The American healthcare system is one of the most innovative in the world. But it’s also riddled with complex challenges, such as access to affordable medications, inefficiency and administrative burdens, and communication barriers between providers. There’s clearly a better way—and at Surescripts, we have a unique sightline into what that may be. In this series, host Melanie Marcus, Chief Marketing Officer of Surescripts, sits down with today’s most inspiring and innovative leaders in healt ...
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TechCheck+ Are AI advancements already slowing down? 12/11/24
MP3•Episode home
Manage episode 454983885 series 2975155
Content provided by CNBC. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CNBC or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Generative AI has developed so quickly in the past two years, massive breakthroughs seemed more a question of “when” rather than “if.” But in recent weeks, Silicon Valley has become increasingly concerned that advancements are slowing. One early indication is the lack of progress between models released by the biggest players in the space. OpenAI is reportedly facing a significantly smaller increase in quality for its next model GPT-5, while Anthropic has delayed the release of its most powerful model Opus, according to wording that was removed from its website. Even at tech giant Google, its upcoming version of Gemini is reportedly not living up to internal expectations. If progress is plateauing, it would call into question a core assumption that Silicon Valley has treated as religion: scaling laws. The idea is that adding more computing power and more data guarantees better models to an infinite degree. But those recent developments suggest they may be more theory than law. The key problem could be that AI companies are running out of data to train models on, hitting what experts call the “data wall.” Instead, they’re turning to synthetic data, or AI-generated data. CNBC’s Deirdre Bosa explores whether AI progress is slowing, and what it means for the industry.
…
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616 episodes
MP3•Episode home
Manage episode 454983885 series 2975155
Content provided by CNBC. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CNBC or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Generative AI has developed so quickly in the past two years, massive breakthroughs seemed more a question of “when” rather than “if.” But in recent weeks, Silicon Valley has become increasingly concerned that advancements are slowing. One early indication is the lack of progress between models released by the biggest players in the space. OpenAI is reportedly facing a significantly smaller increase in quality for its next model GPT-5, while Anthropic has delayed the release of its most powerful model Opus, according to wording that was removed from its website. Even at tech giant Google, its upcoming version of Gemini is reportedly not living up to internal expectations. If progress is plateauing, it would call into question a core assumption that Silicon Valley has treated as religion: scaling laws. The idea is that adding more computing power and more data guarantees better models to an infinite degree. But those recent developments suggest they may be more theory than law. The key problem could be that AI companies are running out of data to train models on, hitting what experts call the “data wall.” Instead, they’re turning to synthetic data, or AI-generated data. CNBC’s Deirdre Bosa explores whether AI progress is slowing, and what it means for the industry.
…
continue reading
616 episodes
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