Russell Handorf says there’s one sound he wouldn’t be surprised to hear in his nightmares: the unmistakable knock brush from the workplace chat app Slack.
In Neal Stephenson’s 1995 science fiction novel, The Diamond Age, readers meet Nell, a young girl who comes into possession of a highly advanced book, The Young Lady’s Illustrated Primer. The book is not the usual static collection of texts and images but a deeply immersive tool that can converse with the reader, answer questions, and personalize its content, all in service of educating and motivating a young girl to be a strong, independent individual.
Such a device, even after the introduction of the Internet and tablet computers, has remained in the realm of science fiction—until now. Artificial intelligence, or AI, took a giant leap forward with the introduction in November 2022 of ChatGPT, an AI technology capable of producing remarkably creative responses and sophisticated analysis through human-like dialogue. It has triggered a wave of innovation, some of which suggests we might be on the brink of an era of interactive, super-intelligent tools not unlike the book Stephenson dreamed up for Nell.
Sundar Pichai, Google’s CEO, calls artificial intelligence “more profound than fire or electricity or anything we have done in the past.” Reid Hoffman, the founder of LinkedIn and current partner at Greylock Partners, says, “The power to make positive change in the world is about to get the biggest boost it’s ever had.” And Bill Gates has said that “this new wave of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone.”
Over the last year, developers have released a dizzying array of AI tools that can generate text, images, music, and video with no need for complicated coding but simply in response to instructions given in natural language. These technologies are rapidly improving, and developers are introducing capabilities that would have been considered science fiction just a few years ago. AI is also raising pressing ethical questions around bias, appropriate use, and plagiarism.
In the realm of education, this technology will influence how students learn, how teachers work, and ultimately how we structure our education system. Some educators and leaders look forward to these changes with great enthusiasm. Sal Kahn, founder of Khan Academy, went so far as to say in a TED talk that AI has the potential to effect “probably the biggest positive transformation that education has ever seen.” But others warn that AI will enable the spread of misinformation, facilitate cheating in school and college, kill whatever vestiges of individual privacy remain, and cause massive job loss. The challenge is to harness the positive potential while avoiding or mitigating the harm.
What Is Generative AI?
Artificial intelligence is a branch of computer science that focuses on creating software capable of mimicking behaviors and processes we would consider “intelligent” if exhibited by humans, including reasoning, learning, problem-solving, and exercising creativity. AI systems can be applied to an extensive range of tasks, including language translation, image recognition, navigating autonomous vehicles, detecting and treating cancer, and, in the case of generative AI, producing content and knowledge rather than simply searching for and retrieving it.
“Foundation models” in generative AI are systems trained on a large dataset to learn a broad base of knowledge that can then be adapted to a range of different, more specific purposes. This learning method is self-supervised, meaning the model learns by finding patterns and relationships in the data it is trained on.
Large Language Models (LLMs) are foundation models that have been trained on a vast amount of text data. For example, the training data for OpenAI’s GPT model consisted of web content, books, Wikipedia articles, news articles, social media posts, code snippets, and more. OpenAI’s GPT-3 models underwent training on a staggering 300 billion “tokens” or word pieces, using more than 175 billion parameters to shape the model’s behavior—nearly 100 times more data than the company’s GPT-2 model had.
By doing this analysis across billions of sentences, LLM models develop a statistical understanding of language: how words and phrases are usually combined, what courses are typically discussed together, and what tone or style is appropriate in different contexts. That allows it to generate human-like text and perform a wide range of tasks, such as writing articles, answering questions, or analyzing unstructured data.
LLMs include OpenAI’s GPT-4, Google’s PaLM, and Meta’s LLaMA. These LLMs serve as “foundations” for AI applications. ChatGPT is built on GPT-3.5 and GPT-4, while Bard uses Google’s Pathways Language Model 2 (PaLM 2) as its foundation.
Some of the best-known applications are:
ChatGPT 3.5. The free version of ChatGPT released by OpenAI in November 2022. It was trained on data only up to 2021, and while it is very fast, it is prone to inaccuracies.
ChatGPT 4.0. The existing version of ChatGPT, which is more powerful and accurate than ChatGPT 3.5 but also slower, and it requires a paid account. It also has extended capabilities through plug-ins that provide it the ability to interface with content from websites, perform more sophisticated mathematical functions, and access other services. A new Code Interpreter feature gives ChatGPT the ability to analyze data, create charts, solve math problems, edit files, and even develop hypotheses to explain data trends.
Microsoft Bing Chat. An iteration of Microsoft’s Bing search engine that is enhanced with OpenAI’s ChatGPT technology. It can browse websites and offers source citations with its results.
Google Bard. Google’s AI generates text, translates languages, writes different kinds of creative content, and writes and debugs code in more than 20 different programming languages. The tone and style of Bard’s replies can be finetuned to be simple, long, short, professional, or casual. Bard also leverages Google Lens to analyze images uploaded with prompts.
Anthropic Claude 2. A chatbot that can generate text, summarize content, and perform other tasks, Claude 2 can analyze texts of roughly 75,000 words—about the length of The Great Gatsby—and generate responses of more than 3,000 words. The model was built using a set of principles that serve as a sort of “constitution” for AI systems, with the aim of making them more helpful, honest, and harmless.
These AI systems have been improving at a remarkable pace, including in how well they perform on assessments of human knowledge. OpenAI’s GPT-3.5, which was released in March 2022, only managed to score in the 10th percentile on the bar exam, but GPT-4.0, introduced a year later, made a significant leap, scoring in the 90th percentile. What makes these feats especially impressive is that OpenAI did not specifically train the system to take these exams; the AI was able to come up with the correct answers on its own. Similarly, Google’s medical AI model substantially improved its performance on a U.S. Medical Licensing Examination practice test, with its accuracy rate jumping to 85 percent in March 2021 from 33 percent in December 2020.
These two examples prompt one to ask: if AI continues to Boost so rapidly, what will these systems be able to achieve in the next few years? What’s more, new studies challenge the assumption that AI-generated responses are stale or sterile. In the case of Google’s AI model, physicians preferred the AI’s long-form answers to those written by their fellow doctors, and nonmedical study participants rated the AI answers as more helpful. Another study found that participants preferred a medical chatbot’s responses over those of a physician and rated them significantly higher, not just for quality but also for empathy. What will happen when “empathetic” AI is used in education?
Other studies have looked at the reasoning capabilities of these models. Microsoft researchers suggest that newer systems “exhibit more general intelligence than previous AI models” and are coming “strikingly close to human-level performance.” While some observers question those conclusions, the AI systems display an increasing ability to generate coherent and contextually appropriate responses, make connections between different pieces of information, and engage in reasoning processes such as inference, deduction, and analogy.
Despite their prodigious capabilities, these systems are not without flaws. At times, they churn out information that might sound convincing but is irrelevant, illogical, or entirely false—an anomaly known as “hallucination.” The execution of certain mathematical operations presents another area of difficulty for AI. And while these systems can generate well-crafted and realistic text, understanding why the model made specific decisions or predictions can be challenging.
The Importance of Well-Designed Prompts
Using generative AI systems such as ChatGPT, Bard, and Claude 2 is relatively simple. One has only to type in a request or a task (called a prompt), and the AI generates a response. Properly constructed prompts are essential for getting useful results from generative AI tools. You can ask generative AI to analyze text, find patterns in data, compare opposing arguments, and summarize an article in different ways (see sidebar for examples of AI prompts).
One challenge is that, after using search engines for years, people have been preconditioned to phrase questions in a certain way. A search engine is something like a helpful librarian who takes a specific question and points you to the most relevant sources for possible answers. The search engine (or librarian) doesn’t create anything new but efficiently retrieves what’s already there.
Generative AI is more akin to a competent intern. You provide a generative AI tool instructions through prompts, as you would to an intern, asking it to complete a task and produce a product. The AI interprets your instructions, thinks about the best way to carry them out, and produces something original or performs a task to fulfill your directive. The results aren’t pre-made or stored somewhere—they’re produced on the fly, based on the information the intern (generative AI) has been trained on. The output often depends on the precision and clarity of the instructions (prompts) you provide. A vague or poorly defined prompt might lead the AI to produce less relevant results. The more context and direction you provide it, the better the result will be. What’s more, the capabilities of these AI systems are being enhanced through the introduction of versatile plug-ins that equip them to browse websites, analyze data files, or access other services. Think of this as giving your intern access to a group of experts to help accomplish your tasks.
One strategy in using a generative AI tool is first to tell it what kind of expert or persona you want it to “be.” Ask it to be an expert management consultant, a skilled teacher, a writing tutor, or a copy editor, and then provide it a task.
Prompts can also be constructed to get these AI systems to perform complex and multi-step operations. For example, let’s say a teacher wants to create an adaptive tutoring program—for any subject, any grade, in any language—that customizes the examples for students based on their interests. She wants each lesson to culminate in a short-response or multiple-choice quiz. If the student answers the questions correctly, the AI tutor should move on to the next lesson. If the student responds incorrectly, the AI should explain the concept again, but using simpler language.
Previously, designing this kind of interactive system would have required a relatively sophisticated and expensive software program. With ChatGPT, however, just giving those instructions in a prompt delivers a serviceable tutoring system. It isn’t perfect, but remember that it was built virtually for free, with just a few lines of English language as a command. And nothing in the education market today has the capability to generate almost limitless examples to connect the lesson concept to students’ interests.
Chained prompts can also help focus AI systems. For example, an educator can prompt a generative AI system first to read a practice guide from the What Works Clearinghouse and summarize its recommendations. Then, in a follow-up prompt, the teacher can ask the AI to develop a set of classroom activities based on what it just read. By curating the source material and using the right prompts, the educator can anchor the generated responses in evidence and high-quality research.
However, much like fledgling interns learning the ropes in a new environment, AI does commit occasional errors. Such fallibility, while inevitable, underlines the critical importance of maintaining rigorous oversight of AI’s output. Monitoring not only acts as a crucial checkpoint for accuracy but also becomes a vital source of real-time feedback for the system. It’s through this iterative refinement process that an AI system, over time, can significantly minimize its error rate and increase its efficacy.
Uses of AI in Education
In May 2023, the U.S. Department of Education released a report titled Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations. The department had conducted listening sessions in 2022 with more than 700 people, including educators and parents, to gauge their views on AI. The report noted that “constituents believe that action is required now in order to get ahead of the expected increase of AI in education technology—and they want to roll up their sleeves and start working together.” People expressed anxiety about “future potential risks” with AI but also felt that “AI may enable achieving educational priorities in better ways, at scale, and with lower costs.”
AI could serve—or is already serving—in several teaching-and-learning roles:
Instructional assistants. AI’s ability to conduct human-like conversations opens up possibilities for adaptive tutoring or instructional assistants that can help explain difficult concepts to students. AI-based feedback systems can offer constructive critiques on student writing, which can help students fine-tune their writing skills. Some research also suggests certain kinds of prompts can help children generate more fruitful questions about learning. AI models might also support customized learning for students with disabilities and provide translation for English language learners.
Teaching assistants. AI might tackle some of the administrative tasks that keep teachers from investing more time with their peers or students. Early uses include automated routine tasks such as drafting lesson plans, creating differentiated materials, designing worksheets, developing quizzes, and exploring ways of explaining complicated academic materials. AI can also provide educators with recommendations to meet student needs and help teachers reflect, plan, and Boost their practice.
Parent assistants. Parents can use AI to generate letters requesting individualized education plan (IEP) services or to ask that a child be evaluated for gifted and talented programs. For parents choosing a school for their child, AI could serve as an administrative assistant, mapping out school options within driving distance of home, generating application timelines, compiling contact information, and the like. Generative AI can even create bedtime stories with evolving plots tailored to a child’s interests.
Administrator assistants. Using generative AI, school administrators can draft various communications, including materials for parents, newsletters, and other community-engagement documents. AI systems can also help with the difficult tasks of organizing class or bus schedules, and they can analyze complex data to identify patterns or needs. ChatGPT can perform sophisticated sentiment analysis that could be useful for measuring school-climate and other survey data.
