Here’s Why OpenAI’s Deal With Hearst Should Scare Google

If AI really is the future of digital tech, it comes with a pretty awful side dish: AI data scraping. Popular large language model applications are so hungry for data that AI makers have frequently resorted to questionable methods when grabbing as much data as possible from wherever they can, regardless of any moral or legal implications. But in a surprising reversal of this trend, the Wall Street Journal reports that market leading AI brand OpenAI has signed a deal with giant publishing house Hearst, giving the AI maker access to the stable of over 20 magazine brands and 40 newspapers that Hearst owns—some of which are world-famous titles like Esquire and Cosmopolitan. The Journal notes that the terms of the deal aren’t disclosed. And why would they be? OpenAI is technically still a startup, albeit a giant one, and it doesn’t want to give away too many business secrets. We can speculate about the number of zeros after the dollar sign, of course, but the amount going into Hearst’s bank account almost doesn’t matter. That’s because it’s what this deal signifies that’s important. OpenAI will have access to millions of words, photos and other content from Hearst’s vast publishing archive. Hearst will see its content “surfaced” inside ChatGPT when users search for a relevant topic—with citations and direct links so that when a user clicks on a link, they’ll be taken to a Hearst site and thus could be shown ads, boosting revenues. OpenAI, by including this material inside its chatbot, helps keep ChatGPT useful and up to date for users, and thus boosts its revenues. Both companies use business jargon to describe this mutual gain, noting the deal will “enhance the utility and reach” of their offerings, the paper says. Interestingly, the Journal also quotes Hearst Newspapers President Jeff Johnson on the complicated topic of AI “theft” of journalistic IP—a tricky issue that has seen newspapers, notably the New York Times, sue the AI maker. Johnson explained how important it was that journalism by “professional journalists” remain at the forefront of AI products’ output. Is this simply a case of “if you can’t beat ‘em, sell it to ‘em?” There is a wrinkle here, and one that the Journal doesn’t investigate. The New York Times’ case, like several others against AI companies from newspapers, book authors and even record labels, rests on the fact that real human work is being used to train a very inhuman AI. Once it has the data, it can create all-but “cloned” output, mirroring the style and content of the training material. If an AI can, say, write pieces like a well-known newspaper journalist, isn’t that a threat to that person’s livelihood? Music A-listers recently penned an open letter decrying AI as a threat to human creativity, exploring a very similar hypothesis. Will giving OpenAI access to Hearst’s archive merely mean the next-gen ChatGPT system can sound like any Cosmo writer on any given topic? Will readers act on sex and fashion tips from a digital creator that has no experience of either? (And, if you think about it, does that even matter?) What this also means is that AI systems really might be the future of the online search experience. Why would users need to exit ChatGPT to find, say, the latest celebrity gossip when they could simply stay logged into the chatbot to read that stuff, contributing to OpenAI’s revenues. This is another shot across Google’s bows, a serious challenge to a search giant that’s kept a stranglehold on search for decades. BY KIT EATON @KITEATON

How AI can level the playing field in learning and education.

