Daily Archives: August 11, 2017

How to build any AI-driven smart service – ZDNet

Posted: August 11, 2017 at 6:17 pm

CXOs: Participate in Constellation's digital transformation survey by Aug.18, 2017 and receive a summary of the results.

The combination of machine learning, deep learning, natural language processing, and cognitive computing will change the ways that humans and machines interact. AI-driven smart services will sense one's surroundings, learn one's preferences are from past behavior, and subtly guide people and machines through their daily lives in ways that will truly feel frictionless. This quest to deliver AI-driven smart services across all industries and business processes will usher the most significant shift in computing and business this decade and beyond.

Organizations can expect AI-driven smart services to impact future of work flows, IoT services, customer experience journeys, and synchronous ledgers (blockchain). Success requires the establishment of AI outcomes (see Figure 1). Once the outcomes are established, organizations can craft AI-driven smart services that orchestrate, automate, and deliver mass personalization at scale.

The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity. When AI is defined through seven outcomes, the business value of AI projects gain meaning and can easily show business value through a spectrum of outcomes:

Because AI-driven smart services require offloading the decision-making responsibility to atomic driven smart services, the foundation of any AI-driven smart service is trust. Below an explanation of how the five key components of an AI-driven smart service orchestrate trust.

Fears of robots taking over the world have been overblown. Successful AI-driven smart services will augment human intelligence just as machines augmented physical capabilities. AI driven smart services play a key role in defining business models for synchronous ledger technologies (blockchain), Internet of Things, customer experience, and future of work by reducing errors, improving decision-making speed, identifying demand signals, predicting outcomes, and preventing "disasters".

Are you a CXO designing an AI-driven smart service? Participate in Constellation's digital transformation survey by Aug. 18, 2017 and receive a summary of the results.

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Can a Crowdsourced AI Medical Diagnosis App Outperform Your Doctor? – Scientific American

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Shantanu Nundy recognized the symptoms of rheumatoid arthritis when his 31-year-old patient suffering from crippling hand pain checked into Marys Center in Washington, D.C. Instead of immediately starting treatment, though, Nundy decided first to double-check his diagnosis using a smartphone app that helps with difficult medical cases by soliciting advice from doctors worldwide. Within a day, Nundys hunch was confirmed. The app had used artificial intelligence (AI) to analyze and filter advice from several medical specialists into an overall ranking of the most likely diagnoses. Created by the Human Diagnosis Project (Human Dx)an organization that Nundy directsthe app is one of the latest examples of growing interest in humanAI collaboration to improve health care.

Human Dx advocates the use of machine learninga popular AI technique that automatically learns from classifying patterns in datato crowdsource and build on the best medical knowledge from thousands of physicians across 70 countries. Physicians at several major medical research centers have shown early interest in the app. Human Dx on Thursday announced a new partnership with top medical profession organizations including the American Medical Association and the Association of American Medical Colleges to promote and scale up Human Dxs system. The goal is to provide timely and affordable specialist advice to general practitioners serving millions of people worldwide, in particular so-called "safety net" hospitals and clinics throughout the U.S. that offer access to care regardless of a patients ability to pay.

We need to find solutions that scale the capacity of existing doctors to serve more patients at the same or cheaper cost, says Jay Komarneni, founder and chair of Human Dx. Roughly 30 million uninsured Americans rely on safety net facilities, which generally have limited or no access to medical specialists. Those patients often face the stark choice of either paying out of pocket for an expensive in-person consultation or waiting for months to be seen by the few specialists working at public hospitals, which receive government funding to help pay for patient care, Komarneni says. Meanwhile studies have shown that between 25 percent and 30 percent (pdf) of such expensive specialist visits could be conducted by online consultations between physicians while sparing patients the additional costs or long wait times.

Komarneni envisions augmenting or extending physician capacity with AI to close this specialist gap. Within five years Human Dx aims to become available to all 1,300 safety net community health centers and free clinics in the U.S. The same remote consultation services could also be made available to millions of people around the world who lack access to medical specialists, Komarneni says.

