Apple has released a public demo of the 4M AI model it developed in collaboration with the Swiss Federal Institute of Technology Lausanne (EPFL). The technology debuts seven months after the model was first open-sourced, allowing informed observers the opportunity to interact with it and assess its capabilities. Apple says 4M was built by applying masked modeling to a single unified Transformer encoder-decoder “across a wide range of input/output modalities — including text, images, geometric and semantic modalities, as well as neural network feature maps.” Continue reading Apple Launches Public Demo of Its Multimodal 4M AI Model

Meta Platforms has unveiled its first natively multimodal model, Chameleon, which observers say can make it competitive with frontier model firms. Although Chameleon is not yet released, Meta says internal research indicates it outperforms the company’s own Llama 2 in text-only tasks and “matches or exceeds the performance of much larger models” including Google’s Gemini Pro and OpenAI’s GPT-4V in a mixed-modal generation evaluation “where either the prompt or outputs contain mixed sequences of both images and text.” In addition, Meta calls Chameleon’s image generation “non-trivial,” noting that’s “all in a single model.” Continue reading Meta Advances Multimodal Model Architecture with Chameleon

Apple researchers have gone public with new multimodal methods for training large language models using both text and images. The results are said to enable AI systems that are more powerful and flexible, which could have significant ramifications for future Apple products. These new models, which Apple calls MM1, support up to 30 billion parameters. The researchers identify multimodal large language models (MLLMs) as “the next frontier in foundation models,” which exceed the performance of LLMs and “excel at tasks like image captioning, visual question answering and natural language inference.” Continue reading Apple Unveils Progress in Multimodal Large Language Models

Amazon has launched five new capabilities to its SageMaker service, including Sagemaker HyperPod, which accelerates large language and foundation model training and tuning. Sagemaker HyperPod is said to shorten the training time by up to 40 percent using its purpose-built infrastructure designed for distributed training at scale. By optimizing acceleration, SageMaker Inference reduces foundation model deployment costs by 50 percent and latency by 20 percent on average, Amazon claims. “SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning infrastructure,” said Amazon. Continue reading SageMaker HyperPod: Amazon Accelerates AI Model Training

In a move to put “generative AI at the fingertips of every business, from startups to enterprises,” Amazon Web Services is commercially rolling out the Bedrock service it announced in April. Bedrock offers a wide range of foundation models from Amazon’s own Titan to products from Anthropic, Stability AI and soon Meta Platforms. The fully managed Bedrock service makes its generative FMs operable through a single, simple API. This means customers can experiment with various leading FMs and customize simple apps in-house, without the need for a third-party diving into their proprietary data. Continue reading AWS Rolls Out Bedrock Generative AI Service, Adds Llama 2

The UK’s Competition and Markets Authority has issued a report featuring seven proposed principles that aim to “ensure consumer protection and healthy competition are at the heart of responsible development and use of foundation models,” or FMs. Ranging from “accountability” and “diversity” to “transparency,” the principles aim to “spur innovation and growth” while implementing social safety measures amidst rapid adoption of apps including OpenAI’s ChatGPT, Microsoft 365 Copilot, Stability AI’s Stable Diffusion. The transformative properties of FMs can “have a significant impact on people, businesses, and the UK economy,” according to the CMA. Continue reading UK’s Competition Office Issues Principles for Responsible AI

Walmart is putting generative AI in the hands of roughly 50,000 non-store U.S. employees who will have access to My Assistant, an LLM trained on information. From speeding the drafting process to serving as a creative partner and summarizing documents, “My Assistant has the potential to change how our associates work and solve problems,” Walmart said, emphasizing the launch goes beyond productivity gains. “We believe the key to unlocking transformation lies in the creativity and innovation of our associates. Ideally, this technology will free them from monotonous, repetitive tasks, allowing more time and focus for improving the customer/member experience.”  Continue reading Walmart Is ‘Empowering’ 50,000 U.S. Associates with GenAI

Bozeman, Montana-based DaaS firm Snowflake has partnered with Nvidia to let clients customize LLMs (large language models) using proprietary data in the Snowflake Data Cloud. Nvidia’s NeMo platform and GPU-accelerated computing will power the effort to tailor models to specific business use cases, such as chatbots with category expertise as opposed to generalists, search engines attuned to context or generative text deep knowledge. Since most companies are eager to harness brand-specific AI without having to build a model from scratch, this category of service is generating a lot of interest. Continue reading Nvidia’s NeMo Delivers AI Customization to Snowflake Cloud

IBM and Adobe are expanding their partnership to help enterprise clients accelerate their content supply chains using artificial intelligence including Adobe Sensei GenAI, released in March, and Adobe Firefly, now in beta, as well as Adobe’s generative AI models. IBM says it will create a portfolio of Adobe-specific consulting services. Leveraging Adobe’s AI solutions and IBM Consulting services, the companies aim to “help clients build an integrated content supply chain ecosystem that drives collaboration, optimizes creativity, increases speed, automates tasks and enhances stakeholders’ visibility across design and creative projects.” Continue reading IBM and Adobe Advance AI Content Workflow for Enterprise

Snorkel AI is offering new capabilities to help companies curate and prep data for generative artificial intelligence. Formed in 2015, Snorkel AI has been developing software for data-centric AI. Its best known product is Snorkel Flow, which helps enterprise clients build and deploy AI applications efficiently using programmatic labeling to automate the process of creating training data for AI models. Now Snorkel AI’s Foundation Model Data Platform is going beyond programmatic labeling with two new core solutions: Snorkel GenFlow for building generative AI applications and Snorkel Foundry for developing custom LLMs with proprietary data. Continue reading Snorkel AI Debuts Products for Model Training, Development

With artificial intelligence development dating back to the 1950s, IBM was clearly ahead of its time. The company has quietly built a commercial portfolio, with more than 100 million customers across 20 industries using its Watson suite, the company says. At its annual Think conference, the company unboxed IBM Watsonx, a next-generation platform that leverages the scale and scope of foundation models to provide custom solutions for data-driven clients. Described as an “enterprise studio for AI builders,” Watsonx is an end-to-end framework that combines the tools, infrastructure and consulting expertise corporations can use to onboard AI. Continue reading IBM Bows Watsonx Suite of Enterprise AI Products, Services