Microsoft is having a hell of a week. On Monday, the company announced a $7.5 billion deal to acquire Zenimax and all of its big video game properties. In one move, the Xbox platform's future suddenly looked a lot brighter. Today, Microsoft announced another deal that could also have a huge long-term impact in the coming years: It's acquired an exclusive license for OpenAI's GPT-3 language model. OpenAI has been making headlines for years--primarily due to the fact that AI-phobe Elon Musk was an early investor.

Microsoft today announced that it will exclusively license GPT-3, one of the most powerful language understanding models in the world, from AI startup OpenAI. In a blog post, Microsoft EVP Kevin Scott said that the new deal will allow Microsoft to leverage OpenAI's technical innovations to develop and deliver AI solutions for customers, as well as create new solutions that harness the power of natural language generation. "We see this as an incredible opportunity to expand our Azure-powered AI platform in a way that democratizes AI technology, enables new products, services and experiences, and increases the positive impact of AI at scale," Scott wrote. "The scope of commercial and creative potential that can be unlocked through the GPT-3 model is profound, with genuinely novel capabilities -- most of which we haven't even imagined yet. Directly aiding human creativity and ingenuity in areas like writing and composition, describing and summarizing large blocks of long-form data (including code), converting natural language to another language -- the possibilities are limited only by the ideas and scenarios that we bring to the table."

One of the most gratifying parts of my job at Microsoft is being able to witness and influence the intersection of technological progress and impact: harnessing the big trends in computing that have the opportunity to benefit everybody on the planet. Frank's post this morning from Ignite shows just how much progress is happening in many of these areas. Today, the foremost computing trend is undoubtedly artificial intelligence (AI). As we increasingly develop the ability to deploy huge AI models at scale in a way that can be leveraged by all developers and businesses, AI is becoming a platform – an environment upon which folks can build amazing new experiences, just like we've seen happen before with personal computers, mobile devices or the internet. Getting this AI platform off the ground requires unprecedented computing horsepower.

After they published the article, many responses came from different media houses and notable people trying to shed some light on what exactly happened. First, let's look at the legitimacy of the article. Surely it was the generated one but with no human intervention? So, it is just another media overhype? I mean cherry-picking the best and presenting it to you in a way that sells.

Since its release, GPT-3, OpenAI's massive language model, has been the topic of much discussion among developers, researchers, entrepreneurs, and journalists. Most of those discussions have been focused on the capabilities of the AI-powered text generator. But much about GPT-3 remains obscure. The company has opted to commercialize the deep learning model instead of making it freely available to the public. And though the AI has shown to be capable of many interesting feats, it's not yet clear if GPT-3 will become a real product or will join the endless array of abandoned projects that never found a viable business model. Earlier this month, as reported by users who have access to the beta version of the language model, OpenAI declared the initial pricing plan of GPT-3.

Trajectory data generation is an important domain that characterizes the generative process of mobility data. Traditional methods heavily rely on predefined heuristics and distributions and are weak in learning unknown mechanisms. Inspired by the success of deep generative neural networks for images and texts, a fast-developing research topic is deep generative models for trajectory data which can learn expressively explanatory models for sophisticated latent patterns. This is a nascent yet promising domain for many applications. We first propose novel deep generative models factorizing time-variant and time-invariant latent variables that characterize global and local semantics, respectively. We then develop new inference strategies based on variational inference and constrained optimization to encapsulate the spatiotemporal validity. New deep neural network architectures have been developed to implement the inference and generation models with newly-generalized latent variable priors. The proposed methods achieved significant improvements in quantitative and qualitative evaluations in extensive experiments.

… created by Elon Musk’s OpenAI, may be considered or appears to exhibit, something like artificial general intelligence (AGI), the ability to understand …

OpenAI, an AI research foundation started by Elon Musk, Sam Altman, Greg Brockman, and a few other leaders in ML, recently released an API and website that allows people to access a new language model called GPT-3. I've had the chance to play with it over the past few days and have been truly amazed by its capabilities. I'd like to start this off by stating that, especially amongst my extremely intelligent ML friends, I am quite the layman, so this post is more aimed for a nontechnical audience and I apologize if I make any technical errors in this post. GPT-3 is essentially a context-based generative AI. What this means is that when the AI is given some sort of context, it then tries to fill in the rest.

In this paper, we expand on our previous research of the potential for abuse of generative language models by assessing GPT-3. Experimenting with prompts representative of different types of extremist narrative, structures of social interaction, and radical ideologies, we find that GPT-3 demonstrates significant improvement over its predecessor, GPT-2, in generating extremist texts. We also show GPT-3's strength in generating text that accurately emulates interactive, informational, and influential content that could be utilized for radicalizing individuals into violent far-right extremist ideologies and behaviors. While OpenAI's preventative measures are strong, the possibility of unregulated copycat technology represents significant risk for large-scale online radicalization and recruitment; thus, in the absence of safeguards, successful and efficient weaponization that requires little experimentation is likely. AI stakeholders, the policymaking community, and governments should begin investing as soon as possible in building social norms, public policy, and educational initiatives to preempt an influx of machine-generated disinformation and propaganda. Mitigation will require effective policy and partnerships across industry, government, and civil society.

San Francisco-based AI research laboratory OpenAI has added another member to its popular GPT (Generative Pre-trained Transformer) family. In a new paper, OpenAI researchers introduce GPT-f, an automated prover and proof assistant for the Metamath formalization language. While artificial neural networks have made considerable advances in computer vision, natural language processing, robotics and so on, OpenAI believes they also have potential in the relatively underexplored area of reasoning tasks. The new research explores this potential by applying a transformer language model to automated theorem proving. Automated theorem proving tends to require general and flexible reasoning to efficiently check the correctness of proofs.