More than 1 million users signed up within a week of the online release of the artificial intelligence (AI) tool ChatGPT on November 30, 2022, a date dystopians think will live in infamy. In just two months, 100 million users signed up, a record for any new software, app, or online tool.

ChatGPT—along with other recent, if less famous, competitors—does in fact constitute a radical advance in software, far more consequential than when IBM’s Deep Blue defeated chess champion Gary Kasparov in 1997, back in the Internet’s first “century.” ChatGPT puts us one giant step closer to normal people having the ability to access supercomputing performance using natural language. So, the chattering classes are all a-twitter with the usual tropes about how automation will bring about the end of work, in this case for “knowledge workers.”

We will, in due course, see news stories about practical “use cases” for ChatGPT. But for now, a lot of people are having a lot of fun playing with it and discovering just how much better this latest AI software is compared with, say, computer interfaces like Alexa or Siri.

Since ChatGPT was trained to recognize and produce language—a fact telegraphed by the word “chat”—I joined the rush to test the new tool, asking it to write a short blurb to promote a Caribbean cruise and to do so in the style of William Shakespeare. Within a fraction of a second, ChatGPT came back with a C+ effort:

Hark! good sirs and fair maidens, lend an ear
To news of grandeur and luxury rare.
A Caribbean cruise doth now appear
For those with hearts that do adventure dare.
Upon the azure seas, our grand barque
Shall bear ye to fair isles of sand and sun
Where comfort and opulence shall embark
And memories forever to be won.

One user gave ChatGPT a standard medical exam; it passed. Another gave it a standard test for Wharton MBA students; passed again. Impressive parlor tricks, but such stunts tell you more about the simplicity of the tests than about the brilliance of ChatGPT. Passing a written test that indicates proficiency with medical, business, or finance rules and nomenclature doesn’t say anything really useful about whether the test taker will be a great surgeon or business leader. Nor would passing the written test for driving a car predict anything about whether the test-taker could be a safe driver, much less a winning Formula One racer.

Engineers love to perform stunts with new technologies. Of course, depending on the stunts, the performance does say something about the state of a technology. The lesson of Kasparov’s loss was to see the “overnight” progress, nearly 20 years later, of Google’s AlphaGo supercomputer beating the world’s Go champion in that much more complex game. Now, almost a decade after that, comes the sonnet-writing, test-passing ChatGPT. The point is that even in the seemingly high-velocity world of computing, so-called exponential change takes quite a while. That’s the reality of commercializing at scale all forms of engineering progress.

For a way to think about what comes next for AI, consider what followed analogous stunts in the history of aviation—a field more relevant to computing than most realize.

The feat that made it clear that an age of useful aviation was possible was Charles Lindbergh’s 1927 barnstorming of all 48 contiguous U.S. states over a period of 95 days, following his better-known stunt of the first nonstop flight across the Atlantic Ocean, which was in retrospect a kind of Kasparov–Deep Blue moment. Even though, following Lindbergh’s odyssey, aircraft would be used in business, industry, and warfighting, it took another three decades until the engineering was good enough to yield, in 1957, the Boeing 707, which launched the age of mass commercial aviation. From there, as entirely new industrial edifices and national infrastructures were built out, the number of revenue-passenger-miles (to use that industry’s term of art) would soar more than a hundredfold by the year 2000.

The emergence of useful, broadly available aviation brought big shifts to the structure and nature of business and employment in transportation. But it ended neither the role of, nor the expansion of uses for, ships, trains, or trucks; and it didn’t end employment in those sectors. Overall, in fact, employment in U.S. transportation services doubled by the turn of the century. It’s no exaggeration to frame ChatGPT as a Boeing 707 moment.

But we’re being told that, well, this time is different. In part, that’s because it seems somehow spookier when technology accelerates tasks performed invisibly in cyberspace—that is, cognitive rather than physical tasks. ChatGPT has reanimated the now-ancient philosophical debates about whether machines think and whether, as they get better at imitating human behaviors, they’ll make a lot of humans redundant and bring on the often-predicted age of unemployable humans.

