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Recently, AI researchers from Stanford University and the University of Washington successfully trained an AI reasoning model named S1, with a training cost of less than $50 and very low cloud computing credits required. This research was published last Friday, indicating that S1 performs comparably to OpenAI's O1 model and DeepSeek's R1 model in mathematical and programming ability tests. The code and data for S1 have been made publicly available on GitHub for other researchers to use. The research team reported that they drew from a

Recently, a new study from the University of Michigan found that an energy-efficient method for training large language models can achieve similar results in the same amount of time while reducing energy consumption by 30%. This method can save enough energy to power 1.1 million American households by 2026. The researchers developed a software tool named Perseus that identifies the critical path, which is a series of subtasks requiring the longest time to complete. Then, Perseus reduces the processor speed on non-critical paths so that all tasks can finish simultaneously.

Meta recently announced that its MobileLLM model is now open to researchers, allowing users to download and use these models for free on the Hugging Face platform, fully integrated with the Transformers library. This initiative aims to promote research and development of large language models (LLMs) on mobile devices, providing a broader range of tools and resources for developers and academia. MobileLLM represents a significant advancement for Meta in the field of mobile AI.

Is AI actually starting to ‘get smarter’?! The latest research from MIT indicates that the internal structure of large language models (LLMs) astonishingly resembles that of the human brain! This study utilized sparse autoencoder techniques to conduct an in-depth analysis of the activation space of LLMs, revealing three hierarchical structural features that are truly remarkable: first, at the microscopic level, researchers discovered the existence of structures akin to ‘crystals’. The surfaces of these ‘crystals’ are composed of parallelograms or trapezoids, similar to familiar lexical analogies such as ‘man:’

Recently, there has been a new trend in the scientific community — AI scientists. Simply put, this type of artificial intelligence can not only propose hypotheses and conduct experiments but also independently write research papers. While I have always believed that the best role of AI is as a tool for humans, rather than a replacement, this AI scientist seems to show considerable potential. Image source note: The image is generated by AI and is licensed from service provider Midjourney. We often hear about cases of scientists utilizing AI, such as the research conducted by MIT in 2019.

The issue of antibiotic resistance has led to a significant number of deaths globally, posing a major public health risk. Recent research utilizing artificial intelligence technology has successfully designed a new antibiotic, Protegrin-1.2, by screening thousands of variants. It effectively kills bacteria while significantly reducing damage to human cells. The study was led by Dr. Bryan Davis from the University of Texas, who used a system called SLAY to screen a vast number of antibiotic peptides, ultimately discovering this new selective antibiotic. Experiments showed that Protegrin-1.

The development of artificial intelligence, particularly large language models (LLMs), has raised concerns about cybersecurity laws. Scholars at Harvard highlighted at the Black Hat conference that the U.S. Computer Fraud and Abuse Act (CFAA) fails to effectively protect AI security researchers, exposing them to legal risks. They noted that existing laws are vague regarding actions like 'prompt injection attacks,' making it difficult for research....

The MIT Computer Science and Artificial Intelligence Laboratory has demonstrated an innovative method for training home robots by utilizing iPhone scans of users' home environments, uploading the data to a simulated environment for training. This method aims to address the limitations of traditional robot training in adapting to complex home layouts, lighting, and object placement. Through simulated training, robots can practice millions of times in a virtual world without any real-world loss, significantly enhancing their adaptability. The construction of the environmental database not only strengthens the robots' performance in known environments but also prepares them to face...

Microsoft researchers have recently unveiled an innovative research called SpreadsheetLLM, aimed at addressing the challenges faced by large language models (LLMs) when parsing electronic spreadsheets. According to a paper published on Arxiv on July 12th, SpreadsheetLLM enables LLMs to "read" spreadsheet content through an encoding framework. This research is expected to significantly enhance the efficiency of data management and analysis in spreadsheets, allowing users to ask questions of AI in

In the field of artificial intelligence, large language models (LLMs) such as GPT-3 and Llama-2 have made significant strides, capable of accurately understanding and generating human language. However, the vast number of parameters in these models poses challenges in terms of the significant computational resources required for both training and deployment, particularly in resource-constrained environments.Paper Access: https://arxiv.org/html/2406.10260v1Traditionally, to achieve a balance betw

A new study has developed a new AI tool that makes it easier to detect lies, but only some people agree to use it. Traditional lie detectors appear to measure a person's breathing rate, pulse, blood pressure, and sweating to determine if they are lying, but this 85-year-old technology has long been proven incorrect by scientists. Essentially, the possibility of false positives and the subjectivity of interpreting the results greatly weaken the practicality of lie detectors. Notably, their result

While large language models like ChatGPT, Claude, and Gemini are impressive, they all share a significant issue: they often generate hallucinations. This is a serious problem in the field of artificial intelligence, and even Apple has expressed concerns about how future Apple Intelligence will handle hallucinations. Fortunately, a team of researchers has now developed an AI hallucination detector that can determine whether the AI is fabricating content.