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and-then-there-were-n0ne · 4 years

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It is crucial to realize that the AI revolution is not just about computers getting faster and smarter. It is fuelled by breakthroughs in the life sciences and the social sciences as well. The better we understand the biochemical mechanisms that underpin human emotions, desires and choices, the better computers can become in analyzing human behavior, predicting human decisions, and replacing human drivers, bankers and lawyers.

In the last few decades research in areas such as neuroscience and behavioral economics allowed scientists to hack humans, and in particular to gain a much better understanding of how humans make decisions. It turned out that our choices of everything from food to mates result not from some mysterious free will, but rather from billions of neurons calculating probabilities within a split second. Vaunted “human intuition” is in reality “pattern recognition” Good drivers, bankers and lawyers don’t have magical intuitions about traffic, investment or negotiation - rather, by recognising recurring patterns, they spot and try to avoid careless pedestrians, inept borrowers and dishonest crooks. It also turned out that the biochemical algorithms of human brain are far from perfect. They rely on heuristics, shortcuts and outdated circuits adapted to the African savannah rather than to the urban jungle. No wonder that even good drivers, bankers and lawyers sometimes make stupid mistakes.

This means that AI can outperform humans even in tasks that supposedly demand “intuition”. If you think AI needs to compete against the human soul in terms of mystical hunches - that sounds impossible. But if AI really needs to compete against neural networks in calculating probabilities and recognising patterns - that sounds far less daunting.

In particular, AI can be better at jobs that demand intuitions about other people. Many lines of work - such as driving a vehicle in a street full of pedestrians, lending money to strangers, and negotiating a business deal - require the ability to correctly assess the emotions and desires of other people. Is that kid about to jump onto the road? Does that man in the suit intend to take my money and disappear? Will that lawyer act on his threats, or is he just bluffing? As long as it was thought that such emotions and desires were generated by an immaterial spirit, it seemed obvious that computers will never be able to replace human drivers, bankers and lawyers. For how can a computer understand the divinely created human spirit? Yet if these emotions and desires are in fact no more that biochemical algorithms, there is no reason why computers cannot decipher these algorithms - and do so far better than any Homo sapiens.

A driver predicting the intentions of a pedestrian, a banker assessing the credibility of a potential borrower, and a lawyer gauging the mood at the negotiation table don’t rely on witchcraft. Rather, unbeknownst to them, their brains are recognizing biochemical patterns by analyzing facial expressions, tones of voice, hand movements, and even body odours. An AI equipped with the right sensors could do all that far more accurately and reliability than a human.

Hence the threat of job losses does not result merely from the rise of infotech. It results from the confluence of infotech with biotech. The way from the fMRI scanner to the labour market is long and tortuous, but it can still be covered within a few decades. What brain scientists are learning today about the amygdala and the cerebellum might make it possible for computers to outperform human psychiatrists and bodyguards in 2050.

AI not only stands poised to hack humans and outperform them in what were hitherto uniquely human skills. It also enjoys uniquely non-human abilities, which make the difference between an AI and a human worker one of kind rather than merely of degree. Two particularly important non-human abilities that AI possesses are connectivity and updatability.

Since humans are individuals, it is difficult to connect them to one another and to make sure that they are all up to date. In contrast, computers aren’t individuals, and it is easy to integrate them into a single flexible network. Hence what we are facing is not the replacement of millions of individual human workers by millions of individual robots and computers. Rather, individual humans are likely to be replaced by an integrated network. When considering automation it is therefore wrong to compare the abilities of a single human driver to that of a single self-driving car, or of a single human doctor to that of a single AI doctor. Rather, we should compare the abilities of a collection of human individuals to the abilities of an integrated network.

For example, many drivers are unfamiliar with all the changing traffic regulations and they often violate them. In addition, since every vehicle is an autonomous entity, when two vehicles approach the same junction at the same time, the drivers might miscommunicate their intentions and collide. Self.driving cars, in contrast, can all be connected to one another. When two such vehicles approach the same junction, they are not really two separate entities - they are part of a single algorithm. The chances that they might miscommunicate and collide are therefore far smaller. And if the Ministry of Transport decides to change some traffic regulation, all self-driving vehicles can be easily updated at exactly the same moment, and barring some bug in the program, they will all follow the new regulation to the letter.

Similarly, if the World Health Organization identifies a new disease, or if a laboratory produces a new medicine, it is almost impossible to update all the human doctors in the world about these developments. In contrast, even if you 10 billion AI doctors in the world - each monitoring the health of a single human being - you can still update all of them within a split second, and they can all communicate to each other their feedback on the new disease or medicine. The potential advantages of connectivity and updatabilily are so huge that in some lines of work it might make to replace aIl humans wlth computers, even if individually some humans still do a better job than the machines.

You might object that by switching from individual humans to a computer network we will lose the advantages of individuality. For example, if one human doctor makes a wrong judgment, he does not kill all the patients in the world. and he does not block the development of all new medications. In contrast, if all doctors are really just a single system, and that system makes a mistake, the results might be catastrophic. In truth, however, an integrated computer system can maximize the advantages of connectivity without losing the benefits of individuality. You can run many alternative algorithms on the same network, so that a patient in a remote jungle village can access through her smartphone not just a single authoritative doctor, but actually a hundred different AI doctors, whose relative performance is constantly being compared. You don’t like what the IBM doctor told you? No problem. Even if you are stranded somewhere on the slopes of Kilimanjaro, you can easily contact the Baidu doctor for a second opinion.

Similarly, self-driving vehicles could provide people with much better transport services, and in particular reduce mortality from traffic accidents. Today close to 1.25 million people are killed annually in traffic accidents (twice the number killed by war, crime and terrorism combined). More than 90 percent of these accidents are caused by very human errors: somebody drinking alcohol and driving, somebody texting a message while driving, somebody falling asleep at the wheel, somebody daydreaming instead of paying attention to the road. The US National Highway Traffic Safety Administration estimated in 2012 that 31 percent of fatal crashes in the USA involved alcohol abuse, 30 percent involved speeding, and 21 percent involved distracted drivers. Self-driving vehicles will never do any of these things. Though they suffer from their own problems and limitations, and though some accidents are inevitable, replacing all human drivers by computers is expected to reduce deaths and injuries on the road by about 90 percent. In other words, switching to autonomous vehicles is likely to save the lives of a million people every year.

Hence it would be madness to block automation in fields such as transport and healthcare just in order to protect human jobs. After all, what we ultimately ought to protect is humans - not jobs. Redundant drivers and doctors will just have to find something else to do.

