If you've ever used a generative artificial intelligence tool, it's lied to you. These recurring fabrications are often called AI hallucinations, and developers are feverishly working to make generative AI tools more reliable by reigning in these unfortunate fibs. One of the most popular approaches to reducing AI hallucinations--and one that is quickly growing more popular in Silicon Valley--is called retrieval augmented generation. The RAG process is quite complicated, but on a basic level it augments your prompts by gathering info from a custom database, and then the large language model generates an answer based on that data. For example, a company could upload all of its HR policies and benefits to a RAG database and have the AI chatbot just focus on answers that can be found in those documents.

The European Union is readying a landmark antitrust law that could limit more advanced generative AI models. The Federal Trade Commission is investigating a deal that Microsoft made with the AI start-up Inflection, probing whether the tech giant deliberately set up the investment to avoid a merger review. And U.S. enforcers reached a deal that will open the company to greater scrutiny of how it wields power to dominate artificial intelligence, including its multibillion-dollar investments in ChatGPT maker OpenAI. That relationship has also exposed Microsoft to new reputational risks, as OpenAI chief executive Sam Altman frequently invites controversy.

Engineering design optimization requires an efficient combination of a 3D shape representation, an optimization algorithm, and a design performance evaluation method, which is often computationally expensive. We present a prompt evolution design optimization (PEDO) framework contextualized in a vehicle design scenario that leverages a vision-language model for penalizing impractical car designs synthesized by a generative model. The backbone of our framework is an evolutionary strategy coupled with an optimization objective function that comprises a physics-based solver and a vision-language model for practical or functional guidance in the generated car designs. In the prompt evolutionary search, the optimizer iteratively generates a population of text prompts, which embed user specifications on the aerodynamic performance and visual preferences of the 3D car designs. Then, in addition to the computational fluid dynamics simulations, the pre-trained vision-language model is used to penalize impractical designs and, thus, foster the evolutionary algorithm to seek more viable designs. Our investigations on a car design optimization problem show a wide spread of potential car designs generated at the early phase of the search, which indicates a good diversity of designs in the initial populations, and an increase of over 20\% in the probability of generating practical designs compared to a baseline framework without using a vision-language model. Visual inspection of the designs against the performance results demonstrates prompt evolution as a very promising paradigm for finding novel designs with good optimization performance while providing ease of use in specifying design specifications and preferences via a natural language interface.

This paper presents a novel approach to extract scientifically valuable information about Earth's physical phenomena from unconventional sources, such as multi-modal social media posts. Employing a state-of-the-art large language model (LLM), Gemini 1.5 Pro (Reid et al. 2024), we estimate earthquake ground shaking intensity from these unstructured posts. The model's output, in the form of Modified Mercalli Intensity (MMI) values, aligns well with independent observational data. Furthermore, our results suggest that LLMs, trained on vast internet data, may have developed a unique understanding of physical phenomena. Specifically, Google's Gemini models demonstrate a simplified understanding of the general relationship between earthquake magnitude, distance, and MMI intensity, accurately describing observational data even though it's not identical to established models. These findings raise intriguing questions about the extent to which Gemini's training has led to a broader understanding of the physical world and its phenomena. The ability of Generative AI models like Gemini to generate results consistent with established scientific knowledge highlights their potential to augment our understanding of complex physical phenomena like earthquakes. The flexible and effective approach proposed in this study holds immense potential for enriching our understanding of the impact of physical phenomena and improving resilience during natural disasters. This research is a significant step toward harnessing the power of social media and AI for natural disaster mitigation, opening new avenues for understanding the emerging capabilities of Generative AI and LLMs for scientific applications.

Nakasone joins OpenAI's board following a dramatic board shake-up. Amid a tougher regulatory environment and increased efforts to digitize government and military services, tech companies are increasingly seeking board members with military expertise. Amazon's board includes Keith Alexander, who was previously the commander of U.S. Cyber Command and the director of the NSA. Google Public Sector, a division of the company that focuses on selling cloud services to governments, also has retired generals on its board.

Apple's otherworldly, flying-saucer headquarters in Cupertino, California, felt like a suitable venue this week for a bold and futuristic revamp of the company's most prized products. With iPhone sales slowing and rivals gaining ground thanks to the rise of tools like ChatGPT, Apple offered its own generative artificial intelligence vision at its Worldwide Developer Conference (WWDC). Apple has lately been perceived as a generative AI laggard. Its WWDC offerings failed to persuade some critics, who have branded WWDC's announcements as downright boring. But with the focus on infusing existing apps and OS features with what the company calls "Apple Intelligence," the big takeaway is that generative AI is a feature rather than a product in and of itself.

Danish media outlets have demanded that the nonprofit web archive Common Crawl remove copies of their articles from past data sets and stop crawling their websites immediately. Common Crawl plans to comply with the request, first issued on Monday. Executive director Rich Skrenta says the organization is "not equipped" to fight media companies and publishers in court. It made the request on behalf of four media outlets, including Berlingske Media and the daily newspaper Jyllands-Posten. The New York Times made a similar request of Common Crawl last year, prior to filing a lawsuit against OpenAI for using its work without permission.

Many burned-out workers have likely dreamed of hiring a career coach or résumé writer. Now, LinkedIn is introducing chats with generative AI career experts based on real people. Other new AI tools within the platform will help people write résumés and cover letters or evaluate their qualifications for jobs posted. LinkedIn has ramped up its generative AI tools in the past year and is moving to incorporate the tech into even more of its offerings. On Thursday, the career site announced new features like a pilot for AI-powered expert advice, an interactive chat to break down information in LinkedIn courses, and more AI features that can be used to search for and apply for jobs for its premium users in English.

Apple executives used the keynote address of this week's annual WWDC developers conference to debut all of the artificial intelligence capabilities that are coming to iPhones, iPads, and Macs. The team showed off how generative tools will help users write emails, clean up iPhone photos, illustrate presentations, and make custom emoji characters. Adding AI to everything is par for the course in 2024, as all of the big tech companies have been loading up their software with similar generative features. But Apple is late to this particular party. The company has been perceived as being "behind" in generative AI, since OpenAI, Microsoft, Google, and a whole bunch of startups have already made massive inroads.

At its annual developers conference on Monday, Apple announced its long-awaited artificial intelligence system, Apple Intelligence, which will customize user experiences, automate tasks and – the CEO Tim Cook promised – will usher in a "new standard for privacy in AI". While Apple maintains its in-house AI is made with security in mind, its partnership with OpenAI has sparked plenty of criticism. OpenAI tool ChatGPT has long been the subject of privacy concerns. Launched in November 2022, it collected user data without explicit consent to train its models, and only began to allow users to opt out of such data collection in April 2023. Apple says the ChatGPT partnership will only be used with explicit consent for isolated tasks like email composition and other writing tools. But security professionals will be watching closely to see how this, and other concerns, will play out.