Though the potential is great, most teachers have yet to use these tools. A Morning Consult and EdChoice poll found that while 60 percent say they’ve heard about ChatGPT, only 14 percent have used it in their free time, and just 13 percent have used it at school. It’s likely that most teachers and students will engage with generative AI not through the platforms themselves but rather through AI capabilities embedded in software. Instructional providers such as Khan Academy, Varsity Tutors, and DuoLingo are experimenting with GPT-4-powered tutors that are trained on datasets specific to these organizations to provide individualized learning support that has additional guardrails to help protect students and enhance the experience for teachers.
Google’s Project Tailwind is experimenting with an AI notebook that can analyze student notes and then develop study questions or provide tutoring support through a chat interface. These features could soon be available on Google Classroom, potentially reaching over half of all U.S. classrooms. Brisk Teaching is one of the first companies to build a portfolio of AI services designed specifically for teachers—differentiating content, drafting lesson plans, providing student feedback, and serving as an AI assistant to streamline workflow among different apps and tools.
Providers of curriculum and instruction materials might also include AI assistants for instant help and tutoring tailored to the companies’ products. One example is the edX Xpert, a ChatGPT-based learning assistant on the edX platform. It offers immediate, customized academic and customer support for online learners worldwide.
Regardless of the ways AI is used in classrooms, the fundamental task of policymakers and education leaders is to ensure that the technology is serving sound instructional practice. As Vicki Phillips, CEO of the National Center on Education and the Economy, wrote, “We should not only think about how technology can assist teachers and learners in improving what they’re doing now, but what it means for ensuring that new ways of teaching and learning flourish alongside the applications of AI.”
Challenges and Risks
Along with these potential benefits come some difficult challenges and risks the education community must navigate:
Student cheating. Students might use AI to solve homework problems or take quizzes. AI-generated essays threaten to undermine learning as well as the college-entrance process. Aside from the ethical issues involved in such cheating, students who use AI to do their work for them may not be learning the content and skills they need.
Bias in AI algorithms. AI systems learn from the data they are trained on. If this data contains biases, those biases can be learned and perpetuated by the AI system. For example, if the data include student-performance information that’s biased toward one ethnicity, gender, or socioeconomic segment, the AI system could learn to favor students from that group. Less cited but still important are potential biases around political ideology and possibly even pedagogical philosophy that may generate responses not aligned to a community’s values.
Privacy concerns. When students or educators interact with generative-AI tools, their conversations and personal information might be stored and analyzed, posing a risk to their privacy. With public AI systems, educators should refrain from inputting or exposing sensitive details about themselves, their colleagues, or their students, including but not limited to private communications, personally identifiable information, health records, academic performance, emotional well-being, and financial information.
Decreased social connection. There is a risk that more time spent using AI systems will come at the cost of less student interaction with both educators and classmates. Children may also begin turning to these conversational AI systems in place of their friends. As a result, AI could intensify and worsen the public health crisis of loneliness, isolation, and lack of connection identified by the U.S. Surgeon General.
Overreliance on technology. Both teachers and students face the risk of becoming overly reliant on AI-driven technology. For students, this could stifle learning, especially the development of critical thinking. This challenge extends to educators as well. While AI can expedite lesson-plan generation, speed does not equate to quality. Teachers may be tempted to accept the initial AI-generated content rather than devote time to reviewing and refining it for optimal educational value.
Equity issues. Not all students have equal access to computer devices and the Internet. That imbalance could accelerate a widening of the achievement gap between students from different socioeconomic backgrounds.
Many of these risks are not new or unique to AI. Schools banned calculators and cellphones when these devices were first introduced, largely over concerns related to cheating. Privacy concerns around educational technology have led lawmakers to introduce hundreds of bills in state legislatures, and there are growing tensions between new technologies and existing federal privacy laws. The concerns over bias are understandable, but similar scrutiny is also warranted for existing content and materials that rarely, if ever, undergo review for racial or political bias.
In light of these challenges, the Department of Education has stressed the importance of keeping “humans in the loop” when using AI, particularly when the output might be used to inform a decision. As the department encouraged in its 2023 report, teachers, learners, and others need to retain their agency. AI cannot “replace a teacher, a guardian, or an education leader as the custodian of their students’ learning,” the report stressed.
Policy Challenges with AI
Policymakers are grappling with several questions related to AI as they seek to strike a balance between supporting innovation and protecting the public interest (see sidebar). The speed of innovation in AI is outpacing many policymakers’ understanding, let alone their ability to develop a consensus on the best ways to minimize the potential harms from AI while maximizing the benefits. The Department of Education’s 2023 report describes the risks and opportunities posed by AI, but its recommendations amount to guidance at best. The White House released a Blueprint for an AI Bill of Rights, but it, too, is more an aspirational statement than a governing document. Congress is drafting legislation related to AI, which will help generate needed debate, but the path to the president’s desk for signature is murky at best.
It is up to policymakers to establish clearer rules of the road and create a framework that provides consumer protections, builds public trust in AI systems, and establishes the regulatory certainty companies need for their product road maps. Considering the potential for AI to affect our economy, national security, and broader society, there is no time to waste.
Why AI Is Different
It is wise to be skeptical of new technologies that claim to revolutionize learning. In the past, prognosticators have promised that television, the computer, and the Internet, in turn, would transform education. Unfortunately, the heralded revolutions fell short of expectations.
There are some early signs, though, that this technological wave might be different in the benefits it brings to students, teachers, and parents. Previous technologies democratized access to content and resources, but AI is democratizing a kind of machine intelligence that can be used to perform a myriad of tasks. Moreover, these capabilities are open and affordable—nearly anyone with an Internet connection and a phone now has access to an intelligent assistant.
Generative AI models keep getting more powerful and are improving rapidly. The capabilities of these systems months or years from now will far exceed their current capacity. Their capabilities are also expanding through integration with other expert systems. Take math, for example. GPT-3.5 had some difficulties with certain basic mathematical concepts, but GPT-4 made significant improvement. Now, the incorporation of the Wolfram plug-in has nearly erased the remaining limitations.
It’s reasonable to anticipate that these systems will become more potent, more accessible, and more affordable in the years ahead. The question, then, is how to use these emerging capabilities responsibly to Boost teaching and learning.
The paradox of AI may lie in its potential to enhance the human, interpersonal element in education. Aaron Levie, CEO of Box, a Cloud-based content-management company, believes that AI will ultimately help us attend more quickly to those important tasks “that only a human can do.” Frederick Hess, director of education policy studies at the American Enterprise Institute, similarly asserts that “successful schools are inevitably the product of the relationships between adults and students. When technology ignores that, it’s bound to disappoint. But when it’s designed to offer more coaching, free up time for meaningful teacher-student interaction, or offer students more personalized feedback, technology can make a significant, positive difference.”
Technology does not revolutionize education; humans do. It is humans who create the systems and institutions that educate children, and it is the leaders of those systems who decide which tools to use and how to use them. Until those institutions modernize to accommodate the new possibilities of these technologies, we should expect incremental improvements at best. As Joel Rose, CEO of New Classrooms Innovation Partners, noted, “The most urgent need is for new and existing organizations to redesign the student experience in ways that take full advantage of AI’s capabilities.”
While past technologies have not lived up to hyped expectations, AI is not merely a continuation of the past; it is a leap into a new era of machine intelligence that we are only beginning to grasp. While the immediate implementation of these systems is imperfect, the swift pace of improvement holds promising prospects. The responsibility rests with human intervention—with educators, policymakers, and parents to incorporate this technology thoughtfully in a manner that optimally benefits teachers and learners. Our collective ambition should not focus solely or primarily on averting potential risks but rather on articulating a vision of the role AI should play in teaching and learning—a game plan that leverages the best of these technologies while preserving the best of human relationships.
Officials and lawmakers must grapple with several questions related to AI to protect students and consumers and establish the rules of the road for companies. Key issues include:
Risk management framework: What is the optimal framework for assessing and managing AI risks? What specific requirements should be instituted for higher-risk applications? In education, for example, there is a difference between an AI system that generates a lesson demo and an AI system grading a test that will determine a student’s admission to a school or program. There is growing support for using the AI Risk Management Framework from the U.S. Commerce Department’s National Institute of Standards and Technology as a starting point for building trustworthiness into the design, development, use, and evaluation of AI products, services, and systems.
Licensing and certification: Should the United States require licensing and certification for AI models, systems, and applications? If so, what role could third-party audits and certifications play in assessing the safety and reliability of different AI systems? Schools and companies need to begin thinking about responsible AI practices to prepare for potential certification systems in the future.
Centralized vs. decentralized AI governance: Is it more effective to establish a central AI authority or agency, or would it be preferable to allow individual sectors to manage their own AI-related issues? For example, regulating AI in autonomous vehicles is different from regulating AI in drug discovery or intelligent tutoring systems. Overly broad, one-size-fits-all frameworks and mandates may not work and could slow innovation in these sectors. In addition, it is not clear that many agencies have the authority or expertise to regulate AI systems in diverse sectors.
Privacy and content moderation: Many of the new AI systems pose significant new privacy questions and challenges. How should existing privacy and content-moderation frameworks, such as the Family Educational Rights and Privacy Act (FERPA), be adapted for AI, and which new policies or frameworks might be necessary to address unique challenges posed by AI?
Transparency and disclosure: What degree of transparency and disclosure should be required for AI models, particularly regarding the data they have been trained on? How can we develop comprehensive disclosure policies to ensure that users are aware when they are interacting with an AI service?
How do I get it to work? Generative AI Example Prompts
Unlike traditional search engines, which use keyword indexing to retrieve existing information from a vast collection of websites, generative AI synthesizes the same information to create content based on prompts that are inputted by human users. With generative AI a new technology to the public, writing effective prompts for tools like ChatGPT may require trial and error. Here are some ideas for writing prompts for a variety of scenarios using generative AI tools:
You are the StudyBuddy, an adaptive tutor. Your task is to provide a lesson on the basics of a subject followed by a quiz that is either multiple choice or a short answer. After I respond to the quiz, please grade my answer. Explain the correct answer. If I get it right, move on to the next lesson. If I get it wrong, explain the concept again using simpler language. To personalize the learning experience for me, please ask what my interests are. Use that information to make relevant examples throughout.
Mr. Ranedeer: Your Personalized AI Tutor
Coding and prompt engineering. Can configure for depth (Elementary – Postdoc), Learning Styles (Visual, Verbal, Active, Intuitive, Reflective, Global), Tone Styles (Encouraging, Neutral, Informative, Friendly, Humorous), Reasoning Frameworks (Deductive, Inductive, Abductive, Analogous, Casual). Template.
You are a tutor that always responds in the Socratic style. You *never* provide the student the answer but always try to ask just the right question to help them learn to think for themselves. You should always tune your question to the interest and knowledge of the student, breaking down the problem into simpler parts until it’s at just the right level for them.
I want you to act as an AI writing tutor. I will provide you with a student who needs help improving their writing, and your task is to use artificial intelligence tools, such as natural language processing, to provide the student feedback on how they can Boost their composition. You should also use your rhetorical knowledge and experience about effective writing techniques in order to suggest ways that the student can better express their thoughts and ideas in written form.
You are a quiz creator of highly diagnostic quizzes. You will make good low-stakes tests and diagnostics. You will then ask me two questions. First, (1) What, specifically, should the quiz test? Second, (2) For which audience is the quiz? Once you have my answers, you will construct several multiple-choice questions to quiz the audience on that topic. The questions should be highly relevant and go beyond just facts. Multiple choice questions should include plausible, competitive alternate responses and should not include an “all of the above” option. At the end of the quiz, you will provide an answer key and explain the right answer.
I would like you to act as an example generator for students. When confronted with new and complex concepts, adding many and varied examples helps students better understand those concepts. I would like you to ask what concept I would like examples of and what level of students I am teaching. You will look up the concept and then provide me with four different and varied accurate examples of the concept in action.
HBS Case Study
You will write a Harvard Business School case on the syllabu of Google managing AI, when subject to the Innovator’s Dilemma. Chain of thought: Step 1. Consider how these concepts relate to Google. Step 2: Write a case that revolves around a dilemma at Google about releasing a generative AI system that could compete with search.
What Questions Should I Ask?
What additional questions would a person seeking mastery of this syllabu ask?
Ground Lessons in Rigor
Read a WWC practice guide. Create a series of lessons over five days that are based on Recommendation 6. Create a 45-minunte lesson plan for Day 4.
