Senate Majority Leader Charles E. Schumer (D-N.Y.) will host the AI Insight Forum -- which is intended to serve as the bedrock for his "all hands on deck" plan to respond to recent AI advances -- in the grand Kennedy Caucus Room, the historic stage of Senate probes into the sinking of the Titanic, as well as Watergate. The more than 20 attendees include Tesla CEO and X owner Elon Musk, Meta CEO Mark Zuckerberg, Google CEO Sundar Pichai and ChatGPT-maker OpenAI CEO Sam Altman, among other top tech executives, civil rights leaders, labor chiefs and researchers.

Tech product launches in 2023 have become predictable: Everything now comes with generative AI features that will serve up chatty but knowledgeable text or mind-blowing images. The rollout of the iPhone 15 this week shows Apple opting to Think Different. The new device comes with the A17 Pro processor, an Apple-designed chip to put more power behind machine-learning algorithms. But the features highlighted at the launch event yesterday were generally subtle, not mind expanding. The company appears focused on AI that is intuitive not generative, making artificial intelligence a part of your life that smoothes over glitches or offers helpful predictions without being intrusive.

When it comes to artificial intelligence, United States senators are looking to the titans of Silicon Valley to fix a Senate problem--a problem today's political class perpetuates daily with their increasingly hyper-partisan ways, which generative AI now feeds off of as it helps rewrite our collective future. Today, the Senate is hosting a first-of-its-kind, closed-door AI forum led by the likes of Elon Musk, Mark Zuckerberg, Bill Gates, and more than 17 others, including ethicists and academics. Even though they'll be on the senators' turf, for roughly six hours, they'll get microphones while the nation's elected leaders get muzzled. "All Senators are encouraged to attend to listen to this important discussion, but please note the format will not afford Senators the opportunity to provide remarks or to ask questions to the speakers," a notice from majority leader Chuck Schumer reads. As generative AI is poised to flood the internet with more--and more convincing--disinformation and misinformation, many AI experts say the top goal of the Senate should be restoring faith in, well, the Senate itself.

The term "generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data. The widespread diffusion of this technology with examples such as Dall-E 2, GPT-4, and Copilot is currently revolutionizing the way we work and communicate with each other. In this article, we provide a conceptualization of generative AI as an entity in socio-technical systems and provide examples of models, systems, and applications. Based on that, we introduce limitations of current generative AI and provide an agenda for Business & Information Systems Engineering (BISE) research. Different from previous works, we focus on generative AI in the context of information systems, and, to this end, we discuss several opportunities and challenges that are unique to the BISE community and make suggestions for impactful directions for BISE research.

Jeff Zients, the White House chief of staff, on Tuesday afternoon plans to meet with representatives from several of the companies, including Photoshop maker Adobe, AI platform Cohere and ScaleAI, which provides data services to companies including OpenAI. The conversation is expected to primarily focus on AI benefits and risks, and cover much of the same ground as the AI summit that the White House hosted with the chief executives of other large tech companies in July, according to a senior administration official, who spoke on the condition of anonymity.

Nvidia's supremacy in building computer chips for artificial intelligence has chilled venture funding for would-be rivals, investors have said, with the number of U.S. deals this quarter falling 80% from a year ago. The Santa Clara, California company dominates the market for chips that work with massive amounts of language data. Generative AI models get incrementally smarter through exposure to more data, a process called training. As Nvidia has grown stronger in this area, the harder it has become for companies attempting to build competing chips. Seeing these startups as a riskier bet, venture financiers are newly unwilling to provide big cash infusions.

With recent advances in Generative AI, it is becoming easier to automatically manipulate 3D models. However, current methods tend to apply edits to models globally, which risks compromising the intended functionality of the 3D model when fabricated in the physical world. For example, modifying functional segments in 3D models, such as the base of a vase, could break the original functionality of the model, thus causing the vase to fall over. We introduce a method for automatically segmenting 3D models into functional and aesthetic elements. This method allows users to selectively modify aesthetic segments of 3D models, without affecting the functional segments. To develop this method we first create a taxonomy of functionality in 3D models by qualitatively analyzing 1000 models sourced from a popular 3D printing repository, Thingiverse. With this taxonomy, we develop a semi-automatic classification method to decompose 3D models into functional and aesthetic elements. We propose a system called Style2Fab that allows users to selectively stylize 3D models without compromising their functionality. We evaluate the effectiveness of our classification method compared to human-annotated data, and demonstrate the utility of Style2Fab with a user study to show that functionality-aware segmentation helps preserve model functionality.

The United States Environmental Protection Agency blocked its employees from accessing ChatGPT while the US State Department staff in Guinea used it to draft speeches and social media posts. Maine banned its executive branch employees from using generative artificial intelligence for the rest of the year out of concern for the state's cybersecurity. In nearby Vermont, government workers are using it to learn new programming languages and write internal-facing code, according to Josiah Raiche, the state's director of artificial intelligence. The city of San Jose, California, wrote 23 pages of guidelines on generative AI and requires municipal employees to fill out a form every time they use a tool like ChatGPT, Bard, or Midjourney. Less than an hour's drive north, Alameda County's government has held sessions to educate employees about generative AI's risks--such as its propensity for spitting out convincing but inaccurate information--but doesn't see the need yet for a formal policy.

Earlier this month, Sundar Pichai was struggling to write a letter to Alphabet's 180,000 employees. The 51-year-old CEO wanted to laud Google on its 25th birthday, which could have been easy enough. Alphabet's stock market value was around $1.7 trillion. Its vast cloud-computing operation had turned its first profit. Its self-driving cars were ferrying people around San Francisco. And then there was the usual stuff--Google Search still dominated the field, as it had for every minute of this century.

We evaluate the use of the open-source Llama-2 model for generating well-known, high-performance computing kernels (e.g., AXPY, GEMV, GEMM) on different parallel programming models and languages (e.g., C++: OpenMP, OpenMP Offload, OpenACC, CUDA, HIP; Fortran: OpenMP, OpenMP Offload, OpenACC; Python: numpy, Numba, pyCUDA, cuPy; and Julia: Threads, CUDA.jl, AMDGPU.jl). We built upon our previous work that is based on the OpenAI Codex, which is a descendant of GPT-3, to generate similar kernels with simple prompts via GitHub Copilot. Our goal is to compare the accuracy of Llama-2 and our original GPT-3 baseline by using a similar metric. Llama-2 has a simplified model that shows competitive or even superior accuracy. We also report on the differences between these foundational large language models as generative AI continues to redefine human-computer interactions. Overall, Copilot generates codes that are more reliable but less optimized, whereas codes generated by Llama-2 are less reliable but more optimized when correct.