The integration of robotics into automatic assembly procedures is highly valuable but has met with issues adapting to high-mix, low-volume manufacturing. Robotic learning which enables robots to acquire assembly skills through demonstrations, not scripted processes, offers a potential resolution to this problem. However, teaching robots to perform assembly tasks from raw sensor data presents a…
Researchers at Massachusetts Institute of Technology (MIT) are seeking to leverage deep learning technology to provide a more detailed and accurate understanding of Earth's planetary boundary layer (PBL). The definition and structure of the PBL are pivotal to improving weather forecasting, climate projections, and issues such as drought conditions.
The PBL is the lowest part of…
Language models (LMs) such as BERT or GPT-2 are faced with challenges in self-supervised learning due to a phenomenon referred to as representation degeneration. These models work by training neural networks using token sequences to generate contextual representations, with a language modeling head, often a linear layer with variable parameters, producing next-token distributions of probability.…
Vector databases, which handle multidimensional data points, have gained significant attention due to their utility in machine learning, image processing, and similarity search applications. This article delves into a comparison of 14 vector databases, assessing their advantages, disadvantages, and unique features.
Faiss, a creation of Facebook AI, excels with efficient, high-performance similarity searching and dense vector…
Due to the need for long-sequence support in large language models (LLMs), a solution to the problematic key-value (KV) cache bottleneck needs addressing. LLMs like GPT-4, Gemini, and LWM are becoming increasingly prominent in apps such as chatbots and financial analysis, but the substantial memory footprint of the KV cache and their auto-regressive nature make…
MLCommons, a joint venture of industry and academia, has built a collaborative platform to improve AI safety, efficiency, and accountability. The MLCommons AI Safety Working Group established in late 2023 focuses on creating benchmarks for evaluating AI safety, tracking its progress, and encouraging safety enhancements. Its members, with diverse expertise in technical AI, policy, and…
Artificial Intelligence (AI) has conventionally been spearheaded by statistical learning methods that are excellent at uncovering patterns from sizeable datasets. However, these tend to uncover correlations rather than causations, a differentiator that is of immense importance given correlation does not infer causation. Causal AI is an emerging, transformative approach that strives to comprehend the 'why'…
Machine learning is the driving force behind data-driven, adaptive, and increasingly intelligent products and platforms. Algorithms of artificial intelligence (AI) systems, such as Content Recommender Systems (CRS), intertwine with users and content creators, in turn shaping viewer preferences and the available content on these platforms.
However, the current design and evaluation methodologies of these AI systems…
Numerous tools have been developed to facilitate the local operation of the powerful open-source language model, Llama 3 on your PC or Mac. Highlighted below are three compelling options that cater to different user needs and technical skills.
The first method involves using Ollama. It's supported on MacOS, Ubuntu, and Windows (Preview version). To use this…
Exploring the interactions between reinforcement learning (RL) and large language models (LLMs) sheds light on an exciting area of computational linguistics. These models, largely enhanced by human feedback, show remarkable prowess in understanding and generating text that mirrors human conversation. Yet, they are always evolving to capture more subtle human preferences. The main challenge lies…
Researchers at UT Austin have developed an effective and efficient method for training smaller language models (LM). Called "Inheritune," the method borrows transformer blocks from larger language models and trains the smaller model on a minuscule fraction of the original training data, resulting in a language model with 1.5 billion parameters using just 1 billion…