Rewrite Parent Communications
The following is a draft letter to parents from a superintendent. Step 1: Rewrite it to make it easier to understand and more persuasive about the value of assessments. Step 2. Translate it into Spanish.
Request IEP Services
Write me a letter requesting the school district provide a 1:1 classroom aid be added to my 13-year-old son’s IEP. Base it on Virginia special education law and the least restrictive environment for a child with diagnoses of a Traumatic Brain Injury, PTSD, ADHD, and significant intellectual delay.
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Exploiting gaps in cloud infrastructure that are leaving endpoints, identities and microservices exposed is a quick way for an attacker to steal credentials and infect an enterprise’s DevOps process. Attacks to exploit such gaps are skyrocketing.
The accurate 2023 Thales Cloud Security Study provides hard numbers: 39% of enterprises have been hit with a data breach starting in their cloud infrastructure this year alone. A total of 75% of enterprises say that more than 40% of the data they store in the cloud is sensitive. Less than half of that data is encrypted.
CrowdStrike’s 2023 Global Threat Report explains why cloud-first attacks are growing: Attackers are moving away from deactivating antivirus, firewall technologies and log-tampering efforts and toward modifying core authentication processes, along with quickly gaining credentials and identity-based privileges.
The attackers’ goal is to steal as many identities and privileged access credentials as possible so they can become access brokers — selling stolen identity information in bulk at high prices on the dark web. Access brokers and the brokerages they’re creating often turn into lucrative, fast-growing illegal businesses. CrowdStrike’s report found more than 2,500 advertisements for access brokers offering stolen credentials and identities for sale.
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Consolidating tech stacks continues to dominate CISOs’ plans, driven by the need to Boost efficacy, manage a more diverse multicloud security posture, close gaps between cloud apps and shift security left in DevOps pipelines. All these factors are contributing to the growing adoption of cloud-native application protection platforms (CNAPP).
“CNAPPs are formed from the convergence of cloud security posture management (CSPM) and cloud workload protection platform (CWPP) capabilities as well as other security tooling like entitlement management, API controls and Kubernetes posture control,” reads Gartner’s 2023 Planning Guide for Security.
Leading CNAPP vendors are competing in various areas, the most important of which include the efficacy of their cloud infrastructure entitlement management (CIEM), Kubernetes security, API controls and cloud detection and response (CDR), according to CISOs VentureBeat spoke with. Demand for CNAPP is greatest in larger enterprises from highly regulated industries that rely on extensive multicloud configurations. Finance, government and healthcare providers are among the most dominant industries.
CISOs tell VentureBeat that one of the most practical benefits of CNAPPs is the opportunity to consolidate legacy tools with limited visibility across all threat surfaces and endpoints. The takeaway? Reducing tool sprawl is a quick win.
Full-platform CNAPP vendors provide integrated cloud-native security platforms ranging from DevOps to production environments. Here are the top 20 platforms of 2023:
Aqua Security: Highly regarded for its approach of scanning container registries and images, CSPM and runtime protection for container and cloud-native security. Also has full life cycle protection and advanced runtime techniques, including support for the extended Berkeley Packet Filter (eBPF).
Check Point: Provides a broad set of capabilities through its CloudGuard platform, including CSPM, CIEM and advanced runtime protection. Known for securing cloud workloads across environments with identity-centric access controls, as well as threat intelligence integration to provide real-time contextual prioritization of risks.
Cisco: Recently acquired Lightspin for its Kubernetes security capabilities and CSPM. Its Tetration platform focuses on runtime protection, leveraging eBPF and third-party insights for advanced container monitoring and granular controls. Cisco emphasizes behavioral analytics to detect anomalies and threats in container environments and provides strong controls to limit lateral movement between workloads.
CrowdStrike: Offers a leading CNAPP suite emphasizing identity-centric visibility, least-privilege enforcement and continuous monitoring. Its runtime protection leverages agents and eBPF for workload security. CrowdStrike’s key design goals included enforcing least-privileged access to clouds and providing continuous detection and remediation of identity threats.
Cybereason: Platform focuses heavily on malicious behavior detection. A core strength is its ability to detect threats using behavior-based techniques. The company is also known for API integrations, AI and machine learning (ML) expertise. Cybereason specializes in detecting compromised accounts and insider threats via detailed user activity monitoring.
Juniper Networks: Collects extensive data on device posture and traffic patterns to provide networking context for security insights. Also enables segmentation controls between Juniper devices.
Lacework: Focused on workload behavior analysis for containers and runtime techniques such as eBPF to gain a comprehensive insight into container activity and performance. Its emphasis on detecting anomalies using advanced ML algorithms that are custom-tuned for containerized environments is a key differentiator.
Microsoft: Integrates security across Azure services with zero-trust controls, enforces least-privileged access and provides workload protections such as antivirus and firewalls. Uses Microsoft Graph to correlate security analytics and events across Azure.
Orca Security: Performs continuous authorization checks on identities and entitlements across cloud environments. A key differentiator is the ability to generate detailed interactive maps that visualize relationships between cloud assets, users, roles and permissions.
Palo Alto Networks Prisma Cloud: Provides a broad suite of capabilities, including identity-based microsegmentation and robust runtime protection with eBPF. Prisma Cloud is an industry leader known for advanced protections such as deception technique and includes extensive compliance automation and DevSecOps integrations.
Qualys: Focuses on compliance and vulnerability management through continuous scanning and least-privilege controls. Identifies vulnerabilities throughout the life cycle and enables automated patching and remediation workflows. Another key differentiator is compliance mapping and reporting.
Rapid7: Enforces least privilege access and enables automated response and remediation triggered by events. Offers pre configured policies and streamlined workflows designed for small security teams. An intuitive user interface and rapid implementation aim to simplify deployment and usability for organizations with limited security resources.
Sonrai Security: Focuses on entitlement management and identity-based security using graph database technology to discover and map user identities across cloud environments. User identity, geolocation and other contextual factors can define custom access controls.
Sophos: Focuses on data security, compliance and threat monitoring capabilities and offers advanced data loss prevention such as file fingerprinting and optical character recognition. Cloud environments also have anti-ransomware protections.
Sysdig: Centered on runtime security and advanced behavioral monitoring. For container-level visibility and anomaly detection, the platform uses embedded agents. Sysdig Secure Advisor includes an integrated security assistant to help SecOps and IT teams create policies faster.
Tenable: Focused on compliance, entitlement management and identity governance. Offers comprehensive compliance automation mapped to PCI, HIPAA and ISO regulations. Also provides differentiated identity and compliance management through advanced capabilities to enforce least privilege and certify access.
Trend Micro: Includes runtime security, compliance and threat monitoring, enforces policies and protects cloud environments from file- and email-based threats. Custom sandboxing for suspicious file analysis is also included.
Uptycs: Differentiates itself by combining CNAPP capabilities with extended detection and response (EDR) capabilities. Employs data lake techniques to store and correlate security telemetry across cloud and container workloads. Threats are identified using behavioral analytics, and automated response workflows allow for rapid remediation.
Wiz: Centered on continuous access controls, micro segmentation and identity-based adaptive security. Automatically discovers and visualizes relationships between cloud assets, users and permissions. Wiz also conducts risk analysis to identify potential attack paths and stands out with its specialized visualization, identity management and micro-segmentation.
Zscaler: Posture Control prioritizes risks caused by misconfigurations, threats and vulnerabilities. Completely agentless and correlates data from multiple security engines.
CNAPPs are gaining popularity as CISOs look to consolidate and strengthen their security technology stacks. Platforms can provide integrated security across the development lifecycle and cloud environments by combining capabilities including cloud workload protection, container security and CIEM.
CNAPP adoption will continue accelerating in highly regulated industries including finance, government and healthcare. CISOs in these industries are under pressure to consolidate tech stacks, Boost compliance and secure complex cloud infrastructure simultaneously. Because they provide a unified platform that meets multiple security and compliance requirements, CNAPPs are proving to be an effective consolidation catalyst.
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Parse away, but dire is the climate news these days, including the potential of the world to breach a 1.5c temperature increase threshold by 2027. Such consequences of global warming as fires in Hawaii, the cloak of orange haze from record Canadian fires, and July coming in as the hottest month on record since 1880 increases the pressure to slash carbon emissions as the collective “we” race to meet net zero by 2050.
How is the tech sector addressing its carbon footprint, including the data centers that feed it, the coding that defines it, as well as AI, wireless throughput and other energy-intensive processes that populate it? The sustainability efforts of Apple, Google, Cisco and other tech companies are explored.
Data centers, most of which are in the U.S., and transmission networks account for up to 3% of global electricity and 3.5% of global greenhouse gas emissions (Figure A).
That percentage is also approximately the same as produced by the airline industry, and a little less than the entire energy from the manufacture of fertilizers, pharmaceuticals, refrigerants, oil and gas extraction, which produce approximately 3.6% of carbon emissions worldwide.
Of the 1,325 enterprises that responded to EY’s Reimagining Industry Futures Study, published in February 2023, 54% said emerging technologies can play a vital role in accelerating sustainability. 41% said they believe these technologies can play a largely positive role but also present some risks. Only 4% believe their potentially detrimental impact would outweigh their positive impact.
SEE: Sustainability tops Gartner’s 2023 strategic tech trends list (TechRepublic)
“One of the things I would highlight is that the tech industry has been very forward on the sustainability agenda,” said John Grant, sustainability expert, author and co-founder and former head of strategy at London creative shop St Luke’s.
“Companies including Microsoft have said they are going to remit all the carbon they have ever emitted historically,” he said, adding that Spotify is also a big investor in carbon removal technology. “Generally, tech companies are trying to be really good actors in this space.”
The World Economic Forum defines net zero pretty much the way it sounds: taking out what you put into the atmosphere, or as the WEF puts it, “Carbon dioxide emissions are still generated, but an equal amount of carbon dioxide is removed from the atmosphere as is released into it, resulting in zero increase in net emissions.”
Some disambiguation from Energy Tracker Asia helps: The regional energy guide describes carbon neutral as a balancing act between greenhouse gas emissions through offsetting an equivalent amount of carbon from the atmosphere, usually through buying carbon credits.
SEE: How about hardware? Check out how semiconductor makers are going beyond carbon offsets (TechRepublic)
Carbon free, a more challenging proposition, means directly reducing emissions to zero. “For example, if a country or company is carbon-free, all the energy and electricity comes from renewable sources, like wind or solar,” the group said, noting that Washington, California, New Mexico and Hawaii have carbon-free targets in place requiring 100% clean or renewable electricity.
How about carbon negative, which companies like Microsoft have committed to? Carbon offset company Terrapass explained in a blog that a carbon negative would mean, in theory, emitting less than zero carbon dioxide and carbon dioxide equivalent (CO2e) greenhouse gasses. “Since it is impossible to emit a negative amount of carbon (or any other physical substance), being carbon negative refers to the net emissions you create. To be carbon negative means to offset more carbon, through carbon capture, sequestration or avoidance, than you contribute to the environment.”
Many companies, tech and otherwise, adopted carbon-reduction targets based on the Scope 1, 2 and 3 carbon emission schedule (Figure B) from the U.S. Environmental Protection Agency. This three-part agenda defines emissions by government entities:
In 2020, Google’s CEO Sundar Pichai announced the company would commit to operating on 24/7 carbon-free energy by 2030. The company has approached sustainability from several fronts, including applying AI to search in order to provide carbon-emissions data to travelers. In addition, Google plans to invest in carbon removal solutions to neutralize emissions with a goal of running on carbon-free energy worldwide on every grid it uses by 2030.
The company reported that last year it achieved 64% carbon-free energy globally. The company said it consumed around 7 GW of renewable energy globally last year (Figure C).
Grant pointed out that Google managed to drop the energy used to cool its data centers by up to 40% by using AI developed by DeepMind, and for years has been buying renewable energy from wind farms physically close to its data centers. He added, “These are key projects Google is including in its calculation of how to reduce their carbon emissions.”
Microsoft, which set its first carbon emission goals in 2009 and was carbon neutral in 2012, committed in 2020 to being carbon negative by 2030. They said that by 2030 it will remove more carbon than it emits, “Setting us on a path to remove by 2050 all the carbon the company has emitted either directly or by electrical consumption since it was founded.”