It is true that there is something different this time, as there is every time. The specifics of the existing machines are different. But what’s not different is the overall effect of automation. Not to diminish the social and political challenges that all disruptions bring to markets and people, but automation has always boosted productivity and thus overall wealth and employment. If labor-saving technologies—namely, automation—were a net job destroyer, unemployment should have been continually rising over the course of modern history as (physical) automation inexorably expanded. It didn’t. MIT economist David Autor has been particularly eloquent on the apparent paradox of seeing continued rise in employment despite advances in labor-reducing technologies, observing that “the fundamental threat [to employment growth] is not technology per se but misgovernance.”

Of course, where and how most people are employed has changed over time. It’s going to change again. And that is disruptive. But the central and unprecedented difference between our time and previous eras is the demographic reality of a shrinking workforce. In the near future, we will need lots of new tools to amplify the efforts of the declining labor supply. Even in our own present, despite the best efforts of the Federal Reserve to increase unemployment (that is, to reduce the pressure employers face to offer “inflationary” salaries to keep workers), job openings still outnumber people available to fill them. Demographics dictate that this gap will widen. Since most jobs in a modern economy are found in so-called knowledge work, the only way to close the labor gap will be with AI tools useful enough to amplify the efficacy of people in those areas.

AI, of course, is not a specific tool per se but a class of tools under that loosely defined term. To extend the earlier analogy, there are many radically different kinds of engines; no single engine is suitable for every class of machine, task, or vehicle—from aircraft to mining trucks. It’s the same for the silicon engines at the core of all AI machines. Much of the misdirection about AI’s implications comes from the sloppy term itself, “artificial intelligence.” It’s no more informative or accurate than calling a car an artificial horse, or an airplane an artificial bird, or an electric motor an artificial waterwheel.

While ChatGPT is a whiz with words, it wasn’t trained on math and, as some users have already observed, performs poorly there. Similarly, ChatGPT couldn’t drive a car, wield a hammer to drive a nail, or carry a box. One needs differently designed and trained AI tools to perform each kind of task. The category confusion about the realities of AI tools is, to put it crudely, the equivalent of seeing that a tool like a hammer makes it easier to push a nail into a board and then trying to use a hammer to drill a precise hole, weld steel, or measure voltage.

The letters GPT in ChatGPT stand for Generative Pre-trained Transformer—computer lingo for an algorithm that, when paired with a powerful computer, can be trained iteratively by looking repetitively at a very large set of samples—in this case, written texts. The same has been done for images and myriad areas where routine tasks entail patterns and rules. The “chat” in ChatGPT will doubtless find early commercial application precisely where chatbots are already used: in online commerce and with the many tasks in all businesses that involve often-confusing or arcane rules, regulations, or standards that a computer can more usefully, quickly, and accurately parse to answer questions put to it in “natural language.”

The management literature is replete with analyses of the productivity-robbing burdens imposed on employees trying to comply with routine tasks in education, health care, business in general, and even in basic research. Such tasks are precisely where the narrow power of AI is most powerful. As it happens, it’s also where one could free up easily re-trainable humans to be redirected to more challenging non-routine tasks.

A exact Federal Reserve analysis divided the U.S. workforce into just two high-level categories: manual and cognitive labor. No surprise that the majority are now employed in the latter. The analysis also created two sub-categories within each: routine and non-routine tasks. Thus classified, about 60 million people in the U.S. work on routine tasks, split almost evenly between manual and cognitive domains. Total employment in both routine manual and routine cognitive tasks hasn’t changed significantly since 1980. Meantime, the non-routine manual labor pool has risen from 15 million to 25 million people since 1980, and employment in non-routine cognitive work has grown from about 30 million to 60 million people.

Four decades of job growth has all been in non-routine tasks. If we want to find more people to take on the jobs where growth is happening—and where they can be paid more—we’ll need to move people out of the routine job domains, while still ensuring that those tasks are fulfilled. That is precisely what’s made possible by AI tools that can increase the efficacy of a shrinking number of people performing routine tasks. Ensuring that that can happen will require AI tools even easier to use, more accurate, and cheaper than what’s available today with ChatGPT and its (jealous) competitors.

