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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Defense Secretary James Mattis Envies Silicon Valley's AI Ascent - WIRED

Making AI models at the University of Southern California (USC) Center for AI in Society does not involve a clean, sorted dataset. Sometimes it means interviewing homeless youth in Los Angeles to map human social networks. Sometimes it involves going to Uganda for better conservation of endangered species.

With AI, we are able to reach 70 percent of the youth population in the pilot, compared to about 25 percent in the standard techniques. So AI algorithms are able to reach far more youth in terms of spreading HIV information compared to traditional methods, saidMilind Tambe, a professor at the USC Viterbi School of Engineering and cofounder of the Center for AI in Society. If I were doing AI normally I might get data from the outside and I would analyze the data, produce algorithms, and so forth, but I wouldnt go to a homeless shelter.

The pilot project will next be expanding to serve 1,000 youth. Other projects currently being taken on by the Center for AI in Society include gang prevention, wildlife conservation with computer vision, and predictive models to improve cybersecurity, prevent suicide, and help homeless youth find housing.

The center has also developed and deployed algorithms for federal agencies such as the U.S. Coast Guard, Air Marshals Service, and Transportation and Security Administration (TSA).

Tambe was one a handful ofauthors of a forward-looking report that examines how AI will evolve and affect business, government, and society between the present and 2030. Commissioned by Stanford University as part of The AI 100 Project, the study found that AI aimed at solving social problems has traditionally lacked investment because it produces no profitable commercial applications. The report prescribes making AI for low resource projects a higher priority and offering AI researchers incentives, but Tambe also believes an entirely new discipline may need to be developed.

[These projects] bring up completely new kinds of AI problems because working with low resource communities, data is sparse, as opposed to being plentiful. When you talk about big data, thats not what were doing here. Whether its wildlife conservation or working with homeless youth, were talking incomplete data and theres no capacity to actually produce that massive clean big data that you can do deep learning on, he said.

Were trying to develop novel AI science as well as novel social science, co-director Eric Rice told VentureBeat in a phone interview. Were not just trying to be data scientists who take advantage of publicly available datasets or social scientists that take advantage of out-of-the-box machine learning tools that are pretty much readily available through canned software packages. What were really trying to build is new science on both sides.

The USC Center for AI in Society is a collaboration between computer science and social science schools at USC, an ambitious initiative created to cross-pollinate ideas between the two disciplines in order to solve some of the worlds biggest problems.

Created in 2013, the program focuses on problems found in the 12 Grand Challenges of social work and the United Nations Sustainable Development Goals.

The 12 Grand Challenges of Social Work was created last year by social workers and espouses goals like ensuring healthy development for all youth, eradicating social isolation, stopping family violence, and ending homelessness.

The Sustainable Development Goals were adopted by U.N. member nations in 2015 and focus on implementing measures to address priorities like access to quality education, gender equity, and the end of poverty and hunger by 2030.

This is the first collaboration as far as we are aware between AI and social work in a center. So were really collaborating across schools in terms of engineering and AI and social work, and its bringing up completely new sets of challenges to the core in terms of problems that the AI community has tackled, Tambe told VentureBeat in a phone interview. Spreading HIV information amongst homeless youth or trying to reduce substance abuse or matching homeless youth to homes, these are challenges that generally have not been tackled within the AI community.

The two schools work together because sometimes an AI data scientist may not understand a social issue if they dont see it emerge in a dataset, and social workers may sometimes fail to understand that an algorithm could significantly impact a social issue.

While there was some initial difficulty in understanding the different vocabularies social scientists and data scientists use, the collaboration leads to completely new kinds of discovery that wouldnt have been possible if either of us were working alone, Tambe said.

Social work tends to be less precise and engineering is very focused, so theres this dance were in, Rice said. Were adding more muddiness to the model and theyre insisting that we are more crisp in our argument, so theres a nice generative aspect to that kind of back and forth.

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AI is targeting some of the world's biggest problems: homelessness, terrorism, and extinction - VentureBeat

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

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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There are still improvements to be made in smartphones and artificial intelligence (AI) will play a critical role in driving further innovation in this space.