- Yuval Noah Harari, When You Grow Up, You Might Not Have a Job in 21 Lessons for the 21st Century

#yuval noah harari #21 lessons #21 lessons for the 21st century

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alicecpacheco · 3 years

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Evaluating the accuracy of three intraoral scanners using models containing different numbers of crown-prepared abutments

Journal of Dental Sciences

Available online 1 June 2021

Abstract

Background/purpose

The scanning accuracy of intraoral scanners’ data collection plays a key role in the success of the final treatment. However, few studies start from scanning technology itself to directly evaluate it. The aim of this study was to evaluate the scanning accuracy of three intraoral scanners, to provide a reference for relevant research and clinical applications.

Materials and methods

Six types of resin models containing different numbers of crown-prepared abutments were three-dimensionally printed, and a model scanner, as well as three intraoral scanners, were used to digitally scan the six models. The obtained data were uploaded to three-dimensional reverse software for registration and comparison, and the accuracy of the models were analyzed.

Results

When scanning the six groups of models, the Omnicam outperformed both the TRIOS and iTero in terms of accuracy in all groups except the second molar group. The TRIOS and iTero scanners also exhibited decreased degrees of accuracy when scanning the long dental arch. The accuracy decreased as the scanning scope increased; however, the Omnicam scanner exhibited a relatively high degree of accuracy when scanning the three-unit fixed bridge and anterior areas. All scanners exhibited the lowest degree of accuracy when scanning the full-arch model. Certain deviations were observed, and the scanning areas at the incisal edges of the anterior teeth and end of the dental arch exhibited relatively large deviations.

Conclusion

With the model scanner data as reference, the scanning accuracy of the three scanners exhibited differences and certain deviations, which were within clinical tolerance.

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fumpkins · 4 years

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Scientists demonstrate quantum radar prototype

Illustration of a quantum radar prototype. Credit: © IST Austria/Philip Krantz

Physicists at the Institute of Science and Technology Austria (IST Austria) have invented a new radar prototype that uses quantum entanglement as a method of object detection. This successful integration of quantum mechanics into devices could significantly impact the biomedical and security industries. The research is published in the journal Science Advances.

Quantum entanglement is a physical phenomenon whereby two particles remain interconnected, sharing physical traits regardless of how far apart they are from one another. Now, scientists from the research group of Professor Johannes Fink at the Institute of Science and Technology Austria (IST Austria) along with collaborators Stefano Pirandola from the Massachusetts Institute of Technology (MIT) and the University of York, UK, and David Vitali from the University of Camerino, Italy—have demonstrated a new type of detection technology called microwave quantum illumination that utilizes entangled microwave photons as a method of detection. The prototype, which is also known as a quantum radar, is able to detect objects in noisy thermal environments where classical radar systems often fail. The technology has potential applications for ultra-low power biomedical imaging and security scanners.

Using quantum entanglement as a new form of detection

The working principles behind the device are simple: Instead of using conventional microwaves, the researchers entangle two groups of photons, which are called the signal and idler photons. The signal photons are sent out towards the object of interest, whilst the idler photons are measured in relative isolation, free from interference and noise. When the signal photons are reflected back, true entanglement between the signal and idler photons is lost, but a small amount of correlation survives, creating a signature or pattern that describes the existence or the absence of the target object—irrespective of the noise within the environment.

“What we have demonstrated is a proof of concept for the microwave quantum radar,” says lead author Shabir Barzanjeh, whose previous research helped advance the theoretical notion behind quantum enhanced radar technology. “Using entanglement generated at a few thousandths of a degree above absolute zero (-273.14 °C), we have been able to detect low reflectivity objects at room-temperature.”

Quantum technology can outperform classical low-power radar

While quantum entanglement in itself is fragile in nature, the device has a few advantages over conventional classical radars. For instance, at low power levels, conventional radar systems typically suffer from poor sensitivity as they have trouble distinguishing the radiation reflected by the object from naturally occurring background radiation noise. Quantum illumination offers a solution to this problem as the similarities between the signal and idler photons—generated by quantum entanglement—makes it more effective to distinguish the signal photons (received from the object of interest) from the noise generated within the environment.

Barzanjeh, who is now an assistant professor at the University of Calgary, says, “The main message behind our research is that quantum radar or quantum microwave illumination is not only possible in theory, but also in practice. When benchmarked against classical low-power detectors in the same conditions, we see that at very low-signal photon numbers, quantum-enhanced detection can be superior.”

Throughout history, basic science has been one of the key drivers of innovation, paradigm shift and technological breakthrough. While still a proof of concept, the group’s research has effectively demonstrated a new method of detection that, in some cases, may be superior to classical radar.

“Throughout history, proofs of concept, such as the one we have demonstrated here, have often served as prominent milestones toward future technological advancements. It will be interesting to see the future implications of this research, particularly for short-range microwave sensors,” says Barzanjeh.

Last author and group leader Professor Johannes Fink says, “This scientific result was only possible by bringing together theoretical and experimental physicists that are driven by the curiosity of how quantum mechanics can help to push the fundamental limits of sensing. But to show an advantage in practical situations, we will also need the help of experienced electrical engineers, and there still remains a lot of work to be done in order to make our result applicable to real-world detection tasks.”

Building a bridge to the quantum world

More information: “Microwave quantum illumination using a digital receiver” Science Advances (2020). DOI: 10.1126/sciadv.abb0451

Provided by Institute of Science and Technology Austria

Citation: Scientists demonstrate quantum radar prototype (2020, May 8) retrieved 9 May 2020 from https://phys.org/news/2020-05-scientists-quantum-radar-prototype.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

New post published on: https://livescience.tech/2020/05/09/scientists-demonstrate-quantum-radar-prototype/

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technuter · 5 years

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Redmi K20 and K20 Pro launched in India

Xiaomi announced the much-anticipated Redmi K20 series of smartphones -- Redmi K20 and Redmi K20 Pro -- as the first flagship series under the Redmi sub-brand.

