FACTS Grounding Leaderboard: Benchmarking LLMs’ Factuality

This notebook describes FACTS Grounding, a new system that tests how well large language models (LLMs) can give accurate answers based on long documents. FACTS Grounding uses a collection of documents and questions created by humans to challenge LLMs. The system then uses other LLMs as judges to decide if the answers are accurate and if they follow the instructions in the question. The goal is to see how well LLMs can understand and use information from long texts, without making things up or ignoring what the question asked. The researchers found that using multiple LLM judges is important because LLMs tend to be biased towards their own answers. FACTS Grounding will be continuously updated with new models, helping researchers improve the accuracy and reliability of LLMs. https://storage.googleapis.com/deepmind-media/FACTS/FACTS_grounding_paper.pdf

Read More

Bipartisan Artificial Intelligence Task Force Report on Artificial Intelligence – December 2024

This report summarizes the findings of the Bipartisan House Task Force on Artificial Intelligence (AI). The report focuses on how the U.S. can lead the way in AI development while also putting in place safety measures to prevent harm. The report discusses how AI can be used in areas like education, national security, and healthcare, and also covers important topics like data privacy and the impact of AI on small businesses. It stresses the need for more research and development in AI, especially in making sure AI systems are fair and trustworthy. The report also emphasizes the importance of training people to understand and use AI, starting from elementary and middle school all the way through adulthood. The goal of the task force is to help Congress create good policies that encourage the positive potential of AI while protecting people from potential risks. https://www.speaker.gov/wp-content/uploads/2024/12/AI-Task-Force-Report-FINAL.pdf

Read More

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

This research paper describes a new approach to sequence modeling called Mamba, which is designed to be faster and more efficient than the commonly used Transformer models. Mamba is based on a different mathematical framework called selective state space models (SSMs), which allow the model to choose which parts of a sequence to focus on, similar to how people can ignore distractions and concentrate on important information. Mamba was tested on different tasks like predicting the next word in a sentence, analyzing DNA sequences, and generating realistic audio, and it outperformed existing models, especially on longer sequences. The key advantage of Mamba is that it can process sequences in linear time, meaning the time it takes to process a sequence increases proportionally to the length of the sequence, unlike Transformers which take much longer for longer sequences. This efficiency makes Mamba a promising alternative to Transformers for various applications involving large amounts of data. https://arxiv.org/pdf/2312.00752 https://x.com/scaling01/status/1869007562034544939

Read More

Relational Neurosymbolic Markov Models

This research paper describes a new type of AI model called a Relational Neurosymbolic Markov Model (NeSy-MM). NeSy-MMs are special because they combine the strengths of two different types of AI: neural networks, which are good at learning from data, and symbolic reasoning, which uses logic and rules. Imagine playing a video game like Mario where you have to follow certain rules to win. NeSy-MMs can learn the rules of the game and use them to make decisions, just like a human player. They can also be used to generate new game levels that follow the same rules. The researchers showed that NeSy-MMs are better at understanding and following rules than other AI models. This makes them more reliable and trustworthy for tasks that require logical reasoning. https://arxiv.org/pdf/2412.13023

Read More

Stable Reasoning in LLMs: A Novel Evaluation Metric and Benchmark

This research paper describes a new way to test how good large language models (LLMs) are at solving math problems. The researchers created a special test called LiveMathBench which uses difficult math problems from contests like the Chinese National Mathematical Olympiad and the American Mathematics Competition. They also created a new scoring system called G-Pass@k that measures not only if the LLM gets the right answer, but also how often it gets the right answer when it tries multiple times. They found that even the best LLMs had trouble consistently getting the right answers on these tough math problems. This means that simply making LLMs bigger doesn’t always make them better at math, and we need to find new ways to teach LLMs how to solve problems reliably. https://arxiv.org/pdf/2412.13147

Read More
man in black crew neck t-shirt wearing black sunglasses holding black smartphone

