In February, an artificial intelligence lab cofounded by Elon Musk informed the world that its latest breakthrough was too risky to release to the public.

OpenAI, a nonprofit focused on creating human-level artificial intelligence, just released an update to its GPT-2 text generator. I'm not being hyperbolic when I say that, after trying it, I'm legitimately terrified for the future of humanity if we don't figure out a way to detect AI-generated content – and soon. GPT-2 isn't a killer robot and my fears aren't that AI is going to rise up against us. I'm terrified of GPT-2 because it represents the kind of technology that evil humans are going to use to manipulate the population -- and in my opinion that makes it more dangerous than any gun. Here's how it works: you give it a prompt and it near-instantly spits out a bunch of words. What's scary about it is that it works.

This week's announcement of Microsoft's major investment in OpenAI sent hopeful waves throughout the growing industry. OpenAI, co-founded by Elon Musk and Y Combinator chairman Sam Altman in 2015, plans to use the billion-dollar investment to create machine learning that mimics the brain. The 100-employee company specializes in what's referred to as AGI, or Artificial General Intelligence, which may eventually replace or stand in for human behaviour. While the nine figure sum is a testament to tech's commitment in the AI race, it also comes at a time when larger philosophical questions on its development are still awaiting answers. "The announcement is quite significant within the AI industry, and it presents an ambitious goal to deliver on the promise of AGI," said Ben Lamm, CEO of Hypergiant, an Austin-based AI products and services company.

OpenAI, the AI company that Elon Musk founded and then quit has just released a more powerful version of its AI text-writing software. The company still won't release their full software - that can be used to write fake news and messages en masse - due to fears it might be misused. OpenAI says its text-writing system is so advanced it can write news stories and even fiction that passes as human. A user can feed the system text - anything from a few sentences to pages of it - and the system will then continue that same text in an uncannily well-written, contextually relevant, human style. However, after releasing its original system, GPT-2, in February, the company said the full software was too dangerous to release to the public - a weaker version was made available. Now, the company has announced it has released a version of GPT-2 that is six times more powerful.

Last week, researchers from OpenAI and Google introduced Activation Atlases, a tool that helps make sense of the inner workings of neural networks by visualizing how they see and classify different objects. At first glance, Activation Atlases is an amusing tool helps you see the world through the eyes of AI models. But it also one of the many important efforts that are helping explain decisions made by neural networks, one of the greatest challenges of the AI industry and an important hurdle in trusting AI in critical tasks. Artificial intelligence, or namely its popular subset deep learning, is far from the only kind of software we're using. We've been using software in different fields for decades.

In this post, you'll learn the main recipe to convert a pretrained TensorFlow model in a pretrained PyTorch model, in just a few hours. We'll take the example of a simple architecture like OpenAI GPT-2 Doing such a conversion assumes a good familiarity with both TensorFlow and PyTorch but it's also one of the best ways to get to know better both frameworks! The first step is to retrieve the TensorFlow code and a pretrained checkpoint. Let's get them from OpenAI GPT-2 official repository: TensorFlow checkpoints are usually composed of three files named XXX.ckpt.data-YYY A trained NLP model should also be provided with a vocabulary to associate the tokens to the embeddings indices (here encoder.json

We study the robust one-bit compressed sensing problem whose goal is to design an algorithm that faithfully recovers any sparse target vector $\theta_0\in\mathbb{R}^d$ uniformly $m$ quantized noisy measurements. Under the assumption that the measurements are sub-Gaussian random vectors, to recover any $k$-sparse $\theta_0$ ($k\ll d$) uniformly up to an error $\varepsilon$ with high probability, the best known computationally tractable algorithm requires $m\geq\tilde{\mathcal{O}}(k\log d/\varepsilon^4)$ measurements. In this paper, we consider a new framework for the one-bit sensing problem where the sparsity is implicitly enforced via mapping a low dimensional representation $x_0 \in \mathbb{R}^k$ through a known $n$-layer ReLU generative network $G:\mathbb{R}^k\rightarrow\mathbb{R}^d$. Such a framework poses low-dimensional priors on $\theta_0$ without a known basis. We propose to recover the target $G(x_0)$ via an unconstrained empirical risk minimization (ERM) problem under a much weaker sub-exponential measurement assumption. For such a problem, we establish a joint statistical and computational analysis. In particular, we prove that the ERM estimator in this new framework achieves an improved statistical rate of $m=\tilde{\mathcal{O}}(kn \log d /\varepsilon^2)$ recovering any $G(x_0)$ uniformly up to an error $\varepsilon$. Moreover, from the lens of computation, despite non-convexity, we prove that the objective of our ERM problem has no spurious stationary point, that is, any stationary point are equally good for recovering the true target up to scaling with a certain accuracy. Furthermore, our analysis also shed lights on the possibility of inverting a deep generative model under partial and quantized measurements, complementing the recent success of using deep generative models for inverse problems.

In 2015, car-and-rocket man Elon Musk joined with influential startup backer Sam Altman to put artificial intelligence on a new, more open course. They cofounded a research institute called OpenAI to make new AI discoveries and give them away for the common good. Now, the institute's researchers are sufficiently worried by something they built that they won't release it to the public. The AI system that gave its creators pause was designed to learn the patterns of language. It does that very well--scoring better on some reading-comprehension tests than any other automated system.

For the first three years of OpenAI, I dreamed of becoming a machine learning expert but made little progress towards that goal. Over the past nine months, I've finally made the transition to being a machine learning practitioner. It was hard but not impossible, and I think most people who are good programmers and know (or are willing to learn) the math can do it too. There are many online courses to self-study the technical side, and what turned out to be my biggest blocker was a mental barrier -- getting ok with being a beginner again. A founding principle of OpenAI is that we value research and engineering equally -- our goal is to build working systems that solve previously impossible tasks, so we need both.

This week Microsoft announced that it is investing $1 Bn in partnership with OpenAI to support the development of an AI framework and models for AI applications across operations and services, built on the company's Azure cloud service. OpenAI is a non-profit AI research group, backed by the likes of Elon Musk, Peter Thiel, Infosys, Amazon Web Services and others. However, Microsoft's investment would be for a for-profit offshoot of OpenAI. OpenAI will be using Microsoft's Azure cloud services to run the group's AI software. The two companies will come together to build a large-scale AI system for Azure.