Redmi K20 series Redmi K20 and K20 Pro promise a flagship, high-end user experience with the latest tech powering both devices. The Redmi K20 series is designed to be ahead of the curve, and is set to catch the fancy of those who aspire to outperform everyday. Both, Redmi K20 and Redmi K20 Pro feature a 16.2cm (6.39) 19.5:9 full HD+ AMOLED Horizon Display with minimal bezels on all sides. Coupled with a 20MP pop-up selfie camera, the Redmi K20 series achieves an incredible 91.9% screen-to-body ratio -- one of the highest in the industry. The Horizon AMOLED Display comes with 7th generation in-display fingerprint scanner and is protected by curved Corning® Gorilla Glass 5 on both phones. Redmi K20 series introduces the beautiful Aura Prime design language, which makes it exceptionally beautiful while retaining functionality at the same time. The ergonomics flow to the rear of the devices with 3D curved Corning Gorilla Glass 5 giving Redmi K20 series an easy handfeel and gorgeous looks. The glass itself is a part of the intricate stack on the rear of the phone that gives Redmi K20 series its characteristic, distinctive looks. The Redmi K20 phones come with an industry-first colours inspired by Fire & Ice, in Glacier Blue, Flame Red and Carbon Black colour variants. The aesthetics department is completed by the pulsating notification LED that sits on top of the pop-up camera mechanism (tested to work at least 300,000 times) along with a stylish halo ring around the camera module. Redmi K20 and K20 Pro are the only flagship phones with the edge-lit module around the pop-up selfie camera, adding to the charisma -- with a bold red accent on the power button. Redmi K20 series comes with an AI triple camera setup, comprising of a 48MP main camera, 8MP telephoto and a 13MP wide-angle. It is powered by a huge 4,000 mAh battery, which can last upto 2 days in regular usage conditions. Both the phones come with USB Type-C port, 3.5mm headphone jack and fast-charge support. Redmi K20 series comes with the highest quality standards; P2i nano-coating makes the phones splash-proof, while Gorilla® Glass 5 on the front and rear of both phones make them durable and long-lasting, beautiful pieces of tech. Other intuitive quality improvements include fall detection, which makes the pop-up camera retract automatically in case of a drop. Speaking on the occasion, Manu Jain, Managing Director, Xiaomi India, and Vice President, Xiaomi, said, “We are thrilled to launch the Redmi K20 series in India. As the first of our Redmi flagship series in India, Redmi K20 and K20 Pro truly redefine the ultimate flagship experience. While the premium 20K+ segment was relatively small traditionally, we are beginning to see massive growth in this segment. We hope Redmi K20 Pro and Redmi K20 will completely disrupt this segment with the kind of experience on offer. “Featuring the new pop-up camera along with an AMOLED screen, as well as an in-display fingerprint sensor, the phones offer our Mi Fans the best of cutting-edge technology with high quality at truly honest pricing. We are also delighted to share that our Mi Fans can experience the all-new Qualcomm Snapdragon 855 and Qualcomm Snapdragon 730 in Redmi K20 Pro and Redmi K20 respectively, thus bringing power-packed performance to all users in India.” Redmi K20 Pro Redmi K20 Pro is a true flagship; a power user’s delight. It is the first Redmi phone to be based on the Qualcomm Snapdragon 855 mobile platform, which is the fastest processor in the world (45% faster than its predecessor SD 845). The Snapdragon 855, with its tri-cluster architecture (1 x Prime core, 3 x Gold cores, 4 x Silver cores) and 7nm architecture, gives Redmi K20 Pro immense processing prowess, making it one of the most powerful smartphones in the world. Redmi K20 Pro comes with 48MP AI primary camera with Sony IMX586 sensor, along with an 8MP 2x telephoto camera and 13MP ultra-wide camera with a 124.8° field of view. Available in two storage variants (6GB/128GB and 8GB/256GB), Redmi K20 Pro will feature the three Fire and Ice colours mentioned previously. A high-res DAC gives Redmi K20 Pro the ability to playback lossless audio (up to 24-bit, 192KHz), while dual GPS antennas ensure pinpoint GPS accuracy. Redmi K20 Pro supports 27W ‘SonicCharge’ and comes with an 18W charger in the box, with an option for users to purchase a 27W SonicCharge adapter from Mi.com. Redmi K20 Redmi K20 is one of the first phones to be launched with the new Qualcomm Snapdragon 730 SoC. Snapdragon 730 is the 3rd fastest chipset to be ever launched by Qualcomm (after SD 845 and SD 855) and is 40% faster than its predecessor - the Snapdragon 710. This makes Redmi K20 one of the most powerful phones in the mid-premium segment. Redmi K20 features a similar AI triple rear camera arrangement as Redmi K20 Pro, with a 48MP Sony IMX582 primary sensor. Redmi K20 comes in two storage variants -- a 6GB/64GB starting variant, and a top-end 6GB/128GB, and will be available in the same Fire & Ice colour scheme as Redmi K20 Pro. It supports 18W fast charging and comes with 18W charger right out of the box. Availability Both Redmi K20 series phones will include 18W chargers and a premium hard case worth INR 999 in the box. Users can buy 27W SonicCharge adapters from Mi.com for INR 999 at a later date. Redmi K20 Pro is being launched at an honest price of INR 27,999 for the 6GB+128GB variant and INR 30,999 for the 8GB+256GB variant. Redmi K20 will be available for INR 21,999 for the 6GB+64GB variant and INR 23,999 for the 6GB+128GB variant. Both phones will go on sale starting 12 pm 22nd July across Mi.com, Flipkart and Mi Homes. It will also be available across all offline retail stores in the coming days - Mi Studio, Mi Stores, Mi Preferred Partners and all other retail shops. Xiaomi also announced Mi Neckband Bluetooth Earphones for INR 1,599 and Mi Rechargeable LED Lamp for INR 1,499 (INR 1,299 for participants during the crowdfunding). Read the full article

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and-then-there-were-n0ne · 4 years

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In the economic sphere too, the ability to hold a hammer or press a button is becoming less valuable than before. In the past, there were many things only humans could do. But now robots and computers are catching up, and may soon outperform humans in most tasks. True, computers function very differently from humans, and it seems unlikely that computers will become humanlike any time soon. In particular, it doesn’t seem that computers are about to gain consciousness, and to start experiencing emotions and sensations. Over the last decades there has been an immense advance in computer intelligence, but there has been exactly zero advance in computer consciousness. As far as we know, computers in 2016 are no more conscious than their prototypes in the 1950s. However, we are on the brink of a momentous revolution. Humans are in danger of losing their value, because intelligence is decoupling from consciousness.

Until today, high intelligence always went hand in hand with a developed consciousness. Only conscious beings could perform tasks that required a lot of intelligence, such as playing chess, driving cars, diagnosing diseases or identifying terrorists. However, we are now developing new types of non-conscious intelligence that can perform such tasks far better than humans. For all these tasks are based on pattern recognition, and non-conscious algorithms may soon excel human consciousness in recognising patterns. This raises a novel question: which of the two is really important, intelligence or consciousness? As long as they went hand in hand, debating their relative value was just a pastime for philosophers. But in the twenty-first century, this is becoming an urgent political and economic issue. And it is sobering to realise that, at least for armies and corporations, the answer is straightforward: intelligence is mandatory but consciousness is optional.

Armies and corporations cannot function without intelligent agents, but they don’t need consciousness and subjective experiences. The conscious experiences of a flesh-and-blood taxi driver are infinitely richer than those of a self-driving car, which feels absolutely nothing. The taxi driver can enjoy music while navigating the busy streets of Seoul. His mind may expand in awe as he looks up at the stars and contemplates the mysteries of the universe. His eyes may fill with tears of joy when he sees his baby girl taking her very first step. But the system doesn’t need all that from a taxi driver. All it really wants is to bring passengers from point A to point B as quickly, safely and cheaply as possible. And the autonomous car will soon be able to do that far better than a human driver, even though it cannot enjoy music or be awestruck by the magic of existence.

