"Google CEO Sundar Pichai was right when he said that while AI companies aspire to create AGI (artificial general intelligence), what we have right now is more like AJI-artificial jagged intelligence. What Pichai meant by this is that today's AI is brilliant at some things, including some tasks that even human experts find difficult, while also performing poorly at some tasks that a human would find relatively easy."
"Thinking of AI in this way partly explains the confusing set of headlines we've seen about AI lately- acing international math and coding competitions, while many AI projects fail to achieve a return on investment and people complain about AI-created "workslop" being a drag on productivity. (More on some of these pessimistic studies later. Needless to say, there is often a lot less to these headlines than meets the eye.)"
AI currently exhibits 'artificial jagged intelligence,' excelling at some complex tasks while failing at others that humans find easy. Many high-profile contrasts—AI acing exams and coding contests versus poor ROI and low-quality outputs—stem from benchmarks that do not match real-world use. OpenAI released GDPval to evaluate leading models on practical, expert-curated tasks from 44 professions across nine economic sectors, with participating experts averaging 14 years of experience in fields such as law, finance, retail, manufacturing, government, and healthcare. GDPval requires long-form professional outputs like a 3,500-word legal memo. Additional developments include a new California AI law, OpenAI Instant Purchases in ChatGPT, and research showing AI can outperform many VCs at selecting promising founders.
Read at Fortune
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