Nearly 70 years since Alan Turing posed the question "Can machines think?" artificial intelligence (AI) is finally beginning to have an impact on the global economy. Proponents of AI believe that it has the potential to transform the world as we know it. So what is AI in business and what are the main themes? Within the AI industry, there are seven key technology categories: machine learning, data science, conversational platforms, computer vision, AI chips, smart robots and context-aware computing. Machine learning (ML) is an application of AI that gives computer systems the ability to learn and improve from data without being explicitly programmed.

If you want to learn how to build artificial intelligence systems, the first step is simple: forget all about artificial intelligence. Instead focus your attention on machine learning. That way, you can be sure you're in the domain of the practical rather than the domain of hype. Okay, this position might sound a little too dramatic. But there are a number of jokes doing the rounds on Twitter along these lines.

OpenAI's researchers knew they were on to something when their language modeling program wrote a convincing essay on a topic they disagreed with. They'd been testing the new AI system by feeding it text prompts, getting it to complete made-up sentences and paragraphs. Then, says David Luan, VP of engineering at the Californian lab, they had the idea of asking it to argue a point they thought was counterintuitive. In this case: why recycling is bad for the world. "And it wrote this really competent, really well-reasoned essay," Luan tells The Verge.

Scientists have developed a deep neural network that sidesteps a problem that has bedeviled efforts to apply artificial intelligence to tackle complex chemistry--a shortage of precisely labeled chemical data. The new method gives scientists an additional tool to apply deep learning to explore drug discovery, new materials for manufacturing, and a swath of other applications. Predicting chemical properties and reactions among millions upon millions of compounds is one of the most daunting tasks that scientists face. There is no source of complete information from which a deep learning program could draw upon. Usually, such a shortage of a vast amount of clean data is a show-stopper for a deep learning project.

I've seen a lot articles about people switching from TensorFlow to PyTorch, but not the other way around. We can have a good understanding about the strengths and weaknesses of the two frameworks if we could hear stories from both sides. I'm one of the people who are slowly moving towards PyTorch, I would like to know more before rewriting tens of thousands lines of old code. Even if both frameworks are similar, there should be far less people switching from PyTorch to TensorFlow because PyTorch came later. So, to have a good discussion are there at least people who are on PyTorch that are considering TensorFlow or people who are regretting switching to PyTorch?

In a previous article (see here) I attempted to approximate a random variable representing real data, by a weighted sum of simple kernels such as uniformly and independently, identically distributed random variables.

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.

We consider the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and millimeter-wave bands) to communicate with the user equipment (UE). While the millimeter-wave band might offer higher data rate, there is a significant probability of outage during which the communication should be carried on the (more reliable) centimeter-wave band. We consider two variations of the BA problem, one-shot and sequential BA. For the former the BS uses only the currently observed information to decide whether to switch to the other frequency band, for the sequential BA, the BS uses a window of previously observed information to predict the best band for a future time step. We provide two approaches to solve the BA problem, (i) a deep learning approach that is based on Long Short Term Memory and/or multi-layer Neural Networks, and (ii) a Gaussian Process based approach, which relies on the assumption that the channel states are jointly Gaussian. We compare the achieved performances to several benchmarks in two environments: (i) a stochastic environment, and (ii) microcellular outdoor channels obtained by ray-tracing. In general, the deep learning solution shows superior performance in both environments.

Capacity analysis has been recently introduced as a way to analyze how linear models distribute their modelling capacity across the input space. In this paper, we extend the notion of capacity allocation to the case of neural networks with non-linear layers. We show that under some hypotheses the problem is equivalent to linear capacity allocation, within some extended input space that factors in the non-linearities. We introduce the notion of layer decoupling, which quantifies the degree to which a non-linear activation decouples its outputs, and show that it plays a central role in capacity allocation through layers. In the highly non-linear limit where decoupling is total, we show that the propagation of capacity throughout the layers follows a simple markovian rule, which turns into a diffusion PDE in the limit of deep networks with residual layers. This allows us to recover some known results about deep neural networks, such as the size of the effective receptive field, or why ResNets avoid the shattering problem.

One endeavour of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D compounds and layered structures atom by atom. The algorithm takes no prior data or knowledge on atomic interactions but inquires a first-principles quantum mechanical program for physical properties. Using reinforcement learning, the algorithm accumulates knowledge of chemical compound space for a given number and type of atoms and stores this in the neural network, ultimately learning the blueprint for the optimal structural arrangement of the atoms for a given target property. ASLA is demonstrated to work on diverse problems, including grain boundaries in graphene sheets, organic compound formation and a surface oxide structure. This approach to structure prediction is a first step toward direct manipulation of atoms with artificially intelligent first principles computer codes.