With the phenomenal rise of generative AI models (e.g., large language models such as GPT or large image models such as Diffusion), there are increasing concerns about human creatives' futures. Specifically, as generative models' power further increases, will they eventually replace all human creatives' jobs? We argue that the answer is "no," even if existing generative AI models' capabilities reach their theoretical limit. Our theory has a close analogy to a familiar insight in financial economics on the impossibility of an informationally efficient market [Grossman and Stiglitz (1980)]: If generative AI models can provide all the content humans need at low variable costs, then there is no incentive for humans to spend costly resources on content creation as they cannot profit from it. But if no human creates new content, then generative AI can only learn from stale information and be unable to generate up-to-date content that reflects new happenings in the physical world. This creates a paradox.

Freelancer has accused Anthropic, the AI startup behind the Claude large language models, of ignoring its "do not crawl" robots.txt Meanwhile, iFixit CEO Kyle Wiens said Anthropic has ignored the website's policy prohibiting the use of its content for AI model training. Matt Barrie, the chief executive of Freelancer, told The Information that Anthropic's ClaudeBot is "the most aggressive scraper by far." His website allegedly got 3.5 million visits from the company's crawler within a span of four hours, which is "probably about five times the volume of the number two" AI crawler. Similarly, Wiens posted on X/Twitter that Anthropic's bot hit iFixit's servers a million times in 24 hours.

Last year, Rachel Antell, an archival producer for documentary films, started noticing AI-generated images mixed in with authentic photos. There are always holes or limitations in an archive; in one case, film-makers got around a shortage of images for a barely photographed 19th-century woman by using AI to generate what looked like old photos. Which brought up the question: should they? And if they did, what sort of transparency is required? The capability and availability of generative AI – the type that can produce text, images and video – have changed so rapidly, and the conversations around it have been so fraught, that film-makers' ability to use it far outpaces any consensus on how.

Y Combinator is famed for its Demo Days, where portfolio companies pitch their apps and wares in hopes of growing from a fledgling company into the next AirBnB. But on Thursday, the startup incubator hosted a mélange of founders, venture capitalists, and US policy makers in its airy industrial space in San Francisco to tackle a defining topic for so many startups today: AI as the latest frontier in the battle between Big Tech and the little guys. For many early-stage tech entrepreneurs, questions around AI can carry existential weight. Ever since ChatGPT was unleashed in late 2022, OpenAI's technology, along with fast follows from Google's and Microsoft's AI teams, has dominated the conversation around this new era of artificial intelligence. But the increasing availability--and potency--of open source AI models has the potential to upend those dynamics.

Apple has joined several other tech companies in agreeing to abide by voluntary AI safeguards laid out by the Biden administration. Those who make the pledge have committed to abide by eight guidelines related to safety, security and social responsibility, including flagging societal risks such as biases; testing for vulnerabilities, watermarking AI-generated images and audio; and sharing trust and safety details with the government and other companies. Amazon, Google, Microsoft and OpenAI were among the initial adoptees of the pact, which the White House announced last July. The voluntary agreement, which is not enforceable, will expire after Congress passes laws to regulate AI. Since the guidelines were announced, Apple unveiled a suite of AI-powered features under the umbrella name of Apple Intelligence.

OpenAI announced a new AI-powered search engine prototype called SearchGPT. It's described SearchGPT as "a temporary prototype of new AI search features that give you fast and timely answers with clear and relevant sources." The company plans to test out the product with 10,000 initial users, then roll it into ChatGPT after gathering feedback. It's a spicy time to launch AI-powered search engines. Last month, Perplexity faced criticism for summarizing stories from Forbes and Wired without adequate attribution or backlinks to the publications.

Google executives may be fearing the worst once again as Microsoft-backed rival OpenAI launches a new AI-powered search tool. 'SearchGPT', which is being trialed as a prototype before a wider rollout, scours the web for live news and information just like Google Search. OpenAI says the new product is particularly useful for queries about current events, recent developments, or specific information that ChatGPT might not know. Social media users have noted the parallels with the world's biggest search engine, with one saying'Google Search is definitely in trouble'. Another said: 'Anyone who has been paying attention knows there will be a new king of search within 10 years.

You might be familiar with such AI tools--they're the ones that can create text, images, or even films from various prompts. Do our genomes really work in the same way? When I was at school, I was taught that the genome is essentially a code for an organism. It contains the instructions needed to make the various proteins we need to build our cells and tissues and keep them working. It made sense to me to think of the human genome as being something like a program for a human being.

Amritha R Warrier & AI4Media / Better Images of AI / error cannot generate / Licenced by CC-BY 4.0 The rapid rise of generative artificial intelligence like OpenAI's GPT-4 has brought remarkable advancements, but it also presents significant risks. One of the most pressing issues is model collapse, a phenomenon where AI models trained on largely AI-generated content tend to degrade over time. This degradation occurs as AI models lose information about their true underlying data distribution, resulting in increasingly similar and less diverse outputs full of biases and errors. As the internet becomes flooded with real-time AI-generated content, the scarcity of new, human-generated or natural data further exacerbates this problem. Without a steady influx of diverse, high-quality data, AI systems risk becoming less accurate and reliable.

Retrieval augmented generation (RAG) enhances the accuracy and reliability of generative AI models by sourcing factual information from external databases, which is extensively employed in document-grounded question-answering (QA) tasks. Off-the-shelf RAG flows are well pretrained on general-purpose documents, yet they encounter significant challenges when being applied to knowledge-intensive vertical domains, such as electronic design automation (EDA). This paper addresses such issue by proposing a customized RAG framework along with three domain-specific techniques for EDA tool documentation QA, including a contrastive learning scheme for text embedding model fine-tuning, a reranker distilled from proprietary LLM, and a generative LLM fine-tuned with high-quality domain corpus. Furthermore, we have developed and released a documentation QA evaluation benchmark, ORD-QA, for OpenROAD, an advanced RTL-to-GDSII design platform. Experimental results demonstrate that our proposed RAG flow and techniques have achieved superior performance on ORD-QA as well as on a commercial tool, compared with state-of-the-arts. The ORD-QA benchmark and the training dataset for our customized RAG flow are open-source at https://github.com/lesliepy99/RAG-EDA.