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© Thomson Reuters FILE PHOTO: Illustration photo of computer code on a screen above a Chinese flag

SHANGHAI/SINGAPORE (Reuters) - Chinese quant hedge fund managers are rushing to explore ChatGPT-style tools, embracing the emerging AI technology that has sparked a global frenzy since the release of the widely popular Microsoft-backed OpenAI chatbot.

© Thomson Reuters FILE PHOTO: Illustration picture of ChatGPT

Quants' focus on advanced artificial intelligence to aid decision-making comes amid a tough investment environment, as China's post-COVID recovery wanes and competition rises in the country's 20 trillion yuan ($3 trillion) private fund industry.

"ChatGPT is an epoch-making application ... It can draw conclusions from a complicated network of relationships with numerous dimensions in ways human brains cannot," said Steve Chen, partner of Shanghai-based MX Capital.

"Exploring its ability is now our main focus."

His hedge fund already uses ChatGPT to better understand a company's fundamentals and avoid value traps, project earnings power, and identify investment opportunities and risks.

ChatGPT, trained using a huge amount of data, can write poems, compose music, draw paintings, and generate other strikingly humanlike responses based on user prompts.

A ChatGPT-like tool boosts quants' ability to process text-related data, said Feng Ji, chairman of Baiont Capital.

"We were also inspired by ChatGPT to build large models using trading data, instead of text," Feng said.

Feng's hedge fund, backed by former Google China chief and AI veteran Kai-Fu Lee, has invested heavily in hardware to enhance computing power required for model-training.

High-Flyer, among China's biggest quant funds, has hailed advanced AI as the "greatest innovation of our times".

In April, High-Flyer announced the setup of a research unit to explore disruptive AI technologies.

MACHINE VS MAN

Last week, Beijing-based asset manager Zhishan Investment said it would deploy AI robot "Cybertron" across all products and use it to help reshape its investment methodology.

Baiont Capital's Feng is more ambitious, seeking to let robots take full control of the investment process - from data analysis and prediction, to decision-making and execution.

Feng's Nanjing-based company uses high-frequency trading strategies and recruits only computer scientists, not Wall Street traders.

Robots do a much better job than humans in forecasting share moves over the next hour as "machine learning is designed to make such predictions", Feng said.

While ChatGPT-like tools have stirred excitement, the race to develop and adopt powerful AI services has also fuelled anxiety about privacy, safety and job security.

Regulators are looking for ways to tackle the impact of generative AI technology. In China, where technology giants such as Alibaba, Sensetime, and Baidu have ramped up AI bets, regulators unveiled draft measures in April giving them greater oversight of the technology.

Larry Cao, senior director of research at CFA Institute, cautioned the technology could put at stake jobs of bankers and fund managers working in areas where data is easily accessible.

"If you're an analyst just telling people the story that everybody is telling other folks, what's your value-add? I can just ask ChatGPT, right?" said Cao, editor of a newly published handbook on how to apply AI and Big Data in investments.

"The threat is real, but it's not tomorrow."

($1 = 7.0827 Chinese yuan)

(Reporting by Samuel Shen and Tom Westbrook; Editing by Himani Sarkar)

With the aim to empower youth in India with future-ready skills, Microsoft has signed a Memorandum of Understanding (MoU) with the Directorate General of Training (DGT), Ministry of Skills Development and Entrepreneurship (MSDE), to train students and educators at government-led Industrial Training Institutes (ITIs) and National Skills Training Institutions (NSTIs). As part of this collaboration, Microsoft will offer a wide range of courses, including training in AI, cloud computing, web development and cybersecurity skills for nearly 6000 students and 200 faculty members.

This training will empower young students with industry-relevant skills, enhancing their employability, and connecting them to relevant job opportunities. In addition to future skills training for AI fluency and fundamentals, cloud computing and web development, students have the opportunity to learn from industry experts and engage in deep-dive sessions, multifaceted capstone projects. Train-the-Trainer workshops will be conducted to empower faculty members on AI fluency and fundamentals, cloud computing, web development, Power BI, and soft skills. These trained faculty members can then train ITI students attending computer operator and programming assistant (COPA) training. The CyberShikshaa programme focusing on basic and intermediate cybersecurity skills training will also be expanded to students and educators at 10 NSTIs for women.

“With acceleration in digitalisation post-Covid, there has been a huge demand for talent and skilled workforce in the tech space. Our platforms like DGTs, ITIs and STIs are driving the skilling efforts from the frontline. I firmly believe that collaborations like that of DGT and Microsoft, will play a pivotal role in nurturing talent and Young Indians will get better access to skills that are more relevant in this fast-changing digital age,” Rajeev Chandrasekhar, minister of state (MoS), Ministry of Skill Development and Entrepreneurship and Ministry of Electronics and IT, said.