Indeed, if we forbid humans to drive taxis and cars altogether, and give computer algorithms monopoly over traffic, we can then connect all vehicles to a single network, and thereby make car accidents virtually impossible. In August 2015, one of Google’s experimental self-driving cars had an accident. As it approached a crossing and detected pedestrians wishing to cross, it applied its brakes. A moment later it was hit from behind by a sedan whose careless human driver was perhaps contemplating the mysteries of the universe instead of watching the road. This could not have happened if both vehicles were steered by interlinked computers. The controlling algorithm would have known the position and intentions of every vehicle on the road, and would not have allowed two of its marionettes to collide. Such a system will save lots of time, money and human lives – but it will also do away with the human experience of driving a car and with tens of millions of human jobs.

Some economists predict that sooner or later, unenhanced humans will be completely useless. While robots and 3D printers replace workers in manual jobs such as manufacturing shirts, highly intelligent algorithms will do the same to white-collar occupations. Bank clerks and travel agents, who a short time ago were completely secure from automation, have become endangered species. How many travel agents do we need when we can use our smartphones to buy plane tickets from an algorithm?

Stock-exchange traders are also in danger. Most trade today is already being managed by computer algorithms, which can process in a second more data than a human can in a year, and that can react to the data much faster than a human can blink. On 23 April 2013, Syrian hackers broke into Associated Press’s official Twitter account. At 13:07 they tweeted that the White House had been attacked and President Obama was hurt. Trade algorithms that constantly monitor newsfeeds reacted in no time, and began selling stocks like mad. The Dow Jones went into free fall, and within sixty seconds lost 150 points, equivalent to a loss of $136 billion! At 13:10 Associated Press clarified that the tweet was a hoax. The algorithms reversed gear, and by 13:13 the Dow Jones had recuperated almost all the losses.

Three years previously, on 6 May 2010, the New York stock exchange underwent an even sharper shock. Within five minutes – from 14:42 to 14:47 – the Dow Jones dropped by 1,000 points, wiping out $1 trillion. It then bounced back, returning to its pre-crash level in a little over three minutes. That’s what happens when super-fast computer programs are in charge of our money. Experts have been trying ever since to understand what happened in this so-called ‘Flash Crash’. We know algorithms were to blame, but we are still not sure exactly what went wrong. Some traders in the USA have already filed lawsuits against algorithmic trading, arguing that it unfairly discriminates against human beings, who simply cannot react fast enough to compete. Quibbling whether this really constitutes a violation of rights might provide lots of work and lots of fees for lawyers.

And these lawyers won’t necessarily be human. Movies and TV series give the impression that lawyers spend their days in court shouting ‘Objection!’ and making impassioned speeches. Yet most run-of-the-mill lawyers spend their time going over endless files, looking for precedents, loopholes and tiny pieces of potentially relevant evidence. Some are busy trying to figure out what happened on the night John Doe got killed, or formulating a gargantuan business contract that will protect their client against every conceivable eventuality. What will be the fate of all these lawyers once sophisticated search algorithms can locate more precedents in a day than a human can in a lifetime, and once brain scans can reveal lies and deceptions at the press of a button? Even highly experienced lawyers and detectives cannot easily spot deceptions merely by observing people’s facial expressions and tone of voice. However, lying involves different brain areas to those used when we tell the truth. We’re not there yet, but it is conceivable that in the not too distant future fMRI scanners could function as almost infallible truth machines. Where will that leave millions of lawyers, judges, cops and detectives? They might need to go back to school and learn a new profession.

When they get in the classroom, however, they may well discover that the algorithms have got there first. Companies such as Mindojo are developing interactive algorithms that not only teach me maths, physics and history, but also simultaneously study me and get to know exactly who I am. Digital teachers will closely monitor every answer I give, and how long it took me to give it. Over time, they will discern my unique weaknesses as well as my strengths. They will identify what gets me excited, and what makes my eyelids droop. They could teach me thermodynamics or geometry in a way that suits my personality type, even if that particular way doesn’t suit 99 per cent of the other pupils. And these digital teachers will never lose their patience, never shout at me, and never go on strike. It is unclear, however, why on earth I would need to know thermodynamics or geometry in a world containing such intelligent computer programs.

Even doctors are fair game for the algorithms. The first and foremost task of most doctors is to diagnose diseases correctly, and then suggest the best available treatment. If I arrive at the clinic complaining about fever and diarrhoea, I might be suffering from food poisoning. Then again, the same symptoms might result from a stomach virus, cholera, dysentery, malaria, cancer or some unknown new disease. My doctor has only five minutes to make a correct diagnosis, because this is what my health insurance pays for. This allows for no more than a few questions and perhaps a quick medical examination. The doctor then cross-references this meagre information with my medical history, and with the vast world of human maladies. Alas, not even the most diligent doctor can remember all my previous ailments and check-ups. Similarly, no doctor can be familiar with every illness and drug, or read every new article published in every medical journal. To top it all, the doctor is sometimes tired or hungry or perhaps even sick, which affects her judgement. No wonder that doctors often err in their diagnoses, or recommend a less-than-optimal treatment.

Now consider IBM’s famous Watson – an artificial intelligence system that won the Jeopardy! television game show in 2011, beating human former champions. Watson is currently groomed to do more serious work, particularly in diagnosing diseases. An AI such as Watson has enormous potential advantages over human doctors. Firstly, an AI can hold in its databanks information about every known illness and medicine in history. It can then update these databanks every day, not only with the findings of new researches, but also with medical statistics gathered from every clinic and hospital in the world.

Secondly, Watson can be intimately familiar not only with my entire genome and my day-to-day medical history, but also with the genomes and medical histories of my parents, siblings, cousins, neighbours and friends. Watson will know instantly whether I visited a tropical country recently, whether I have recurring stomach infections, whether there have been cases of intestinal cancer in my family or whether people all over town are complaining this morning about diarrhoea.

Thirdly, Watson will never be tired, hungry or sick, and will have all the time in the world for me. I could sit comfortably on my sofa at home and answer hundreds of questions, telling Watson exactly how I feel. This is good news for most patients (except perhaps hypochondriacs). But if you enter medical school today in the expectation of still being a family doctor in twenty years, maybe you should think again. With such a Watson around, there is not much need for Sherlocks.