KPMG 20th annual Global Semiconductor Outlook

The semiconductor industry, which makes tiny computer chips for everything from phones to cars, is expected to grow in 2024! After a bit of a slump in 2023, companies are hopeful as sales of chips for artificial intelligence (AI) and cars are going up. The biggest concern, though, is finding enough skilled workers. There are simply not enough people with the right training to fill all the jobs, so companies are partnering with universities and trying to make their workplaces more attractive to keep their employees happy. Companies are also focused on making their supply chains more diverse and resilient, meaning they want to source materials and parts from different places around the world in case problems arise in one location. While companies are excited about the potential of AI, they are also cautious about the economy and government regulations, so they are being careful about how much money they spend on new equipment and research. https://kpmg.com/kpmg-us/content/dam/kpmg/pdf/2024/global-semiconductor-industry-outlook.pdf

Read More

Apollo: An Exploration of Video Understanding in Large Multimodal Models

This document is all about a new computer program called Apollo that can understand videos really well! It was created by researchers who wanted to see how well computers can understand videos. They found that a lot of the ways computers currently understand videos aren’t very good because they rely on understanding the words that go with the video more than actually looking at the video. To make their program better, they had to look at lots of different ways that videos can be broken up and understood by computers. They also found that they didn’t have to train Apollo on the absolute biggest computers to get good results, which will help other people do similar research without needing huge computers. In the end, the researchers found that Apollo is really good at understanding videos, even better than some other programs that use much bigger computers. They think that Apollo will help other researchers create even better video understanding programs in the future. https://arxiv.org/pdf/2412.10360

Read More

Artificial Narrow Intelligence • ANI

What is artificial narrow intelligence? Artificial Narrow Intelligence (ANI) is a type of artificial intelligence that focuses on a single task. Unlike artificial general intelligence (AGI), which has the ability to learn and perform any task that a human can, ANI is limited to a specific range of tasks. However, within that range, ANI can often outperform humans. For example, there are now many ANI systems that can beat humans at chess or Go. ANI systems are typically designed using a combination of rule-based systems, machine learning, and deep learning. As ANI systems become more advanced, they are increasingly being used in a wide range of applications, from self-driving cars to medical diagnosis. In the future, it is likely that ANI will play an increasingly important role in our lives. AI is a process of programming a computer to make decisions for itself. This can be done in a number of ways, but the most common is through the use of algorithms. These are sets of rules that can be followed by a machine in order to complete a task. For example, an algorithm might be used to sort a list of numbers from smallest to largest. AI can also be used to create models of how humans think and behave. These models are then used to make predictions about how people will react in certain situations. Narrow AI is a form of AI that is designed to perform a single task. This is in contrast to general AI, which is designed to handle multiple tasks. Narrow AI is sometimes also referred to as weak AI or applied AI. It is the most common form of AI in use today and includes applications such as voice recognition, facial recognition, and language translation.

Read More

Guide to Essential Competencies for AI

This guide explains what artificial intelligence (AI) is and why it’s important to learn about it. AI is when computers think like humans and can do things that used to need human intelligence. The guide teaches you about different parts of AI, like how to use it safely and responsibly, how to understand the data it uses, and how to analyze data. It also describes different jobs that will use AI, from regular people using AI tools to experts who build AI systems. The guide believes that everyone needs to understand AI, because it will affect our lives in many ways. It encourages readers to share their thoughts and ideas to help improve the guide as AI technology changes. https://thealliance.ai/docs/guide-to-essential-competencies-for-ai.pdf

Read More

Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance

This research paper explored whether using ChatGPT to help students write essays is better than getting help from a teacher, using a checklist, or getting no help at all. Researchers asked 117 college students to write an essay and then revise it using one of these four methods. They found that students who used ChatGPT got the best scores on their essays, but they didn’t learn the information as well as the other students. The researchers think this might be because the students relied too much on ChatGPT to do the work for them instead of thinking about the task on their own. They also found that none of the types of help made a difference in students’ motivation to do the task. Overall, the study suggests that ChatGPT can be helpful for writing, but teachers need to make sure students are still learning and thinking for themselves when they use it. https://arxiv.org/pdf/2412.09315

Read More
Back To Top