Semi-supervised node classification involves learning to classify unlabelled nodes given a partially labeled graph. In transductive learning, all unlabelled nodes to be classified are observed during training and in inductive learning, predictions are to be made for nodes not seen at training. In this paper, we focus on both these settings for node classification in attributed graphs, i.e., graphs in which nodes have additional features. State-of-the-art models for node classification on such attributed graphs use differentiable recursive functions. These differentiable recursive functions enable aggregation and filtering of neighborhood information from multiple hops (depths). Despite being powerful, these variants are limited in their ability to combine information from different hops efficiently. In this work, we analyze this limitation of recursive graph functions in terms of their representation capacity to effectively capture multi-hop neighborhood information. Further, we provide a simple fusion component which is mathematically motivated to address this limitation and improve the existing models to explicitly learn the importance of information from different hops. This proposed mechanism is shown to improve over existing methods across 8 popular datasets from different domains. Specifically, our model improves the Graph Convolutional Network (GCN) and a variant of Graph SAGE by a significant margin providing highly competitive state-of-the-art results.

"If you know Machine Learning, then you will not believe in Deep Learning(artificial neural nets). If you believe in Deep Learning, that means you don't understand Machine Learning" The word'believe'.....i used it in a totally an informal way, in the sense that, "I dont believe means, I dont accept it as a theory"....and don't want it to have any bearing on its practicality. I clarify, I am not in anyway intending to say that they are not practically useful, infact they are in much vogue in many practical applications, which everyone knows. I am talking in a theoretical sense, some things we cannot accept as a theory in some abstract sense, although they may be very useful in parcticality. By using word, "believe" i mean "accepting as a theory", and not bearing anything on practical engineering significance.

The Open Philanthropy Project is proud to announce our first class of AI Fellows – seven very promising machine learning researchers to whom we're collectively recommending a total of about $1.1 million in PhD fellowship support over the next five years. These fellows were selected from more than 180 applicants for their academic excellence, technical knowledge, careful reasoning, and interest in making the long-term, large-scale impacts of AI a central focus of their research. We believe that progress in artificial intelligence may eventually lead to changes in human civilization that are as large as the agricultural or industrial revolutions; while we think it's most likely that this would lead to significant improvements in human well-being, we also see significant risks. The AI Fellows have a broad mandate to think through which kinds of AI and ML research are likely to be most valuable, to share ideas and form a community with like-minded students and professors, and ultimately to act in the way that they think is most likely to improve outcomes from progress in AI. For more on the Open Philanthropy Project's views about the potential impacts of AI, see our previous blog posts.

There are many possible reasons that deep learning systems might be attacked for medical fraud, the researchers say. With eye images, they note insurers might want to reduce the rate of surgeries they have to pay for. With chest X-rays, they note companies running clinical trials might want to get the results they want, given that one 2017 study estimated the median revenues across individual cancer drugs was as high as $1.67 billion four years after approval. With skin photos, the researchers note that dermatology in the United States operates under a model wherein a physician or practice is paid for the procedures they perform, causing some dermatologists to perform a huge number of unnecessary procedures to boost revenue.

Artificial intelligence technology is now making its way into manufacturing, and the machine-learning technology and pattern-recognition software at its core could hold the key to transforming factories of the near future. While AI is poised to radically change many industries, the technology is well suited to manufacturing, says Andrew Ng, the creator of the deep-learning Google Brain project and an adjunct professor of computer science at Stanford University. "AI will perform manufacturing, quality control, shorten design time, and reduce materials waste, improve production reuse, perform predictive maintenance, and more," Ng says. The term artificial intelligence is used today as something of a catch-all for software that can train itself to perform certain tasks and to get better at those tasks over time, he says. For example, AI is behind the software that identifies your friends' faces in photographs.

The gamers hoping, wishing, and praying for a new generation of GeForce cards to arrive this week got some bad news from the company's CEO: They won't show up for a "long time." When asked by Tom's Hardware reporter Paul Acorn when the launch for the next GeForce will happen during a closed-door press briefing at Computex 2018, Nvidia CEO Jensen Huang responded, "It's a long time from now." Huang had just finished announcing Nvidia's Jetson Xavier, which Huang said is the first computer designed specifically for robotics. Powered by six processors--including a Volta Tensor core GPU, eight ARM64 cores, and deep learning accelerators and image processors--Xavier is the kind of computer that could be used to build a robot to hand you a wrench in the lab, like Tony Stark's Jarvis. Most of Nvidia's press conference focused on deep learning, AI, and other advances the company has been focused on recently, but nothing to feed the insatiable hunger of PC gamers for more performance. Keep in mind, Nvidia has never said when new GeForce graphics cards would arrive, but that hasn't stopped numerous sites from reporting when the hardware would break cover.

As interest in artificial intelligence (AI) continues to increase rapidly, they say data is the new oil. The machine and deep learning algorithms that drive AI applications require access to massive amounts of data to work. A new survey of 100 business executives conducted by Corinium Digital, an online community focused on analytics, and commissioned by Paxata, Accenture Applied Intelligence, and Microsoft, finds 72 percent of large enterprise IT organizations have already allocated more than $2 million to AI initiatives in the 2018/2019 timeframe. A full 93 percent of respondents say their organizations are investing more than $1 million in analytics initiatives in the same timeframe as well. But when it comes to AI and analytics, there's a significant hurdle that needs to be overcome.

Soon you could be choosing a computer over a doctor when it comes to a cancer diagnosis. According to a new study, an artificial intelligent (AI) system outperformed dermatologists when it came to diagnosing skin cancer. The computer, a deep learning convolutional neural network (CNN), was trained by a team from Germany, France, and the US, by looking at over 100,000 images of cancerous moles and benign spots. After its training, the scientists put the computer to work by pitting it against 58 dermatologists from 17 countries around the world. After being shown images of different types of moles, the CNN was able to accurately detect skin cancer in 95 per cent of the images.

Grab your beverage of choice and enjoy the read! Do hit reply if you're up for a brainstorming session on use cases, new research or ways to future proof your SaaS or enterprise product by implementing ML where it makes sense. On the current "AI revolution": In a lovely piece, Prof. Michael Jordan of Berkeley explores many of the central tenets driving the excitement around AI today. He makes the case for a new engineering discipline, defines the differences between human-imitative AI (i.e. "The current focus on doing AI research via the gathering of data, the deployment of "deep learning" infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills -- with little in the way of emerging explanatory principles -- tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems that perform natural language processing, the need to infer and represent causality, the need to develop computationally-tractable representations of uncertainty and the need to develop systems that formulate and pursue long-term goals. These are classical goals in human-imitative AI, but in the current hubbub over the "AI revolution," it is easy to forget that they are not yet solved."