This threat hovers over the heads not only of general practitioners, but also of experts. Indeed, it might prove easier to replace doctors specialising in a relatively narrow field such as cancer diagnosis. For example, in a recent experiment a computer algorithm diagnosed correctly 90 per cent of lung cancer cases presented to it, while human doctors had a success rate of only 50 per cent. In fact, the future is already here. CT scans and mammography tests are routinely checked by specialised algorithms, which provide doctors with a second opinion, and sometimes detect tumours that the doctors missed.

A host of tough technical problems still prevent Watson and its ilk from replacing most doctors tomorrow morning. Yet these technical problems – however difficult – need only be solved once. The training of a human doctor is a complicated and expensive process that lasts years. When the process is complete, after ten years of studies and internships, all you get is one doctor. If you want two doctors, you have to repeat the entire process from scratch. In contrast, if and when you solve the technical problems hampering Watson, you will get not one, but an infinite number of doctors, available 24/7 in every corner of the world. So even if it costs $100 billion to make it work, in the long run it would be much cheaper than training human doctors.

And what’s true of doctors is doubly true of pharmacists. In 2011 a pharmacy opened in San Francisco manned by a single robot. When a human comes to the pharmacy, within seconds the robot receives all of the customer’s prescriptions, as well as detailed information about other medicines taken by them, and their suspected allergies. The robot makes sure the new prescriptions don’t combine adversely with any other medicine or allergy, and then provides the customer with the required drug. In its first year of operation the robotic pharmacist provided 2 million prescriptions, without making a single mistake. On average, flesh-and-blood pharmacists get wrong 1.7 per cent of prescriptions. In the United States alone this amounts to more than 50 million prescription errors every year!

Some people argue that even if an algorithm could outperform doctors and pharmacists in the technical aspects of their professions, it could never replace their human touch. If your CT indicates you have cancer, would you like to receive the news from a caring and empathetic human doctor, or from a machine? Well, how about receiving the news from a caring and empathetic machine that tailors its words to your personality type? Remember that organisms are algorithms, and Watson could detect your emotional state with the same accuracy that it detects your tumours.

This idea has already been implemented by some customer-services departments, such as those pioneered by the Chicago-based Mattersight Corporation. Mattersight publishes its wares with the following advert: ‘Have you ever spoken with someone and felt as though you just clicked? The magical feeling you get is the result of a personality connection. Mattersight creates that feeling every day, in call centers around the world.’ When you call customer services with a request or complaint, it usually takes a few seconds to route your call to a representative. In Mattersight systems, your call is routed by a clever algorithm. You first state the reason for your call. The algorithm listens to your request, analyses the words you have chosen and your tone of voice, and deduces not only your present emotional state but also your personality type – whether you are introverted, extroverted, rebellious or dependent. Based on this information, the algorithm links you to the representative that best matches your mood and personality. The algorithm knows whether you need an empathetic person to patiently listen to your complaints, or you prefer a no-nonsense rational type who will give you the quickest technical solution. A good match means both happier customers and less time and money wasted by the customer-services department.

The most important question in twenty-first-century economics may well be what to do with all the superfluous people. What will conscious humans do, once we have highly intelligent non-conscious algorithms that can do almost everything better?

Throughout history the job market was divided into three main sectors: agriculture, industry and services. Until about 1800, the vast majority of people worked in agriculture, and only a small minority worked in industry and services. During the Industrial Revolution people in developed countries left the fields and herds. Most began working in industry, but growing numbers also took up jobs in the services sector. In recent decades developed countries underwent another revolution, as industrial jobs vanished, whereas the services sector expanded. In 2010 only 2 per cent of Americans worked in agriculture, 20 per cent worked in industry, 78 per cent worked as teachers, doctors, webpage designers and so forth. When mindless algorithms are able to teach, diagnose and design better than humans, what will we do?

This is not an entirely new question. Ever since the Industrial Revolution erupted, people feared that mechanisation might cause mass unemployment. This never happened, because as old professions became obsolete, new professions evolved, and there was always something humans could do better than machines. Yet this is not a law of nature, and nothing guarantees it will continue to be like that in the future. Humans have two basic types of abilities: physical abilities and cognitive abilities. As long as machines competed with us merely in physical abilities, you could always find cognitive tasks that humans do better. So machines took over purely manual jobs, while humans focused on jobs requiring at least some cognitive skills. Yet what will happen once algorithms outperform us in remembering, analysing and recognising patterns?

The idea that humans will always have a unique ability beyond the reach of non-conscious algorithms is just wishful thinking. True, at present there are numerous things that organic algorithms do better than non-organic ones, and experts have repeatedly declared that something will ‘for ever’ remain beyond the reach of non-organic algorithms. But it turns out that ‘for ever’ often means no more than a decade or two. Until a short time ago, facial recognition was a favourite example of something which even babies accomplish easily but which escaped even the most powerful computers on earth. Today facial-recognition programs are able to recognise people far more efficiently and quickly than humans can. Police forces and intelligence services now use such programs to scan countless hours of video footage from surveillance cameras, tracking down suspects and criminals.

In the 1980s when people discussed the unique nature of humanity, they habitually used chess as primary proof of human superiority. They believed that computers would never beat humans at chess. On 10 February 1996, IBM’s Deep Blue defeated world chess champion Garry Kasparov, laying to rest that particular claim for human pre-eminence.

Deep Blue was given a head start by its creators, who preprogrammed it not only with the basic rules of chess, but also with detailed instructions regarding chess strategies. A new generation of AI uses machine learning to do even more remarkable and elegant things. In February 2015 a program developed by Google DeepMind learned by itself how to play forty-nine classic Atari games. One of the developers, Dr Demis Hassabis, explained that ‘the only information we gave the system was the raw pixels on the screen and the idea that it had to get a high score. And everything else it had to figure out by itself.’ The program managed to learn the rules of all the games it was presented with, from Pac-Man and Space Invaders to car racing and tennis games. It then played most of them as well as or better than humans, sometimes coming up with strategies that never occur to human players.

Computer algorithms have recently proven their worth in ball games, too. For many decades, baseball teams used the wisdom, experience and gut instincts of professional scouts and managers to pick players. The best players fetched millions of dollars, and naturally enough the rich teams got the cream of the market, whereas poorer teams had to settle for the scraps. In 2002 Billy Beane, the manager of the low-budget Oakland Athletics, decided to beat the system. He relied on an arcane computer algorithm developed by economists and computer geeks to create a winning team from players that human scouts overlooked or undervalued. The old-timers were incensed by Beane’s algorithm transgressing into the hallowed halls of baseball. They said that picking baseball players is an art, and that only humans with an intimate and long-standing experience of the game can master it. A computer program could never do it, because it could never decipher the secrets and the spirit of baseball.