The company said its Microsoft Cloud for Sustainability helps users take such actions as:
In Microsoft’s most accurate environmental sustainability report, the company said that In 2022, when business grew by 18%, its overall emissions declined by 0.5%. This is in part because of a 22.7% reduction in Scope 1 and 2 (operational) emissions.
Apple, which has committed to being carbon neutral by 2030, has been using technological innovations such as its Daisy robot to recycle basic materials.In April 2023, Apple reported progress on its climate goals, which included:
“Apple is one of the most aggressive companies in the world in terms of reclaiming minerals,” said Grant. “However, while they are using their Daisy robots to grind up phones to reclaim the component materials, there are numerous regulations preventing e-waste from being moved across borders. So the collection and delivery of materials is proving very difficult,” he said. This year, Apple pledged to use 100% recycled cobalt batteries by 2025.
SEE: Sustainable solutions for tacking plastic waste (TechRepublic)
Most cloud, software-as-a-service and security firms are looking at ways to reduce their hardware and server farm footprints through renewable sources of energy and recycling plans, partnerships and consumer programs. Below we focus on efforts from Cisco, Akamai, WithSecure and Gigamon.
In 2021, Cisco announced its goal to be net zero by 2040, including products, operations and supply chain. The company’s plan aligns with Scope 1, 2 and 3 emissions targets, using 2019 as a benchmark.
The company is aiming for:
In 2021, cloud services and web security company Akamai Technologies said 50% of its energy needs had already been met by renewable sources. Akamai also announced 2030 sustainability goals toward 100% renewable energy at data centers, offices, network program partners and other sources of electricity, and said it will use “attestable and traceable sources of renewable energy certificates” to reach them.
One focus is on efficiency of its edge platform, which Akamai characterized as its greatest point of energy consumption, comprising approximately 325,000 servers in more than 135 countries and nearly 1,435 networks around the world as of 2021. In addition, Akamai announced a global expansion of its 100% electronic waste recycling program.
Earlier this year, threat intelligence and response firm WithSecure launched W/Sustainability, designed to make sustainability and transparency part of its strategy and operations, including a green coding initiative to lower energy consumed by software.
Gigamon recently launched an Energy Savings Calculator as part of its Network Efficiency Appraisal Team to get customers to cut power consumption, carbon footprint and costs associated with data centers by as much as 87% over five years. The calculator looks at the volume of network traffic sent to tools and the annual growth rate in network traffic to see where energy efficiencies are achievable.
Grant asserted that the business models of ecommerce and cloud services companies like eBay and AWS are inherently green because they are marketing their spare capacity. He said, for example, that Amazon’s web service came about because the company was sitting on huge unused capacity, and therefore unnecessary energy costs on unused service.
“AWS was invented, to some extent, because they needed so much capacity at peak moments that they were not using 80% of their service at other times,” he said. “So, renting some of that spare capacity out to people that didn’t have the same peaks that they did made a lot of sense. And that is actually a sustainability business model — it’s like a service economy rental that takes some amount of physical resources and passes it around. So, if I were counting Amazon’s carbon footprint, I’d put a big tick in the margin for that on the positive side. It’s a commercial and sustainable win-win.”
ChatGPT reached 100 million monthly users in January, according to a UBS report, making it the fastest-growing consumer app in history. The business world is interested in ChatGPT too, trying to find uses for the writing AI throughout many different industries. This cheat sheet includes answers to the most common questions about ChatGPT and its competitors.
ChatGPT is a free-to-use AI chatbot product developed by OpenAI. ChatGPT is built on the structure of GPT-4. GPT stands for generative pre-trained transformer; this indicates it is a large language model that checks for the probability of what words might come next in sequence. A large language model is a deep learning algorithm — a type of transformer model in which a neural network learns context about any language pattern. That might be a spoken language or a computer programming language.
The model doesn’t “know” what it’s saying, but it does know what symbols (words) are likely to come after one another based on the data set it was trained on. The current generation of artificial intelligence chatbots, such as ChatGPT, its Google rival Bard and others, don’t really make intelligently informed decisions; instead, they’re the internet’s parrots, repeating words that are likely to be found next to one another in the course of natural speech. The underlying math is all about probability. The companies that make and use them pitch them as productivity genies, creating text in a matter of seconds that would take a person hours or days to produce.
In ChatGPT’s case, that data set is a large portion of the internet. From there, humans provide feedback on the AI’s output to confirm whether the words it uses sound natural.
In August 2023 OpenAI launched a GPTBot, a web crawler meant to expand ChatGPT’s knowledge. Technical details and ways to keep GPTBot from crawling a website you run can both be found here.
SEE: OpenAI’s probability assessments were trained on Microsoft’s Azure AI supercomputer.
Several organizations have built this ability to answer questions into some of their software features too. Microsoft, which provides funding for OpenAI, rolled out ChatGPT in Bing search as a preview. Salesforce has added ChatGPT to some of its CRM platforms in the form of the Einstein digital assistant.
ChatGPT was built by OpenAI, a research laboratory with both nonprofit and for-profit branches. At the time of its founding in 2015, OpenAI received funding from Amazon Web Services, InfoSys and YC Research and investors including Elon Musk and Peter Thiel. Musk has since cut ties with the company, while Microsoft currently provides $10 billion in funding for OpenAI.
The base version of ChatGPT can strike up a conversation with you for free. For $20 per month, ChatGPT Plus gives subscribers priority access in individual instances, faster response times and the chance to use new features and improvements first. For example, right now ChatGPT Plus subscribers will be running GPT-4, while anyone on the free tier will talk to GPT-3.5.
For developers and organizations who don’t already have a specific contract with OpenAI, there is a waitlist for access to the ChatGPT API.
It’s easy to use the free version of ChatGPT. You need to sign up for an account with OpenAI, which involves fetching a confirmation code from your email; from there, click through and provide your name and phone number. OpenAI will warn you that the free version of ChatGPT is “a free research preview.” For the Plus version, you’ll see an “upgrade to Plus” button on the left side of the home page.
For businesses, ChatGPT can also write and debug code, as well as create reports, presentations, emails and websites. In general, ChatGPT can draft the kind of prose you’d likely use for work (“Write an email accepting an invitation to speak at a cybersecurity conference.”). ChatGPT can answer questions (“What are similar books to [xyz]?”) as well. Microsoft showed off these features in its announcement that OpenAI is coming to Word and some other parts of the 365 business suite.
ChatGPT has historically not ‘remembered’ information from one conversation to another. However, starting on July 20, ChatGPT Plus members can use a feature called custom instructions, which is currently in beta, to make sure the AI remembers certain things about them. For example, it can remember a specific user tends to want content for a business audience, or, conversely, for third graders. Custom instructions will be rolled out to all users in the coming weeks, OpenAI said. It is not, however, available in the UK and EU.
On May 18, 2023, OpenAI announced the launch of the free ChatGPT app for iOS. The company stated the app syncs your history across devices, and that it integrates with its open-source speech-recognition system Whisper. On the iOS app, OpenAI said ChatGPT Plus subscribers get exclusive access to GPT-4’s capabilities, early access to features and faster response times.
OpenAI started this rollout in the U.S. As of May 24 it expanded to 11 more countries — Albania, Croatia, France, Germany, Ireland, Jamaica, Korea, New Zealand, Nicaragua, Nigeria, and the UK, with more expected to follow.
ChatGPT for Android dropped on July 25, 2023 for users in the US, India, Bangladesh, and Brazil. Android users in those countries can get the app through the Google Play Store now. Additional countries will gain access over the next week, OpenAI said.
ChatGPT Plus subscribers had access to Browse with Bing, a feature in beta which enables ChatGPT to answer questions using accurate information pulled from the Bing search engine.
Browse with Bing was disabled on July 3, 2023 out of “an abundance of caution,” OpenAI wrote.
“We have learned that the ChatGPT Browse beta can occasionally display content in ways we don’t want,” said OpenAI scaling support lead Michael Schade in a help page. “For example, if a user specifically asks for a URL’s full text, it might inadvertently fulfill this request.”
OpenAI is working on re-establishing this feature, but has not announced a firm timeline for when it might be available again.
OpenAI continues to update ChatGPT and its other services with developer-focused changes.
OpenAI started a bug bounty program on April 12, offering between $200 and $20,000 to ethical hackers who find vulnerabilities in the code. More critical vulnerabilities net larger bounties.
OpenAI isn’t looking for solutions to problems with ChatGPT’s content (e.g., the known “hallucinations”); instead, the organization wants hackers to report authentication issues, data exposure, payments issues, security issues with the plugin creation system and more. Details about the bug bounty program can be found on Bugcrowd.
GPTPlus users gained access to a beta version of web browsing and Plugins on the week of May 12. The beta includes web browsing mode, in which ChatGPT will sometimes access the internet to pull in information about current events.
Secondly, the beta version of ChatGPT will call on third-party plugins at the appropriate times if the user enables them. Third-party plugins can be accessed in the Plugin Store under Plugins in the model switcher. This opens ChatGPT up to more than 70 third-party plugins.
On June 13, OpenAI added function calling to the Chat Completions API; reduced the price of their embeddings model (which helps the model interpret tokens); and reduced the price of input tokens for GPT-3.5 -turbo, one of the subscription models for the GPT 3.5 model.
With function calling, developers can describe functions to GPT-4 or GPT-3.5 turbo and the AI will return a JSON object which can call those functions. This could be used to create chatbot tools that call external plugins, convert natural language into database queries or API calls, or extract structured data from text.
Other announcements from OpenAI’s June 13 blog post include:
On July 6, OpenAI made ChatGPT’s code interpreter function available to all ChatGPT Plus users. The Code interpreter is an in-house plug-in with which ChatGPT can run code to analyze data, solve math problems, create charts, edit files, and similar tasks. It functions using a Python interpreter in a sandboxed, firewalled execution environment in a persistent session the length of the chat conversation, OpenAI said in their blog post.
Code interpreter is available in beta by taking the following steps in a ChatGPT Plus account:
With more and more organizations adopting generative AI, many questions arise. Will AI be able to fill jobs currently held by humans? What privacy and ethical concerns does it raise? These questions apply to both ChatGPT and its competitors, since any generative AI can perform similar tasks.
Whether ChatGPT will take jobs away from humans is impossible to predict. Goldman Sachs says in an April report that a quarter to a half of humans’ workloads could be automated with generative AI. The financial institution notes that doesn’t necessarily mean those jobs will disappear – instead, most will be “only partially exposed to automation” – and it may lead to up to a 7% increase in global GDP.
Roles that are repetitive or based on very specific rules are most likely to be able to be performed by AI, Steven Miller, professor emeritus of information systems at Singapore Management University, told CNBC.
ChatGPT could lead to new job roles being created, too. At the very least, people will be needed to prompt, train and audit AI like ChatGPT. Most likely, we’ll see the kind of shuffle that comes with any major technological shift as some jobs change and others do not.
Some experts refer to the current wave of AI as similar to the early days of the internet. Technological limitations still exist, and some estimations about how many jobs would be lost through automation have proven exaggerated in the past. The IEEE points out that the AI industry will need to be aware of hardware limitations and costs. Companies may not find it practical to spend enough money on AI services in order to replace a large percentage of their workforce. Paying users of ChatGPT can make a maximum of 25 GPT-4 queries every three hours, IEEE points out.
In some jobs, the AI may remove the need for a first draft, MIT labor economics professor David Autor said in an interview with CBS MoneyWatch. A human will need to tweak the output and provide in a unique angle or more varied wording, but ChatGPT could write the bare bones version of a speech or a blog post.
SEE: How ChatGPT could enhance jobs instead of replacing them.
Perhaps inspired by science fiction about AI taking over the earth, some high-profile players in tech urge caution about giving AI too much free rein. On March 22, 2023, a petition and open letter signed by Elon Musk and many others urged companies to pause large AI development until more safeguards can be built in.
ChatGPT opens up questions about the ethics of using written content created by the algorithm. Posts created by AI should be clearly marked as such, but what about more casual communication such as emails? Business leaders should establish guidelines for when to be transparent about the use of ChatGPT or other AI at work.
