Cutting-edge AI tech has been described as'conscious' by a leading philosophy of mind expert. New York University's Professor David Chalmers made the bombshell claim while discussing the highly-controversial Generative Pre-trained Transformer 3 (GPT-3) - OpenAI's powerful new language generator able to create content better than anything else ever made.

Robust estimation of ground motions generated by scenario earthquakes is critical for many engineering applications. We leverage recent advances in Generative Adversarial Networks (GANs) to develop a new framework for synthesizing earthquake acceleration time histories. Our approach extends the Wasserstein GAN formulation to allow for the generation of ground-motions conditioned on a set of continuous physical variables. Our model is trained to approximate the intrinsic probability distribution of a massive set of strong-motion recordings from Japan. We show that the trained generator model can synthesize realistic 3-Component accelerograms conditioned on magnitude, distance, and $V_{s30}$. Our model captures the expected statistical features of the acceleration spectra and waveform envelopes. The output seismograms display clear P and S-wave arrivals with the appropriate energy content and relative onset timing. The synthesized Peak Ground Acceleration (PGA) estimates are also consistent with observations. We develop a set of metrics that allow us to assess the training process's stability and tune model hyperparameters. We further show that the trained generator network can interpolate to conditions where no earthquake ground motion recordings exist. Our approach allows the on-demand synthesis of accelerograms for engineering purposes.

The song in question not a genuine track, but a convincing fake created by "research and deployment company" OpenAI, whose Jukebox project uses artificial intelligence to generate music, complete with lyrics, in a variety of genres and artist styles. Along with Sinatra, they've done what are known as "deepfakes" of Katy Perry, Elvis, Simon and Garfunkel, 2Pac, Céline Dion and more. Having trained the model using 1.2m songs scraped from the web, complete with the corresponding lyrics and metadata, it can output raw audio several minutes long based on whatever you feed it. Input, say, Queen or Dolly Parton or Mozart, and you'll get an approximation out the other end.

How Does a Neural Network Work? A neural net is not much more than a collection of logical units that each has an input weight and a weight per connector. If the input weight multiplied by the connector weight is more than a threshold value defined, the unit is set to fire, which then triggers the unit to its right with a new input value. In the graph on the right, due to the fact that not all threshold values are exceeded, the tree will shrink until only a couple of output units or even only one have fired. This is the basis of how it learns.

Many real-world problems require complex coordination between multiple agents -- e.g., people or algorithms. A machine learning technique called multi-agent reinforcement learning (MARL) has shown success with respect to this, mainly in two-team games like Go, DOTA 2, Starcraft, hide-and-seek, and capture the flag. But the human world is far messier than games. That's because humans face social dilemmas at multiple scales, from the interpersonal to the international, and they must decide not only how to cooperate but when to cooperate. To address this challenge, researchers at OpenAI propose training AI agents with what they call randomized uncertain social preferences (RUSP), an augmentation that expands the distribution of environments in which reinforcement learning agents train.

Microsoft and OpenAI, the AI research lab in which Microsoft has invested over $1 billion, today submitted a document to the U.S. government describing how a "digitally transformed" export controls system might work and the benefits it could provide. The organizations suggest that their proposed solutions could bring commercial benefits to users, as well as a more powerful, dynamic, and targeted method for controlling U.S. exports of fundamental technologies. Following a mandate in the Export Control Reform Act of 2018, the U.S. Department of Commerce's Bureau of Industry and Security (BIS) undertook efforts to identify and control exports of "emerging" or "foundational" technologies ostensibly vital to national security. In comment periods ending in January 2019 and earlier this week, BIS solicited comment from the industry on how to identify and approach control of these technologies. Microsoft and OpenAI take issue with the restrictions promulgated via traditional export control approaches.

In practice, few studies are fully replicated because most researchers are more interested in producing new results than reproducing old ones. But in fields like biology and physics--and computer science overall--researchers are typically expected to provide the information needed to rerun experiments, even if those reruns are rare. AI is feeling the heat for several reasons. For a start, it is a newcomer. It has only really become an experimental science in the past decade, says Joelle Pineau, a computer scientist at Facebook AI Research and McGill University, who coauthored the complaint.

Artificial intelligence (AI) is being used to create new'deepfake' pop songs that sound like they're being performed by dead musicians, including Elvis Presley, Frank Sinatra, David Bowie and Michael Jackson. Jukebox, created by California-based company OpenAI, is a neural network that generates eerie approximates of pop songs in the style of multiple artists. The neural network generates music, including rudimentary singing complete with lyrics in English and a variety of instruments like guitar and piano. OpenAI has created a expansive library of new tracks, imitating a diverse selection of artists, including the Beatles, Nirvana, Katy Perry, Simon and Garfunkel, Stevie Wonder, Elton John and Ed Sheeran, as well as deceased heroes that almost appear to be brought back to life. Most of the samples have a bizarre, faraway quality to them, as if they're poorly produced demos from the 1950s that haven't seen the light of day until now.

Not quite the imitation of existing performers or interpretation of famous pieces -- but the discovery of hidden gems. Uncanny Valley is a passé. Indeed, the works are unique: every time a new never before existed music piece is generated -- and you can be sure (like in the case of GPT-3) that this sequence will never be repeated. My first experiment brought me goosebumps. Already the 2nd level was something special, not really in a way of music pieces.

Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL). Although algorithmic advances combined with convolutional neural networks have proved to be a recipe for success, current methods are still lacking on two fronts: (a) data-efficiency of learning and (b) generalization to new environments. To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms. We perform the first extensive study of general data augmentations for RL on both pixel-based and state-based inputs, and introduce two new data augmentations - random translate and random amplitude scale. We show that augmentations such as random translate, crop, color jitter, patch cutout, random convolutions, and amplitude scale can enable simple RL algorithms to outperform complex state-of-the-art methods across common benchmarks. RAD sets a new state-of-the-art in terms of data-efficiency and final performance on the DeepMind Control Suite benchmark for pixel-based control as well as OpenAI Gym benchmark for state-based control. We further demonstrate that RAD significantly improves test-time generalization over existing methods on several OpenAI ProcGen benchmarks.