To build OpenAI--a new artificial intelligence lab that seeks to openly share its research with the world at large--Elon Musk and Sam Altman recruited several top researchers from inside Google and Facebook. But if this unusual project is going to push AI research to new heights, it will need more than talent. It will needs enormous amounts of computing power. Google and Facebook have the resources needed to build the massive computing clusters that drive modern AI research, including vast networks of machines packed with GPU processors and other specialized chips. Google has even gone so far as to build its own AI processor.

A BSTRACT Restricted Boltzmann Machine (RBM) is a bipartite graphical model that is used as the building block in energy-based deep generative models. Due to numerical stability and quantifiability of the likelihood, RBM is commonly used with Bernoulli units. Here, we consider an alternative member of exponential family RBM with leaky rectified linear units - called leaky RBM. We first study the joint and marginal distributions of leaky RBM under different leakiness, which provides us important insights by connecting the leaky RBM model and truncated Gaussian distributions. The connection leads us to a simple yet efficient method for sampling from this model, where the basic idea is to anneal the leakiness rather than the energy; - i.e., start from a fully Gaussian/Linear unit and gradually decrease the leakiness over iterations. This serves as an alternative to the annealing of the temperature parameter and enables numerical estimation of the likelihood that are more efficient and more accurate than the commonly used annealed importance sampling (AIS). We further demonstrate that the proposed sampling algorithm enjoys faster mixing property than contrastive divergence algorithm, which benefits the training without any additional computational cost. 1 I NTRODUCTION In this paper, we are interested in deep generative models. There is a family of directed deep generative models which can be trained by back-propagation (e.g., Kingma & Welling, 2013; Goodfellow et al., 2014). The other family is the deep energy-based models, including deep belief network (Hinton et al., 2006) and deep Boltzmann machine (Salakhutdinov & Hinton, 2009).

We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (VAEs). However, these models typically assume that modalities are forced to have a conditioned relation, i.e., we can only generate modalities in one direction. To achieve our objective, we should extract a joint representation that captures high-level concepts among all modalities and through which we can exchange them bi-directionally. As described herein, we propose a joint multimodal variational autoencoder (JMVAE), in which all modalities are independently conditioned on joint representation. In other words, it models a joint distribution of modalities. Furthermore, to be able to generate missing modalities from the remaining modalities properly, we develop an additional method, JMVAE-kl, that is trained by reducing the divergence between JMVAE's encoder and prepared networks of respective modalities. Our experiments show that our proposed method can obtain appropriate joint representation from multiple modalities and that it can generate and reconstruct them more properly than conventional VAEs. We further demonstrate that JMVAE can generate multiple modalities bi-directionally.

Right on cue, this past week heralded in an announcement of OpenAI, a new non-profit started by a number of tech luminaries to spearhead AI research that is publicly accessible. The motivation is that apparently these scions of capitalism lose faith in Adam Smith's invisible hand when it comes to AI R&D. Musk continues to promote the idea that AI will be humanity's largest existential threat. Challenging this view, the HBR asks if "OpenAI [is] Solving the Wrong Problem", pointing to the implied lack of trust in capitalism. This is similar to my own parry: that the biggest existential threat to humanity is humanity.

OpenAI Gym is a open-source Python toolkit for developing and comparing reinforcement learning algorithms. This R package is a wrapper for the OpenAI Gym API, and enables access to an ever-growing variety of environments. If you encounter a clear bug, please file a minimal reproducible example on github.

Abstract: A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several... Read More

In recent months, security researchers have shown that machine learning algorithms can be reverse-engineered and made to expose user data, like personal photos or health data. So how can we protect that information? New research from OpenAI and Google shows a way to build AI that never sees personal data, but is able to function as if it had. Ian Goodfellow, a researcher at OpenAI, compares the system to medical school. "The doctors who teach in medical school have learned everything they know from decades of experience working with specific individual people, and as a side effect they know a lot of private medical histories," Goodfellow says.

The Billionaire, who also runs a not-for-profit artificial intelligence research company, warned that the futuristic technology could be deadly if it falls into the wrong hands. He has previously said that the research company, OpenAI, wants to "contract large corporations who may gain too much power by owning super-intelligence systems devoted to profits, as well as governments which may use AI to gain power and even oppress their citizens" but has extended that warning further. Speaking to Sam Altman, co-chairman of OpenAI, he claimed that countries would attempt to steal control away from its owner. However, Mr Musk reassured the public that AI technology would not develop a mind of its own and attack like scenes fictionalised in science fiction hit Terminator.

LONDON Jaguar Land Rover, Ford and Tata Motors are testing connected cars which can communicate with each other using technology designed to speed up journeys and cut accidents, the first such trials in Britain.

OpenAI wants to build the technology that will finally create a computer that can converse in a way that is indistinguishable humans. The nonprofit, backed by Tesla CEO Elon Musk and his PayPal co-founder Peter Thiel, brought on NVIDIA's supercomputer DGX-1, which has 170 teraflops of computing power, to help hone machine learning systems to create algorithms that can comprehend language and teach robots to respond appropriately. That should solve one of the biggest hindrances to making AI systems that can learn complex interactions: the slowness of current computers. "The speed of our computers is in some sense the lifeblood of deep learning," OpenAI research director Ilya Sutskever in an NVIDIA video. The goal of this project is to allow a robot to become smart enough to not only recognize speech, but to also use the data it gathers to formulate appropriate responses on its own--and to do that, computers need to digest data more quickly than they are currently capable of. The DGX-1, which is optimized for an arm of machine learning called deep learning, can feed copious amounts of natural language data into OpenAI's network much quicker than ever before.