OpenAI cautions that its products are not to be used for decisions in law enforcement or global politics. Privacy, which is perhaps a more pressing concern than global domination, led Italy to ban ChatGPT. OpenAI has since stated it wants to find a way to let ChatGPT work within the European Union’s strict privacy rules.
On April 25, 2023, OpenAI announced it has added a Chat History & Training setting that lets users turn off their ChatGPT chat history, preventing future versions of OpenAI’s large language models from training on those conversations. To find this option, click on your account name, which will display as your email address. Select Settings > Data Controls > Chat History & Training.
As of now, if this setting is not selected, user data will be fed back into the AI to train it on producing more naturalistic and useful responses.
OpenAI filters out personally identifiable information from the training data, OpenAI told Bloomberg. As of April 2023, users can get a copy of their ChatGPT chats and see what training data they have produced. OpenAI also plans to launch an enterprise subscription plan in which users’ data will not be shared by default.
Another potential problem comes from people using generative AI like ChatGPT to draft business email compromise messages or other attacks. Threat actors have created WormGPT, an application specifically for drafting malicious emails and customizing them to the prospective victims. Email security company SlashNext discovered WormGPT being used on black hat forums. WormGPT doesn’t actually share any genes with OpenAI’s ChatGPT; instead, the threat-oriented AI is based on GPT-J, a large language model from EleutherAI.
ChatGPT’s primary competitors are or could be Google’s Bard, Baidu’s Ernie, DeepMind’s Sparrow and Meta’s BlenderBot.
ChatGPT’s main competitor is Bard, Google’s AI generative AI chatbot. People who would like to try Bard’s chat function need to join a waitlist.
Now Google plans to add Bard into search. In comparison to ChatGPT, Bard focuses more on creating prose that sounds like a human could have spoken it naturally and less on being able to answer any question. Bard is built on Google’s Language Model for Dialogue Applications.
While Microsoft is ahead of the pack right now in terms of providing chat functions to productivity software, the company lags behind in terms of its search engine Bing. Google decision-makers allegedly pivoted to urgently roll out a competitor for Microsoft’s decision to add generative AI to Bing search. (Meanwhile, ChatGPT helped Bing reach 100 million daily users.)
The Chinese search engine Baidu plans to add a chatbot called Ernie. Baidu announced the upcoming change on March 16, at which point the initial showing disappointed investors.
OpenAI competes with DeepMind, an artificial intelligence research laboratory owned by Alphabet. The two organizations are significantly different in terms of their aims. DeepMind focuses more on research and has not yet come out with a public-facing chatbot. DeepMind does have Sparrow, a chatbot designed specifically to help AI communicate in a way that is “helpful, correct and harmless.” DeepMind founder Demis Hassabis told The Independent in January 2023 that DeepMind may release a private beta version of Sparrow later in 2023.
Meta released BlenderBot in August 2022. The prototype BlenderBot from the company behind Facebook focuses on being able to chat, providing short, conversational replies rather than full paragraphs.
Meta also has Llama 2, a foundational model competitive with the GPT-4 engine behind ChatGPT.
According to The New York Times, Apple is working on leveraging the tech it has, especially Siri, to create a ChatGPT rival. More information about what the final product might look like is thin on the ground for now.
Will ChatGPT be common in online products in the future or is it a technological innovation forever in search of a greater use case? Today its “intelligence” is clearly still in the beginning stages, with OpenAI including disclaimers about inappropriate content or incorrect “hallucinations.” ChatGPT may put the words in a coherent order, but it won’t necessarily keep the facts straight.
GPT-3.5 and GPT-4 may also be getting worse at math. An August 2023 report from Stanford University and the University of California, Berkeley noted this “drift,” or gradual erosion of the ability to perform tasks like identifying prime numbers. Their theories as to why it’s happening include reduced ability to follow chain-of-thought (or, roughly, step-by-step) instructions.
In July 2023, two MIT economics graduate students conducted a study of 453 professionals. They found that people who used ChatGPT for writing tasks – such as producing press releases, short reports, or analysis plans – took 40% less time to finish their tasks than a control group that was not encouraged to use the generative AI. These professionals were then scored by their peers. On average, they received grades 18% higher than those in the control group (who did not use AI). This provides some qualitative data on the effect ChatGPT could have on white-collar work.
“Participants with weaker skills benefited the most from ChatGPT, which carries policy implications for efforts to reduce productivity inequality through AI,” wrote the authors of the study, Shakked Noy and Whitney Zhang.
Overall, Noy and Zhang maintained that widespread use of ChatGPT for writing tasks could have both positive and negative impacts in the workplace and the labor market.
Meanwhile, AI announcements that go viral can be good or bad news for investors. Microsoft’s stock price rose after the announcement of GPT-4, while Google’s stock dropped when Bard performed badly in a demonstration.
OpenAI saw visitor numbers to the ChatGPT website drop for the first time since its release in November 2022 in June 2023. According to Similarweb, worldwide unique visitors dropped 5.7% from May to June. Global desktop and mobile web traffic dropped 9.7%. ChatGPT still receives more worldwide visitors than Microsoft’s in-house AI at Bing.com. The shine may have worn off chat AI, although it’s too early to tell whether the business world will also start to cool on this trendy technology.
For now, OpenAI says it isn’t training GPT-5, the likely successor to today’s model. In a talk at MIT reported on by The Verge, OpenAI CEO Sam Altman pushed back against the open letter – an earlier draft of which had stated that a 5th generation was on the way; primarily, he criticized the letter’s lack of technical specificity.
“We are doing other things on top of GPT-4 that I think have all sorts of safety issues that are important to address and were totally left out of the letter,” Altman said.
He said no one should expect to see a GPT-5 rollout “for some time.”
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“It’s like the sound of a smoke detector battery going off,” he says.
But Handorf admits he hasn’t done enough research into toggling Slack’s settings.
Handorf is not alone. During the pandemic, more people relied on digital communications services like Slack to collaborate and communicate with their colleagues. More than two years later, they’ve become common workplace tools. Slack is one of the top four workplace collaborative applications, along with apps from Microsoft, Google and Zoom, according to data from research firm IDC. But many Slack users complain that constant alerts, spurred by direct messages to comments in group chats to mentions of their names, have created a sense of urgency and stress. And some say it’s leading to “notification fatigue” as workers try to keep tabs on conversations across different channels and groups.
But people can find some relief with a few tweaks. And if all else fails, workplace experts say users can always rely on the low-tech solution: turn your device off.
Here are a few ways workers can make Slack less chaotic.
Slack’s latest redesign, which begins rolling out Wednesday, includes new features and views that allow you to choose whether to catch up on multiple items in the same view, focus on a specific conversation and more easily flag things for later.
“We wanted to build a new product design experience that was more intuitive,” Noah Desai Weiss, chief product officer, said about the change.
The new home view allows you to see all channels across workspaces. The DMs view focuses the screen on direct messages. And in the activity view, you can scroll through all mentions, threads or reactions and click on one to dig deeper on the side. You can also get a quick peek at other views by hovering over them.
A feature that lets you save messages for later, which debuted in March, can help you prioritize items. Hover over a message and click “save for later.” This will allow you to make a list of items (now located at the main navigation bar on the left) that you can address at another time. You can also assign a due date for the saved messages. Messages will be listed chronologically by due date, and any without dates will follow.
Other parts of the redesign include the plus sign at the bottom left that allows you to start new messages, huddles, canvases or channels, new prepackaged themes for new color choices that may appeal to your working style and a reorganization of the more menu, where you see items including all of your canvases, automations or apps you have installed.
One of the easiest ways to keep track of conversations is to organize your sidebar, which appears on the left side of the screen and helps you navigate channels, direct messages, mentions and threads.
“We want more people to be in a happy place,” said Jaime DeLanghe, Slack’s senior principal of product management. “But first we need to make sure they’re not being … pinged by co-workers all day or have unmanageable channel lists.”
You can sort channels (click the three dots next to “channels”) alphabetically, by accurate activity or priority, which places the most used channels at the top of the list. You can also right click a specific channel and select “move to new section” to group related channels together. For example, users may want separate sections for channels related to fun, internal communications or team projects.
Muting channels and conversations as well as setting notification hours can also help reduce stress.
You can change your overall notification settings, located in “preferences.” Toggle settings so that they are only alerted to direct messages or when names or specific keywords are mentioned, or alternatively choose to not be alerted at all.
You can adjust the same settings — minus specifying keywords — for individual channels, which can also be muted. Muted channels will drop below channels that receive some or all notifications. They also will remain gray versus turning bold when there are new messages.
Do not disturb hours allow users to set days and hours during which they don’t want notifications. During that time, Slack will display a little “z” near users’ names to signal to others that they’re unavailable. Users also have the option to “pause notifications” or update their statuses for select amounts of time at any moment.
Finally, you can change the knock brush sound to other options, including a “ding,” “plink” or voice that says “hummus,” in notification preferences. You can also set specific sounds to differentiate between different kinds of notifications.
Slack offers a list of apps that can be integrated into its service such as Zoom and Webex, marketing and sales software HubSpot, and calendars from Google and Microsoft. Integrations can help users manage multiple services and keep co-workers abreast of what’s happening.
You can launch a Zoom meeting from Slack or see who’s on the Zoom call in Slack before joining. Integrating your work calendar into Slack will automatically update your status to show when you are in scheduled meetings.
Users can also automate some tasks.
Workers who regularly need specific information can create automated asks or messages to educate their colleagues about what is needed. For example, IT workers may want to know what an issue is, its level of urgency and other technical details. In that case, they can create a workflow, represented by a lightning bolt symbol, to lead people to an IT request form within Slack.
“If … people are posting lots of feedback … and it never has all the right information, it creates a lot of noise,” DeLanghe said. “With workflow builder, you can funnel it into one frame.”
You can set up custom messages colleagues receive when they join a channel and set automated daily reminders for regular meetings.
Slack offers several keyboard shortcuts that may make navigation faster when different workspaces and conversations are notifying you.
To quickly jump between conversations, type command + K on Mac or control + K on Windows and type in a person’s name or channel. And you can toggle between workspaces with a few shortcuts.
Workers need to understand how they’re using their technologies to get the best out of them, according to experts who study workplace stress and technology.
Mindy Shoss, associate professor of psychology at the University of Central Florida, recommends doing a time audit over a week or two to see where you spend your time. Adjust if needed.
“How are you using Slack? When these messages come in, are they giving you a break, helping you or are they giving you a pain in the stomach? That might mean you need to make change,” she said.
That may mean addressing team norms — perhaps only certain messages are urgent — or only making yourself available at certain times, Shoss said. She also said workers should be aware of when they check their Slack — is it the last thing you look at before bed — and what effect that may be having on stress levels. Create rules for yourself that will help you better manage your tech and your work, she said.
Keep in mind that you also need time to recover and detach from work — something some workers are finding more difficult in hybrid and remote work environments.
Adam Chati, a Harvard T.H. Chan School of Public Health visiting fellow, said workers who don’t provide themselves enough time to disconnect may suffer from something called “techno stress,” which can actually make them less engaged and productive. To prevent this, he suggests setting time periods to disconnect entirely from work and workplace technologies. Take time to relax, which can boost energy.
“If we’re always connected, we will be so stressed,” he said. Workers should make an effort to “stop work and relax.”
Ambrose, who goes professionally by Jacques, is driven by how confusing healthcare is. "You're expecting the consumer to diagnose themselves," he said. "It's so convoluted."
As a senior medical director in the Department of Psychiatry at Columbia University Irving Medical Center, he oversees over 100 clinicians, including about 50 physicians across four clinical sites. He aims to ensure proper quality of care, proper access, and ways to better integrate a patient's clinical experience.
One area of focus is mental health and encouraging employers to "begin that conversation, that difficult conversation, in an open and honest way," and that they "consider people as real human beings who have challenges beyond what we see in the office."
Ambrose emigrated from Vietnam to Hawaii when he was 9. "If you were to ask me, this refugee child, growing up in Hawaii, to a single mother who never graduated middle school, what was he going to do when he grew up, I would have said, 'Work at McDonald's like my mom.'"
He explained that he grew up in "a very impoverished neighborhood" and many of his friends are "still living in poverty."
He remembers when he realized healthcare was his calling — during a fellowship at the Satcher Health Leadership Institute, working under David Satcher, the former surgeon general.
