Labs For AI Are Competing Fiercely
Enterprise funders gave 110 generic-AI startups $2.7bn last year, demonstrating that big tech won’t get all the value. Alphabet, Microsoft, their counterparts, and the CCP will strive to disprove these investors. Start-up AI.
OpenAI and Google Research
Pre-hype is hard to assess. However, ChatGPT and its competitors’ generative AI models are already transforming the tech industry’s view of innovation and its engines—company development labs such OpenAI and Google Research which are merging big tech’s computing power with computer science’s sharpest sparks. These labs—owned by huge tech companies, affiliated with organizations, or independent startups—are striving for AI supremacy.
In America, company R&D has long advanced science. Thomas Edison’s telegraph and lightbulb inventions funded his Menlo Park, New Jersey factory 150 years ago. After WWII, America Inc. heavily invested in basic science to create useful products. IBM, Xerox, and DuPont—chemical and hardware manufacturers—had large research labs. AT&T Bell Labs invented the transistor, laser, and photovoltaic cell, winning 9 Nobel Awards.
However, most recent improvements in the field have come from huge organizations due to their computer power. Amazon, whose Alexa voice assistant uses AI, and Meta, which recently beat humans at “Diplomacy,” a strategy board game, produce two-thirds and four-fifths as much AI research as Stanford University, a computer-science powerhouse. Alphabet and Microsoft produce more, even without DeepMind, Google Research’s sister lab that the parent company acquired in 2014, and Microsoft-affiliated OpenAI.
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Experts disagree on who deserves what. For instance, Chinese labs dominate the subdiscipline of computer vision, which analyzes photographs, with the most highly referenced publications. Microsoft ranks the top five computer-vision groups as Chinese. The BAAI claims Wu Dao 2.0 is the world’s best natural-language model. Cicero, Meta’s “Diplomacy” participant, will be praised for its strategic thinking and manipulation of humans. DeepMind’s models can predict protein shapes and beat Go champions.
Neither AI excelled. Google answered 5 math questions correctly, whereas ChatGPT answered three. Their relationship advice was inconsistent: fed some explicit interactions in a dating app, each suggested specific methods on one event and vague platitudes like “be open-minded” and “speak successfully” on another. ChatGPT solved 9 SAT questions correctly, whereas Google answered seven. It was more responsive to our ideas and answered some questions correctly on the second try. Riley Goodside of Scales AI, an AI firm, found that Anthropic’s chatbot, Claude, may perform better as ChatGPT at realistic-sounding discourse but poorer at writing code.
Early-mover benefits may also self-reinforce. Insiders say OpenAI’s rapid growth has enabled it to poach a few consultants from rivals like DeepMind, which despite its many successes may debut a version of its chatbot, Sparrow, later this year. Given the regulatory attention from governments throughout the world, Alphabet, Amazon, and Meta may need to rediscover their ability to move quickly and solve problems to keep up.
Technological advancement is another key factor. Generative AI has increased. That benefited affluent tech firms greatly. Measurement may not matter later. Models can only be so big. Epoch, a non-profit research institute, predicts that large language models will burn out of high-quality material on the web by 2026. As Mr. Ha of Stability AI notes, fine-tuning a model to a certain process “dramatically reduced down the requirement to scale up”. New ways to do more with less are created regularly.
Generic-AI startups received $2.7bn in 109 offers last year, indicating that enterprise funders believe big tech won’t capture all the value. Alphabet, Microsoft, their peers, and the Chinese Communist Party will try to disprove these investors. AI is only starting.