Patricia Scanlon, Ireland’s first AI ambassador, thinks that AI can make learning more efficient and accessible if we rethink our education system. AI is rapidly transforming almost all spheres of human activity; from the way we work and the way we create content to even the way we find novel solutions to age-old problems. While there is much excitement around what AI brings with it, some are also cautious about its implications on the future of jobs and the damage advanced AI can do if in the wrong hands. One such area of human endeavour that AI has already started to upend is learning. The power of generative AI is disrupting classrooms in schools and colleges world-over, with educators scrambling to devise policies to prevent the technology’s misuse. Individualised help But with every disruption comes an opportunity to makes things better. Patricia Scanlon, Ireland’s first AI ambassador, thinks that when it comes to the impact of AI on education, the novel tech can actually be used as a force for good in learning and development. “There’s a lot of power in being able to take AI and level the playing field in some ways,” Scanlon said in her keynote speech to an audience of more than 350 people at the Learnovation Summit held in the Aviva Stadium in Dublin today (5 October). The annual summit was organised by the Learnovate Centre, a learning technology research centre based in Trinity College Dublin. “Not everybody has access to low student-teacher ratios, after school tutors, helpful parents at home, English as their first language – you can see how that more individualised help can really help in education.” But it’s not just children in schools that can benefit from the equalising ability of AI technology. Adults, too, Scanlon said, can make the most out of LLMs [large language model] that can help them learn things they never had access to learning before. “Maybe somebody never gets to go to college, but they can educate themselves to a certain point with AI. Not the point of a full format education system, but a tool to help and that’s where the productivity aspect comes in,” she explained. “And then in the working world, AI can be hugely helpful – particularly for people with dyslexia. You’re levelling the playing field for people like that, who struggle to write in the blank page.” ‘Turn education system upside down’ But is the effect of AI on learning, especially for younger people and children, really such a bed of roses? Scanlon said there are ways in which these tools could be harmful to our development if we start to rely on it too much. “We wonder if kids are ever going to be able to write for themselves or engage in critical thinking. Conversely, AI can help to ensure that integrity – but it’s going to take work,” she went on. “You can use the LLM to create live questioning that somebody couldn’t possibly be prepared for, and change the questions based on the answers to drill downs someone’s knowledge. “Then, together with a little bit of security and analytics, or maybe their style of writing or what they said before or what we know that LLMs produce, you can get to something more like an oral assessment or a defence of a thesis if you want, and AI can help that.” According to Scanlon, the easy thing to do would be to ban AI. But it’s not necessarily the best way forward. What’s far more beneficial, she argued, is to “turn our whole education system upside down” and look at AI in a different light. “It’s not going away, so we need to think about how we can use this tool to help with critical thinking, to help them [learners] progress in all aspects of teaching and learning.”

What Your Business Can Learn From the World’s Greatest Mathematician’s View of AI

Artificial intelligence is capable of some stunning feats, from generating mind-bogglingly convincing imagery and video to chatting in an incredibly human-like voice. Users’ growing embrace of AI for fun and help at work shows the technology already has practical applications–even before it evolves to super-genius levels–and may take over some roles in the workplace. But UCLA math professor Terence Tao, known as the “Mozart of Math,” isn’t particularly worried that AI is coming for his job soon. Tao, considered the world’s greatest living mathematician, spoke to the Atlantic recently about AI, and his words have impact far beyond the world of equations, proofs and hypotheses. Tao was asked about the impact of AI on the field of math, because he’d recently posted some scathing comments about OpenAI’s latest and supposedly greatest GPT o1 model. Touted as the first model from the leading AI brand that can “reason” as well as simply answer back to user queries, Tao posted on social platform Mathstodon that in his opinion the cutting edge AI was only as smart as a “mediocre, but not completely incompetent” graduate student. The magazine elicited a more in-depth explanation of Tao’s views, and what he said was deeply interesting for anyone who’s thinking about embracing AI into their workplace, or those who are worried that AI will displace people from their jobs. Entrepreneur Peter Thiel, for example, recently suggested AI could actually “come for” roles that rely on math first. Expanding on his criticism of ChatGPT, Tao said his remarks were misinterpreted. What he had been trying to do, rather than dismiss GPT o1’s capabilities, was point out that he “was interested in using these tools as research assistants.” That’s because a research project has “a lot of tedious steps: You may have an idea and you want to flesh out computations, but you have to do it by hand and work it all out.” This sort of methodical task is exactly what “reasoning” AI models should be great at, saving time and money for people like Tao, whose job involves this kind of data processing. Tao thinks AI tech–at least for now–is only good at this type of “assistant” role, and not necessarily a shining example of excellence here either. And Tao’s concluding remarks are even more telling. Asked about how AI is taking over some methodical math work, Tao pointed out that this has always been true of technology. To adapt, humans simply “have to change the problems you study.” In terms of AI use, Tao noted he’s “not super interested in duplicating the things that humans are already good at. It seems inefficient.” Instead he foresees a time when, “at the frontier, we will always need humans and AI. They have complimentary strengths.” So far, so very math nerdy. But, sitting at your desk in your office, working at tasks that seemingly have little to do with sophisticated mathematics, why should you care what Tao thinks? At least partly because of the kind of sentiment that Thiel voiced about the future of work. In terms of the question “will AI steal my job?” Tao is very definitely on the side of voices that argue “no.” In a similar way that the PC changed the average office job, AI will simply change what employees do hour by hour. The tech will mechanize some humdrum “research” tasks, and actually allow some of your workers to work more efficiently at tasks that directly generate revenues. So if you’ve been hesitant to embrace AI’s promise thus far, maybe you can go ahead—and reassure your staff that they’re not at risk. BY KIT EATON @KITEATON