When a physican needs help diagnosing or treating a patient they open the Human Dx smartphone app or visit the projects Web page and type in their clinical question as well as their working diagnosis. The physician can also upload images and test results related to the case and add details such as any medication the patient takes regularly. The physician then requests help, either from specific colleagues or the network of doctors who have joined the Human Dx community. Over the next day or so Human Dxs AI program aggregates all of the responses into a single report. It is the new digital equivalent of a curbside consult where a physician might ask a friend or colleague for quick input on a medical case without setting up a formal, expensive consultation, says Ateev Mehrotra, an associate professor of health care policy and medicine at Harvard Medical School and a physician at Beth Israel Deaconess Medical Center. It makes intuitive sense that [crowdsourced advice] would be better advice, he says, but how much better is an open scientific question. Still, he adds, I think its also important to acknowledge that physician diagnostic errors are fairly common. One of Mehrotra's Harvard colleagues has been studying how the AI-boosted Human Dx system performs in comparison with individual medical specialists, but has yet to publish the results.

Mehrotra's cautionary note comes from research that he and Nundy published last year in JAMA Internal Medicine. That study used the Human Dx service as a neutral platform to compare the diagnostic accuracy of human physicians with third-party symptom checker Web sites and apps used by patients for self-diagnosis. In this case, the humans handily outperformed the symptom checkers computer algorithms. But even physicians provided incorrect diagnoses about 15 percent of the time, which is comparable with past estimates of physician diagnostic error.

Human Dx could eventually help improve the medical education and training of human physicians, says Sanjay Desai, a physician and director of the Osler Medical Training Program at Johns Hopkins University. As a first step in checking the service's capabilities, he and his colleagues ran a study where the preliminary results showed the app could tell the difference between the diagnostic abilities of medical residents and fully trained physicians. Desai wants to see the service become a system that could track the clinical performance of individual physicians and provide targeted recommendations for improving specific skills. Such objective assessments could be an improvement over the current method of human physicians qualitatively judging their less experienced colleagues. The open question, Desai says, is whether the algorithms can be created to provide finer insights into an [individual] doctors strengths and weaknesses in clinical reasoning.

Human Dx is one of many AI systems being tested in health care. The IBM Watson Health unit is perhaps the most prominent, with the company for the past several years claiming that its AI is assisting major medical centers and hospitals in tasks such as genetically sequencing brain tumors and matching cancer patients to clinical trials. Studies have shown AI can help predict which patients will suffer from heart attacks or strokes in 10 years or even forecast which will die within five. Tech giants such as Google have joined start-ups in developing AI that can diagnose cancer from medical images. Still, AI in medicine is in its early days and its true value remains to be seen. Watson appears to have been a success at Memorial Sloan Kettering Cancer Center, yet it floundered at The University of Texas M. D. Anderson Cancer Center, although it is unclear whether the problems resulted from the technology or its implementation and management.

The Human Dx Project also faces questions in achieving widespread adoption, according to Mehrotra and Desai. One prominent challenge involves getting enough physicians to volunteer their time and free labor to meet the potential rise in demand for remote consultations. Another possible issue is how Human Dx's AI quality control will address users who consistently deliver wildly incorrect diagnoses. The service will also require a sizable user base of medical specialists to help solve those trickier cases where general physicians may be at a loss.

In any case, the Human Dx leaders and the physicians helping to validate the platform's usefulness seem to agree that AI alone will not take over medical care in the near future. Instead, Human Dx seeks to harness both machine learning and the crowdsourced wisdom of human physicians to make the most of limited medical resources, even as the demands for medical care continue to rise. The complexity of practicing medicine in real life will require both humans and machines to solve problems, Komarneni says, as opposed to pure machine learning.

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Can a Crowdsourced AI Medical Diagnosis App Outperform Your Doctor? - Scientific American

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AI Helps Magicians Perform Mind Reading Tricks – IEEE Spectrum

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Illustration: iStockphoto Computer algorithms can help magicians create magic tricks that exploit human psychology

You are presented with two decks, one with images and the other with words. The magician shuffles and distributesthe decks into piles of four cards. You get to choose twopiles, one from the word deck and one from the image deck, to make a hand of eight cards. Then youre invited to picka word card and and an imagecardfrom yourhand.Once youve selected a pair, youwatch the magician reveal a previously written prediction about the cards youve chosen. The prediction is correct!