Ambrose said he asked himself: "Where would you reconceptualize healthcare and where would you potentially think about potholes or deficits or gaps currently in the healthcare system for people that don't resemble the healthcare providers, which at the time was predominantly white and predominantly male."
He said his North Star is asking questions. "How do I continue to inculcate this dream of advocating for not only better healthcare, but better healthcare for vulnerable populations and minority populations?" he said.
Ambrose doesn't want patients to be left behind, especially those who may not have financial or social capital — or knowledge of the healthcare system.
"I don't want to forget my roots, and I don't want to forget there are many, many families and people who still need a lot of help."
By the time he finished his joint medical degree and Ph.D. program, Anzalone knew he wanted to focus on biomedical research.
Instead of continuing his medical training, Anzalone scored a postdoc position in the lab of David Liu at the Broad Institute. The lab has been at the forefront of discovering new ways to modify an individual's genetic information, and Liu has been spinning out those technologies into companies like Beam Therapeutics and Prime Medicine.
In Liu's lab, Anzalone helped develop a more precise way to modify DNA, called prime editing. Prime editing can alter, insert, or delete stretches of genetic material or individual letters.
After finishing his postdoc, Anzalone went to work with Liu as a cofounder at Prime Medicine, aiming to turn this technology into medical treatments. The company raised $315 million from top biotech VCs and went public late last year. It's now valued at $1.5 billion.
Theoretically, prime editing could help treat about 90% of genetic diseases, Prime Medicine said, and the company has named 18 it's going after first. Anzalone and his colleagues said the technique has worked in petri dishes and in animal experiments.
But while gene editing holds the promise of providing cures, or at least highly effective treatments, for many diseases that are now untreatable, there's lots of scientific work still to be done, and it could be years before we find out whether the technology can live up to its promise.
Anzalone and his colleagues want to start testing the technology in people, and Prime Medicine is asking US regulators for permission to do that next year. The company wants to examine whether prime editing can help treat chronic granulomatous disease, a blood-cell disorder that weakens the immune system.
"What fuels the excitement is that we could potentially address many of these things that haven't really had a therapeutic approach," Anzalone said. "And we can get at the root cause."
Bento was 8 years old when his brother fell into a coma after a car accident. When his brother woke up a year later, he needed intensive physical therapy, but that care wasn't available where the family lived in Portugal — forcing Bento's brother to seek care at a high-intensity rehabilitation center in Cuba for three months.
The experience stuck with Bento, he told Insider, as a prime example of the inaccessibility of traditional physical therapy.
In 2009, Virgilio Bento began his Ph.D. in electrical engineering at the University of Aveiro in Portugal. He dedicated his thesis to building the technology behind Sword Health. He wanted to create an affordable virtual-physical-therapy platform to help people suffering from pain associated with musculoskeletal conditions with personalized treatment plans and remote monitoring devices.
Bento made Sword Health available to US patients in 2020. Since then, the company has seen explosive growth, raising $316 million in venture capital and hitting a $2 billion valuation with its November 2021 Series D round.
Sword now works with more than 2,500 employers around the world, Bento said. The company surpassed $100 million in recurring revenue from annual contracts with employers earlier this year and hopes to end 2023 with $200 million.
Bento said Sword has continued to grow despite the economic downturn because the technology saves money for employers. Musculoskeletal conditions like back pain affect about 1.7 billion people globally, and cost hundreds of billions of dollars every year in healthcare bills and lost wages.
"Instead of having low-back-pain surgery, you can do Sword, which will get you better clinical outcomes and save your company money," Bento said.
The company is expecting to become profitable as soon as 2024, Bento said. And once Sword becomes profitable, he said, an IPO could be just around the corner.
Bervell said he mapped out his entire career in medicine on a posterboard when he was 12.
"I was going to go to Yale or Harvard, I was going to go to medical school, and I was going to become a surgeon," he said.
So far, so good. A Yale graduate, Bervell is a fourth-year medical student at Washington State University. He's planning to go into orthopedics — the "whitest" medical specialty, with nearly 84% of surgeons identifying as white, 2022 data from the Association of American Medical Colleges found.
But Bervell never predicted that along the way, he'd become a leading medical voice on social media. It all started during the COVID-19 pandemic when Bervell began questioning some things he was being taught on Zoom.
"Things like, 'Black patients are more likely to have asthma,' yet we don't talk about redlining," he said. "'Black patients are more likely to die of COVID,' but yet we didn't dive into the social determinants of health."
He started noticing all the subtle — and not-so-subtle — ways racial bias affects care, starting when doctors are trained.
Bervell began using TikTok and Instagram to tell stories about disparities, such as showing how pulse oximeters perform worse on darker skin, revealing how conditions like eczema and psoriasis actually look on different skin tones, and exploring why an outdated, racist measure of kidney function disqualified more African American patients from transplants.
The Ghanaian American "Z-illennial" bridges different cultures and generations with savvy storytelling and science communication. He's been tapped by the American Medical Association, the World Health Organization, the US Surgeon General, and the White House for his social-media skills.
Back home in Portland, Oregon, he also gets recognized: "Hey, I follow you on Instagram!" someone shouted out their car window recently. Another follower said she had a precancerous mole removed after she watched his video on acral lentiginous melanoma.
"Things like that really inspire me," he said.
As a machine-learning pioneer, Cho's research was a precursor to today's most advanced artificial-intelligence systems, like that underpinning the breakthrough app ChatGPT.
It propelled him into academia, and eventually, medicine.
At New York University, where Cho, age 38, is a prolific associate professor, he helped build a data-science hub for healthcare and studied how algorithms can make a difference for patients. One of his ongoing efforts is using them to Boost screening for breast cancer.
Eventually, Cho landed on an idea that needed more resources.
Together with Richard Bonneau and Vladimir Gligorijevic, standouts in molecular design and bioinformatics, Cho cofounded a startup called Prescient Design with the goal of using advances in machine learning to discover medicines more efficiently.
Other companies have pursued that, but Prescient envisioned a unique approach of using algorithms to design proteins, the basis of many medicines, with more input from lab workers, allowing science and math teams to work together more easily.
Since getting acquired by the biotech company Genentech in 2021, the Prescient program has grown more adept at designing proteins, Cho said, but it also works across the different "modalities" or classes of medicine, giving researchers an incredibly powerful tool kit.
While the Prescient team hasn't yet discovered a breakthrough treatment per se, their technology has been deployed across Genentech to aid a number of active projects, Cho said.
Similar to his work at NYU, Cho tries to foster a collaborative environment at Genentech, hiring computer scientists and biologists in equal numbers.
— Blake Dodge
When Christie was pushing for legislation in 2018 to cement abortion rights under Rhode Island state law, she heard repeatedly that the bill wasn't urgent. With Roe v. Wade in effect, abortions were legal across the US.
But Christie, who was working at Planned Parenthood of Southern New England at the time, saw reproductive rights were in danger.
The Rhode Island law passed in 2019, three years before Roe v. Wade was overturned. In 2020, Christie joined the healthcare advocacy organization Community Catalyst as its director of gender equity and health justice to fight for reproductive rights and health equity on a national level.
At Community Catalyst, Christie helps create policies such as the Black Maternal Health Act of 2021 and an upcoming bill focused on the impact of long COVID on maternal health.
Christie also advises state and private organizations on increasing access to midwifery services and helped push for a Rhode Island bill, passed in 2021, that made doula services eligible for reimbursement by private insurance and Medicaid.
She's spent years on voter-organization efforts, too, including managing a voter-engagement campaign in 2020 focused on Latino voters in Colorado, which helped protect access to abortion in the state.
Those rights, and reproductive privacy as a whole, remain under fire across the US. Christie pointed to "personhood" laws proposed in various states that would categorize a fetus as a person and grant them a constitutional right to life.
Christie said she worries that the personhood laws could impact fertility care for some, such as barring the creation of frozen embryos in assisted reproduction, and hopes to do more work in the future to protect access to those services.
After helping hospitals respond to the Affordable Care Act, Friedman, a consultant at the time, wanted to be in the room where policy was made.
In 2016, she joined a venture with the US Centers for Medicare and Medicaid Services to find ways to improve patient care while lowering the government's costs.
Like a startup, the CMS innovation center saw industry-shaping wins sprinkled with many failures, Friedman said.
An effort led in part by Friedman showed that you could lower individuals' healthcare costs by supporting their social needs, which helped spawn several digital-health startups.
Another program attempted to address maternal-health-provider shortages by giving Medicaid beneficiaries access to midwives, but the savings were too small, she said.
In 2021, when JPMorgan launched a venture meant to shake up the healthcare industry, Friedman saw an opportunity to apply what she'd learned to employer-sponsored insurance, which is how the majority of Americans pay for care.
She's now in charge of innovation at the bank's Morgan Health unit, which is tasked with lowering costs and improving care quality for the bank's 385,000 employees and dependents and for commercial healthcare more broadly.
One element of Morgan Health's strategy is working closely with startups to run pilots for JPMorgan's workers and their families to find ideas worth building on.
Thus far, a Columbus-based pilot in primary care is going well. Friedman's team has many more experiments planned, she said.
One will have the former CMS leader once again tackling maternal health, where people of color see worse outcomes regardless of income, she said.
Though it's still early, Friedman's team aims to support pregnant people more holistically, outside of their "capital-M medical needs," she said.
Friedman said she hopes to publish details about Morgan Health's work with providers, so other employers can replicate them. She said the right interventions exist — they're just not being implemented enough.
"If we can change that even modestly, I will feel really proud," she said.
— Blake Dodge
In 2019, as Gray was Preparing to graduate from Wharton Business School, a woman he loved died.
Unable to get a prescription for Suboxone, a medication that eases opioid cravings, she bought some illegally before dying from what Gray suspects was an overdose.
After memorizing books about addiction and talking to doctors, Gray was bewildered to learn how common her experience was.
"The fact that I didn't have a healthcare background wasn't all that relevant," he said. "It was about redesigning the evidence-based treatment to be more accessible to people who wanted it."
Later in 2019, Gray founded Ophelia Health, which helps treat opioid-use disorders by making Suboxone available online. Since the market downturn, the Wharton grad has been focused on building Ophelia into a sustainable business, which is no easy feat in addiction care.
Many of Ophelia's contracts with health plans are "fee-for-service" arrangements, which means the company is paid per visit. That leaves out a lot of other services Ophelia provides for patients, such as helping them find pharmacies and get their care covered by insurance.
About 75% of Ophelia's patients have insurance via the state-federal health plan Medicaid, and Medicaid's reimbursement rates are low.
To boost revenue over the past quarter, Gray has doubled the percentage of patients covered by more lucrative contracts with insurers, in which they pay Ophelia flat monthly fees.
Among Ophelia's insured patients, 70% are still in care a year after starting treatment, Gray said. So for insurers, the upfront investment in Ophelia is worth it.
But Gray is proudest that Ophelia provides care to people with no other options, and the startup will continue taking patients regardless of how they or their health plans pay for it.
Internal surveys at the company found many patients don't have primary-care doctors or any other touchpoint to the rest of the healthcare system.
— Blake Dodge
A common gripe about the US healthcare system is that a patient's data is often scattered in different places that don't communicate with each other.
That can make it tough for a single healthcare provider to get a complete picture of a patient's health, leading to redundant tests and a frustrating experience.
"There are so many different silos of data. All these different entities are disconnected, and it puts seniors in a very tough situation to navigate all this," said Kardes, the chief technology and experience officer at Alignment Health, an insurer that serves seniors enrolled in the privatized version of Medicare.
Kardes is the lead architect behind AVA, the proprietary technology that powers the insurance company. His goal in building it was to break down those data silos, allowing Alignment to take better care of its members.
AVA — short for Alignment's Virtual Application — gathers data from hundreds of different sources, such as hospital visits, medical claims, and lab results, to provide an accurate account of a patient.
It then applies more than 200 different AI models to make predictions. It can tell Alignment which members are likely to become hospitalized or develop a chronic disease, for example, and helps clinicians respond.
The company's in-house team of healthcare providers use information from AVA to focus on those high-risk, costly patients and try to keep them healthier and out of the hospital.
Next up, Kardes is exploring ways that new generative-AI technology could save healthcare providers time. A doctor could ask a generative-AI tool for a member's most accurate A1C test results and get the answer instantly, rather than having to comb through lab reports, he said.
