Yoshua Bengio: Yoshua BengioOCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3] He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.[4] He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC[11] (born 6 December 1947) is an English Canadiancognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.

Viral hepatitis is the regularly found health problem throughout the world among other easily transmitted diseases, such as tuberculosis, human immune virus, malaria and so on. Among all hepatitis viruses, the uppermost numbers of deaths are result from the long-lasting hepatitis C infection or long-lasting hepatitis B. In order to develop this system, the knowledge is acquired using both structured and semi-structured interviews from internists of St.Paul Hospital. Once the knowledge is acquired, it is modeled and represented using rule based reasoning techniques. Both forward and backward chaining is used to infer the rules and provide appropriate advices in the developed expert system. For the purpose of developing the prototype expert system SWI-prolog editor also used. The proposed system has the ability to adapt with dynamic knowledge by generalizing rules and discover new rules through learning the newly arrived knowledge from domain experts adaptively without any help from the knowledge engineer.

The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, we examine the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in the target DML. Most of the current Transfer DML (TDML) methods are not applicable to the scenario where data are drawn from heterogeneous domains. Some existing heterogeneous transfer learning (HTL) approaches can learn target distance metric by usually transforming the samples of source and target domain into a common subspace. However, these approaches lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. This motivates us to develop a general flexible heterogeneous TDML (HTDML) framework. In particular, any (linear/nonlinear) DML algorithms can be employed to learn the source metric beforehand. Then the pre-learned source metric is represented as a set of knowledge fragments to help target metric learning. We show how generalization error in the target domain could be reduced using the proposed transfer strategy, and develop novel algorithm to learn either linear or nonlinear target metric. Extensive experiments on various applications demonstrate the effectiveness of the proposed method.

Slate Plus members get extended, ad-free versions of our podcasts--and much more. Sign up today and try it free for two weeks. Copy this link and add it in your podcast app. For detailed instructions, see our Slate Plus podcasts page. Click on the play button to hear this episode, or listen to the show via Apple Podcasts, Overcast, Spotify, Stitcher, or Google Play.

In an earlier post today, we distilled half a dozen DARPA-dense docs into an easy-to-follow overview of the DARPA Subterranean Challenge (SubT), a new competition that will task teams of humans and robots to explore complex underground environments. In this post, we have an interview with SubT program manager Timothy Chung, whom we met late last year at DARPA's D60 Conference. "I think for many of the technologies we're seeking to advance--it's one of those, aim for the moon, even if you miss you hit the stars type of an approach," he told us about the new challenge. "So we envision some component technologies being immediately operationally of value, but we've set the bar ambitiously high enough for it to be DARPA-worthy and also provide a vision for how that kind of impact could be magnified if and when we're successful." IEEE Spectrum: What are the SubT courses going to be like?

Yoshua Bengio, who last month won the prestigious Turing award, alongside Geoffrey Hinton and Yann LeCun, for his work on AI, is worried about what the technology is being made into behind closed doors. In an interview with Nature, he explains his concerns, but takes care to avoid sounding like a doomsayer. A professor at the Montreal Institute for Learning Algorithms, his main concern is not a particular nightmare scenario but simply that AI is being pursued by people who have few controls in place. "A lot of what is most concerning is not happening in broad daylight," he said. This we have certainly seen, with all the major tech companies in one way or another providing or considering government and military work, from the benign to the clearly conflict-oriented.

Submissions for HCOMP-19 Are Due in June! The Seventh AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2019) will be held October 28-30 at Skamania Lodge in Washington State near the Columbia Gorge River, just 45 minutes from Portland, Oregon. This year is the 10-year anniversary of the very first HCOMP workshop in Paris, and to celebrate, there will be special events, talks, and panels throughout the conference. HCOMP is the premier venue for disseminating the latest research findings on crowdsourcing and human computation. While artificial intelligence (AI) and human-computer interaction (HCI) represent traditional mainstays of the conference, HCOMP believes strongly in inviting, fostering, and promoting broad, interdisciplinary research.

Turing Award winners (from left to right) Yoshua Bengio, Yann LeCun, and Geoffrey Hinton at the ReWork Deep Learning Summit, Montreal, October 2017. AI "Sputnik moment" (say it in Chinese*) is at hand China is overtaking the US not just in the sheer volume of AI research papers submitted and published, but also in the production of high-impact papers as measured by the top 50%, top 10%, and top 1% most-cited papers. "By projecting current trends, we see that China is likely to have more top-10% papers by 2020 and more top-1% papers by 2025" (Allen Institute for Artificial Intelligence). Cisco attributes the decline to their increased confidence that "migrating to the cloud will improve protection efforts, while apparently decreasing reliance on less proven technologies such as artificial intelligence" (Cisco). Nearly 90% of IT leaders see their use of AI/ML increasing in the future and 41% look for technology that is powered by AI, a top factor in their purchasing decisions.

The 2018 Turing Award, known as the "Nobel Prize of computing," has been given to a trio of researchers who laid the foundations for the current boom in artificial intelligence. Yoshua Bengio, Geoffrey Hinton, and Yann LeCun -- sometimes called the'godfathers of AI' -- have been recognized with the $1 million annual prize for their work developing the AI subfield of deep learning. The techniques the trio developed in the 1990s and 2000s enabled huge breakthroughs in tasks like computer vision and speech recognition. Their work underpins the current proliferation of AI technologies, from self-driving cars to automated medical diagnoses. In fact, you probably interacted with the descendants of Bengio, Hinton, and LeCun's algorithms today -- whether that was the facial recognition system that unlocked your phone, or the AI language model that suggested what to write in your last email.

It's probably fair to say that in the history of politicking, few politicians have publicly declared what to do about America's crumbling malls, or how to provide free marriage counseling for all, or how to make filing taxes fun. But Andrew Yang, who's gunning to be the Democratic presidential candidate in 2020, certainly has--and those are the more minor concerns among a dizzying list of 80 policy positions on his campaign website. It's an indication that Yang is running a rather methodical, data-driven, science-happy campaign. He's applying that approach to more standard issue problems, like labor, climate change, and the economy, but giving them a decidedly tech-forward approach: how (and why) we should define robots, what use might geoengineering have in saving the planet, and should the government embrace universal basic income and give every American a $1,000 check. Yang talked with WIRED about all this and more in a recent interview.