They soon had to eat their baseball caps. Beane’s shoestring-budget algorithmic team ($44 million) not only held its own against baseball giants such as the New York Yankees ($125 million), but became the first team ever in American League baseball to win twenty consecutive games. Not that Beane and Oakland could enjoy their success for long. Soon enough, many other baseball teams adopted the same algorithmic approach, and since the Yankees and Red Sox could pay far more for both baseball players and computer software, low-budget teams such as the Oakland Athletics now had an even smaller chance of beating the system than before.

In 2004 Professor Frank Levy from MIT and Professor Richard Murnane from Harvard published a thorough research of the job market, listing those professions most likely to undergo automation. Truck drivers were given as an example of a job that could not possibly be automated in the foreseeable future. It is hard to imagine, they wrote, that algorithms could safely drive trucks on a busy road. A mere ten years later, Google and Tesla not only imagine this, but are actually making it happen.

In fact, as time goes by, it becomes easier and easier to replace humans with computer algorithms, not merely because the algorithms are getting smarter, but also because humans are professionalising. Ancient hunter-gatherers mastered a very wide variety of skills in order to survive, which is why it would be immensely difficult to design a robotic hunter-gatherer. Such a robot would have to know how to prepare spear points from flint stones, how to find edible mushrooms in a forest, how to use medicinal herbs to bandage a wound, how to track down a mammoth and how to coordinate a charge with a dozen other hunters. However, over the last few thousand years we humans have been specialising. A taxi driver or a cardiologist specialises in a much narrower niche than a hunter-gatherer, which makes it easier to replace them with AI.

Even the managers in charge of all these activities can be replaced. Thanks to its powerful algorithms, Uber can manage millions of taxi drivers with only a handful of humans. Most of the commands are given by the algorithms without any need of human supervision. In May 2014 Deep Knowledge Ventures – a Hong Kong venture-capital firm specialising in regenerative medicine – broke new ground by appointing an algorithm called VITAL to its board. VITAL makes investment recommendations by analysing huge amounts of data on the financial situation, clinical trials and intellectual property of prospective companies. Like the other five board members, the algorithm gets to vote on whether the firm makes an investment in a specific company or not.

Examining VITAL’s record so far, it seems that it has already picked up one managerial vice: nepotism. It has recommended investing in companies that grant algorithms more authority. With VITAL’s blessing, Deep Knowledge Ventures has recently invested in Silico Medicine, which develops computer-assisted methods for drug research, and in Pathway Pharmaceuticals, which employs a platform called OncoFinder to select and rate personalised cancer therapies.

As algorithms push humans out of the job market, wealth might become concentrated in the hands of the tiny elite that owns the all-powerful algorithms, creating unprecedented social inequality. Alternatively, the algorithms might not only manage businesses, but actually come to own them. At present, human law already recognises intersubjective entities like corporations and nations as ‘legal persons’. Though Toyota or Argentina has neither a body nor a mind, they are subject to international laws, they can own land and money, and they can sue and be sued in court. We might soon grant similar status to algorithms. An algorithm could then own a venture-capital fund without having to obey the wishes of any human master.

If the algorithm makes the right decisions, it could accumulate a fortune, which it could then invest as it sees fit, perhaps buying your house and becoming your landlord. If you infringe on the algorithm’s legal rights – say, by not paying rent – the algorithm could hire lawyers and sue you in court. If such algorithms consistently outperform human fund managers, we might end up with an algorithmic upper class owning most of our planet. This may sound impossible, but before dismissing the idea, remember that most of our planet is already legally owned by non-human inter-subjective entities, namely nations and corporations. Indeed, 5,000 years ago much of Sumer was owned by imaginary gods such as Enki and Inanna. If gods can possess land and employ people, why not algorithms?

So what will people do? Art is often said to provide us with our ultimate (and uniquely human) sanctuary. In a world where computers replace doctors, drivers, teachers and even landlords, everyone would become an artist. Yet it is hard to see why artistic creation will be safe from the algorithms. Why are we so sure computers will be unable to better us in the composition of music? According to the life sciences, art is not the product of some enchanted spirit or metaphysical soul, but rather of organic algorithms recognising mathematical patterns. If so, there is no reason why non-organic algorithms couldn’t master it.

David Cope is a musicology professor at the University of California in Santa Cruz. He is also one of the more controversial figures in the world of classical music. Cope has written programs that compose concertos, chorales, symphonies and operas. His first creation was named EMI (Experiments in Musical Intelligence), which specialised in imitating the style of Johann Sebastian Bach. It took seven years to create the program, but once the work was done, EMI composed 5,000 chorales à la Bach in a single day. Cope arranged a performance of a few select chorales in a music festival at Santa Cruz. Enthusiastic members of the audience praised the wonderful performance, and explained excitedly how the music touched their innermost being. They didn’t know it was composed by EMI rather than Bach, and when the truth was revealed, some reacted with glum silence, while others shouted in anger.

EMI continued to improve, and learned to imitate Beethoven, Chopin, Rachmaninov and Stravinsky. Cope got EMI a contract, and its first album – Classical Music Composed by Computer – sold surprisingly well. Publicity brought increasing hostility from classical-music buffs. Professor Steve Larson from the University of Oregon sent Cope a challenge for a musical showdown. Larson suggested that professional pianists play three pieces one after the other: one by Bach, one by EMI, and one by Larson himself. The audience would then be asked to vote who composed which piece. Larson was convinced people would easily tell the difference between soulful human compositions, and the lifeless artefact of a machine. Cope accepted the challenge. On the appointed date, hundreds of lecturers, students and music fans assembled in the University of Oregon’s concert hall. At the end of the performance, a vote was taken. The result? The audience thought that EMI’s piece was genuine Bach, that Bach’s piece was composed by Larson, and that Larson’s piece was produced by a computer.

Critics continued to argue that EMI’s music is technically excellent, but that it lacks something. It is too accurate. It has no depth. It has no soul. Yet when people heard EMI’s compositions without being informed of their provenance, they frequently praised them precisely for their soulfulness and emotional resonance.

Following EMI’s successes, Cope created newer and even more sophisticated programs. His crowning achievement was Annie. Whereas EMI composed music according to predetermined rules, Annie is based on machine learning. Its musical style constantly changes and develops in reaction to new inputs from the outside world. Cope has no idea what Annie is going to compose next. Indeed, Annie does not restrict itself to music composition but also explores other art forms such as haiku poetry. In 2011 Cope published Comes the Fiery Night: 2,000 Haiku by Man and Machine. Of the 2,000 haikus in the book, some are written by Annie, and the rest by organic poets. The book does not disclose which are which. If you think you can tell the difference between human creativity and machine output, you are welcome to test your claim.

In the nineteenth century the Industrial Revolution created a huge new class of urban proletariats, in the twenty-first century we might witness the creation of a new massive class: people devoid of any economic, political or even artistic value, who contribute nothing to the prosperity, power and glory of society.