We know from history that when new technologies are found to be broadly useful, engineers drive down costs and make them easier to use. The latter is the “user interface,” in the jargon of tech. Again, witness the capabilities of ChatGPT versus, say, Alexa. With Natural Language Processing (NLP), the human-machine interface makes it easier for non-experts to engage casually in computational feats previously reserved for supercomputers and the expert class. The overall effect of NLP, in addition to taking up the burden of routine tasks, will also be to reduce routine burdens for employees in non-routine types of work. It will also enable the upskilling of more people to become “knowledge workers,” including even coding. It’s no coincidence that AI tools are bringing greater productivity to writing computer code. One company touts that its AI-based tool can help a coder write software ten to 100 times faster.

The good news, at least from a macroeconomic perspective, is that there’s been a land-rush of activity to develop mission-specific machine-learning algorithms. One measure of the scale of that activity is in the amount of private capital chasing AI deals and companies. We’re in the early stages of billions of dollars directed at another tech hype cycle.

Another measure of the scale of AI activity can be found in the total quantity of the world’s computer processing power used to “train” deep-learning models; it’s been doubling every few months for the past half-dozen years. That translates into a 300,000-fold increase in computing power used for AI training over that short time. You don’t need a crystal ball to predict that such prodigious efforts will soon yield a fusillade of useful AI tools to succeed ChatGPT.

Coming back to our aviation analogy, it’s the inescapably physical world of energy that reveals the implications of the scale of AI and machine learning. Even AI cognoscenti are surprised to learn that the energy equivalent of the fuel used to fly a jumbo jet from Austin to Asia is gobbled up by an AI-centric computer being trained on “large language models” or other similar sets of “parameters” needed for machine learning. That’s not a one-time investment; it happens every time and for each kind of similar application of learning. As “use cases” for AI expand, the proliferation of AI training will follow apace.

Ah, but for those who are anxious about energy issues, we also know that emerging and next-generation AI chips and algorithms are far more energy-efficient—some are already tenfold better. This will tamp down AI’s voracious energy appetite, even as the tools improve. But it’s that reality—more efficiency and higher performance—that will lead to a repeat of the trajectory of the first, pre-cloud era of the Internet.

Radical gains in efficiency have always been critical to unlocking the commercial viability of any new machine or infrastructure for society. In 1958, when Pan Am began passenger jet service with the 707, no one forecasted (much less exhibited angst about) the aggregate fuel consumption that commercial aviation would induce. Since then, aircraft have become 300 percent more energy-efficient, not to mention safer and more reliable. Those features are what enabled today’s trillions of passenger-miles flown, an activity that consumes some 4 billion barrels of oil each year, compared with a trivial amount in 1958.

Similarly, decades of inexorable gains in computing energy efficiency are illustrated by the fact that, if today’s smartphones operated at 1980 computing-efficiency levels, just one phone would use as much electricity as an office building. A single datacenter at 1980 efficiency levels would require the entire U.S. grid to power it. Instead, staggering reductions in energy-per-logic-operation, again along with gains in performance, are what made possible a commercial world of billions of smartphones and thousands of datacenters. And that yielded today’s global cloud infrastructure—still in a pre-AI era—that already uses about as much energy as global aviation.

In a future when AI machines perform not dozens but tens of thousands of simulations entailing trillions of computing-hours, overall energy use will balloon again. And that will happen because of the economic benefits AI offers to people, businesses, and even—and especially—in the pursuit of science and new discovery.

As Regina Barzilay, an AI researcher at MIT, put it when asked about the power of AI-assisted discoveries to invent new life-saving drugs: it’s “not the machine that invented the molecule. It’s that the machine helped humans to scan the huge space of possibilities and zoom in on the fruitful set of hypotheses that they tested.” Or, as economist Alexander Salter succinctly observed: “Data doesn’t interpret itself.” The AI machines are knowledge amplifiers.

Even so, we will see disruptions to the nature of jobs and businesses. Indeed, the scale of those disruptions will echo the magnitude of the opportunities that AI creates. Some educators have voiced worries about disruptions to teaching, including detecting cheaters. ChatGPT will indeed require adjustments, perhaps even a return to Socratic methods of in-class learning and testing—hardly a new idea. Nor is dealing with cheating, especially in the age of the Internet. Teachers found ways to teach math in the age of the calculator. Adaption to AI is not just possible, but arguably beneficial.