That kind of mind-reading magic trick could benefitfrom new AI computer algorithms. These algorithms are designed to exploithuman psychology andhelpmagicians choosethe best card combinations.

Thisassociation magic trickrelies upon making a spectator believe that the magician hasmanaged to predict his or herfree choice from a random combination ofshuffled cards. In reality, the magician has preselected two decks of cards that together containa category of card pairs that triggera particularly powerful mental association for most people. To help pull off this mind-reading illusion, computer scientists created a computer algorithm that can automatically help find compellingword and image combinations.

First and foremost its an entertaining magic trick we have built, but it does potentially allow insight into the processes that humans use to decide associations, saysPeter McOwan, a professor of computer science at Queen Mary University London in the UK.There are a range of mentalism tricks that use associations to accomplish their effects and similar computational frameworks could be applied across that range, he said.

McOwan began practicing magic as a hobby in his teens. He has since used magic tricks to teach computer algorithms and haswrittenfree e-books on the intersection between the two subjects. In recent years, McOwan has teamed up withHoward Williams, another computer scientistat Queen Mary University London, to develop computer algorithms that can help create new magic tricks. Their latest study on the association magic trick was published in the 9 Aug 2017 issue of the journal PLOS One.

The association magic trick takes advantage of how the human subconscious tends to formstrong mental associations between certain concepts. For example, people may quickly make food associations between images of burgers or fruitand related words such asbites,treats,snack andfeast. The human subconscious can quickly recognize and process such associations in a way that appears almost automatic to the conscious mind.

Another key part of the trick involves an appreciation of two psychological systems that underlay our decision making, as described byDaniel Kahneman, a psychologist and Nobel Prize-winner. System 1 covers the swift and seemingly automatic mental processing. System 2 refers to the more active, conscious thinking involved in planning, puzzle solvingor calculations.

The magician wants the spectator participating in the magic show to use the first system and make the automatic association because it makes his or her choice predictableespecially when the decks of cards are organized and shuffled in a way that ensures a matched pair of cards that belongto a certain category will always be among the choices. So the magician adds time pressure by asking the spectator to make a quick decision. That pressure typically ensures the spectator makes the predictable choice rather than making a more idiosyncratic pairingbased on the more conscious thought processes of the second system.

To collect relevant data in making the magic trick, the Queen Mary University London researchersperformed an online psychology experiment by showing human participants various selections of 10 trademarks from a pool of 100 of the most famous trademarks. The researchers then askedparticipantsto write down any words about how the trademarks made them feel, along with any otherassociations they had with each mark.

But theresearchers alsodeveloped an AI to help themfindstrong associations for the magic trick. First, their computer algorithm ran Internet searches on popular trademarks and plucked words from the webpages linked by the top ten search results for each trademark. Second, itused a previously developed search algorithm, called BM25, to organizeand rank the collecteddata according to certain association categories (such as food-related words). Additional AI techniques called word2vec and Wordnet helpedby providingsimilarity scores for certain word pairings.

The AI by itself was not necessarily able to find the strongest or most useful associations for the magic trick without human help. But suchautomated data gathering and organization could prove a handy time-saving tool for complementing data collected from the more time-consuming experimental surveys, according to Williams at Queen Mary University London. He described the tradeoff as follows:

Automated data gathering is useful as it is quick and can gather large sets of data. Experiments take longer to organize, perform, process data, etc., but provide more specific and targeted data. [Its] essentially a tradeoff between quality and quantity. Though quantity provides broadness, and is useful in its own right.

That process led Williams and McOwan to create image and word card decks that contained the food category as the likeliest choice. Theytested out their association magic trick on 143individuals during theBig Bang 2013 science fair in Birmingham, UK, where it succeeded in all but 15 cases. Those more unusual word and image pairings chosen in the unsuccessful cases could potentially be excluded by the computer algorithm or by hand in the future.

Even though there is a fairly clear pathway we have created in the trick for them to follow in the performances, some people just had left field associationsprobably influenced by their life experiences, McOwan says.Its an area worth looking at more.

