Whether your chip is running a vintage computer game or the latest DeepSeek model, it'll reward you for speaking its native language. But if you took a look beneath the pixels--the rickety rides, the crowds of hungry, thirsty, barfing people (and the janitors mopping in their wake)--deep down at the level of the code, you saw craftsmanship so obsessive that it bordered on insane. Chris Sawyer, the game's sole developer, wrote the whole thing in assembly. Because if/when the machines take over, we should at least speak their language. Certain programming languages, like Python or Go or C++, are called "high-level" because they work sort of like human language, written in commands and idioms that might fit in at a poetry slam.

D2C requires only a few examples of desired outcomes and works in any environment, regardless of its geometry or the distribution of the desired outcome examples.

AI models are trained on vast swathes of data from every corner of the internet, by humans. AI models are trained on vast swathes of data from every corner of the internet, by humans. How thousands of'overworked, underpaid' humans train Google's AI to seem smart In the spring of 2024, when Rachael Sawyer, a technical writer from Texas, received a LinkedIn message from a recruiter hiring for a vague title of writing analyst, she assumed it would be similar to her previous gigs of content creation. On her first day a week later, however, her expectations went bust. Instead of writing words herself, Sawyer's job was to rate and moderate the content created by artificial intelligence. The job initially involved a mix of parsing through meeting notes and chats summarized by Google's Gemini, and, in some cases, reviewing short films made by the AI.

First-order logic (FOL) reasoning, which involves sequential deduction, is pivotal for intelligent systems and serves as a valuable task for evaluating reasoning capabilities, particularly in chain-of-thought (CoT) contexts. Existing benchmarks often rely on extensive human annotation or handcrafted templates, making it difficult to achieve the necessary complexity, scalability, and diversity for robust evaluation. To address these limitations, we propose a novel framework called ProverGen that synergizes the generative strengths of Large Language Models (LLMs) with the rigor and precision of symbolic provers, enabling the creation of a scalable, diverse, and high-quality FOL reasoning dataset, ProverQA. ProverQA is also distinguished by its inclusion of accessible and logically coherent intermediate reasoning steps for each problem. Our evaluation shows that state-of-the-art LLMs struggle to solve ProverQA problems, even with CoT prompting, highlighting the dataset's challenging nature. We also finetune Llama3.1-8B-Instruct on a separate training set generated by our framework. The finetuned model demonstrates consistent improvements on both in-distribution and out-of-distribution test sets, suggesting the value of our proposed data generation framework. Code available at: https://github.com/opendatalab/ProverGen

To address the global challenge of population aging, our goal is to enhance successful aging through the introduction of robots capable of assisting in daily physical activities and promoting light exercises, which would enhance the cognitive and physical well-being of older adults. Previous studies have shown that facial expressions can increase engagement when interacting with robots. This study aims to investigate how older adults perceive and interact with a robot capable of displaying facial emotions while performing a physical exercise task together. We employed a collaborative robotic arm with a flat panel screen to encourage physical exercise across three different facial emotion conditions. We ran the experiment with older adults aged between 66 and 88. Our findings suggest that individuals perceive robots exhibiting facial expressions as less competent than those without such expressions. Additionally, the presence of facial expressions does not appear to significantly impact participants' levels of engagement, unlike other state-of-the-art studies. This observation is likely linked to our study's emphasis on collaborative physical human-robot interaction (pHRI) applications, as opposed to socially oriented pHRI applications. Additionally, we foresee a requirement for more suitable non-verbal social behavior to effectively enhance participants' engagement levels.

After the bus driver ordered him to observe a rule requiring passengers to wear face masks, he approached the fare box and began arguing with her. "I hit bitches," he said, leaning over a plastic shield that the driver was sitting behind. When she pulled out her iPhone to call the police, he reached around the shield, snatched the device, and raced off. The bus driver followed the man outside, where he punched her in the face repeatedly. He then stood by the curb, laughing, as his victim wiped blood from her nose. By the time police officers canvassed the area, the assailant had fled, but the incident had been captured on surveillance cameras.

Maryland is a unique place to debate face recognition regulation, says Andrew Northrup, an attorney in the forensics division of the Maryland Office of the Public Defender. He calls Baltimore "a petri dish for surveillance technology," because the city spends more money per capita on police among 72 major cities in the US, according to a 2021 analysis by the nonprofit Vera Institute of Justice, and has a long history of surveillance technology in policing. The use of invasive surveillance technology including face recognition in Baltimore during protests following the 2015 death of Freddie Gray led former House Oversight and Reform Committee chair Elijah Cummings to interrogate the issue in Congress. And in 2021, the Baltimore City Council voted to place a one-year moratorium on face recognition use by public and private actors, but not police, that expired in December. Northrup spoke in favor of the bill and its requirement for proficiency testing at the same House of Delegates Judiciary Committee hearing addressed by Carronne Sawyer this month.

Carronne Sawyer took the week off work to get her husband Alonzo out of jail. She knew he was asleep on the couch with her at the time police alleged he assaulted a bus driver near Baltimore and stole their smartphone. But an intelligence analyst using face recognition software had labeled him a possible match with the suspect seen on CCTV footage from the bus, police records show, and an officer had confirmed it. At a police station and in a meeting with her husband's former parole officer, the person who had confirmed the software's suggested match, Carronne drew attention to details in photos on her phone taken recently by her daughter. Her husband is taller than the suspect in the video, she explained, and has facial hair and gaps between his teeth.

Since businesses often don't have the time or resources to support the long and tedious work required to complete data science projects, most of them never come to fruition. The fairly recent development of automated machine learning, or AutoML, rectifies this by speeding up the work data scientists perform through automation. Dennis Michael Sawyers, data scientist and author of Automated Machine Learning with Microsoft Azure, uses Azure's AutoML product as the foremost example of how automated ML software expedites and simplifies this otherwise arduous work. In this Q&A, Sawyers discusses the evolution of automated machine learning platforms and how they are used to develop ML models. Editor's note: The following interview was edited for length and clarity.