Ascalculatingthese residuals requires out-of-sample inference, we fit the models and half of the data and evaluate on the other half, before switching the training and testing sets and training/inferring again. We used the Adam optimizer [4] and PyTorch's exponential learning rate scheduler with gammasetto0.99.

Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here's a handy roundup of the last week's stories in the world of machine learning, along with notable research and experiments we didn't cover on their own. In one of the more surprising stories of the past week, Italy's data protection authority (DPA) blocked OpenAI's viral AI-powered chatbot, ChatGPT, citing concerns that the tool breaches the European Union's General Data Protection Regulation. The DPA is reportedly opening an investigation into whether OpenAI unlawfully processed people's data, as well as over the lack of any system to prevent minors from accessing the tech. It's unclear what the outcome might be; OpenAI has 20 days to respond to the order.

Recent work proposed the UCTMAXSAT algorithm to address Maximum Satisfiability Problems (MaxSAT) and shown improved performance over pure Stochastic Local Search algorithms (SLS). UCTMAXSAT is based on Monte Carlo Tree Search but it uses SLS instead of purely random playouts. In this work, we introduce two algorithmic variations over UCTMAXSAT. We carry an empirical analysis on MaxSAT benchmarks from recent competitions and establish that both ideas lead to performance improvements. First, a nesting of the tree search inspired by the Nested Monte Carlo Search algorithm is effective on most instance types in the benchmark. Second, we observe that using a static flip limit in SLS, the ideal budget depends heavily on the instance size and we propose to set it dynamically. We show that it is a robust way to achieve comparable performance on a variety of instances without requiring additional tuning.

Run:ai, the company simplifying AI infrastructure orchestration and management, today announced that it has raised $75M in Series C round led by Tiger Global Management and Insight Partners, who led the previous Series B round. The round includes the participation of additional existing investors, TLV Partners and S Capital VC, bringing the total funding raised to date to $118M. Run:ai has grown sharply, with a 9x increase in Annual Recurring Revenue in the last year, while the company's staff more than tripled over the same period. The company plans to use the investment to further grow its global teams and will also be considering strategic acquisitions as it develops and enhances the company's Atlas software platform. Omri Geller, Run:ai CEO and co-founder, said, "It may sound dramatic, but AI is really the next phase of humanity's development. When we founded Run:ai, our vision was to build the de-facto foundational layer for running any AI workload. Our growth has been phenomenal, and this investment is a vote of confidence in our path. Run:ai is enabling organizations to orchestrate all stages of their AI work at scale, so companies can begin their AI journey and innovate faster."

A robot-building lab and contest was held at the Eleventh National Conference on Artificial Intelligence. Teams of three worked day and night for 72 hours to build tabletop autonomous robots of legos, a small microcontroller board, and sensors. The robots then competed head to head in two events. The contest was a chance to learn about building machines that operate in the real world. The lab was in a roped-off area of the main exhibition area.

I will review the state of solving incomplete-information games. They encompass many practical problems such as auctions, negotiations, and security applications. I will discuss them in the context of how they have transformed computer poker. In short, game-theoretic reasoning now scales to many large problems, outperforms the alternatives on those problems, and in some games beats the best humans. This is nontrivial because an agent's utility-maximizing strategy generally depends on the other agents' strategies.

Human readers comprehend vastly more, and in vastly different ways, than any existing comprehension test would suggest. An ideal comprehension test for a story should cover the full range of questions and answers that humans would expect other humans to reasonably learn or infer from a given story. ICCG uses structured crowdsourcing to comprehensively generate relevant questions and supported answers for arbitrary stories, whether fiction or nonfiction, presented across a variety of media such as videos, podcasts, and still images. While the AI scientific community had hoped that by 2015 machines would be able to read and comprehend language, current models are typically superficial, capable of understanding sentences in limited domains (such as extracting movie times and restaurant locations from text) but without the sort of widecoverage comprehension that we expect of any teenager. Comprehension itself extends beyond the written word; most adults and children can comprehend a variety of narratives, both fiction and nonfiction, presented in a wide variety of formats, such as movies, television and radio programs, written stories, YouTube videos, still images, and cartoons.

As good as devices like the Amazon Echo have become at relaying information verbally, there are still some tasks that are better accomplished using a touchscreen. That's perhaps why Amazon in June released the $229.99 Echo Show, which includes a 7-inch touchscreen placed above a speaker. But the convenience of having a tablet-sized screen also introduced trade-offs -- the Echo Show is bulkier, pricier, and generally less attractive compared to the $99.99 cylindrical Echo. Echo Spot, Amazon hopes to solve some of those issues.