Furthermore, with support from DGT, Microsoft will facilitate the placement of successful learners from this project, through collaborations with recruitment and placement agencies, and statewide job fairs to promote employment opportunities.

“Empowering India’s youth with digital skills is crucial to building economic resilience and ensuring a brighter future for all. Our collaborate with DGT is a step towards this goal. Together, we can build an inclusive future for the young leaders of tomorrow and unlock the full potential of the country’s workforce through this sustainable flywheel of skilling and employment,” Jean-Philippe Courtois, executive vice president and president, National Transformation Partnerships, Microsoft, said.

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Like most (if not all) Hackaday readers, I like to know how the technology I use works. I’m always amazed, for example, how many otherwise smart people have no idea how the cellphone network works other than “it’s a radio.” So now that I have two phones with fingerprint scanners on them, I decided I needed to know more about what’s going on in there.

Sure, I assumed the sensor was capacitive (but maybe not, I found out). Plus we all know some super glue, scotch tape, and gummy bears are all you need to fake one out. However, that’s been known for about 15 years and we are still seeing phones and other devices rolling out with the same scanners. So for now, put aside the debate about whether we should be using fingerprint scanners. Let’s talk about how those sensors work.

Three Tech

There are at least three common ways to scan a fingerprint: Take a picture of it, sense it capacitively, or sense it using ultrasound. However you do it, you wind up with an image of the print. Then from that image, you have to work out if it is the right finger or not.

Optical

Makes sense that you can take a picture of a fingerprint using a camera-like device. In fact, I’ve seen this used when getting fingerprints made for identification. The latest Samsung Galaxy S8+ uses an optical sensor under the phone’s screen. This was planned, apparently for the Galaxy S8, but was scrapped at the last minute due to technical issues. However, some optical sensors can be easy to fool with a picture of a finger and a dirty finger can cause issues, too. Some sensors use a second method to detect a live finger such as detecting a pulse or body heat.

Capacitance

There are two ways capacitive fingerprint sensors–the kind in most phones–can work: active or passive. Either way, each sensor element acts like a capacitor. For passive scanning, your finger forms the other plate of each capacitor. In active scanning, the sensor has both plates and your finger changes the expected capacitance.

Either way, these are cheap and fairly robust. The only problem is your finger has to come in close contact with the silicon sensor and that can cause problems if your finger has an electrostatic charge on it. Asking people to wear a wrist strap to unlock their phones isn’t practical, so the sensors require special construction to help them handle high voltages due to electrostatics.

Ultrasound

Some latest phones use ultrasound to sense the dermal layer of your finger which also has the fingerprint ridges. These don’t have problems with dirt and even a scar on your finger won’t stop it from identifying you. It may even be less susceptible to fake-finger spoofing, but time will tell if that’s true or not.

Identification, Please

So one way or another, you have a bitmapped image of a fingerprint. Now what? Apparently, there are three types of fingerprint patterns: arch, loop, and whorl. The arch is just what it sounds like and starts on one side of the finger and goes to the other. The loop doubles back and exits the same side it started. The whorl circles around a central point. Interestingly, family members often have similar fingerprint patterns, but even identical twins don’t have the exact same prints.

There are several algorithms for matching prints, but the most common one is the minutia matching algorithm. This looks at three things: where your ridges end, very short ridges, and places where ridges split into two. Based on those, there’s enough information to differentiate your print from most other people’s. You can find a short but scholarly paper describing the process if you want to try your own implementation. Or, if you search, there are multiple variations on GitHub, some based on later papers.

The algorithm isn’t foolproof, of course. But the chances of someone trying to unlock your phone randomly having the same pattern is pretty low. Google’s guidelines, for example, says the scanner can have no more than a 0.002% false positive rate.  If 50,000 people try to unlock your phone, then, you can expect one of them will get in. Of course, if the one that gets in is the first one of the 50,000… maybe you should buy a lottery ticket if that happens.

Giving Your Phone the Finger

That’s how a fingerprint scanner works in a nutshell. Like I said, though, faking out a sensor isn’t that hard if you are committed (see the video below) so maybe you shouldn’t lock your bank password behind your fingerprint.

Despite that, we’ve’ seen fingerprints open garage doors and you can find USB scanners that will work with a Raspberry Pi, if you still want to use this technology.

Of course, you have to wonder why we even have fingerprints or why they are just on our fingers. Scientists don’t know, but they have disproved the old notion that it helped us grip better.