A clear-eyed recognition of benefits from any new technology doesn’t constitute a Pollyanna’s perspective. It’s also true that AI machines won’t all be useful or put to good use; such is the (sometimes sad) state of human nature. As science-fiction author and technology seer Cory Doctorow recently quipped in a long interview, “I think that the problems of A.I. are not its ability to do things well but its ability to do things badly, and our reliance on it nevertheless.” His cautions—and these are a constant refrain in his dystopian fiction—center around the need to recognize the limits of any machine and the kinds of risks arising from misuses.

Coming back to where we started, looking over the long period since the emergence of the modern information era, circa 1970, Census data show a significant shift in the structure of employment—away from production and toward services. Economists David Autor and Anna Salomons have done pioneering work in mapping those dynamics as a kind of hollowing out of highly paid “middle-skilled” jobs that don’t typically require a college degree, and a simultaneous shift toward more low- and high-skilled employment.

Autor recently posed a question as to “whether a countervailing set of economic forces will soon reverse the decline of middle-skill work?” I think the answer to that question is yes. The countervailing forces will come from the fact that computing has finally become widely useful with the advent of commercially viable AI. And that’s happening just in time to rescue the economy from demographic dystopia.

Mark P. Mills is a senior fellow at the Manhattan Institute, a strategic partner in the energy-tech venture fund Montrose Lane, and author of The Cloud Revolution: How the Convergence of New Technologies Will Unleash the Next Economic Boom and a Roaring 2020s. He hosts The Last Optimist podcast.

By Wachira Kigotho

In the last year's Kenya Certificate of Secondary Education, about 3,000 students had their results cancelled or withheld for cheating.

However, Kenya National Examinations Council (KNEC) has been under intense pressure from students and politicians to release the results.

Nonetheless, there are concerns that examination dishonesty is not just perpetrated by students but the integrity of public examinations has been compromised by many actors, including education officials. While releasing KCSE results, Education Minister Sam Ongeri said education officials might be involved in the unending incidents of examination fraud.

Unless the academic fraud is severely punished, it is going to graduate from merely a nagging educational issue to national crisis like in Nigeria where universities have petitioned the Government to be allowed to set their own admission tests instead of relying on the West African Senior School Certificate Examination, which is characterised by massive malpractices.

The crux of the matter is that success in academic life has great value and examinations are the means of selecting candidates for higher education and employment. Success in KCSE optimally opens doors for admission to the best degree programmes in public universities.

Academic fraud

Academic fraud appears to be on the increase worldwide according to United Nations Educational Scientific and Cultural Organisation (Unesco).

"Education has opened new avenues of advancement to growing number of citizens but many complex forms of misconduct have developed, amounting to a wide range of academic fraud," says Muriel Poisson, a programme specialist at Unesco.

But whereas academic fraud can take many forms examination dishonesty can occur at any stage of the examinations. Basically, students or their agents arrive at the venue prepared to use various methods. The most common is known as ¡®giraffing¡¯ where a student sticks out one the neck to see another student¡¯s answer sheet and it is often coordinated by examiners and teachers. It is related to ¡®lateral connection¡¯ implemented through a sitting arrangement, whereby the bright candidate is flanked on both sides by weaker candidates. The bights ones are then asked by teachers to let others peep on his work.

Alice Arinlade Jekayinfa, a professor of Social Education Studies at the University of Ilorin in Nigeria, says a common cheating method in Sub-Saharan Africa is known as ¡®nothing-nothing.¡¯ "This involves the use of empty biro pen to trace information on a blank white piece of paper. Seeing this on the table, one would think there is nothing on the paper, but closer observation reveals well loaded with facts about the examination," says Jekayinfa.

In Kenya, it is called mwakenya and apart from being used during KCPE and KCSE, it is commonly used by university students.

Other forms of mwakenya are dubbing and tattoos.

For tattoos, a candidate ¨C mainly female ¨C writes information on thighs where she can easily adjust to read and copy. The offending material can be deleted quickly when the invigilator becomes suspicious.

There is also a ¡®missile catch¡¯ that represents answers written on a piece of paper, squeezed and thrown to a colleague during the examination.