OpenAI Just Announced 4 New AI Features, and They’re Available Now

OpenAI announced a slew of updates to its API services at a developer day event today in San Francisco. These updates will enable developers to further customize models, develop new speech-based applications, reduce prices for repetitive prompts, and get better performance out of smaller models. OpenAI announced four major API updates during the event: model distillation, prompt caching, vision fine-tuning, and the introduction of a new API service called Realtime. For the uninitiated, an API (application programming interface) enables software developers to integrate features from an external application into their own product. Model Distillation The company introduced a new way to enhance the capabilities of smaller models like GPT-4o mini by fine-tuning them with the outputs of larger models, called model distillation. In a blog post, the company said that “until now, distillation has been a multi-step, error-prone process, which required developers to manually orchestrate multiple operations across disconnected tools, from generating datasets to fine-tuning models and measuring performance improvements.” To make the process more efficient, OpenAI built a model distillation suite within its API platform. The platform enables developers to build their own datasets by using advanced models like GPT-4o and o1-preview to generate high-quality responses, fine-tune a smaller model to follow those responses, and then create and run custom evaluations to measure how the model performs at specific tasks. OpenAI says it will offer 2 million free training tokens per day on GPT-4o mini and 1 million free training tokens per day on GPT-4o until October 31 in order to help developers get started with distillation. (Tokens are chunks of data that AI models process in order to understand requests.) The cost of training and running a distilled model is the same as OpenAI’s standard fine-tuning prices. Prompt Caching OpenAI has been laser-focused on driving down the price of its API services, and has taken another step in that direction with prompt caching, a new feature that enables developers to reuse commonly-occurring prompts without paying full price every time. Many applications that use OpenAI’s models include lengthy prefixes in front of prompts that detail how the model should act when completing a specific task, like directing the model to respond to all requests with a chipper tone or to always format responses in bullet points. Longer prefixes typically improve the model and help keep responses consistent, but they also increase the cost per API call. Now, OpenAI says the API will automatically save or “cache” lengthy prefixes for up to an hour. If the API detects a new prompt with the same prefix, it will automatically apply a 50-percent discount to the input cost. For developers of AI applications with very focused use cases, the new feature could save a significant amount of money. OpenAI rival Anthropic introduced prompt caching to its own family of models in August. Vision Fine-Tuning Developers will now be able to fine-tune GPT-4o with images in addition to text, which OpenAI says will enhance the model’s ability to understand and recognize images, enabling “applications like enhanced visual search functionality, improved object detection for autonomous vehicles or smart cities, and more accurate medical image analysis.” By uploading a dataset of labeled images to OpenAI’s platform, developers can hone the model’s performance when it comes to understanding images. OpenAI says that Coframe, a startup building an AI-powered growth engineering assistant, has used vision fine-tuning to improve the assistant’s ability to generate code for websites. By giving GPT-4 hundreds of images of websites and the code used to create them, “they improved the model’s ability to generate websites with consistent visual style and correct layout by 26% compared to base GPT-4o.” To get developers started, OpenAI will give out 1 million free training tokens every day during the month of October. From November on, fine-tuning GPT-4o with images will cost $25 per one million tokens. Realtime Last week, OpenAI made its human-sounding advanced voice mode available for all ChatGPT subscribers. Now, the company is enabling developers to build speech-to-speech applications using its technology. If a developer had previously wanted to create an AI-powered application that could speak to users, they’d first need to transcribe the audio, pass the text over to a language model like GPT-4 in order to be processed, and then send the output to a text-to-speech model. OpenAI says this approach “often resulted in loss of emotion, emphasis, and accents, plus noticeable latency.” With the Realtime API, audio is immediately processed by the API without needing to link multiple applications together, making it much faster, cheaper, and more responsive. The API also supports function calling, meaning applications powered by it will be able to take actions, like ordering a pizza or making an appointment. Realtime will eventually be updated to handle multimodal experiences of all kinds, including video. To process text, the API will cost $5 per one million input tokens and $20 per 1 million output tokens. When processing audio, the API will charge $100 per 1 million input tokens and $200 per 1 million output tokens. OpenAI says this equates to “approximately $0.06 per minute of audio input and $0.24 per minute of audio output.”