The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domains such as continuous control. In this work, we explore a method for learning a single policy that manipulates various forms of agents to solve various tasks by distilling a large amount of proficient behavioral data. In order to align input-output (IO) interface among multiple tasks and diverse agent morphologies while preserving essential 3D geometric relations, we introduce morphology-task graph, which treats observations, actions and goals/task in a unified graph representation. We also develop MxT-Bench for fast large-scale behavior generation, which supports procedural generation of diverse morphology-task combinations with a minimal blueprint and hardware-accelerated simulator. Through efficient representation and architecture selection on MxT-Bench, we find out that a morphology-task graph representation coupled with Transformer architecture improves the multi-task performances compared to other baselines including recent discrete tokenization, and provides better prior knowledge for zero-shot transfer or sample efficiency in downstream multi-task imitation learning. Our work suggests large diverse offline datasets, unified IO representation, and policy representation and architecture selection through supervised learning form a promising approach for studying and advancing morphology-task generalization.

Check out all the on-demand sessions from the Intelligent Security Summit here. All other AI startup news got blown out of the water this morning, when word got out that Google will invest over $300 million into AI lab Anthropic, one of the buzziest AI startups in recent memory (partly thanks to its massive early investment by Sam Bankman-Fried and FTX) and one of OpenAI's biggest rivals for the LLM space. But, there were a few other startups that made news as well -- from those in autonomous driving to retail self-checkout. According to new reporting from the Financial Times, Google has invested $300 million in one of the most buzzy OpenAI rivals, AI lab startup Anthropic, whose recently-debuted generative AI model Claude is considered competitive with ChatGPT. According to the reporting, Google will take a stake of around 10% and Anthropic will be required to use the money to buy computing resources from Google Cloud.

"ChatGPT is basically auto-complete on steroids." I heard that quip from a computer scientist at the University of Rochester as my fellow professors and I attended a workshop on the new reality of artificial intelligence in the classroom. Like everyone else, we were trying to grapple with the astonishing capacities of ChatGPT and its AI-driven ability to write student research papers, complete computer code, and even compose that bane of every professor's existence, the university strategic planning document. That computer scientist's remark drove home a critical point. If we really want to understand artificial intelligence's power, promise, and peril, we first need to understand the difference between intelligence as it is generally understood and the kind of intelligence we are building now with AI. That is important, because the kind we are building now is really the only kind we know how to build at all -- and it is nothing like our own intelligence.

Large Language Models (LLM) are on fire, capturing public attention by their ability to provide seemingly impressive completions to user prompts (NYT coverage). They are a delicate combination of a radically simplistic algorithm with massive amounts of data and computing power. They are trained by playing a guess-the-next-word game with itself over and over again. Each time, the model looks at a partial sentence and guesses the following word. If it makes it correctly, it will update its parameters to reinforce its confidence; otherwise, it will learn from the error and give a better guess next time.

It is safe to say that the Chat GPT function from OpenAI has created a firestorm of conversation about the applications of artificial intelligence (AI) in knowledge work and scholarship, which includes cyber threat intelligence. Can ChatGPT really replace the thought and knowledge work done by many people? That question is outstanding and I cannot answer, nor can anyone yet with any certainty. But, it's application to various topics, including cyber threat intelligence, is in question – and by proxy, it's impact on those topics. So, let me provide some perspective after 20 years of cyber threat intelligence AND having employed artificial intelligence and machine learning in this space for the last 10 years at least.

For a product that its own creators, in a marketing pique, once declared "too dangerous" to release to the general public, OpenAI's ChatGPT is seemingly everywhere these days. The versatile automated text generation (ATG) system, which is capable of outputting copy that is nearly indistinguishable from a human writer's work, is officially still in beta but has already been utilized in dozens of novel applications, some of which extend far beyond the roles ChatGPT was originally intended for -- like that time it simulated an operational Linux shell or that other time when it passed the entrance exam to Wharton Business School. The hype around ChatGPT is understandably high, with myriad startups looking to license the technology for everything from conversing with historical figures to talking to historical literature, from learning other languages to generating exercise routines and restaurant reviews. But with these technical advancements come with a slew of opportunities for misuse and outright harm. And if our previous hamfisted attempts at handling the spread of deepfake video and audio technologies were any indication, we're dangerously underprepared for the havoc that at-scale, automated disinformation production will wreak upon our society.

Hi, my name's Daniel and I want to introduce you to Gooly a chatbot with which you can interact with OpenAI's GPT3 artificial intelligence engine, both by typing on the keyboard and by conversing with your voice. Now Gooly is destined to answer questions for the little ones!! Developed with by Daniel Atik danielatik.com

OpenAI is ready to start capitalizing on ChatGPT's buzz. On Wednesday, the firm announced it will offer a pilot $20-a-month subscription version of the chatbot called ChatGPT Plus, which gives priority access to users during peak time and faster responses. The free version remains available but is so popular that it is often at capacity or slow to give responses. In a clear push for commercialization, OpenAI also said it will roll out an API waitlist, different paid tiers, and business plans. OpenAI, it seems, believes enterprises will be willing to pay for its chatbot's capabilities.

That's because AI is able to read, write, and understand text-based data well, she added. "Analyzing and interpreting vast amounts of language based data and information is a skill that you'd expect generative AI technologies to ramp up on," Madgavkar said. Economist Paul Krugman said in a New York Times op-ed that ChatGPT may be able to do tasks like reporting and writing "more efficiently than humans." The media industry is already beginning to experiment with AI-generated content. Tech news site CNET used an AI tool similar to ChatGPT to write dozens of articles -- though the publisher has had to issue a number of corrections -- and BuzzFeed announced that it will use tech from the ChatGPT maker to generate new forms of content.

In this blog, Nadia Seaborne at GEMS Wellington International School in Dubai, explains how AI might change teaching and classroom practice. Is it a case of'keep your friends close and your enemies closer'? With an optimistic mindset the benefits could be endless and Nadia ends the blog with twenty examples of how ChatGPT could be used to improve critical thinking. Scrolling through any education forum or Facebook post, I would be very surprised if ChatGPT is not too far away from any conversation. Threads are full of panic, hesitation and a relentless worry by teachers who cannot believe what they are seeing.