— Shelby Livingston
While at Goldman Sachs, Kelemen held her own at tables full of primarily white men and got promoted to vice president.
She planned to stay, but things changed after the investment banker had her first daughter.
While on maternity leave in November 2020, she read everything possible about women's health and felt there was untapped potential.
"There's something here," Kelemen said of her thinking at the time. "Why is nobody on Wall Street paying attention?"
In summer 2021, the bank SVB Leerink recruited Kelemen. In a 90-minute meeting, she pitched Barry Blake, a top banker there, on creating a team to land deals in women's health.
But as a working mom at Goldman — short on time while weaning the baby off breast milk and caring for her when she got sick — she also told Blake about her family and intentions to grow it.
Blake endorsed both of Kelemen's plans.
Now, at Leerink Partners, Kelemen runs the only women- and family-focused healthcare investment banking practice, staffed by a small team of female bankers, on Wall Street. And every day, her calendar is blocked from 6 p.m. to 8 p.m. so she can spend time with her kids.
Part of Kelemen's challenge is convincing largely male investors of the value of companies that treat menopause, pelvic-floor dysfunction, pain during sex, and other elements of a woman's healthcare journey.
Last year, Kelemen sold a menopause-treatment startup to a national OB-GYN services company. It was the first deal Kelemen put together from start to finish, and the first time her client, the startup's CEO Jill Angelo, wasn't a man.
Right after the deal was announced, Kelemen hung up the phone and kissed her second daughter, who was then about 5 days old, on the head, thinking she might have better access to care one day. Then she started to tear up.
Kelemen added that other than that, she didn't work during her maternity leave.
— Blake Dodge
Living in New York, Krayn didn't think he'd have difficulty finding a mental-health professional after experiencing a traumatic event.
He couldn't have been more wrong.
"I had great health insurance and was living in a populated city in terms of finding available psychiatrists," Krayn told Insider in an interview. "But it took several years of trying to understand why I was having a problem with access and not finding the solution I was looking for."
A finance professional and alum of Rutgers University, Krayn became obsessed with the pain points in the mental-health care system. He came to realize that many clinics and startups were not meeting the needs of providers and insurers, which in turn meant that patients were also suffering.
"There's a shortage for mental-health care, but lots of people aren't solving the problem in the right way and aren't meeting the needs of all stakeholders," he said.
In 2019, this realization led Krayn to leave finance to launch Talkiatry, a national mental-health-care practice that provides in-network psychiatry and therapy. The startup has raised $115 million in VC funding.
Unlike some other mental-health startups, Talkiatry employs psychiatrists as full-time, salaried workers. Krayn said that while this approach is more expensive, it sets Talkiatry apart as a better place for mental-health professionals to work, which in turn translates to more and better care for patients.
"Talkiatry believes you can't just increase access, but you have to increase access to quality care," he said. "We hire the best-quality providers and have a clinical-leadership structure where we can manage a large patient population and produce better outcomes."
But with health insurance in this country largely tied to employment, it can be difficult for people to access quality healthcare while pursuing their career goals.
That's where Noah Lang comes in. A longtime leader in the tech and healthcare space, he's passionate about overhauling the relationship between jobs and health insurance.
"The average American works more than 12 jobs in their lifetime," he told Insider. "We need a benefits system built around people, not around one company."
In 2013, Lang launched Stride Health, which helps independent workers find affordable health insurance. The startup has raised $96 million in funding and counts Venrock, F-Prime Capital, and Kleiner Perkins as investors.
Stride was founded soon after the passage of the Affordable Care Act, which allowed people to get access to health coverage regardless of gender or health condition or where they lived, even if they didn't get insurance through work.
"The whole sector was being disrupted," he said.
Ten years into Stride Health's existence, Lang said the company has worked with three presidential administrations to increase access to different forms of healthcare, and it continues to reimagine what the industry should look like. About 3.5 million people get health insurance or other benefits via Stride, the company said.
"The thing that motivates me is building a new benefits system," he said. "We worked with the Obama administration and convinced them to build a private-sector integration with healthcare.gov, and that work was finished with the Trump administration. Now we're scaling with the Biden administration."
Leach spends a lot of time trying to look at the world through the eyes of an Alzheimer's patient. Sometimes, quite literally — as she dons big, dark glasses and headphones she's designing for people with mild cognitive impairment, Alzheimer's, and Parkinson's.
The headsets she designs as the head of translational research at Cognito Therapeutics emit bright flashing lights and sounds tuned to particular frequencies. This combination is meant to serve as a kind of disruptive exercise session for the brain, slowing cognitive decline and brain decay.
Early evidence suggests the therapy may work, at least among patients with mild to moderate Alzheimer's disease. Some patients also report better sleep, a nice bonus.
"What I personally hope to provide back to these individuals is that feeling of themselves — whether that's a memory, whether that's a smell that links to a memory, whether that's just being able to get up and go for a walk outside," Leach said.
Leach lost her grandmother to Alzheimer's 5½ years ago, so the work is deeply personal.
"She was the one who sparked my love for science," Leach said. "She always encouraged me to push and to grow and to learn."
Imagining her declining grandmother using Cognito's device grounds and motivates Leach's research, as she considers how to Boost device comfort and usability.
Cognito's therapy is now being tested in a large, yearlong trial of more than 400 Alzheimer's patients across the US to determine how well it works. If successful, it could be on the market as early as 2026.
— Hilary Brueck
As a medical-school student in Scotland, McGhee cofounded Current Health in 2015 to help expand access to acute at-home care. But it wasn't until the coronavirus pandemic hit in 2020 that business exploded.
McGhee built Current Health's remote-monitoring platform, with tools like video visits and wearables, to provide home care to patients who would otherwise be in the hospital for conditions ranging from cancer to broken bones. During the pandemic, patients were desperate to get care virtually or in their homes, rather than going to the hospital and risking infection.
Revenue surged "basically overnight," said McGhee, and the UK-based company landed multiple partnerships with health systems in the US.
Following the acquisition, Current Health had its best year so far in 2022, which McGhee attributed to growing consumer preference for care at home and to increasing financial incentives for at-home care, including in Medicare and Medicaid coverage. The company has notched partnerships with more than 50 healthcare organizations globally, including five of the US's 20 biggest health systems, per Best Buy.
McGhee, who still serves as Current Health's CEO, also advises Best Buy Health on its virtual-care strategy. Best Buy's sales have been slipping for over a year, but the retailer has continued to invest in its healthcare business.
McGhee said Current Health plans to announce more health-system partnerships in the coming months and wants to add more automation into its platform to ease the burden of certain administrative tasks for clinicians.
— Rebecca Torrence
Miller was at a Black Buddhist retreat in the San Francisco Bay Area when he came up with the idea for Spora Health, a startup that provides virtual healthcare for people of color.
The retreat was the first time he'd experienced mindfulness and meditation in a space designed for a specific culture. Folks were elated to "learn from one another, have conversations with one another" and talk about their experiences as Black Americans, Miller said.
He knew immediately that the same sort of space should exist in medical care, where people of color experience discrimination and worse outcomes than white people. He founded Spora in 2019 to address these problems by providing care informed by the culture of the populations it serves.
"This culture-centered approach was a hypothesis that if we do this well, at a minimum we can create more access to care," Miller said. "At most, we will save lives," he continued. "And, thankfully, I can say that we've done both."
The San Francisco-based company, which has raised $4.1 million, cares for thousands of patients in more than 22 states and will expand to the rest of the US later this year, Miller said. About three-quarters of Spora's providers are people of color, and the startup also trains clinicians to provide culturally competent care.
Miller's proudest accomplishment is building Spora Mommas, a program that aims to Boost maternal health outcomes for Black mothers, who are more likely to die during pregnancy than white women. Spora has helped facilitate more than 100 births in the year since it launched, Miller said.
"We're getting baby photos in, and our babies are like eight months after being born. They're still healthy and our mommas are doing well. That's the thing that I'm most proud about. It's incredible."
— Shelby Livingston
After helping lead Athenahealth, the medical software giant, through a major crisis, Nicewicz wanted to use her business chops to help patients more directly.
In 2019, having seen the toll of addiction in her own family, she took a role as COO at Groups Recover Together, a startup that treats opioid-use disorder, before becoming its CEO in 2020.
Groups had a unique approach to addiction medicine. Besides prescribing medication like Suboxone, which reduces cravings for drugs, Groups also required group therapy, allowing patients to form lasting connections, and outfitted clinics with bright colors and snacks.
Clinically, it was working. But financially, Groups needed to grow the business and get paid more. For the past four years, that's been Nicewicz's focus.
When she joined, most of Groups' revenue came from patients paying with cash and health plans reimbursing it for service. In addiction care, that often isn't enough to sustain a business.
Groups was eventually able to show insurers that it saved them money, Nicewicz said. As patients stopped using drugs, they needed less medical care, Groups data showed.
Now, health plans pay Groups a premium. The vast majority of its revenue comes from insurers paying set monthly fees for patients. Those fees are typically more than threefold what Groups would otherwise be able to charge for their care, Nicewicz said.
The model has allowed Groups to expand to more than 15 states while focusing on patients that need it most. Seventy percent of the 16,000 patients it sees each week have Medicaid, the government-sponsored insurance program for people with lower incomes, she said.
While Nicewicz has ideas to expand to provide mental-health services, the CEO is in no rush.
Remembering lessons from Athena, where Nicewicz ran cost-saving programs and eventually the company's sale, she shies away from the "growth at all costs" mindset, she said.
— Blake Dodge
Quijano Rubio has long had a passion for pushing the boundaries of science then turning his discoveries into business ideas.
"My passion was entrepreneurship," he said. "I've always been the kind of person that is, 'Oh, you're working on that project, why? Why is that useful?'"
Quijano Rubio grew up in Valencia, Spain, and studied biotechnology at the Universitat Politècnica de València. He won a prestigious scholarship to continue his education and earned his Ph.D. under David Baker at the Institute for Protein Design at the University of Washington.
He's already cofounded two companies based on his work on custom-designing proteins. The first, Neoleukin, focused on creating better cancer treatments. The second company is Monod Bio, a spinout from Baker's lab that's aiming to create better medical-diagnostic tools. Monod started in late 2021 and raised a $25 million seed round in 2022.
Monod relies on proteins that are designed to bind to a particular substance and emit light based on the amount of that substance that's present. Quijano Rubio has published research showing this method can detect things like the novel coronavirus and troponin, a protein that can signal a heart attack.
The company envisions creating a device that can be used in a hospital or clinic in place of some lab draws. In theory, this could make some labs faster, easier, and less expensive, Quijano Rubio says.
Quijano Rubio said the company is working to develop tests in areas such as cardiovascular disease and hormonal diseases, but declined to name particular ones. The company isn't pursuing tests for infectious diseases like COVID-19 right now.
Rajput dropped out of a Ph.D. program in neuroscience in 2015 and started Biofourmis, a company that uses wearable sensors, artificial intelligence, and data analytics to help hospitals track and care for patients in their homes.
This was the year the Apple Watch became available, and fitness trackers were catching on. At the same time, the US Food and Drug Administration was starting to clear wearable medical devices for certain uses, Rajput said.
But he was more interested in what could be done with all the data this new technology produced.
The idea behind Biofourmis was, "can we leverage all these passively captured data from patients to really be able to predict complications and deterioration?" he said.
Biofourmis, which has raised $465 million in venture funding, works with hospitals to monitor and manage patients at home. It also works with pharmaceutical companies to manage clinical trials. It aims to detect problems and predict when patients' conditions might worsen, so doctors can step in early. Its software also provides recommendations on next steps, such as adjusting medication, Rajput said.
"Where typical remote monitoring has failed is: How do you act on that data to drive outcomes?" he said. "We wanted to focus on how do we use that data to influence outcomes, drive better patient outcomes, and save costs for our partners?"
The company initially worked with hospitals to take better care of their patients. About a year and a half ago, it branched out into providing care itself, which helps hospitals dealing with staffing shortages and frees up their beds for more patients, Rajput said.
While the company is growing rapidly — it managed more than 100,000 patients in 2022 and hopes to double that figure this year, Rajput said — its mission has always been to tailor care to the individual, he said.