There remained significant differences today in terms of the functions offered in a $200- and $1,000-priced smartphone, said Bruce Lee, Huawei's global vice president of handsets business. He dismissed suggestions that innovation in the handset market had plateaued, with little separating low-end and high-end devices, and that manufacturers should move their focus elsewhere.

Speaking to ZDNet in an interview Friday, Lee said Huawei continued to focused its R&D efforts on introducing more functionalities and improving existing capabilities, such as camera, battery life and processing speed. It also needed to ensure its handsets could support faster internet connection, especially when 5G networks become available, he added.

Earlier this year, Pacific Crest's analyst for emerging technologies Ben Wilson opined in a research report, titled "There Is No 'Next Smartphone'", that the smartphone revolution was a "singular event in compute platform history" that was unlikely to repeat. Others also debated the "death of the smartphone" and impact of wearables.

While he acknowledged there was tremendous growth potential in wearables and smart devices, Lee said these still were challenged by the same issues faced in the smartphone market. He pointed to existing limitations in compute performance and battery life.

This further indicated that, far from "dying", there was still some ways to go in terms of smartphone innovation and development, he noted, adding that the industry must continue to invest in these key areas--of improving battery life and compute performance--to enhance user experience.

In this aspect, he said Huawei believed AI would play an important role in the future of handsets and would facilitate many critical developments in smartphones.

In its 2016 annual report, the Chinese manufacturer described an era of "+Intelligence" in which all devices, people, and processes would be supported by AI. "Building intelligence into our devices, networks, and industries will open up new worlds," it said, adding that it would impact the role of smartphones in future.

Huawei believed phones would be able to think contextually and engage humans in dialogue to understand their needs. The devices then could deliver the information and services humans required and would evolve into personal assistants to provide expertise and personalised services.

"AI will disrupt the user experience, but before it can do so, we will need a quantum leap in the functionality of our smart devices, chipsets, and cloud services," it said. "Artificial intelligence will place heavy demands on computing performance, energy efficiency, and device-cloud synergy. Meeting these demands and creating a better intelligent experience will take a synthesis of capabilities across both chipsets and the cloud."

Lee said Huawei had invested heavily in building a development team focused on AI, which included both hardware and software.

"We hope to use AI in our phones to have more learning capabilities...[so], together with big data, we will be able to understand consumer habits and better incorporate voice and image capabilities into the phone," he said. "This will enable the phone to become smarter and offer increased efficiencies for consumers."

Lee also underscored the need to embed this intelligence on the device itself, rather than push data into the cloud to be analysed.

Because machine learning and AI algorithms required significant amount of compute power, much of these processes were carried out in the cloud, and not on the local device, he explained. This, however, was not efficient, he said, stressing the need for more AI capabilities to be supported on the smartphone itself in order to reduce latency.

"We can then have faster responses because we don't need to upload data from the device into the cloud, do the computing, and send it back into the device," he noted. "And when we do the computing on the local device, we can also safeguard user privacy since we don't need to upload data into the server."

In terms of handset performance, Huawei had a stellar start to the year, bypassing Oppo in the first quarter to claim pole position in China's smartphone market. It shipped 20.8 million units, which was up 25.5 percent from the year before, and held a 20 percent market share.

Worldwide, it placed third behind Samsung and Apple, with a 9 percent market share for the first quarter 2017. The Chinese vendor shipped 34.18 million units, compared to Samsung's 78.67 million and Apple's 51.9 million.

Lee attributed the growth to its high-end P and Mate product lines. He further revealed that the company's future growth strategy would see more investment towards its high-end smartphone products.

In addition, Huawei would be looking to increase its market share outside its domestic market. Noting that China contributed about 60 percent of its smartphone business, he said the vendor was targeting for its overseas revenue to outweigh that of its home market.

While Europe currently was its biggest region outside of China, he added that the rest of Asia-Pacific would play a pivotal role in its future growth due to Huawei's geographical advantage in this region. Due to its heritage, it also had a better understanding of Asian consumers so the region should offer higher growth potential, he said.

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Huawei sees AI, not death, in smartphone future - ZDNet

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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How to build any AI-driven smart service - ZDNet