Microsoft has invested $1 billion in the Elon Musk-founded artificial intelligence venture that plans to mimic the human brain using computers. OpenAI said the investment would go towards its efforts of building artificial general intelligence (AGI) that can rival and surpass the cognitive capabilities of humans. "The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity," said OpenAI CEO Sam Altman. "Our mission is to ensure that AGI technology benefits all of humanity, and we're working with Microsoft to build the supercomputing foundation on which we'll build AGI." The two firms will jointly build AI supercomputing technologies, which OpenAI plans to commercialise through Microsoft and its Azure cloud computing business.

Microsoft has invested $1 billion in the Elon Musk-founded artificial intelligence venture that plans to mimic the human brain using computers. OpenAI said the investment would go towards its efforts of building artificial general intelligence (AGI) that can rival and surpass the cognitive capabilities of humans. "The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity," said OpenAI CEO Sam Altman. "Our mission is to ensure that AGI technology benefits all of humanity, and we're working with Microsoft to build the supercomputing foundation on which we'll build AGI." The two firms will jointly build AI supercomputing technologies, which OpenAI plans to commercialise through Microsoft and its Azure cloud computing business.

It is a truism that artificial intelligence (AI) is set to change the world in unimaginable ways. The giants of the tech industry have realised it and are investing heavily in it, as can be seen, for instance, from Microsoft's $1 billion investment in OpenAI, which in turn was founded by Tesla's Elon Musk, and seeks to use AI to benefit all of mankind. Again, Twitter has acquired four AI companies - the biggest of them being Magic Pony for $150 million in 2016 - in its bid to improve its system of recommending specific tweets in users' timelines. Even traditional businesses are using AI to improve their services, such as UK-based grocer Nisa Retail employing Amazon Web Services to meet its business challenges. India too has plunged headlong into AI and machine learning (ML) with numerous start-ups offering AI solutions in areas such as banking, logistics and transportation.

In a media briefing at Dell's AI Experience Zone in Singapore, Dell Technologies announced that AI Singapore has chosen Dell Technologies to deliver High-Performance Computing (HPC) infrastructure that's optimised for AI workloads. AI Singapore, first announced in 2017, is a national program office launched by the National Research Foundation (NRF) to drive the adoption of artificial intelligence, develop the country's AI talent and help seed high-quality research efforts to develop fundamental AI novel techniques, algorithms and adjacent technologies. In the collaboration, Dell Technologies will provide three key computational building blocks for the new supercomputer at AI Singapore to help drive performance and flexibility for its researchers and to scale up its flagship 100 Experiments (100E) program. According to Laurence Liew, Director, AI Industry Innovation, for the 100E program, AI Singapore would partner with companies or industries that need AI solutions, but there are no commercially available solutions available for them in the market, or when they're committed to building their own products to compete globally. "The way we support them is by bringing our professors, researchers and engineering teams to work together with the companies to build their AI products and solutions," he explained.

But machine learning continues to be one of the toughest skills to acquire. The domain is as vast and as complex as the field of computer science. Developers will have to learn new languages, algorithms, frameworks, tools from an extremely diverse and fragmented ecosystem. They need to learn how to use the cloud to train the models and optimizing those models to integrate with a variety of environments and platforms. The complexity multiplies when we attempt to take the models to the edge.

Yesterday, artificial intelligence(AI) powerhouse OpenAI astonished the world by unveiling a prototype of a robotic arm that could solve a Rubik's cube with one hand. The prototype didn't only represent a milestone for the robotics ecosystem in solving high complexity tasks that actively require sensorial information but it also resulted on a major achievement for the AI community. The reason is that the OpenAI robot was completely trained using simulations based on the reinforcement learning models that the OpenAI Five system used to beat human players in Dota2. The research was discussed in a paper that accompanied the news. The importance of OpenAI's achievement was not about designing a robot that could solve a Rubik's cube.