Body aids

Some cheating methods involve body aids. While announcing last year¡¯s KCPE, Ongeri produced a slipper that was used to write answers. Other body aids that are used to smuggle materials into examination venues include under-pants and handkerchiefs.

Often, students use coded sign language to communicate during examinations particularly in multiple-choice examinations. Another trick allowed by teachers is the ¡®table top guide¡¯ where anticipated answers are written on desks before an examination starts. Mostly, formulas, diagrams and maps are written in short form with complicity of teachers and compromised invigilators.

Ingenuity of examination fraud has also spread to use of tokens. These are short notes on the mathematical set, razor blades, rulers, handkerchiefs and other items for referencing during examinations.

A new form of cheating called ¡®coms¡¯ involves use of calculators that facilitate multiple entries. Quite often, such special calculators are put in casings of ordinary calculators and might look ordinary and escape invigilators.

Direct access is an act whereby an examiner helps candidates during examinations. There is also the ¡®mercenary service,¡¯ which involves another person writing the examination.

In last year¡¯s KCSE, some university students were arrested sitting examinations for private candidates.

The ¡®mercenary service¡¯ is related to ¡®rank zeroxing¡¯ which happens when a candidate collects and copies a colleague¡¯s answers word for word.

In universities where examination procedures are not very strict, academic corruption known as ¡®contract¡¯ is used.

This occurs when a student¡¯s grade is influenced with the assistance of a friendly lecturer. It can occur when a lecturer is paid money or sex for marks to enhance grades.

There is also the ¡®time out¡¯ method in which a student pretends to be suffering from diarrhoea and visits the toilet several times to read prepared answers.

Besides dishonesty during the examination time, academic corruption is often extended after the papers are collected by examiners and this is one of the most difficult examinations cheating to detect.

Tracing marking

With complicity of examiners and education officials, some parents are able to trace the results of their children to the last point of call, be it, the computer rooms where marks are stored for final processing and grading.

Unesco says this form of cheating is directly related to leakage of examination from administrative offices to local examination sites.

"Stakes are very high for cheating when examinations are for entry to higher education," says Unesco, in a report, Combating Academic Fraud: Towards a Culture of Integrity.

Academic dishonesty is rarely punished and is purely driven by greed and quest for profit.

¡¡

© Provided by The Indian Express

- Saurabh Kumar

The National Eligibility cum Entrance Test or NEET 2023 has been confirmed to take place on May 7 this year by the National Testing Agency (NTA). Last year the highly competitive entrance exam saw over 18 lakh registrations and with just a little over 3 months remaining for the 2023 exam, it is time for the candidates appearing for the examination to push their preparations for acing this entrance exam.

By achieving a good rank in the national-level entrance exam, students get a chance to enroll in the top medical colleges and universities in the country providing MBBS, BDS, AYUSH & other nursing courses.

Read |NEET UG 2023: Before NTA begins registration process, here’s answers to all FAQs

Preparing for any competitive examination relies upon the discipline shown by the candidate. With a good strategy along with the right guidance, any applicant willing to go the extra mile can ace NEET 2023.

Exam Pattern of NEET 2023

The test will be conducted offline in pen and paper mode with the exam duration being 3 hours and 20 minutes. A total of 200 questions will be provided to the candidates, out of which they will have to attempt 180 with the total achieving marks being 720.

The paper will be divided into three subjects- Physics, Chemistry, and Biology (Botany and Zoology). Candidates will gain 4 marks for each correct answer while 1 negative marking for each incorrect answer. The physics and chemistry sections will have a total of 50 questions whereas the biology section will have a total of 100 questions.