Magicians could eventually makeuseof popular AI techniques such as machine learning and deep learning that can automatically find and learn from patterns in data. McOwan speculated that such techniques could prove useful in cold reading, which is when a magician uses psychological tricks and a data-driven understanding of population trends to pretend to divine personal details about a stranger.

The researchers have already commercialized magic tricks that were created with the help of computer algorithms. In 2014, they used a computer algorithm to help create a magic jigsaw puzzle that makes certain shapes seem to disappear upon reassembly based on certain geometric principles. That jigsaw puzzlesold out two production runs in a well known London magic shop, McOwan says.

The idea of computer algorithms helping create magic tricks may lack the emotional drama ofChristopher Nolans film The Prestige,where rival magicians vie to perfecttheir magic illusions. But even some of thefictional wizards in the magical world of Harry Potter might appreciate muggle AI technology that can help magicians seem toperform mind reading without wands and spells.

Of course a trick is only as good as the performer and our work is simply giving new tools to create new methods to perform with, McOwan says.The real magic still lies with the magician.

IEEE Spectrums general technology blog, featuring news, analysis, and opinions about engineering, consumer electronics, and technology and society, from the editorial staff and freelance contributors.

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AI Helps Magicians Perform Mind Reading Tricks - IEEE Spectrum

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There is a good case to unleash job-killing AI on the high seas – New Scientist

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US blockchain company in tie-up on medical artificial intelligence – Reuters

Posted: at 6:16 pm

NEW YORK (Reuters) - U.S. technology company The Bitfury Group has formed a partnership with Insilico Medicine, a Baltimore-based medical artificial intelligence (AI) firm, to create new applications for the healthcare industry using blockchain, Bitfury's chief executive officer said on Friday.

Blockchain is a digital ledger of transactions that gained prominence as the software underpinning the digital currency bitcoin. The technology, being developed in the public and private sectors, has gained attention globally for its ability to permanently record and track assets or transactions across all industries.

The two companies signed a memorandum of understanding last month for collaboration to study and develop blockchain and AI solutions for sharing, managing, tracking and validating healthcare data, said Bitfury founder and CEO Valery Vavilov in an email to Reuters. The collaboration is in an early stage and there were no details available about potential projects or specific uses.

Artificial intelligence in the healthcare sector uses algorithms and software to mimic human ability in analyzing complex medical data. Vast amounts of healthcare data are pushing the development of AI applications.

Vavilov said both companies will use Bitfury's Exonum blockchain platform to store and secure health data in a system compatible with artificial intelligence.

"AI has not reached its full potential for the healthcare industry yet because it requires a large and diverse range of data to learn from in order to ensure accuracy and provide actionable results," said the Bitfury chief executive.

Healthcare AI is expanding by an annual rate of 40 percent, research firm Frost & Sullivan said in a recent study. It said global revenue generated by artificial intelligence systems will soar to $6.7 billion by 2021 from $811 million in 2015.

"A blockchain-based medical records system could safeguard patient data and allow for improved interoperability between doctors and hospitals, while also giving patients more ownership over their own records," Vavilov said.

Reporting by Gertrude Chavez-Dreyfuss; Editing by David Gregorio

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US blockchain company in tie-up on medical artificial intelligence - Reuters

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Artificial intelligence identifies plant species by looking at them – Boing Boing

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Machine learning algorithms have successfully identified plant species in massive herbaria just by looking at the dried specimens. According to researchers, similar AI approaches could also be used identify the likes of fly larvae and plant fossils. From Nature:

There are roughly 3,000 herbaria in the world, hosting an estimated 350 million specimens only a fraction of which has been digitized. But the swelling data sets, along with advances in computing techniques, enticed computer scientist Erick Mata-Montero of the Costa Rica Institute of Technology in Cartago and botanist Pierre Bonnet of the French Agricultural Research Centre for International Development in Montpellier, to see what they could make of the data.

Researchers trained... algorithms on more than 260,000 scans of herbarium sheets, encompassing more than 1,000 species. The computer program eventually identified species with nearly 80% accuracy: the correct answer was within the algorithms top 5 picks 90% of the time. That, says (Penn State paleobotanist Peter) Wilf, probably out-performs a human taxonomist by quite a bit.