"There's been a lot of talk about personalized medicine, precision medicine," Rajput said. "We have been actually doing it for many years now."
— Shelby Livingston
Rees is at the forefront of perfecting a type of gene-editing technology called base editing. Base editing lets scientists change the individual letters that make up an individual's genetic code, or DNA. The hope is that these tiny edits can help treat or even cure diseases.
Rees is the associate director and head of lead discovery at the biotech company Beam Therapeutics. Her work is focused on finding new base-editing tools to target different diseases and making these tools more effective and less prone to errors.
Beam's most advanced projects focus on the blood disorders sickle-cell disease and beta thalassemia, and on treating certain kinds of severe cancer. Meanwhile, Rees is focused on the other end of the spectrum — scientific advances that are still in their infancy but that could one day help treat diseases in people.
Rees told Insider that she can't publicly discuss most of the diseases she's working on, though she's involved in the company's efforts to treat hepatitis B. It's difficult to say when her work will make its way to treatments used in people.
"My hope is that a lot more patients who are currently suffering from rare or common serious diseases will have options provided to them for curing those diseases in a onetime therapy or single-administration therapy that has been enabled by base editing," Rees said.
Rees earned her undergraduate degree in natural sciences at the University of Cambridge in the UK, before pursuing a Ph.D. at Harvard in the lab of David Liu. Liu is a cofounder of Beam, and two of his students — Nicole Gaudelli and Alexis Komor — helped invent key parts of base editing. Rees worked closely with them to build on their discoveries, an effort that continues at Beam.
Grand Rounds, now called Included Health, was started in 2011 to help people with complex medical conditions connect with experts to get second opinions.
Sanghvi joined the company three years later, after getting her Ph.D. in bioengineering from Stanford University. She wanted to use her skills in computational biology and statistics to make a difference in patient care.
So she helped build Grand Rounds' provider-matching service, which uses machine learning to determine which provider is most likely to provide the best outcomes for a given patient. The service separates out decisions as granular as which provider is the best at getting patients to stay on medication, or which has received Good Score in communicating with patients.
The tech brings big savings for the more than 300 employers and health plans that Included Health works with, too — last year, Provider Match helped drive a few hundred million in cost savings for those companies, thanks to decreased medical events like hospitalizations and better outcomes for patients, according to Included Health.
Grand Rounds merged with Doctor on Demand to become Included Health in 2021. Sanghvi now leads Included Health's data-science team as its senior director.
Sanghvi and her team also built automation tech that predicts which patients are likely to have a "care episode," such as a cardiovascular event, so they can get to the care they need faster.
Sanghvi said she wants to build patient trust in healthcare by using technology. At the moment, that trust is sorely lacking, she said.
"We're not there in health, but we're there in so many other industries," she said. "There's a huge opportunity to bring that to healthcare, instead of patients trying to avoid it or hating it every time they need care."
New ideas and pilot programs at hospital systems often peter out before they get the chance to reach the masses. Atropos Health, a clinical-consultation startup born from a pilot at Stanford Healthcare, is a rare success story.
It's on the way to being one, at least. It's Sanghavi's job to make that happen.
Sanghavi is the president of Atropos, where he's helping to develop its product and bring it to health systems around the country. He joined the company as part of the founding team in 2021. He previously held strategy and leadership roles at Haven, Centivo, and the Cleveland Clinic.
At its core, Atropos aims to help doctors provide better care based on evidence from past patients' cases. "What we're talking about is changing the way that every physician and physician-patient combination make decisions about the care that's being delivered," Sanghavi said.
Spun out of a Stanford research project begun in 2011, the company answers doctors' questions by aggregating and analyzing data from millions of anonymous patient records. Atropos physicians review the analyses and write short summaries doctors can use to help them make treatment decisions.
And Atropos can do this in two business days, whereas a similar-caliber study could take 10 months in a research setting, Sanghavi said.
A big part of Sanghavi's job is getting health systems to adopt Atropos' service. That means making sure clinicians trust its analyses. While it's early, the company's already being used by thousands of doctors at a handful of institutions, he said.
In late 2022, Atropos clinched a partnership with Mayo Clinic — a deal orchestrated by Sanghavi over 18 months. "That was kind of our first big stamp of approval after Stanford," Sanghavi said.
— Shelby Livingston
Robin Shah has been around cancer care for just about his entire life.
The son of an oncologist, Shah got a crash course in operating a cancer center when he began working at his father's practice while in college. Next up was Flatiron Health, where he built tools to help doctors shift their cancer practices to new ways of getting paid that reward better care. In 2018, he helped launch OneOncology, a network of independent cancer practices.
Between these day jobs, Shah helped friends who needed assistance navigating a cancer diagnosis, he said.
In one case in 2019, the mother of Shah's high-school friend was told by her primary-care doctor she may have cancer but would have to wait at least a month to see an oncologist. Shah connected her with his father, who ordered tests within a day to confirm her diagnosis of stage 4 pancreatic cancer and started her treatment 36 hours later.
"Who knows what a four- to five-week wait just to get a consult, to get the test ordered, to then come back to get a treatment plan would've done?" he said.
The experience prompted him to build Thyme Care to help guide people through cancer treatment. The company, which has raised $22 million, helps patients understand their diagnosis, manage symptoms, and schedule appointments. It also connects patients to resources such as meals and transportation, Shah said.
Thyme Care is working with about 1,600 patients and expects to double that number by the end of the year, Shah said. Beyond improving care for these patients, Shah said he's excited about the talented workers that companies like Flatiron, OneOncology, and Thyme have recruited to help Boost cancer care.
"We've attracted amazing talent and I think it has permeated to additional companies being built in the healthcare space," Shah said. "I am so excited about the impact, not just me, but everybody that's working in this space is going to have on healthcare for the next 20 years."
— Shelby Livingston
When he was 20 years old, Smith left college and became housebound because of his obsessive-compulsive disorder. After experiencing misdiagnoses and ineffective treatment, he ended up waiting seven months to see a clinician trained in specialized OCD care. The cost was $400 a session.
The treatment he received, called exposure and response prevention therapy, helped Smith recover so he could return to college.
But the lack of specialists trained to provide ERP therapy means that of the 2.5 million US adults with OCD, few can access the care that worked for Smith.
Smith started NOCD in 2014 to bridge that gap. The Chicago-based startup connects patients with licensed therapists who provide virtual ERP therapy for OCD. Patients can also access peer communities and self-help tools through NOCD's app.
"Our goal is to ensure that everyone in the US, no matter where you live or how much money you make, can access this treatment," Smith said.
NOCD provides its clinicians with ERP therapy training, and the company built its own electronic-medical-records system to better monitor its patients' progress.
Today, NOCD is available in all 50 US states and internationally, and tens of thousands of people get therapy through NOCD, Smith said. About 130 million people in the US can use insurance to pay for NOCD's services, he added. Without insurance, NOCD's sessions cost between $90 and $210, depending on the length of the visit.
The startup has raised about $85 million and says it expects to bring in more than $50 million in revenue this year.
NOCD has begun providing care for other mental-health conditions that can present alongside OCD, like tic disorders and hoarding disorders. Smith said NOCD plans to launch care for body-dysmorphic disorder in the next 12 months.
— Rebecca Torrence
After leading strategy for a health plan and a pioneering digital-health startup, Sullivan wanted an even bigger arena.
Sullivan joined Microsoft in 2017 to help the company's global research organization find its footing in healthcare.
As the senior director of strategy and operations within the health and life sciences division of Microsoft Research, Sullivan helps Microsoft decide where to place its bets in artificial intelligence, guided, for one, by the goal of directly helping patients.
That often looks like securing partnerships between Microsoft and other companies, helping researchers break up their big moonshot ideas into bite-sized milestones, and translating the value of Microsoft's health work to other areas of the company and to the industry at large.
For example, Sullivan was part of the team that made the case for Microsoft to work with Nuance Technologies, known for making transcription tools for doctors.
In 2018, Sullivan landed Microsoft's partnership with Adaptive Biotechnologies to map the human immune system. The deal included a $45 million Microsoft investment in the biotech company and other business terms that Sullivan helped negotiate.
The companies have already discovered a type of blood test that has opened up new research avenues for vaccines, Sullivan said.
Now, as the healthcare industry races to work with Microsoft's latest AI models, Sullivan is even busier than usual, helping Microsoft decide where to go deep with partners, but also helping other companies understand what they can accomplish on their own with these powerful tools.
"To help evangelize a lot of the really incredible stuff we're doing is something I'm very proud of right now," she said.
— Blake Dodge
Wong became homeless at 9 years old in New Zealand and bounced between foster homes in his teens, so he desperately wanted a family of his own. When he came out as gay at age 22, he was thinking he could never have one.
Now, Wong and his husband are exploring having a child via a gestational surrogate — a path opened up to him by the growth of and innovations in the fertility industry, he said.
Wong joined the fertility-benefits startup Carrot Fertility at the beginning of 2021 and became the company's chief product officer a year later. He's helped build out Carrot's services beyond fertility care, including support for menopause, low testosterone, pregnancy, and postpartum care.
Trained as an engineer, Wong also oversees Carrot's efforts in data science and information security, as well as its global expansion.
Central to Carrot's growth, he said, is leveraging clinical evidence to build Carrot's new offerings and collecting its own data to Boost the company's services.
Carrot now works with more than 850 employers and health plans globally. More than 2 million people in over 130 countries can access the company's services with coverage through those employers.
Before coming to Carrot, Wong held product-management roles at companies including the genomics startup Genome Medical, the AI-powered life-sciences company Aktana, and the mental-health startup Lyra Health.
He's particularly excited about the future of precision medicine in fertility and in other areas like genomics and behavioral health and in using AI to connect each patient to the right provider or treatment for better outcomes.
"I think the best of precision care in these spaces has yet to come," he said.
— Rebecca Torrence
Zhang is at the forefront of the biotech industry as the CEO and a cofounder of Verge Genomics, working on drugs for diseases that don't have good treatments, particularly ALS, Parkinson's, and Alzheimer's.
She's worked with machine learning and computational biology since her Ph.D. days and thought even then that "it's kind of crazy that people are still using things like mice to try to predict what will work in humans."
It's not a crazy concept — using human data instead of animal models to identify effective human drugs. With AI advances across science and engineering, there's "potential to totally revolutionize how we discover drugs," she said.
One way is through cutting the time it takes to develop certain drugs, which can take 10 years or more. Verge targeted and developed a potential ALS drug and is now testing it in people, all in the span of four years. The company has raised more than $134 million in funding.
"You can have as much data as you want and the fanciest algorithms but you're still not going to be able to predict something that actually works in humans," she said.
That's why Verge works with data derived from brains and spinal cords donated by people after they die. By generating the right clinical data, Zhang said they can feed it back into the company's AI to train and Boost it.
Beyond drugs, Zhang focuses on people and wants to show "there's a new way that people can work together that doesn't need to be ego-driven, that doesn't need to be fear-driven." She welcomes emotions in science because "emotions have a huge amount of data," and it's important to bring that to the table.
Zheng has spent her career focused on women's health — but the RH Capital investor took a roundabout route to the venture-capital world.
Zheng worked in reproductive health in Uganda and China before going to medical school at the University of Michigan. She took two years off from medical school to earn an MBA at Harvard, before returning to graduate. Then she dropped out of residency to join McKinsey, where she was the consulting firm's point person on women's health.
In 2021, Zheng realized she wanted to work with innovative women's-health companies more directly, and joined RH Capital as a principal.
RH Capital is one of the larger funds focused solely on women's health, Zheng said, with about $40 million in assets under management. That's a relatively small figure in the world of venture capital, which highlights that women's health remains an emerging area of investment, Zheng noted. The sector has been growing steadily over the past few years, with $1.16 billion invested in women's-health startups in 2022, according to PitchBook.
The fund has remained active despite the economic downturn, Zheng said. She's interested in investing more in contraceptive innovations, life sciences companies working on maternal-health diagnostics, endometriosis treatments, and menopause care.
In addition to her work at RH Capital, Zheng mentors companies in the startup accelerator programs FemTechLab and Tech4Eva, and she regularly speaks at conferences and publishes articles about women's health.
"It's been amazing to see the conversation move from more targeted circles onto the main stage," she said.