In September 2013 two Oxford researchers, Carl Benedikt Frey and Michael A. Osborne, published ‘The Future of Employment’, in which they surveyed the likelihood of different professions being taken over by computer algorithms within the next twenty years. The algorithm developed by Frey and Osborne to do the calculations estimated that 47 per cent of US jobs are at high risk. For example, there is a 99 per cent probability that by 2033 human telemarketers and insurance underwriters will lose their jobs to algorithms. There is a 98 per cent probability that the same will happen to sports referees, 97 per cent that it will happen to cashiers and 96 per cent to chefs. Waiters – 94 per cent. Paralegal assistants – 94 per cent. Tour guides – 91 per cent. Bakers – 89 per cent. Bus drivers – 89 per cent. Construction labourers – 88 per cent. Veterinary assistants – 86 per cent. Security guards – 84 per cent. Sailors – 83 per cent. Bartenders – 77 per cent. Archivists – 76 per cent. Carpenters – 72 per cent. Lifeguards – 67 per cent. And so forth. There are of course some safe jobs. The likelihood that computer algorithms will displace archaeologists by 2033 is only 0.7 per cent, because their job requires highly sophisticated types of pattern recognition, and doesn’t produce huge profits. Hence it is improbable that corporations or government will make the necessary investment to automate archaeology within the next twenty years.

Of course, by 2033 many new professions are likely to appear, for example, virtual-world designers. But such professions will probably require much more creativity and flexibility than your run-of-the-mill job, and it is unclear whether forty-year-old cashiers or insurance agents will be able to reinvent themselves as virtual-world designers (just try to imagine a virtual world created by an insurance agent!). And even if they do so, the pace of progress is such that within another decade they might have to reinvent themselves yet again. After all, algorithms might well outperform humans in designing virtual worlds too. The crucial problem isn’t creating new jobs. The crucial problem is creating new jobs that humans perform better than algorithms.

- Yuval Noah Harari, The Great Decoupling in Homo Deus: A Brief History of Tomorrow

#yuval noah harari #the great decoupling #homo deus: a brief history of tomorrow #homo deus

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un-enfant-immature · 6 years

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MacBook Air review

For three years, the MacBook Air was conspicuously absent. The ultraportable never left Apple’s site, of course, but we finished keynote after keynote wondering why Apple continued to neglect one of its most popular products, all while overhauling the rest of the MacBook line.

At an event last month in Brooklyn, however, Apple finally acquiesced, delivering the largest single update since the product was introduced ten and a half years prior. In an event stuffed to the gills with an enthusiastic audience, the Air got what was easily the biggest applause break — more than the iPad Pro and certainly more than the Mac Mini.

The fan base was clearly ready for a new Air.

Getting the Air right is a tricky proposition. Not only is it the slimmest model in the line, it’s also the cheapest, a combination that’s made it a popular selection for frequent travelers and those just looking for the least expensive route into the MacOS ecosystem. Every hardware addition to the line comes with a potential price increase — something we saw play out with the evolution of the Mini, which jumped from $499 to $799, removing some of the device’s entry-level appeal.

The Air has also seen a price increase, though Apple was able to rein things in a bit more here, in terms of both overall and relative price. At $1,199, the low-end version of the laptop remains the least pricey entry point into the Mac ecosystem (excluding the older Air, which is still available for $999).

This latest update finds the Air finally assuming its place in the current MacBook line, whose current iteration began life with a major overhaul in 2015. Becoming part of the club means an aesthetic upgrade, a move to USB-C, souped up internals and, of course, the long-awaited addition of a Retina Display.

The device arrives amid a shift for the company, as it once again embraces creative professionals with both MacOS devices and iOS through the addition of the iPad Pro. The latter continues to blur the line between Apple’s operating systems, with computation power rivaling — and in some cases outperforming — some of its MacOS models.

Currently, the Air sits between the iPad Pro and low-end MacBook — though given the $100 price difference between it and the former, I don’t know that anyone would be entirely shocked to see Apple quietly sunset the baseline product in favor of the reborn Air. There simply aren’t enough compelling reasons to keep that model around in its current configuration, especially given the Air’s enduring popularity.

Certain sacrifices were made in favor of keeping the Air’s price down — most notably the Touch Bar. There was some speculation that Apple’s decision to drop the technology on this device was some clear sign that the company was moving away from the touchscreen-adjacent tech, but the reason is likely far more simple: Adding it would have further driven up the entry-level price — and eclipsed the MacBook in the process.

Instead, the company did something even better, breaking out Touch ID from the bar. After a couple of years with a Touch Bar on both my work and personal machines, the fingerprint scanner remains the one feature (outside of the standard function keys like volume) that I use on a daily basis. In the long run, the company may have done the Touch Bar a bit of a disservice by consciously uncoupling Touch ID, but for the Air, it was the ideal decision, bringing its most useful feature without driving up the price in the process.

The keyboard is the same found on the most recent MacBook Pros, as well. That, along with other shifts, is bound to be polarizing among longtime Air users. I will say this, however, if you haven’t tried a MacBook keyboard since the infamous butterfly switch overhaul of 2015, visit your local Apple store to give them another shot. It’s true that they’re still a fair bit shallower than the previous model, but things have been improved in the past three years, courtesy of two major updates.

This latest generation is quieter, has a better feel and has the added benefit of a new rubberized bladder, which should protect from spills, along with particulate matter, which has become a bane of everyone with an earlier model’s existence. Seriously, I once found myself roaming around Seattle desperately trying to find a can of compressed air before an Amazon event.

Those who’ve been holding out to upgrade from an earlier Air model will likely have a bit of an adjustment period, but it’s a much easier transition that it was on those initial 2015 MacBooks. The track pad, too, is now in line with its MacBook brethren. It’s 20 percent larger than the previous Air and utilizes Force Touch for a more uniform response across the surface, welcome changes the both of them.

The new Air’s internals are, naturally, an upgrade across the board over the 2015 model, but it’s more of a mixed bag when compared to the MacBook. In fact, the concurrent existence of the two products is likely to cause confusion among buyers — and understandably so. If you’ve been having trouble deciding between MacBooks, Apple’s made that task even more complex.

RAM is the same on both systems at either 8 or 16GB. No surprise there — that’s pretty consistent across the entire MacBook line. The base-level storage configuration, on the other hand, starts lower but goes higher than the MacBook, with an entry of 128GB (to the MacBook’s 256), all the way up to 1.5TB. Of course, storage upgrades are always costly, and if you max this one out, it’s going to run you another $1,000.

