In the growing field of warehouse automation, managing hundreds of robots zipping through a large warehouse is a logistical challenge. Delivery paths, potential collisions and congestion all pose significant issues, making the task a complex problem that even the best algorithms find hard to manage. To solve this, a team of MIT researchers has developed…
Internal links play a significant role in optimising a website's visibility and ranking on Search Engine Result Pages (SERPs). They also enhance website navigation and user experience. The evolution of internal links has been affected by numerous factors over the years. Initially, search engines paid attention to the number of internal links to determine a website's…
Chinese tech firm, Kuaishou Technology, has launched a text-to-video (T2V) generator named 'Kling,' which may compete with OpenAI's unreleased 'Sora.' Kuaishou, based in Beijing, cultivates content sharing platforms that speed up the process of content production, distribution, and consumption. Their short video platform, also named 'Kuaishou', is second only to TikTok in terms of daily…
The AI Summit London 2024 is set to be one of the most anticipated artificial intelligence (AI) events of the year. As the highlight of London Tech Week, it promises to gather leaders, innovators, and enthusiasts from the AI industry. Attendees can secure a pass and join a community dedicated to pushing the boundaries of…
Matrix multiplication (MatMul) is a fundamental process in most neural network topologies. It is commonly used in vector-matrix multiplication (VMM) by dense layers in neural networks, and in matrix-matrix multiplication (MMM) by self-attention mechanisms. Significant reliance on MatMul can be attributed to GPU optimization for these tasks. Libraries like cuBLAS and the Compute Unified Device…
Researchers have identified cultural accumulation as a crucial aspect of human success. This practice refers to our capacity to learn skills and accumulate knowledge over generations. However, currently used artificial learning systems, like deep reinforcement learning, frame the learning question as happening within a single "lifetime." This approach does not account for the generational and…
Language Learning Models (LLMs) can come up with good answers and even be honest about their mistakes. However, they often provide simplified estimations when they haven't seen certain questions before, and it's crucial to develop ways to draw reliable confidence estimations from them. Traditionally, both training-based and prompting-based approaches have been used, but these often…
Stanford University researchers have developed a new method called Demonstration ITerated Task Optimization (DITTO) designed to align language model outputs directly with users' demonstrated behaviors. This technique was introduced to address the challenges language models (LMs) face - including the need for big data sets for training, generic responses, and mismatches between universal style and…
Large language models (LLMs) have significantly advanced code generation, but they develop code in a linear fashion without access to a feedback loop that allows for corrections based on the previous outputs. This creates challenges in correcting mistakes or suggesting edits. Now, researchers at the University of California, Berkeley, have developed a new approach using…
In 2010, Media Lab students at MIT, Karthik Dinakar and Birago Jones, embarked on a class project to create a tool aimed at aiding content moderation teams at companies such as Twitter and YouTube. The innovative project generated considerable interest, leading to an opportunity to demonstrate the tool at a White House cyberbullying summit. However,…