Advancements in ML and AI require enterprises to continuously adapt, focusing on robust MLOps for effective governance and agility. Capital One emphasizes the importance of standardized tools, inter-team communication, business-aligned tool development, collaborative expertise, and a customer-centric product mindset to maintain a competitive edge in the fast-paced AI/ML landscape.

ALERTA-Net is a deep neural network that forecasts stock prices and market volatility by integrating social media, economic indicators, and search data, surpassing conventional analytical approaches.

MIT researchers have developed an Automatic Surface Reconstruction framework using machine learning to design new compounds or alloys for catalysts without reliance on chemist intuition. The method provides dynamic, thorough characterization of material surfaces, revealing previously unidentified atomic configurations. It operates more cost-effectively, efficiently, and is available for global use.

Elon Musk is seeking a $1 billion investment for xAI, aiming to explore universal secrets with AI. After raising $135 million from undisclosed investors, he touts xAI’s potential and strong team with ties to top AI organizations. xAI’s tool, Grok, offers edgy, humorous AI interactions, setting it apart from peers.

Researchers from Microsoft and Georgia Tech have found statistical lower bounds for hallucinations in Language Models (LMs). These hallucinations can cause misinformation and are concerning in fields like law and medicine. The study suggests that pretraining LMs for text prediction can lead to hallucinations but can be mitigated through post-training procedures. Their work also offers…

Deep Active Learning (DAL) streamlines AI model training by efficiently selecting the most instructive data for labeling. This technique can halve the amount of data required, saving time and costs, while enhancing model performance. DAL’s future looks promising, with potential applications across various fields.

Large Language Models (LLMs) like OpenAI’s GPT have become more prevalent, enhanced by Generative AI for human-like textual responses. Techniques such as Retrieval Augmented Generation (RAG) and fine-tuning improve responses’ precision and contextuality. RAG uses external data for accurate, up-to-date answers, while fine-tuning adapts pre-trained models for specific tasks. RAG excels at dynamic data environments…

Google introduces Gemini, a versatile AI model family capable of processing text, images, audio, and video. Gemini will integrate into Google products like search, Maps, and Chrome. Its performance surpasses GPT-4 in benchmarks, with versions for Android, AI services, and data centers. Google highlights Gemini’s efficiency, speed, and ethical commitment, offering developer access through AI…

AI advancements aim to improve accessibility and usefulness across various communities, ensuring it addresses diverse needs and offers solutions that enhance daily life for all individuals.

ETH Zurich researchers developed an approach using Fast Feedforward Networks (FFF) to increase the speed of Large Language Models (LLM). By engaging only a small fraction of neurons for individual inferences, their UltraFastBERT model could potentially run 341x faster, although a software workaround currently yields a 78x improvement.