We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a densely connected autoencoder architecture capable of learning a joint representation of both local graph structure and available external node features for the multi-task learning of link prediction and node classification. To the best of our knowledge, this is the first architecture that can be efficiently trained end-to-end in a single learning stage to simultaneously perform link prediction and node classification. We provide comprehensive empirical evaluation of our models on a range of challenging benchmark graph-structured datasets, and demonstrate significant improvement in accuracy over related methods for graph representation learning. Code implementation is available at https://github.com/vuptran/graph-representation-learning

Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signal cognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given via points.

We are on the verge of a technological revolution that will fundamentally alter the way we live, work, and relate to one another unlike anything humankind has experienced before. The main driver for this technological revolution is Artificial Intelligence (AI). Technological change driven by AI will change not only what we do but also who we are. It will affect our identity and all the issues associated with it: our sense of privacy, our notions of ownership, our consumption patterns, the time we devote to work and leisure, and how we develop our careers, cultivate our skills, and nurture relationships. But the development and applications of artificial intelligence can also present a dystopian threat to our collective and individual well being. From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google's search algorithms to IBM's Watson to autonomous robots and weapons systems. Artificial intelligence today is often referred to as narrow AI (or weak AI), which is designed to perform a narrow task (eg:facial recognition or only internet searches or driving a car). The other kind of Artificial Intelligence is termed general AI (AGI or strong AI) which is designed to "think," and solve problems much like humans.

Stationed around the class, each robot has a mounted video screen controlled by the remote user that lets the student pan around the room to see and talk with the instructor and fellow students participating in-person. The study, published in Online Learning, found that robot learning generally benefits remote students more than traditional videoconferencing, in which multiple students are displayed on a single screen. Christine Greenhow, MSU associate professor of educational psychology and educational technology, said that instead of looking at a screen full of faces as she does with traditional videoconferencing, she can look a robot-learner in the eye -- at least digitally. "It was such a benefit to have people individually embodied in robot form -- I can look right at you and talk to you," Greenhow said. The technology, Greenhow added, also has implications for telecommuters working remotely and students with disabilities or who are ill.

AI teaching computers to make business sense of ill-lit 3D objects. We invested in Doxel, because of Saurabh Ladha, and his co-founder Robin Singh. They are without much parallel when it comes to the tech of 3D semantic understanding, and with their team of CS PhDs essentially writing software using AI to teach computers to make sense of the 3D world around them -- even when in less than ideal, real world sites that have little to no light. Both founders come with exceptionally strong engineering backgrounds, having met on the Dubai campus and then they split off to respectively Stanford and Ann Arbor Michigan for further education. This technology has broad application to industries of any kind wanting to know what's going on on any physical project of theirs, be it construction, agriculture, shipping, manufacture and many more have been relegated to a 2D static world.

One of the problems in demystifying artificial intelligence is that once an AI-based product reaches the public, "we stop calling it AI," said Tara Chklovski, the CEO and founder of Iridescent, a nonprofit that aims to educate and empower children and their parents on engineering and …

Their uses once seemed far off for companies, but with the introduction of Machine Learning-as-a-Service (MLaaS), data science is being brought to the masses. Machine learning, a branch of artificial intelligence, is the process of using self-iterating algorithms to analyse massive amounts of data by learning from the information and processing it with minimal supervision. Essentially, machines can learn from themselves through advanced algorithms data scientists create. This technology has implications across all fields, which is why financial institutions, health services, and more are all scrambling to hire skilled data scientists. Given the demand for machine learning services, MLaaS offerings have recently sprouted up to meet this need.

IT services and consulting company Accenture is launching new services for testing artificial intelligence systems to help companies build own AI-driven products and services based locally or on the cloud. "The adoption of AI is accelerating as businesses see its transformational value to power new innovations and growth," Bhaskar Ghosh, group chief executive, Accenture Technology Services, said in a statement. "As organisations embrace AI, it is critical to find better ways to train and sustain these systems – securely and with quality – to avoid adverse effects on business performance, brand reputation, compliance and humans," Ghosh said. The Dublin-headquartered company said the new testing services works in two phases. While the first phase helps companies focus on choice of data, models and algorithms to teach the machine learning engine, the second phase helps them compare results of the engine with key performance indicators and understand if the engine can explain the decision-making process.

MIT researchers have developed a chip designed to speed up the hard work of running neural networks, while also reducing the power consumed when doing so dramatically – by up to 95 percent, in fact. The basic concept involves simplifying the chip design so that shuttling of data between different processors on the same chip is taken out of the equation. The big advantage of this new method, developed by a team led by MIT graduate student Avishek Biswas, is that it could potentially be used to run neural networks on smartphones, household devices and other portable gadgets, rather than requiring servers drawing constant power from the grid. Because it means that phones of the future using this chip could do things like advanced speech and face recognition using neural nets and deep learning locally, rather than requiring more crude, rule-based algorithms, or routing information to the cloud and back to interpret results. Computing'at the edge,' as its called, or at the site of sensors actually gathering the data, is increasingly something companies are pursuing and implementing, so this new chip design method could have a big impact on that growing opportunity should it become commercialized.

Since the 1980s the number of women completing computer science degrees has plummeted, and in most large tech companies the representation of women in technical roles is below 30%. This lack of diversity prevents us from building products that work for everybody. It can foster toxic "brogrammer" cultures which harm everybody who works within them, and it deprives teams of the well-documented performance boost that women bring. Many of the early superstars in computer science were women -- from Lord Byron's polymath daughter Ada Lovelace, the first person to envisage a general purpose computer, to Rear Admiral Grace Hooper, who pioneered the use of natural language in writing computer programs. Similarly, the post-war computing scene was dominated by women.