NEET 2023 Syllabus

Physics

Physical world and measurement, Electro statistics, Kinematics, Current electricity, Magnetic effects of current and magnetism, Work, energy, and power, Motion of systems of particles and rigid body, Laws of Motion, Electromagnetic waves, Optics, Electromagnetic induction and alternating currents, Properties of bulk matter, Dual nature of matter and radiation, Gravitation, Thermodynamics, Atoms and nuclei, Electronic devices, Oscillations and wave, and Behavior of perfect gas and kinetic theory

Chemistry

Some basic concepts of chemistry, Solid state, Structure of atom, Solutions, Electrochemistry, Chemical kinetics, States of matter: Gases and liquids, Classification of elements and periodicity in properties, Thermodynamics, Surface chemistry, General principles and processes of isolation of elements, Equilibrium, P-block elements, Redox reactions, Chemical bonding and molecular structure, D- and f-block elements, Hydrogen, Coordination compounds, S-block elements (Alkali and Alkaline earth metals), Alcohols, Some p-Block elements, phenols, and ethers, Organic chemistry – Some basic principles and techniques, Haloalkanes and haloarenes, Aldehydes, ketones and carboxylic acids, Hydrocarbons, Environmental chemistry, Biomolecules, polymers, and chemistry in everyday life, Organic compounds containing nitrogen

Biology

Diversity in the living world, Genetics and evolution, Cell structure and function, Reproduction, Biology and human welfare, Structural organization – Plants and animals, Plant physiology, Human physiology, Ecology and environment, Biotechnology and its applications

NEET 2023 Preparation Tips

1) Preparing a timetable- As there are 3 months remaining for the examination, candidates need to prepare a timetable by dividing time equally for all subjects (or as they see best). The timetable will help keep the speed of the student on track and will help them complete the preparation on time.

2) Understanding the syllabus and mastering NCERT Books- As the questions in NEET 2023 are majorly based upon NCERT, students need to master the NCERT books while understanding the fundamentals behind every topic.

Read |Medical colleges increase by 69%, 94% rise in MBBS seats since 2014: Govt

3) Conceptual clarity is key- For acing the medical exam, the candidates need to keep clearing their doubts and understand the courses with clarity. Referring to other study materials can be beneficial for students.

4) Test yourself with Mock papers- Mock exams are game-changers in any difficult entrance exam. Taking them will help the candidate evaluate oneself while finding the weaknesses where they need to focus.

5) Expert guidance- Students need to seek expert guidance to clear their doubts and problems to move on with their preparation.

6) Maintaining a healthy lifestyle will lead to better brain functioning- Doctors say that the human brain has the best functioning in the morning which is why candidates need to give their brains enough time to relax. By encouraging themselves to study early in the morning rather than late at night, candidates will be able to maximize their brain functioning.

The correct strategy and effective time management are important in determining an aspirant's rank. This time is best for aspirants to master their preparation and time-management skills. Learning how to maintain accuracy and quality when responding to a single question while identifying and addressing the weak areas is also essential.

(The author is Chief Academic Officer, Vidyamandir Classes)

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Whether you have the latest iPhone or an older model, your device is chock-full of features and services you may not know about

No medical exam life insurance — also called simplified issue — offers coverage without a physicals, blood test or urine test. If you need insurance on short notice or have a preexisting condition that would make it hard to pass a medical exam, simplified issue life insurance can be convenient and affordable.

No medical exam life insurance policies

No medical exam policies are available for both term and whole life insurance. One key difference between term and whole life is that the death benefits for whole life coverage without an exam are typically limited to $50,000, whereas term life benefits without an exam can be twice that.

When applying for no medical exam insurance, you'll be asked several questions about your health and medical history, and your responses will be used to determine whether you qualify for coverage. Each insurer has their own list of questions, meaning you may be rejected by one company but find coverage at another.

A life insurance policy that doesn't require any medical screening and doesn't ask for responses to any health questions is called guaranteed issue or guaranteed acceptance.

Regardless of what you're asked, honesty is important. Insurers can cancel your policy during the first two years of coverage without a refund if they find that you lied or misrepresented anything. Keep in mind that even without a health exam, insurers still have ways to confirm the information you provide. Insurance companies regularly check your responses against:

No medical exam term life insurance

If you don't qualify for fully underwritten life insurance and need more than $50,000 in coverage, your best option is a no medical exam term life policy. Term policies are among the cheapest forms of no medical exam insurance and are offered in lengths up to 30 years. They can typically be purchased until age 75, but some insurers restrict term lengths based on age.

When shopping for simplified issue term life insurance, you should make sure the policy is described as "level term" or has guaranteed level premiums for the term length. These phrases mean the life insurance quotes you receive will reflect the price you'll pay for the entire length of the policy.