Facebook's head of artificial intelligence, Yann LeCun, seems to fit that profile to a T. "I work mostly by intuition," he writes in When the Machine Learns, a new book that is part biography, part science lecture, part AI history, published Wednesday in French as Quand la machine apprend. "I project in my head the borderline cases, that which Einstein called the'thought experiments'," writes LeCun. LeCun is animated on stage, clearly energized by trying to convey things at the edges of AI that have come from his thought experiments. That ability to imagine something that doesn't exist, perhaps at the limit of what's generally thought feasible, is the mark of engineers and innovators. LeCun is something of a rarity among the AI crowd, a scientist who is at home in algorithm design but also has one foot firmly in computer engineering.

Since deep learning regained prominence in 2012, many machine learning frameworks have clamored to become the new favorite among researchers and industry practitioners. From the early academic outputs Caffe and Theano to the massive industry-backed PyTorch and TensorFlow, this deluge of options makes it difficult to keep track of what the most popular frameworks actually are. If you only browsed Reddit, you might assume that everyone's switching to PyTorch. Judging instead by Francois Chollet's Twitter, TensorFlow/Keras may appear as the dominant framework while PyTorch's momentum is stalling. In 2019, the war for ML frameworks has two remaining main contenders: PyTorch and TensorFlow. My analysis suggests that researchers are abandoning TensorFlow and flocking to PyTorch in droves.

We present a framework for recovering/approximating unknown time-dependent partial differential equation (PDE) using its solution data. Instead of identifying the terms in the underlying PDE, we seek to approximate the evolution operator of the underlying PDE numerically. The evolution operator of the PDE, defined in infinite-dimensional space, maps the solution from a current time to a future time and completely characterizes the solution evolution of the underlying unknown PDE. Our recovery strategy relies on approximation of the evolution operator in a properly defined modal space, i.e., generalized Fourier space, in order to reduce the problem to finite dimensions. The finite dimensional approximation is then accomplished by training a deep neural network structure, which is based on residual network (ResNet), using the given data. Error analysis is provided to illustrate the predictive accuracy of the proposed method. A set of examples of different types of PDEs, including inviscid Burgers' equation that develops discontinuity in its solution, are presented to demonstrate the effectiveness of the proposed method.

V ARIBAD: A V ERY G OOD M ETHOD FOR B AYES-A DAPTIVE D EEP RL VIA M ETA-L EARNING Luisa Zintgraf University of Oxford Kyriacos Shiarlis Latent Logic Maximilian Igl University of Oxford Sebastian Schulze University of Oxford Y arin Gal OA TML Group, University of Oxford Katja Hofmann Microsoft Research Shimon Whiteson University of Oxford Latent Logic A BSTRACT Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is however intractable for all but the smallest tasks. In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference in an unknown environment, and incorporate task uncertainty directly during action selection. In a grid-world domain, we illustrate how variBAD performs structured online exploration as a function of task uncertainty. We also evaluate variBAD on MuJoCo domains widely used in meta-RL and show that it achieves higher return during training than existing methods. 1 I NTRODUCTION Reinforcement learning (RL) is typically concerned with finding an optimal policy that maximises expected return for a given Markov decision process (MDP) with an unknown reward and transition function. If these were known, the optimal policy could in theory be computed without interacting with the environment. By contrast, learning in an unknown environment typically requires trading off exploration (learning about the environment) and exploitation (taking promising actions). Balancing this tradeoff is key to maximising expected return during learning . A Bayes-optimal policy, which does so optimally, conditions actions not only on the environment state but on the agent's own uncertainty about the current MDP . In principle, a Bayes-optimal policy can be computed using the framework of Bayes-adaptive Markov decision processes (BAMDPs) (Martin, 1967; Duff & Barto, 2002). The agent maintains a belief, i.e., a posterior distribution, over possible environments. Augmenting the state space of the underlying MDP with this posterior distribution yields a BAMDP, a special case of a belief MDP (Kaelbling et al., 1998).