Such results often worry botanists, Bonnet says, many of whom already feel that their field is undervalued. People feel this kind of technology could be something that will decrease the value of botanical expertise, he says. But this approach is only possible because it is based on the human expertise. It will never remove the human expertise. People would also still need to verify the results, he adds.

"Going deeper in the automated identification of Herbarium specimens" (BMC Evolutionary Biology)

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Artificial intelligence and creativity: If robots can make art, what’s left for us? – ABC Online

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Posted August 11, 2017 11:58:38

Artificial intelligence is becoming commonplace, from your smartphone and your Amazon account to the driverless cars that will soon grace public roads in Australia.

Often, the response to this reality is one of trepidation and concern, about mass unemployment and the dominance of Big Tech. But that's not always the case.

"Art is one of the last domains in AI where there is an optimistic view on how humans and machines can work together," says Dave King, founder of Move 37, a creative AI company.

He says creativity is not a God-given thing. It's a process, and it takes practice.

"One of the most interesting aspects of creativity is that ability to combine ideas or to draw things together," he says.

"If you have an algorithm that is working for you in the way you want it to it can source and discover lots and lots of different things."

AI is already being used in a range of artistic fields. Algorithms trained on millions of pages of romance novels have been used to write poems, and the recent Robot Art Competition showed a range of paintings with brushwork so sophisticated it could have been done by a human hand.

Jon McCormack is an artist and professor of computer science at Monash University whose work incorporates algorithms.

His series Fifty Sisters (2012) featured images of futuristic-looking plants that were "algorithmically grown" from computer code. In another work, titled Eden, he created an installation featuring "virtual creatures" whose movements were influenced by gallery visitors entering the space.

McCormack says when there is concern about AI, it is understandable.

"We're naturally scared of anything where we take away something from people, particularly something as precious as being creative and art, which we associate with being the most fundamental human [trait] that thing that differentiates us from every other species on the planet," he said.

After all, as AI expert Professor Toby Walsh notes: "We have one of the most creative brains out there."

"One of the oldest jobs on the planet, being a carpenter or an artisan, we will value most [in the future] because we will like to see an object carved or touched by the human hand, not a machine."

Artists have always used tools to create their work: for Van Gogh, it was a paint brush; for Henri Cartier-Bresson, a Leica camera.

With AI, however, the question becomes one of authorship.

"I see myself as being the artist," McCormack says of his compositions. "The computer is still very primitive it doesn't have the same capabilities as a human creative, but it's capable of doing things that complement our intelligence.

King says AI currently can only bring a limited perspective to artistic practice.

"They can only draw on what they've been trained on," he says, referring to the reams of data used to create artificial intelligence. "Whereas the human condition is expansive and broad and brings a lot more depth of perspective to it."

By itself, AI can certainly generate things that look like art, McCormack says. Whether you could consider it art is a harder question.

"So much of what we think about art is humans communicating to each other," he says.

"As soon as you bring a computer into the mix, suddenly you've got a non-human entity trying to fulfil the role that used to be occupied exclusively by people."

Soon, however, it may be possible to go further; to consider the machine not just a tool, but a partner or collaborator, with its own ability to create.

"We always think of Lennon and McCartney as being the great musical creative partnership," McCormack says.

"Will we eventually see a point in time where we have a human and computer partnership that we acknowledge as being more than the sum of its parts?

"If the art was really, really good if it moved us emotionally in the way the best art does then I think we would come to start to accept art that's made by machines."

Topics: arts-and-entertainment, robots-and-artificial-intelligence, science-and-technology, australia

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Artificial Intelligence Is Likely to Make a Career in Finance, Medicine or Law a Lot Less Lucrative – Entrepreneur

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Growing up, theres a good chance you heard the mantra go to a good school, get a good job, and make lots of money. On the surface, that seems like sound advice. After all, college graduates, on average,earn almost $1 million morein their lifetimes than those with only a high school education.

Perhaps you were encouraged to get a professional degree to land a high paying job like a doctor, dentist, lawyeror something similar.This also seems like great advice, considering a professional degree holder typically earns more than $2 million more in their lifetimes than the average college graduate.

