Lauren Bannon says ChatGPT helped diagnose her with cancer. 'LUCKY TO BE ALIVE': A mother of two credits ChatGPT for saving her life, claiming the artificial intelligence chatbot flagged the condition leading to her cancer when doctors missed it. AUTONOMY TEST RUN: Robotaxis are closer to becoming a reality, after Tesla launched a full self-driving (FSD) supervised ride-hailing service in Austin, Texas, and the San Francisco Bay Area "for an early set of employees." HARVESTING YOUR DATA?: A powerful House Committee is demanding information from DeepSeek on what U.S. data it used to train the AI model as members accuse the company of being in the pocket of the Chinese government. EDUCATION REFORMS: President Donald Trump signed multiple Executive Orders relating to education Wednesday afternoon, with several tied to the theme of returning meritocracy back to the education system.

You can want different things from a university--superlative basketball, an arts center, competent instruction in philosophy or physics, even a cure for cancer. No wonder these institutions struggle to keep everyone happy. The Trump Administration has effectively declared open war on higher education, targeting it with deep cuts to federal grant funding. University presidents are alarmed, as are faculty members, and anyone who cares about the university's broader role. Because I'm a historian of science and technology, part of my terrain is the evolving role of the university--from its medieval, clerical origins to the entrepreneurial R. & D. engines of today.

Copilot, Microsoft's AI assistant, appears to be struggling to match its competition in terms of popularity. The number of people using Copilot has remained around 20 million weekly users for the last year, according to tech newsletter Newcomer, while OpenAI's ChatGPT has hit as high as 400 million weekly users. The data was shared at an annual executive meeting in March by Microsoft's chief financial officer Amy Hood, Newcomer reports, and raise some concerns about the AI future Microsoft is pitching. Microsoft uses OpenAI's models to power Copilot, and the assistant offers similar features to ChatGPT, but they clearly don't draw the same interest from users. The company has also built Copilot into Windows 11, Microsoft 365 and the Edge browser, without apparently reaping the benefit of additional user growth.

If you've performed a Google search lately, you've undoubtedly come across an AI Overview in your search results. This tool, powered by Google's Gemini, tries to save you some clicks by aggregating information from the links populated in your search results and succinctly delivering what it believes to be the information you're looking for. The accuracy of these overviews, however, often leaves a lot to be desired, and the tool has been plagued with hallucinations since its launch (with varying degrees of hilarity). Now Google is bringing the tool to YouTube, testing a video version of AI overviews for a small number of YouTube Premium members in the US across limited English search queries. While Google search results show LLM-generated text summaries, YouTube's AI overviews will function as something of a highlight reel for certain videos.

Google has a monopoly, and that's the official line of the US federal government. In fact, it has two of them, losing two separate antitrust cases that threaten to cripple the tech giant. The Department of Justice has proposed forcing Google to sell or otherwise divest itself of the Chrome browser as its first and preferred remedy. But who would buy it? Unsurprisingly, there are beaucoup business beaus lining up around the block for this browser bachelorette.

OpenAI is giving free ChatGPT users limited access to its Deep Research tool without the need to pay for it. In addition, the company has expanded the tool's limits for all users by rolling out a lightweight version of it powered by its o4-mini model. It says the o4-mini Deep Research feature produces slightly shorter responses, but is "nearly as smart, more cost-efficient and delivers similarly high-quality results" as the original version. OpenAI previously released the tool for use by paying Pro, Plus, Team, Edu and Enterprise subscribers. But even they have a limited number of Deep Research queries per month.

Microsoft has good news for anyone with corner office ambitions. In the future we're all going to be bosses – of AI employees. The tech company is predicting the rise of a new kind of business, called a "frontier firm", where ultimately a human worker directs autonomous artificial intelligence agents to carry out tasks. Everyone, according to Microsoft, will become an agent boss. "As agents increasingly join the workforce, we'll see the rise of the agent boss: someone who builds, delegates to and manages agents to amplify their impact and take control of their career in the age of AI," wrote Jared Spataro, a Microsoft executive, in a blogpost this week.

Arizona State University, USA Deep Foundation Models (DFMs) offer unprecedented capabili ties but their increasing complexity presents profound challenges to understanding their internal worki ngs - a critical need for ensuring trust, safety, and accountability. As we grapple with explaining these sys tems, a fundamental question emerges: Are the difficulties we face merely temporary hurdles, awaiting more sophisticated analytical techniques, or do they stem from intrinsic barriers deeply rooted in the nature of these large-scale models them selves? This paper delves into this critical question by examining the fundamental characteristics of DFMs and scrutinizing the limitations encountered by current explainability methods when confronted with this inherent challenge. We probe the feasibility of achieving satisfactory explanati ons and consider the implications for how we must approach the verification and governance of these powerful technologies. Introduction Deep Foundation Models (DFMs) - such as large language models a nd multimodal architectures - are a class of neural networks trained on vast amounts of data, de signed to serve as general-purpose engines for downstream tasksacross diverse domains [10].With the emergence ofsystems like GPT, Gemini, and CLIP, artificial intelligence is undergoing aprofound transformation.

Large language models (LLMs) have revolutionized natural language processing by solving a wide range of tasks simply guided by a prompt. Yet their performance is highly sensitive to prompt formulation. While automated prompt optimization addresses this challenge by finding optimal prompts, current methods require a substantial number of LLM calls and input tokens, making prompt optimization expensive. We introduce CAPO (Cost-Aware Prompt Optimization), an algorithm that enhances prompt optimization efficiency by integrating AutoML techniques. CAPO is an evolutionary approach with LLMs as operators, incorporating racing to save evaluations and multi-objective optimization to balance performance with prompt length. It jointly optimizes instructions and few-shot examples while leveraging task descriptions for improved robustness. Our extensive experiments across diverse datasets and LLMs demonstrate that CAPO outperforms state-of-the-art discrete prompt optimization methods in 11/15 cases with improvements up to 21%p. Our algorithm achieves better performances already with smaller budgets, saves evaluations through racing, and decreases average prompt length via a length penalty, making it both cost-efficient and cost-aware. Even without few-shot examples, CAPO outperforms its competitors and generally remains robust to initial prompts. CAPO represents an important step toward making prompt optimization more powerful and accessible by improving cost-efficiency.

Traditional fine-tuning methods, while effective, often require substantial computational resources and large, static datasets, making them impractical for real-time applications. Moreover, these models notoriously suffer from catastrophic forgetting, rapid performance degradation on previously learned tasks when presented with new data (Luo et al., 2023). Recent literature addresses catastrophic forgetting via techniques such as replay buffers, which periodically reintroduce previously learned data, and Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning approach designed to reduce computational overhead (Smith & Jones, 2024; Hu et al., 2021). Although these methods individually show promise, there remains a notable gap in understanding their efficacy and interaction within real-time, streaming learning environments. In this work, we bridge this gap by integrating LoRA with a lightweight replay mechanism under stringent streaming constraints, simulating real-world conditions where models must continually adapt using limited computational resources and data batches. We focus specifically on three distinct domains,medical, genetic, and legal,to evaluate the generalizability and robustness of our approach.