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

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

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.

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

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

TapeAgents are like helpful robots that can do tasks for you, like searching the web or filling out forms. TapeAgents use a special list, called a “tape,” to keep track of everything they do and think. Imagine it like a notebook where they write down their plans, actions, and observations. TapeAgents can work alone or in teams, and they can even learn from their past experiences (the tapes) to get better at their jobs. For example, the sources discuss a TapeAgent that learned how to fill out forms correctly by studying examples from a “teacher” TapeAgent that used a really big and powerful brain (a large language model). This allows companies to build helpful AI assistants that are cheaper and faster to run. You can see examples of how TapeAgents work in Figures 3 and 5, which show the “tapes” they create while working on different tasks. https://arxiv.org/pdf/2412.08445

This research paper explores whether training large language models (LLMs) to be truthful could make them politically biased, specifically leaning towards liberal viewpoints. The researchers trained different models on datasets designed to teach the models about truthfulness in everyday facts and scientific information. They then tested these models using a dataset of paired statements on various political topics, with one statement leaning left and the other leaning right. They found that most models trained on truthfulness datasets showed a left-leaning bias, especially larger models. The researchers also tested pre-existing models trained on general human preferences and found a similar left-leaning bias, particularly with larger models. This suggests that focusing on truthfulness during training might unintentionally introduce a political slant. However, the researchers acknowledge the limitations of using datasets to represent truth and the complexities of defining political leanings, calling for further investigation into this relationship. https://arxiv.org/pdf/2409.05283v2