Over the last week Apple has dominated AI headlines and not for the reason you'd expect. Their research team released a paper called "The Illusion of Thinking (https://machinelearning.apple.com/research/illusion-of-thinking) ." (Authors: Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, Maxwell Horton, Samy Bengio, Mehrdad Farajtabar)
This quote from Gary Marcus' newsletter (https://garymarcus.substack.com/p/a-knockout-blow-for-llms) sums it best:
"What the Apple paper shows, most fundamentally, regardless of how you define AGI, is that LLMs are no substitute for good well-specified conventional algorithms. (They also can’t play chess as well as conventional algorithms, can’t fold proteins like special-purpose neurosymbolic hybrids, can’t run databases as well as conventional databases, etc.)
In the best case (not always reached) they can write python code, supplementing their own weaknesses with outside symbolic code, but even this is not reliable. What this means for business and society is that you can’t simply drop o3 or Claude into some complex problem and expect it to work reliably."
This was then followed by Apple's developer event this week where Apple continued to under-deliver on market expectations of AI releases in iOS and beyond.
In this episode we explore what it means for builders and business users of AI that Apple is playing the spoiler role compared to OpenAI and Google's role as hypers of GAI.
We present a path forward for leveraging the increasing task accuracy and task accuracy of the latest LLMs. And we discuss the risk of the illusion of reasoning within LLMs as it relates to employers looking to reduce headcount.
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This episode was hosted by:
Arpy Dragffy Guerrero (Founder & Principal Strategist PH1 Research) https://www.linkedin.com/in/adragffy/
Brittany Hobbs (VP Insights, Huge) https://www.linkedin.com/in/brittanyhobbs/
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