Blog

Your blog category

Lenovo’s latest ThinkPad and IdeaPad laptops include new Intel Core Ultra chips

has unveiled its latest productivity-focused ThinkPad and IdeaPad laptops ahead of CES. The systems are equipped with , which include three compute engines — a central processing unit, graphics processing unit and neural processing unit. Those are designed to work together for greater efficiency. Lenovo notes that some compute tasks can be offloaded to the […]

Lenovo’s latest ThinkPad and IdeaPad laptops include new Intel Core Ultra chips Read More »

Training Diffusion Models with Reinforcement Learning – The Berkeley Artificial Intelligence Research Blog

Training Diffusion Models with Reinforcement Learning replay Diffusion models have recently emerged as the de facto standard for generating complex, high-dimensional outputs. You may know them for their ability to produce stunning AI art and hyper-realistic synthetic images, but they have also found success in other applications such as drug design and continuous control. The key

Training Diffusion Models with Reinforcement Learning – The Berkeley Artificial Intelligence Research Blog Read More »

How AWS Prototyping enabled ICL-Group to build computer vision models on Amazon SageMaker

This is a customer post jointly authored by ICL and AWS employees. ICL is a multi-national manufacturing and mining corporation based in Israel that manufactures products based on unique minerals and fulfills humanity’s essential needs, primarily in three markets: agriculture, food, and engineered materials. Their mining sites use industrial equipment that has to be monitored

How AWS Prototyping enabled ICL-Group to build computer vision models on Amazon SageMaker Read More »

Rethinking the Role of PPO in RLHF – The Berkeley Artificial Intelligence Research Blog

Rethinking the Role of PPO in RLHF TL;DR: In RLHF, there’s tension between the reward learning phase, which uses human preference in the form of comparisons, and the RL fine-tuning phase, which optimizes a single, non-comparative reward. What if we performed RL in a comparative way? Figure 1: This diagram illustrates the difference between reinforcement

Rethinking the Role of PPO in RLHF – The Berkeley Artificial Intelligence Research Blog Read More »

Improve your Stable Diffusion prompts with Retrieval Augmented Generation

Text-to-image generation is a rapidly growing field of artificial intelligence with applications in a variety of areas, such as media and entertainment, gaming, ecommerce product visualization, advertising and marketing, architectural design and visualization, artistic creations, and medical imaging. Stable Diffusion is a text-to-image model that empowers you to create high-quality images within seconds. In November

Improve your Stable Diffusion prompts with Retrieval Augmented Generation Read More »

Threads’ new hashless tags have opened the door for the silliest form of trolling

Last week, Meta finally rolled out searchable tags for all users on Threads, its microblogging Instagram offshoot, and users are taking advantage of a design quirk for a bit of dumb fun. Threads’ “topic tags” are a lot like hashtags, but not entirely the same. For one, there’s no hash (#). They’re also able to

Threads’ new hashless tags have opened the door for the silliest form of trolling Read More »

Goal Representations for Instruction Following – The Berkeley Artificial Intelligence Research Blog

Goal Representations for Instruction Following A longstanding goal of the field of robot learning has been to create generalist agents that can perform tasks for humans. Natural language has the potential to be an easy-to-use interface for humans to specify arbitrary tasks, but it is difficult to train robots to follow language instructions. Approaches like

Goal Representations for Instruction Following – The Berkeley Artificial Intelligence Research Blog Read More »

Streamlining ETL data processing at Talent.com with Amazon SageMaker

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Established in 2011, Talent.com aggregates paid job listings from their clients and public job listings, and has created a unified, easily searchable platform. Covering over 30 million job listings across more than 75 countries and spanning various

Streamlining ETL data processing at Talent.com with Amazon SageMaker Read More »

Scroll to Top