Given that it’s a newer model, the process is an upgrade over the pricier MacBook on the baseline, from a 1.2GHz dual-core Intel Core i3 to a 1.6GHz dual-core Intel Core i5 processor. That said, there’s only one configuration here, at present, so if you want more power, seriously consider upgrading to the Pro. Our model, the Core i5 coupled with 8GB (standard on everything but storage) scored a 4,297 and 7,723 on Geekbench’s single and multi-core tests, respectively.

A quick glance at the above graphic really highlights the gulf between the Air and Pro, though the new chips do mark an upgrade over the 2017 MacBook’s single- and multi-core scores of 3,527 and 6,654. The new silicon is plenty zippy for most users’ daily tasks, but if you need more out of your system — be it for gaming or resource-intensive tasks like video edit — it’s worth the jump to the Pro.

Battery, meanwhile, is a pretty sizable bump over the MacBook, owing to the larger footprint on the Air’s 13.3-inch frame (versus the 12-inch MacBook), with a stated “up to 12 hours” on a charge to the MacBook’s 10. I found that to be pretty on the money, in my own testing. I was able to stream video for just a hair under 12 hours — plenty enough to get you through most flights.

Of course, the larger screen and battery also mean a heftier laptop. The Air’s 2.75 pounds is around 3/4 of a pound more than the MacBook. In spite of retaining the iconic beveled design, it’s also a bit thicker than the 12-inch model. That said, the company’s managed to both shrink the footprint and reduce the weight from the older Air, which weighed in at 2.96 pounds.

The display is, as advertised, a massive upgrade over the last model. If you’ve spent any time with a Retina display, you know the deal. It’s big and bright, with a nice color balance. In terms of sheer numbers, we’re talking about a bump from 1440 x 900 to 2560 x 1600 pixels. That amounts to 227 PPI, compared to the old model’s 128. It’s an immediately apparent upgrade — there’s a reason so many Air owners have been holding out for the addition. The multimedia experience is rounded out by upgraded speakers that are capable of getting LOUD, in spite of taking up very little real estate on either side of the keyboard.

The design language was overdue for an update, and now the system looks nearly identical to the 13-inch Pro at first glance, aside from the familiar tapered design. And, of course, you can pick it up in Gold, keeping with Apple’s theme of more colorful options on lower-cost devices like the iPhone XR.

The most polarizing aspect on the frame is no doubt the continued shift to all Thunderbolt 3 (USB-C). No surprise there, of course. Get ready to lead the #donglelife until time comes to upgrade all of your accessories. The two USB-C ports are located on the same side, which means a bit more maneuvering when charging — though the new ports are much more diverse than the old power model. It’s the same set up you’ll find on the MacBook. Upgrade to the Pro, meanwhile, and you’ll get twice the number.

There’s no doubt the new Air marks a sizable update. It’s pricier, too, though Apple’s kept things more in check here than with the Mac Mini. With all of its upgrades and lower price point to boot, the Air is the clear pick over the 12-inch MacBook in practically every way.

As a matter of fact, barring some major future upgrade, the 12-inch likely isn’t long for this world. And that’s perfectly fine. The new Air is very clearly the better buy.

#TechCrunch

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gordonwilliamsweb · 5 years

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AI could tackle prostate data

More than 10 million prostate tissue samples are collected each year in the U.S., creating a veritable mountain of information for health care employees to process. (For Spectrum Health Beat)

In another step toward using artificial intelligence in medicine, a new study shows that computers can be trained to match human experts in judging the severity of prostate tumors.

Researchers found that their artificial intelligence system was “near perfect” in determining whether prostate tissue contained cancer cells.

And it was on par with 23 “world-leading” pathologists in judging the severity of prostate tumors.

No one is suggesting computers should replace doctors. But some researchers do think AI technology could improve the accuracy and efficiency of medical diagnoses.

Typically, it works like this: Researchers develop an algorithm using “deep learning”—where a computer system mimics the brain’s neural networks. It’s exposed to a large number of images—digital mammograms, for example—and it teaches itself to recognize key features, such as signs of a tumor.

Earlier this month, researchers reported on an AI system that appeared to best radiologists in interpreting screening mammograms.

Other studies have found that AI can outperform doctors in distinguishing harmless moles from skin cancer and detecting breast tumor cells in lymph node samples.

The new study looked at whether it’s possible to train an AI system to detect and “grade” prostate cancer in biopsied tissue samples.

Normally, that’s the work of clinical pathologists—specialists who examine tissue under the microscope to help diagnose disease and judge how serious or advanced it is.

It’s painstaking work and, to a certain degree, subjective, according to study leader Martin Eklund, a senior researcher at the Karolinska Institute in Sweden.

Then there’s the workload.

In the United States alone, more than 1 million men undergo a prostate biopsy each year—producing more than 10 million tissue samples to be examined, Eklund’s team noted.

To create their AI system, the researchers digitized more than 8,000 prostate tissue samples from Swedish men ages 50 to 69, creating high-resolution images.

They then exposed the system to roughly 6,600 images—training it to learn the difference between cancerous and noncancerous tissue.

Next came the test phase.

The AI system was asked to distinguish benign tissue from cancer in the remaining samples, plus around 300 from men who’d had biopsies at Karolinska. The AI results, the researchers reported, were almost always in agreement with the original pathologist’s assessment.

And when it came to grading the severity of prostate tumors with what’s called a Gleason score, the AI system was comparable to the judgment of 23 leading pathologists from around the world.

Much work, however, remains.

A next step, Eklund said, is to see how the AI system performs across different labs and different pathology scanners, which are used to create digital images.

But one day, he said, AI could be used in a number of ways—including as a “safety net” to make sure a pathologist didn’t miss a cancer. It might also improve efficiency by prioritizing suspicious biopsies that pathologists should examine sooner.

Studies like this are a necessary step toward incorporating AI into medical practice, said Dr. Matthew Hanna, a pathologist at Memorial Sloan Kettering Cancer Center in New York City.

But, he stressed, “there’s still a long road ahead.”

Hanna, who was not involved in the study, is also a spokesperson for the College of American Pathologists.

Like Eklund, he said that any AI system would have to be validated across different centers and different pathology scanners. And ultimately, Hanna said, studies will need to show that such technology can be used effectively in pathologists’ real-world practice.

There are practical realities, too.

At the moment, Hanna pointed out, only a relative minority of pathology labs use digital systems in patient care.

That’s key because for any AI algorithm to work, there have to be digital images to analyze. Most often, pathologists still study tissue using the classic approach—glass slides and a microscope.

What’s clear is that machines won’t be replacing humans—at least in the foreseeable future.

“This technology is coming,” Hanna said. “But as opposed to replacing doctors, it will transform how they deliver care—hopefully for the better.”

The study was reported online recently in The Lancet Oncology.

AI could tackle prostate data published first on https://nootropicspowdersupplier.tumblr.com/

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michellelinkous · 5 years