This AI Impact Tour stop will bring together leaders in generative AI and enterprise security, and features case studies from industry giants like Honeywell and Ally Financial, showcasing how they're both leveraging generative AI applications, but also using AI to revolutionize security operations.
We propose a notion of causal influence that describes the ‘intrinsic’ part of the contribution of a node on a target node in a DAG. By recursively writing each node as a function of the upstream noise terms, we separate the intrinsic information added by each node from the one obtained from its…
Standard AI shifts focus from autonomous checkout to AI-powered vision analytics, aiming to help retailers gain actionable insights into shopper behavior, optimize store performance, and drive immediate ROI while protecting customer privacy.
Foundational raises $8M seed round to automate data quality and AI readiness through advanced code analysis, helping enterprises build trust in data and streamline machine learning initiatives.
Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers that, however, are not always grounded in the input image. We investigate this phenomenon, usually referred to as “hallucination” and show that it stems from an excessive reliance on the language…
From detecting anomalies in security logs to improving developer productivity through automation of code migrations, CIOs and IT leaders shared how they're harnessing gen AI's potential across a broad base of use cases to achieve productivity gains.
Multi-objective optimization is a class of optimization problems with multiple conflicting objectives. We study offline optimization of multi-objective policies from data collected by a previously deployed policy. We propose a pessimistic estimator for policy values that can be easily plugged into…
In his GTC keynote, Jensen Huang demonstrated multiple GR00T-powered humanoid robots, including those from Agility Robotics, Apptronik, Fourier Intelligence and Unitree Robotics.
Google researchers have developed 'VLOGGER', an AI system that generates realistic talking head videos from a single image, using advanced diffusion models, enabling new applications while raising concerns about deepfakes.
In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, explores the origins of NumPy and SciPy with their creator, Dr. Travis Oliphant. Dr. Oliphant shares his journey from personal need to global impact, the challenges overcome, and the future of these essential Python libraries in scientific computing and data science.
Apple researchers achieve state-of-the-art results in multimodal AI with MM1 models, combining text and images for breakthroughs in image captioning, visual question answering, and few-shot learning, as the company invests heavily in AI to enhance Siri, Messages, and future products.
How can we effectively generate missing data trans-formations among tables in a data repository? Multiple versions of the same tables are generated from the iterative process when data scientists and machine learning engineers fine-tune their ML pipelines, making incremental improvements. This…
Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.
In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many of our followers that this feature will enable them to catch up with important news and features flowing across our many channels.
Sketches have rich spatial information to help the robot carry out its tasks without getting confused by the clutter of realistic images or the ambiguity of natural language instructions.
Federated Learning (FL) is a popular algorithm to train ma-chine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns. Typically, FL is trained with the assumption that no part of the user data can be egressed from the edge. However, in many…