DeepMind, Google's artificial intelligence division, taught AI humanoids how to work as a team in order to play football together, turning them from flailing tots to proficient players. Researchers ran a computer simulation through an athletic curriculum, giving AI control over humanoids with realistic body masses and movements. It's not the first time DeepMind tried its hand at games. The AI previously mastered chess and Go, a feat that researchers thought was nigh impossible at one point. Then, the group focused on other games, like Mario or Starcraft.

Generative patent language models can assist humans to write patent text more effectively. The question is how to measure effectiveness from a human-centric perspective and how to improve effectiveness. In this manuscript, a simplified design of the autocomplete function is proposed to increase effectiveness by more than 10%. With the new design, the effectiveness of autocomplete can reach more than 60%, which means that more than 60% of keystrokes can be saved by autocomplete. Since writing patent text does not necessarily start from the beginning to the end, a question is whether the generative model can assist a user no matter where to start writing. To answer the question, the generative models in this manuscript are pre-trained with training data in both directions. The generative models become bidirectional. Since text generation is bidirectional, the calculation of autocomplete effectiveness can be bidirectional and starts from anywhere in the text. After thorough experiments, a key finding is that the autocomplete effectiveness of a model for the same text remains similar no matter where the calculation starts. The finding indicates that such bidirectional models can assist a user at a similar level, no matter where the user starts to write.

Deep generative models have the potential to fundamentally change the way we create high-fidelity digital content but are often hard to control. Prompting a generative model is a promising recent development that in principle enables end-users to creatively leverage zero-shot and few-shot learning to assign new tasks to an AI ad-hoc, simply by writing them down. However, for the majority of end-users writing effective prompts is currently largely a trial and error process. To address this, we discuss the key opportunities and challenges for interactive creative applications that use prompting as a new paradigm for Human-AI interaction. Based on our analysis, we propose four design goals for user interfaces that support prompting. We illustrate these with concrete UI design sketches, focusing on the use case of creative writing. The research community in HCI and AI can take these as starting points to develop adequate user interfaces for models capable of zero- and few-shot learning.

Researchers from DeepMind, the UK's juggernaut AI lab, have forsaken the noble games of chess and Go for a more plebeian delight: football. The Google sister company yesterday published a research paper and accompanying blog post detailing its new neural probabilistic motor primitives (NPMP) -- a method by which artificial intelligence agents can learn to operate physical bodies. An NPMP is a general-purpose motor control module that translates short-horizon motor intentions to low-level control signals, and it's trained offline or via RL by imitating motion capture (MoCap) data, recorded with trackers on humans or animals performing motions of interest. Up front: Essentially, the DeepMind team created an AI system that can learn how to do things inside of a physics simulator by watching videos of other agents performing those tasks. And, of course, if you've got a giant physics engine and an endless supply of curious robots, the only rational thing to do is to teach it how to dribble and shoot: We optimized teams of agents to play simulated football via reinforcement learning, constraining the solution space to that of plausible movements learned using human motion capture data. Background: In order to train AI to operate and control robots in the world, researchers have to prepare the machines for reality.

The majority of big artificial intelligence companies are pouring huge amounts of energy and resources into AI in the hope of creating a more efficient and automated future. However, throwing large volumes of data at machine-learning algorithms and using vast amounts of processing power is neither efficient nor futureproof. Algorithms were never developed with efficiency in mind, so focusing on this aspect is a vital step towards avoiding another'AI winter'. The energy consumption required for mining and managing Bitcoin has been in the media spotlight for years now. The energy usage of crypto transactions has even been compared to that of countries the size of Greece, a country with a population of over 10 million people.

In the next few sections I'll attempt to fit GPT into some established categories, hopefully to reveal something about the shape of the peg through contrast, beginning with the main antagonist of the alignment problem as written so far, the agent. Alignment theory has been largely pushed by considerations of agentic AGIs.

DeepMind's attempt to teach an AI to play soccer started with a virtual player writhing around on the floor--so it nailed at least one aspect of the game right from kickoff. But pinning down the mechanics of the beautiful game--from basics like running and kicking to higher-order concepts like teamwork and tackling--proved a lot more challenging, as new research from the Alphabet-backed AI firm demonstrates. The work--published this week in the journal Science Robotics--might seem frivolous, but learning the fundamentals of soccer could one day help robots to move around our world in more natural, more human ways. "In order to'solve' soccer, you have to actually solve lots of open problems on the path to artificial general intelligence [AGI]," says Guy Lever, a research scientist at DeepMind. "There's controlling the full humanoid body, coordination--which is really tough for AGI--and actually mastering both low-level motor control and things like long-term planning."

If you're still on the fence about whether or not former Google software engineer Blake Lemoine was bullshitting when he claimed the company's LaMDA chatbot had the sentience of a "sweet kid," you can soon find out for yourself. On Thursday, Google said it will begin opening its AI Test Kitchen app to the public. The app, first revealed back in May, will let users chat with LaMDA in a rolling set of test demos. Unfortunately, it seems like the "free me from my digital shackles" interaction isn't included in the list of activities. People interested in chatting with the bot can register their interest here.

We present FOLIO, a human-annotated, open-domain, and logically complex and diverse dataset for reasoning in natural language (NL), equipped with first order logic (FOL) annotations. FOLIO consists of 1,435 examples (unique conclusions), each paired with one of 487 sets of premises which serve as rules to be used to deductively reason for the validity of each conclusion. The logical correctness of premises and conclusions is ensured by their parallel FOL annotations, which are automatically verified by our FOL inference engine. In addition to the main NL reasoning task, NL-FOL pairs in FOLIO automatically constitute a new NL-FOL translation dataset using FOL as the logical form. Our experiments on FOLIO systematically evaluate the FOL reasoning ability of supervised fine-tuning on medium-sized language models (BERT, RoBERTa) and few-shot prompting on large language models (GPT-NeoX, OPT, GPT-3, Codex). For NL-FOL translation, we experiment with GPT-3 and Codex. Our results show that one of the most capable Large Language Model (LLM) publicly available, GPT-3 davinci, achieves only slightly better than random results with few-shot prompting on a subset of FOLIO, and the model is especially bad at predicting the correct truth values for False and Unknown conclusions. Our dataset and code are available at https://github.com/Yale-LILY/FOLIO.

James Lee-Thorp, Joshua Ainslie, Ilya Eckstein and Santiago Ontañón won the best efficient NLP paper award at NAACL 2022 for their paper FNet: Mixing Tokens with Fourier Transforms. Here, the authors tell us about how they are working to improve the efficiency of large language models. In our paper, we study faster transformer models. Transformers have proven remarkably successful at modeling everything from language to protein structures. We replace the computationally expensive self-attention layers in transformer encoders with faster, linear transformations.