But that was then, and this now.

Related: The Future of Productivity: AI and Machine Learning

Thanks to rapid advances in robotics, automation and artificial intelligence, jobs are falling to machines left and right. And its not just blue-collar jobs that are being taken over by automation. It's white-collar professions as well. According to an Oxford study, 47 percentof U.S. jobs could fall to automation in the next 20 years.

The safe, high paying jobs of the past are starting to look much less secure going forward. If youre currently in one of the following professions, or going to school to get into these fields, you should think twice before continuing.

If Wall Street is known for anything, its known for crazy high salaries and bonuses. For those who have wanted to get rich quickly post-college, there have been few better industries than finance. Alas, finance is one of the industries with the highest risk of automation.

Bridgewater Associates, the worlds largest hedge fund, announced late last year that it was going to be cutting staff in favor of more automation. Its getting harder to compete with AI driven hedge funds like Sentient and high-frequency traders in general, and an impressive swath of financial management services are now being handled by robo-advisors like Betterment and Wealthfront, both of which are growing rapidly.

In fact, according to Angel List, there are more than 15,000 finance startups right now working to actively disrupt finance, many of them utilizing artificial intelligence and other forms of automation. If youre in this field or planning to enter it, you might want to reconsider.

So, if not finance, then what? If youre good with numbers and detail-oriented, you should consider getting into data science. Data scientist salaries are rising rapidly, and theyre considered the new rock stars of the tech world. Of course, you could always try getting into venture capital to ride the massive transitional wave thats coming, but being a VC isnt all its cracked up to be either. Either way, traditional finance jobs are on the way out.

Related: How AI Machines Coudl Save Wall Street Brokers' Jobs

The work that doctors do is tremendously important, and on average, theyre very well paid for it. That said, there are numerous areas of medicine that are ripe for automation and improved efficiencies. One key example would be medical imaging and the fields of radiology, pathology and dermatology.

Using AI, IBMs Watson is now considered at least on-par with a professional radiologist in terms of ability to analyze an image and diagnose a patient, and it can do the analysis much faster while considering vastly larger amounts of information than any human could ever hope to. This is fantastic news for the people who need a diagnoses, but not so great for medical imaging jobs. If youre already in the field, or working to get into it, you could consider transitioning into some aspect of computer vision, be it research or training. If you cant beat the machines, you can always help to make them better.

There are numerous other technologies, such as telemedicine and mobile medical devices, that will also heavily disrupt this field going forward. And while there will still be a strong need for certain medical skills going forward, youll need to be highly selective in what you choose.

Ahh, lawyers. The world could probably use far fewer lawyers, and the machines are well on their way to making that a reality. While you can still make a pretty penny as a lawyer, depending on your specialty, its worth noting that lawyers spend a lot of time gathering and parsing data, creating or reviewing legal documentsand numerous other mundane tasks. For most lawyers, its far from a glamorous profession.

Much of this grunt work has already been automated, and there are more than 1,500 startups out there trying to streamline the legal world even further. While this wont immediately eliminate all legal jobs, it means that it will take far fewer lawyers -- and especially paralegals --to handle the same level of work.

Because lawyers tend to pay excellent attention to detail, and are highly versed in logic, a good alternative field would be programming. Programming languages are built around logicand require every bit as much attention to detail as any contract. Best of all, there are a ton of courses online that can help you learn, including some from top-tier universities like Stanford, MITand even Harvard.

And of course, programmers are incredibly well paid and in high demand virtually everywhere. Here are a few of the most in-demand programming languages to help you along if you decide to make the switch.

Related: Advancing Automation Means Humans Need to Embrace Lifelong Learning

There have been numerous times throughout history when a large number of old jobs have gone away, only to be replaced by new jobs as new technologies came along. Sometimes the transition from old to new is protracted enough to make a semi-smooth transition possible. That may or may not be the case this time around.

Sam McRoberts is the CEO ofVUDU Marketing, and the author ofScrew the Zoo. He has delved deep into the worlds of philosophy, cognitive psychology and neuroscience to better help his clients achieve their goals. When he isn't...

