After seven rocky years, the company's assets will be sold to Dazzle, a new AI firm that Mayer founded. Sunshine cofounder and CEO Marissa Mayer speaks at TechCrunch Disrupt in San Francisco in 2023. Sunshine, the consumer AI startup founded by former Yahoo CEO Marissa Mayer in 2018, has seen brighter days. The small startup is shutting down, and its assets are being sold to a new entity incorporated by Mayer called Dazzle, according to an email viewed by WIRED. Mayer sent the email to Sunshine shareholders on September 17, informing them that Dazzle has officially incorporated and is ready to acquire Sunshine's holdings.

Last weekend the name was all over the internet – just not on ChatGPT. David Mayer became famous for a moment on social media because the popular chatbot appeared to want nothing to do with him. Legions of chatbot wranglers spent days trying – and failing – to make ChatGPT write the words "David Mayer". But the chatbot refused to comply, with replies alternating between "something seems to have gone wrong" to "I'm unable to produce a response" or just stopping at "David". This produced a blizzard of online speculation about Mayer's identity.

Marissa Mayer didn't say AI is Death, destroyer of worlds or even AI needs ethical guardrails. Instead, she said it's the sun--life-giving, bright, shiny, endlessly giving. Thus, the former Google engineer and CEO of Yahoo, who has worked on artificial intelligence for 25 years, christened her startup Sunshine. It's devoted to AI-empowering family and social life with photo sharing, contact managing, and event planning. As I spoke with Mayer in Sunshine's candy-colored digs in Palo Alto, I was so stunned by her boosterism that I ended up mirroring it.

We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to accomplish complex tasks by providing high-level intentions, while a pre-trained AI agent determines low-level details. This approach enlarges the target audience of BCIs to individuals with cognitive impairments, a population often excluded from the benefits of conventional BCIs. We present the general concept of BAIs and illustrate the potential of this new approach with a Conversational BAI based on EEG. In particular, we show in an experiment with simulated phone conversations that the Conversational BAI enables complex communication without the need to generate language. Our work thus demonstrates, for the first time, the ability of a speech neuroprosthesis to enable fluent communication in realistic scenarios with non-invasive technologies.

This study explores the integration of generative artificial intelligence (AI), specifically large language models, with multi-modal analogical reasoning as an innovative approach to enhance science, technology, engineering, and mathematics (STEM) education. We have developed a novel system that utilizes the capacities of generative AI to transform intricate principles in mathematics, physics, and programming into comprehensible metaphors. To further augment the educational experience, these metaphors are subsequently converted into visual form. Our study aims to enhance the learners' understanding of STEM concepts and their learning engagement by using the visual metaphors. We examine the efficacy of our system via a randomized A/B/C test, assessing learning gains and motivation shifts among the learners. Our study demonstrates the potential of applying large language models to educational practice on STEM subjects. The results will shed light on the design of educational system in terms of harnessing AI's potential to empower educational stakeholders.

Abstract: Hypergraph partitioning is an NP-hard problem that occurs in many computer science applications where it is necessary to reduce large problems into a number of smaller, computationally tractable sub-problems. Current techniques use a multilevel approach wherein an initial partitioning is performed after compressing the hypergraph to a predetermined level. This level is typically chosen to produce very coarse hypergraphs in which heuristic algorithms are fast and effective. This article presents a novel memetic algorithm which remains effective on larger initial hypergraphs. This enables the exploitation of information that can be lost during coarsening and results in improved final solution quality.

We present several new algorithms for bidirectional best-first search that employ a front-to-front strategy of estimating distances from newly-generated frontier nodes in one search direction to existing frontier nodes in the other search direction, rather than estimating distances to terminal nodes in both searches. Unlike previous front-to-front strategies that use a shared priority queue to manage both frontiers, we use a separate data structure for each search, and choose that data structure to minimize the amount of computational effort required by the best-first search algorithm it supports.

Python programmers will improve their computer science skills with these useful one-liners. Python One-Liners will teach you how to read and write "one-liners": concise statements of useful functionality packed into a single line of code. You'll learn how to systematically unpack and understand any line of Python code, and write eloquent, powerfully compressed Python like an expert. The book's five chapters cover tips and tricks, regular expressions, machine learning, core data science topics, and useful algorithms. Detailed explanations of one-liners introduce key computer science concepts and boost your coding and analytical skills.

When Marissa Mayer decided to start her own company, after nearly five years as Yahoo's CEO and 13 years at Google, she turned to her rolodex of contacts. For a startup in its early stages, success often has less to do with what you're building than who is building it. And Mayer, one of Silicon Valley's marquee names, had a lot of numbers she could call. There are over 14,000 people stored in her iPhone. So it's not surprising that Mayer assembled a fine team at Lumi Labs.

Although machine learning and artificial intelligence (AI) are terms that are often used interchangeably, they are quite different. That difference becomes more important as applications for these technologies become more prevalent. Tech Briefs posed questions to machine learning/AI industry executives to get their views on issues such as machine learning platform selection, interpreting data created by these platforms, and pros and cons of implementing machine learning. Our participants are Dr. Florian Baumann, Chief Technology Officer - Automotive & AI, at Dell Technologies; Mario Bergeron, Technical Marketing Engineer at Averna Technologies; Zach Mayer, Vice President of Data Science at Data Robot; George Rendell, Senior Director of NX Design at Siemens Digital Industries Software; and Rajesh Ramachandran, Chief Digital Officer - Industrial Automation, at ABB Inc. Tech Briefs: Machine learning is a term that has confused many people, partly because its definition has taken on multiple forms. How do you define machine learning and how do you see it being used in manufacturing, medical, transportation, or other industrial applications?