An OpenAI research team came up with a model for which they trained about 40GB internet text, the performance of the model was unbelievable, the NLP techniques or model, we knew, was able to predict the new text, but this model is such a powerful model that it can predict a whole article or story only with the few sentences or words, and the result was so optimum that you cannot even guess that it has been generated by a machine.

Earlier this summer, the Economist announced a competition for young people. They asked contestants to answer this question: "What fundamental economic and political change, if any, is needed for an effective response to climate change?" More than 2,400 people responded, from over 110 countries. And the Economist slipped one essay into the stack of submissions that their judges would review: an essay written by an artificial intelligence. The AI in question was GPT-2, a language-generating system developed by San Francisco AI lab OpenAI and announced this spring.

This post was co-written with Baptiste Rocca. In the last few years, deep learning based generative models have gained more and more interest due to (and implying) some amazing improvements in the field. Relying on huge amount of data, well-designed networks architectures and smart training techniques, deep generative models have shown an incredible ability to produce highly realistic pieces of content of various kind, such as images, texts and sounds. Among these deep generative models, two major families stand out and deserve a special attention: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). In a previous post, published in January of this year, we discussed in depth Generative Adversarial Networks (GANs) and showed, in particular, how adversarial training can oppose two networks, a generator and a discriminator, to push both of them to improve iteration after iteration.

The AI lab gets to throw Microsoft's supercomputing and cloud computing muscle at its bid to build artificial general intelligence (AGI). The news: Microsoft says OpenAI will help it jointly develop and train new AI technologies for its Azure cloud computing service. It will also work with it to develop new supercomputing hardware to try to achieve AGI--machines with the capacity to learn tasks the way human beings do. That's a holy grail of AI that still remains (and may always remain) out of reach. OpenAI's founders, which include Elon Musk and other tech leaders, reckon AGI could help solve longstanding challenges in areas that range from climate change to health care.

Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP). A novel approach using variational inference for learning the reward function is proposed in this research. Using this technique, the intractable posterior distribution of the continuous latent variable (the reward function in this case) is analytically approximated to appear to be as close to the prior belief while trying to reconstruct the future state conditioned on the current state and action. The reward function is derived using a well-known deep generative model known as Conditional Variational Auto-encoder (CVAE) with Wasserstein loss function, thus referred to as Conditional Wasserstein Auto-encoder-IRL (CWAE-IRL), which can be analyzed as a combination of the backward and forward inference. This can then form an efficient alternative to the previous approaches to IRL while having no knowledge of the system dynamics of the agent. Experimental results on standard benchmarks such as objectworld and pendulum show that the proposed algorithm can effectively learn the latent reward function in complex, high-dimensional environments.

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. We'll tell you what's true. You can form your own view.

The Analog Ensemble (AnEn) method tries to estimate the probability distribution of the future state of the atmosphere with a set of past observations that correspond to the best analogs of a deterministic Numerical Weather Prediction (NWP). This model post-processing method has been successfully used to improve the forecast accuracy for several weather-related applications including air quality, and short-term wind and solar power forecasting, to name a few. In order to provide a meaningful probabilistic forecast, the AnEn method requires storing a historical set of past predictions and observations in memory for a period of at least several months and spanning the seasons relevant for the prediction of interest. Although the memory and computing costs of the AnEn method are less expensive than using a brute-force dynamical ensemble approach, for a large number of stations and large datasets, the amount of memory required for AnEn can easily become prohibitive. Furthermore, in order to find the best analogs associated with a certain prediction produced by a NWP model, the current approach requires searching over the entire dataset by applying a certain metric. This approach requires applying the metric over the entire historical dataset, which may take a substantial amount of time. In this work, we investigate an alternative way to implement the AnEn method using deep generative models. By doing so, a generative model can entirely or partially replace the dataset of pairs of predictions and observations, reducing the amount of memory required to produce the probabilistic forecast by several orders of magnitude. Furthermore, the generative model can generate a meaningful set of analogs associated with a certain forecast in constant time without performing any search, saving a considerable amount of time even in the presence of huge historical datasets.

Graphs and networks are a key research tool for a variety of science fields, most notably chemistry, biology, engineering and social sciences. Modeling and generation of graphs with efficient sampling is a key challenge for graphs. In particular, the non-uniqueness, high dimensionality of the vertices and local dependencies of the edges may render the task challenging. We apply our recently introduced method, Generative Examination Networks (GENs) to create the first text-based generative graph models using one-line text formats as graph representation. In our GEN, a RNN-generative model for a one-line text format learns autonomously to predict the next available character. The training is stopped by an examination mechanism checking validating the percentage of valid graphs generated. We achieved moderate to high validity using dense g6 strings (random 67.8 +/- 0.6, canonical 99.1 +/- 0.2). Based on these results we have adapted the widely used SMILES representation for molecules to a new input format, which we call linear graph input (LGI). Apart from the benefits of a short compressible text-format, a major advantage include the possibility to randomize and augment the format. The generative models are evaluated for overall performance and for reconstruction of the property space. The results show that LGI strings are very well suited for machine-learning and that augmentation is essential for the performance of the model in terms of validity, uniqueness and novelty. Lastly, the format can address smaller and larger dataset of graphs and the format can be easily adapted to define another meaning of the characters used in the LGI-string and can address sparse graph problems in used in other fields of science.

We're possibly less than a decade away from self-conscious artificial intelligence capable of learning on its own, and this latest project gets us one step closer. Researchers OpenAI took an unusual approach at developing AI, one inspired by natural selection and competition. Put simply, they pit multiple AI agents against each other to compete for conflicting goals and found that it developed new sophisticated behavior in the long term. These AI agents are split between "hiders" / "seekers," and their roles are exactly as they sound. The hiders get a set time to hide, while the seekers are frozen, with each of the AI agents left on their own to discover ways to make use of their environment to achieve their goals.

For decades, artificial intelligence scientists have sought to create intelligent machines by trying to study and replicate the structure and functionality of the human brain. Last week, researchers at AI research lab OpenAI introduced a more fundamental approach at developing AI, a project inspired by natural selection and competition, the simple rules that have led to the evolution of all living beings, including humans. The AI researchers pitted multiple AI agents against each other to compete for conflicting goals. They observed that the AI developed new and sophisticated behavior in the long term. While the project draws on existing AI techniques and concepts, it might provide new approaches and ideas to creating AI applications.