Monthly Archives: August 2017

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.

Read the rest here:

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

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.

Excerpt from:

How to build any AI-driven smart service - ZDNet

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.

Read more here:

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.

Sign up for the Tech Alert newsletter and receive ground-breaking technology and science news from IEEE Spectrum every Thursday.

A laptop-sized system could make it easier to diagnose and study sleep disorders 10Aug

Minor changes to street sign graphics can fool machine learning algorithms into thinking the signs say something completely different 4Aug

Engineers at Knowles bring the hearing aid industry together to fight feedback with simulation.

The DNA-as-malware hackthough difficultpoints to weaknesses in bioinformatics software 10Aug

From consumer audio to ultrasound imaging, the impact of new metamaterial structures for acoustic cloaking is far-reaching.

Deep learning AI can identify individual anthrax spores in seconds within special microscope images 4Aug

An audio technology startup delivers manufacturable transducers for high-end headphones.

In designing acoustical systems, engineers must account for multiple physics and their interactions at multiple scales and frequencies.

Glassdoor's latest research shows software jobs are diffusing beyond traditional geographies and industries 31Jul

A race is on to discover Planet Nine using classical astronomy and new computational techniques 31Jul

Blind opens its tech gossip app to anyone who works in tech, but only some get into closed company rooms 27Jul

Software engineering has highest share of foreign-born workers 25Jul

This body-tracking software could help robots read your emotions 22Jul

Find the programming languages that are most important to you 18Jul

Python jumps to No.1, and Swift enters the Top Ten 18Jul

We analyze the languages that are indemandbyemployers 18Jul

Blind quantum computing in the cloud could keep computation results secret even for remote classical-computer users 14Jul

Personalized learning has to get social. Students learn better through conversation 10Jul

Social and computer scientists parse online bot discourse 6Jul

Hedge funds are testing new quantitative strategies that could supplant traditional fund managers 28Jun

More:

AI Helps Magicians Perform Mind Reading Tricks - IEEE Spectrum

Log in

Create an account for free access to:

With a free New Scientist account you'll enjoy increased access to New Scientist content and ideas.

Every week the editors release a selection of articles to New Scientist account holders. These articles are available exclusively to logged in account holders and subscribers. The editors selection can range from new features, opinions and interviews to fascinating content from the New Scientist archive.

You'll also receive the latest news and top stories in your inbox every week with the New Scientist email newsletter.

Get more from New Scientist. To create your free account, simply complete this quick form.

Special rates for students, teachers, libraries, schools, colleges and universities

Special rates for companies and group subscriptions

Give New Scientist to a friend or loved one, or activate your gift subscription

View original post here:

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