Weekly AI Roundup — Benchmark wars and the impending plateau of AI
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Every decade has its own benchmark wars. In the 1990s, it was processor speeds, in the 2000s, it was browser speeds, and in the 2020s it is AI model speeds, feeding the human desire to pit competitors against each other to see who wins. With Anthropic’s release of Claude 3.5 Sonnet this week, the headline had to be “how handily it beat ChatGPT 4o” in order for it to garner attention.
Meanwhile, Meta played catch up on the multi-modal bandwagon, with their release of 5 new AI models to handle tasks like image processing, text-to-music, AI-generated speech and text-to-image. What makes Meta different is its benign-sounding team names like FAIR (Fundamental AI Research) and their “open” models.
As new models promise faster and more capable AI features, researchers are ringing alarm bells that the lynchpin of AI — training data — may exhaust by 2026. The quality of AI results rely directly on the quality of training data.
And the dearth of training data is already being felt in fields like chemistry, where AI’s ability to advance the field is facing limitations. Researchers are exploring alternative ways to solve the data scarcity issue, but they all come with tradeoffs in quality and scalability.
If you’re worried AI will take your jobs, then AGI might set your pants on fire. AGI is basically AI with capabilities that rival those of a human.
Did you know generating a single AI-generated image takes as much energy as fully charging your smartphone? That’s because AI heavily relies on cloud computing and data centers, which consume significant amounts of energy and water.
And the dearth of training data is already being felt in fields like chemistry, where AI’s ability to advance the field is facing limitations. Researchers are exploring alternative ways to solve the data scarcity issue, but they all come with tradeoffs in quality and scalability.
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