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Artificial Intelligence Is Likely to Make a Career in Finance, Medicine or Law a Lot Less Lucrative - Entrepreneur

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Defense Secretary James Mattis Envies Silicon Valley’s AI Ascent – WIRED

Posted: at 6:16 pm

Secretary of Defense Jim Mattis waves as he walks to his vehicle after speaking at the Defense Innovation Unit Experimental in Mountain View, Aug. 10, 2017.

Jeff Chiu/AP

Defense Secretary James Mattis has a lot on his mind these days. North Korea , obviously. China's expanding claims on the South China sea. Afghanistan, Iraq, Syria. And, closer to home, the Pentagon lagging behind the tech industry in leveraging artificial intelligence.

Mattis admitted to that concern Thursday during the Silicon Valley leg of a West Coast tour that includes visits to Amazon and Google . When WIRED asked Mattis if the US had ambitions to harness recent progress in AI for military purposes like those recently espoused by China, he said his department needed to do more with the technology.

It's got to be better integrated by the Department of Defense, because I see many of the greatest advances out here on the West Coast in private industry, Mattis said.

Mattis, speaking in Mountain View, a stones throw from Googles campus, hopes the tech industry will help the Pentagon catch up. He was visiting the Defense Innovation Unit Experimental, an organization within the DoD started by his predecessor Ashton Carter in 2015 to make it easier for smaller tech companies to partner with the Department of Defense and the military. DIUx has so far sunk $100 million into 45 contracts, including with companies developing small autonomous drones that could explore buildings during military raids, and a tooth-mounted headset and microphone.

Mattis said Thursday he wanted to see the organization increase the infusion of tech industry savvy into his department. Theres no doubt in my mind DIUx will continue to exist; it will grow in its influence on the Department of Defense, he said.

The Pentagon has a long record of researching and deploying artificial intelligence and automation technology. But AI is rapidly progressing, and the most significant developments have come out of the commercial and academic spheres.

Over the past five years, leading tech companies and their lavishly funded AI labs have sucked up ideas and talent from universities. They're now in a race to spin up the best new products and experimental projects. Google, for example, has recently used machine learning research to power up its automatic translation and cut data-center cooling bills. Waymo, Alphabet's autonomous-car company, uses AI in developing the technology in its self-driving vehicles.

Making smart use of artificial intelligence looks to be crucial to military advancement and dominance. Just last month, Chinas State Council released a detailed strategy for artificial intelligence across the economy and in its military. China's strategic interest in AI led DIUx to prepare an internal report this year suggesting scrutiny and restrictions on Chinese investment in Silicon Valley companies. Texas senior senator John Cornyn has proposed legislation that could enable that policy.

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A recent Harvard report commissioned by the Office of the Director of National Intelligence found that AI-based technologies, like autonomous vehicles, are poised to make advance militaries much more powerfuland possibly cause a transformation similar in scale to the advent of nuclear weapons. But the US does not have a public, high-level national or defense strategy for artificial intelligence in the same way as Chinaperhaps owing mostly to differences of political style.

On Thursday, Mattis professed confidence that his department would figure out how to make more with AI, without offering specifics. The bottom line is well get better at integrating advances in AI that are being taken here in the Valley into the US military, he said.

There is another bottom line to consider. The Trump administrations proposed budget would increase funding for DIUx, which might help fulfill Mattis' dreams of an AI acceleration. It also expands support to Pentagon research agency DARPA , which has many AI-related projects. But the White Houses budget proposal also includes cuts to the National Science Foundation, an agency that has long supported AI research, including work on artificial neural networks, the very technique that now has companiesand nationssuddenly so interested in the field's potential.

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Using only alternative medicine for cancer linked to lower survival rate – Yale News

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Yale News
Using only alternative medicine for cancer linked to lower survival rate
Yale News
Patients who choose to receive alternative therapy as treatment for curable cancers instead of conventional cancer treatment have a higher risk of death, according to researchers from the Cancer Outcomes, Public Policy and Effectiveness Research ...
Alternative medicine only treatment linked to lower cancer survival ...UPI.com

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Using only alternative medicine for cancer linked to lower survival rate - Yale News

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