Artificial intelligence could put jobs in jeopardy

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Artificial intelligence has recently become a hot syllabu around the world as tech companies like Alibaba, Microsoft, and Google have released conversational chatbots that the everyday person can use. While we're already using AI in our daily lives, often unknowingly, these forms of computer science are very interesting to a large population.

Some are hoping to simply learn to properly use the chatbots to make extra money on the side, experiment with robot interactions, or simply catch sight of what the fuss is all about. Others, however, are hoping to inspire change and become part of the history by physically advancing AI technology alongside tech tycoons.

No matter the contribution or footprint you plan to have on such a controversial and competitive industry, there is plenty of education for you to find.

ARTIFICIAL INTELLIGENCE: FAQ

© iStock Artificial Intelligence is the leading innovation in technology today. iStock

Provided you are seeking a comprehensive understanding of AI and the ability to contribute to the industry, there are countless opportunities to absorb a mastery of data science, machine learning, engineering and computer skills, and more.

A Bachelor’s Degree in Science is a four-year undergraduate program and a Master’s Degree in Artificial Intelligence, while it can vary from person to person, is typically a two-year program.

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If you’re simply hoping to better grasp how to use natural language processing tools like ChatGPT or Bard, or AI image programs like Midjourney, there are a myriad of books, online courses, blogs, forums, video tutorials, and more which educate users.

Follow the social media platforms, websites, and email newsletters of artificial intelligence experts and tech titans like Elon Musk, Bill Gates, or Andy Jassy, published content from AI giants like Microsoft, or general intelligence companies like OpenAI, Deepmind, and Google Brain.

Here are a few resources to get you started on understanding the basics of AI, using sophisticated artificial intelligence chatbots, the advancements and dangers of AI, its history, and more.

If you’re looking to become a contributor to the advancements in AI or develop a greater understanding of computer science, machine learning and more, consider a Bachelor of Science degree.

A Bachelor of Science with a concentration in Data and Computational Science is a degree "based on the combination of real-world computer science skills, data acquisition and analysis, scientific modeling, applied mathematics, and simulation," according to George Mason University’s site.

ARTIFICIAL INTELLIGENCE QUIZ! HOW WELL DO YOU KNOW AI?

A number of universities offer a BS in Data and Computational Science. You can also seek a degree in related subjects including information technology, computer engineering, statistics, or data science. Those with a computer science, mathematics or programming background will have the fundamentals to get started with a degree to become an AI professional.

There are a multitudinous array of variations of Master’s Degrees in Artificial Intelligence around the U.S. and Canada. A few of them include the Artificial Intelligence Master’s Program Online at Johns Hopkins University, the Master of Science in Artificial Intelligence at Northwestern University, and the Master’s in Artificial Intelligence at The University of Texas at Austin.

© Provided by The Register

Gartner asks for some fundamentals in a slew of AI-in-analytics announcements

Following a fortnight festooned with analytics and data management announcements, Gartner has warned that users are not keeping pace with analytics vendors' fashion-following desire to inject just about anything they get their hands on with some AI serum.…

Speaking as the curtain fell on global analyst company’s Data & Analytics Summit in London this week, Jason Medd, Director Analyst at Gartner, said despite the slew of announcements from Microsoft, SAP and Google, many customers still had to catch up in terms of data quality.

“Any shiny new thing that comes along, very often data quality gets dismissed, they try and implement it, they try and get some value, and… the quality issues start to creep in. There are so many ways bad data can creep into a system. People start to lose track of it as they keep chasing that shiny new thing,” he told The Reg.

Gartner analysts estimate that through 2024, half of organizations will adopt modern data quality technology to better support their digital business initiatives.

Earlier this week, Microsoft relaunched its analytics platform under the new name Microsoft Fabric, which encompasses a data lake called OneLake, Data Science, Data Warehousing, and Power BI. Microsoft promised that Copilot would allow users to create dataflows and data pipelines, generate code and entire functions, build machine learning models, or visualize results using conversational language.

Meanwhile, Google formed a partnership with enterprise software company SAP to bring together its Datasphere analytics tools with Google Cloud’s data and analytics technology, including its AI and machine learning (ML) models.

However, Medd said users were still struggling to find the right business case for analytics rollouts.

“The challenge is connecting the business use case [to the technology]. One of the things we did see a lot at the conference was people asking how to get value out of data. To a certain degree, it doesn't necessarily matter how fast the technology runs until you get that business case right and until you understand how you're going to get value from data,” he said.

It was also a challenge getting the right culture and awareness of business data as an asset, as much as the technology, Medd said.

Gartner has a four-step process it recommends for data quality. It starts with understanding which data influences business outcomes most, introducing data quality accountability, then validating data quality, and finally, integrating data quality into corporate culture. ®