Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions. While the employed learning procedures typically assume that training data is drawn i.i.d. from the distribution of interest, it may be desirable to model distinct distributions which are observed sequentially, such as when different classes are encountered over time. Although conditional variations of deep generative models permit multiple distributions to be modeled by a single network in a disentangled fashion, they are susceptible to catastrophic forgetting when the distributions are encountered sequentially. In this paper, we adapt recent work in reducing catastrophic forgetting to the task of training generative adversarial networks on a sequence of distinct distributions, enabling continual generative modeling.

Most of today's artificial intelligence (AI) systems rely on machine learning algorithms that can predict specific outcomes by drawing on pre-established values, but now researchers from OpenAI, a company funded by no less than Elon Musk and Peter Thiel, who are trying to democratise AI for "human good" just discovered – literally – that a machine learning system they created to predict the next character in the text of reviews from Amazon evolved into an unsupervised learning system that could learn how to read sentiment. That's a pretty big deal, and it's also something that, at the moment, even the researchers themselves can't explain. "We were very surprised that our model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment," said OpenAI in a blog. According to the post OpenAI's neural network was able to train itself and analyse sentiment accurately by classifying Amazon's reviews as either positive or negative – and it then generated follow on text that fit with the sentiment. The AI the team used was what's known as a multiplicative long short-term memory (LSTM) model that was trained for a month, processing 12,500 characters a second using Nvidia Pascal GPU's – which Nvidia's own CEO gifted to Elon Musk last year – with "4,096 units on a corpus of 82 million Amazon reviews to predict the next character in a chunk of text."

If imitation is the sincerest form of flattery, OpenAI's newest robot system should leave humanity blushing. Not only can it successfully replicate human behaviors, it can do so after just a single demonstration of the task. The research company co-founded and chaired by Elon Musk used two separate neural networks to develop its one-shot imitation learning system. The first, a vision network, analyzes an image from the robot's camera to determine the location of objects in reality (in OpenAI's video example, these objects are blocks of wood on a table). The network is able to do this despite never having seen the actual table or blocks before.

OpenAI, the artificial intelligence research company set up by Elon Musk, has come up with a new method for teaching robots -- giving them a demo in virtual reality. The non-profit, which is funded to the tune of $1 billion, trained a self-learning algorithm to complete a task after a human demonstrated it once in virtual reality. In this case, the task was stacking coloured blocks. The team got a programmed robot to reproduce the behaviour shown during the demonstration in the virtual environment. "We've developed and deployed a new algorithm, one-shot imitation learning, allowing a human to communicate how to do a new task by performing it in VR," OpenAI wrote in a blog post on Tuesday.

Elon Musk's artificial intelligence platform OpenAI introduced a new program to train robots entirely in simulation. Now they've added a new algorithm, named one-shot imitation learning, which will only require humans to demonstrate a task once in VR for a robot to learn it. The system is powered by two neural networks. The first takes a camera image and determines objects' spatial position in relation to the robot -- but it was trained only with a host of simulated images, meaning it was taught how to interact with the real world before it ever actually saw the real world. The second imitates tasks shown by the demonstrator by scanning through recorded action and paying attention to frames that tell it what to do next.

We look forward to collaborating with other industry-leading Partnership on AI members to address the challenges and opportunities within the AI field including companies, nonprofits and institutions and with founding members Apple, Amazon, Facebook, Google / DeepMind, IBM and Microsoft; existing Partners AAAI, ACLU, OpenAI; and new Partners: AI Forum of New Zealand (AIFNZ), Allen Institute for Artificial Intelligence (AI2), Centre for Democracy & Tech (CDT), Centre for Internet and Society, India (CIS), Cogitai, Data & Society Research Institute (D&S), Digital Asia Hub, eBay, Electronic Frontier Foundation (EFF), Future of Humanity Institute (FHI), Future of Privacy Forum (FPF), Human Rights Watch (HRW), Intel, Leverhulme Centre for the Future of Intelligence (CFI), McKinsey & Company, SAP, Salesforce, Sony, UNICEF, Upturn, XPRIZE Foundation and Zalando.

I would've thought to tie both networks together but allowing the two to be trained independently makes much more sense given the training data.

Imagine how easy it would be to learn skating, if only it doesn't hurt everytime you fall. Unfortunately, we, humans, don't have that option. Robots, however, can now "learn" their skills on a simulation platform without being afraid of crashing into a wall. This is possible with the reinforcement learning algorithms provided by OpenAI Gym and the ROS Development Studio. You can now train your robot to navigate through an environment filled with obstacles just based on the sensor inputs, with the help of OpenAI Gym. In April 2016, OpenAI introduced "Gym", a platform for developing and comparing reinforcement learning algorithms.

One of our core aspirations at OpenAI is to develop algorithms and techniques that endow computers with an understanding of our world. It's easy to forget just how much you know about the world: you understand that it is made up of 3D environments, objects that move, collide, interact; people who walk, talk, and think; animals who graze, fly, run, or bark; monitors that display information encoded in language about the weather, who won a basketball game, or what happened in 1970. This tremendous amount of information is out there and to a large extent easily accessible -- either in the physical world of atoms or the digital world of bits. The only tricky part is to develop models and algorithms that can analyze and understand this treasure trove of data. Generative models are one of the most promising approaches towards this goal.

Most of today's artificial intelligence (AI) systems rely on machine learning algorithms that can predict specific outcomes by drawing on pre-established values, but now researchers from OpenAI, a company funded by no less than Elon Musk and Peter Thiel, who are trying to democratise AI for "human good" just discovered – literally – that a machine learning system they created to predict the next character in the text of reviews from Amazon evolved into an unsupervised learning system that could learn how to read sentiment. That's a pretty big deal, and it's also something that, at the moment, even the researchers themselves can't explain. "We were very surprised that our model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment," said OpenAI in a blog. According to the post OpenAI's neural network was able to train itself and analyse sentiment accurately by classifying Amazon's reviews as either positive or negative – and it then generated follow on text that fit with the sentiment. The AI the team used was what's known as a multiplicative long short-term memory (LSTM) model that was trained for a month, processing 12,500 characters a second using Nvidia Pascal GPU's – which Nvidia's own CEO gifted to Elon Musk last year – with "4,096 units on a corpus of 82 million Amazon reviews to predict the next character in a chunk of text."