Some insurers offer a no medical exam term insurance product where quotes are based on your age group (typically a five-year period, such as ages 50–54). With these renewable policies, the term length is essentially one year, and premiums increase each time you enter a new age group. This means the policy becomes incredibly expensive over the course of 15 to 20 years.

As an example, let's say you're a 45-year-old man and want $150,000 of coverage for 20 years. New York Life offers coverage according to your age, so the monthly premium would increase each time you enter a new age bracket.

Even though your initial quote was for $119, the average you would pay over the term would be $182 per month. Over 20 years, you'll have paid $43,680.

If you had a level term policy and paid $119 per month, you would pay $28,560 over the course of 20 years instead — a savings of $15,120. Level term policies are usually more affordable because premiums can vary based on factors other than age, so the insurer can better price your risk profile.

Another product you’ll want to look out for when shopping for life insurance is accidental death insurance, which is similar to term life insurance but only pays a death benefit if you die as the result of an accident. Because only about 5% of deaths are caused by accidents, premiums are cheap and coverage often requires no medical exam. However, accidental death coverage does not apply to natural causes, such as stroke or heart attack.

No medical exam whole life insurance

No medical exam whole life insurance is typically used as a form of final expense insurance, because the coverage is lifelong and death benefits are generally capped at $25,000 or $50,000. After your death, your beneficiaries may receive a payout large enough to cover your funeral, other end-of-life costs and potentially a small loan. However, it won’t be enough to pay for a mortgage or provide income replacement for an extended period.

If you want final expense insurance and do not qualify for traditional coverage, simplified issue whole life insurance will be less expensive than a guaranteed acceptance policy.

Should I buy no medical exam life insurance?

We don't recommend no-exam insurance if you would likely qualify for traditional coverage. No medical exam life insurance is more expensive than fully underwritten coverage and typically provides fewer options. For example, you usually can't increase your death benefit or convert a term policy to permanent coverage. In addition, the medical exam for traditional coverage is free, typically lasts less than 30 minutes and can take place at your home or work, so it’s a fairly easy process if you would likely qualify.

However, you should consider a no medical exam policy if:

If any of these scenarios apply to you, we recommend first looking for an insurer that offers fully underwritten coverage for your situation, as insurance companies all have different restrictions. Independent insurance agents represent multiple companies and are familiar with each insurer's underwriting requirements, so an agent may be able to help you find an insurer that accepts your health profile.

No medical exam life insurance is also useful if you need instant life insurance to secure a personal or business loan, which is a common requirement from institutional lenders. No medical exam policies often provide coverage the same day or may take up to five business days, whereas traditional policies can take several weeks for approval. In these cases, we recommend getting no medical exam coverage and then applying for a fully underwritten term policy. This way, you can satisfy the bank or other lender’s immediate requirements but minimize your long-term costs.

No medical exam versus guaranteed acceptance life insurance

Guaranteed acceptance life insurance is similar to no medical exam coverage, in that you don't need to get a physical or provide blood or urine tests to apply. The main difference is that there are no health questions for guaranteed issue life insurance, so anyone who falls within a particular age range will be accepted. This makes guaranteed issue policies even more costly than no-exam insurance, but they're still a good alternative if you don’t qualify for no medical exam coverage.

Each insurer has its own standards and questions for no-exam life insurance applicants. In general, you should probably consider a guaranteed acceptance policy if:

Guaranteed issue policies are only available for whole life insurance, and coverage is typically less than $25,000. It's usually intended as final expense insurance, offering a death benefit that’s enough to cover a funeral and other costs associated with your death. There are no alternatives for term life insurance without providing some personal information.

No exam term or whole life vs guaranteed acceptance whole life

Because guaranteed acceptance policies offer life insurance coverage without health or medical questions, they generally have a two- to three-year waiting period, during which the insurer will not pay the full death benefit to your beneficiary. Instead, they’ll provide your beneficiary with the amount you've paid in premiums, plus interest (usually 6–10%).

No medical exam life insurance policies usually have no waiting period, but the company will investigate the circumstances of your death if it occurs within the first two years of coverage. If they find any evidence that you died from suicide or provided any misleading medical or personal information during the application process, they can deny the claim, and your beneficiaries won't receive a payout.