"As we move into 2017, Baidu's strategic evolution from a mobile-first to an AI-first company continues to gain momentum." There was a time when Baidu, Inc. (BIDU) was primarily a Chinese language Internet search provider, often dubbed as the'Google of China.' In recent years, Baidu embarked on a new journey that makes it a search, artificial intelligence (AI), and autonomous driving company that is working towards innovative, next-generation products and revenue streams. Baidu is leading the AI revolution in the mainland with huge investments, collaborations and acquisitions. AI technologies encompass deep learning, image recognition, computer vision, robotics, collaborative systems, machine learning and natural learning process, among other things.

Nearly eight years later, the magazine revisited the list to see how "Goodson plus the machine" had performed. The list featured not only names widely known to the public and leaders of industries, but also high performers such as Ibibo, which had eight employees in 2009 when selected and now has $2 billion annual sales as the top hotel booking site in India. Twenty percent of the companies chosen had reached billion-dollar valuations.

Some of the world's top researchers in AI have proved their mettle by taking top honors in three challenges posed by the Seattle-based Allen Institute for Artificial Intelligence. The institute, also known as AI2, was created by Microsoft co-founder Paul Allen in 2014 to blaze new trails in the field of artificial intelligence. One of AI2's previous challenges tested the ability of AI platforms to answer eighth-grade-level science questions. The three latest challenges focused on visual understanding – that is, the ability of a computer program to navigate real-world environments and situations using synthetic vision and machine learning. These aren't merely academic exercises: Visual understanding is a must-have for AI applications ranging from self-driving cars to automated security monitoring to sociable robots.

PDF: We made a fancy PDF of this post for printing and offline viewing. Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what's happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1--Part 2 is here. We are on the edge of change comparable to the rise of human life on Earth. What does it feel like to stand here? It seems like a pretty intense place to be standing--but then you have to remember something about what it's like to stand on a time graph: you can't see what's to your right. So here's how it actually feels to stand there: Imagine taking a time machine back to 1750--a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything.

Now, scientists have taken inspiration from such creatures, replicating their colour-changing abilities with artificial electronic skin. Their incredible material could be used in a variety of ways in the future, including in robotics, prosthetics and wearable technology used by armed forces. The material is made from graphene - a form of pure carbon that is 200 times stronger than steel. Two layers of graphene are included - a highly-resistive strain sensor, alongside a stretchable organic electrochromic device (ECD) that changes colour when a current is applied. While other colour-changing materials require enormous strain of 100-500 per cent to change colour, the researchers found that subtle strain - between zero and 10 per cent - was enough to cause an obvious colour change.

LONDON/NEW YORK – SoftBank Group Corp. has taken a stake in Roomba vacuum-cleaner maker IRobot Corp., people familiar with the matter said, as the Japanese company builds its holdings in robotics firms. SoftBank has built a less than 5 percent stake in IRobot, below the amount that would require a regulatory disclosure in the U.S., the people said, asking not to be identified because the purchase was private. The Bedford, Massachusetts-based technology company has a market value of $2.4 billion. Shares in IRobot rose 22 percent in late trading in New York. Representatives for SoftBank and IRobot declined comment.

A deep neural network based architecture was constructed to predict amino acid side chain conformation with unprecedented accuracy. Amino acid side chain conformation prediction is essential for protein homology modeling and protein design. Current widely-adopted methods use physics-based energy functions to evaluate side chain conformation. Here, using a deep neural network architecture without physics-based assumptions, we have demonstrated that side chain conformation prediction accuracy can be improved by more than 25%, especially for aromatic residues compared with current standard methods. More strikingly, the prediction method presented here is robust enough to identify individual conformational outliers from high resolution structures in a protein data bank without providing its structural factors. We envisage that our amino acid side chain predictor could be used as a quality check step for future protein structure model validation and many other potential applications such as side chain assignment in Cryo-electron microscopy, crystallography model auto-building, protein folding and small molecule ligand docking.

Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a symmetric strategy to learn one deep hash function for both query points and database (retrieval) points. The training of these symmetric deep supervised hashing methods is typically time-consuming, which makes them hard to effectively utilize the supervised information for cases with large-scale database. In this paper, we propose a novel deep supervised hashing method, called asymmetric deep supervised hashing (ADSH), for large-scale nearest neighbor search. ADSH treats the query points and database points in an asymmetric way. More specifically, ADSH learns a deep hash function only for query points, while the hash codes for database points are directly learned. The training of ADSH is much more efficient than that of traditional symmetric deep supervised hashing methods. Experiments show that ADSH can achieve state-of-the-art performance in real applications.

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.

His understanding of the subject is limited.