Category: AI
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Hallucination isn’t a thing; it’s just what we call a thing.
Lemme explain. There is no feature or any different behavior when GenAI “hallucinates”. GenAI is always using probability to predict the next token to output. We just choose to call it a hallucination when it does something we don’t want. Why is this distinction important? I heard a training explaining hallucination as I did above,…
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Why GenAI Systems are Biased
There are two categories of AI systems: Symbolic AI and Deep Learning. Symbolic AI requires the builders to define the rules it will apply. If you are trying to understand language, you would need to add grammatical rules. Deep Learning, on the other hand, starts with no rules and creates rules that match the patterns…
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This might be as good as it gets for genAI
“The AI you use today is going to be the worst AI you ever use” is a phrase commonly shared by AI educators. This makes sense from a technical development perspective. Even if the scaling laws don’t hold, surely, we will find other ways to improve current genAI systems. While that is likely true, it…
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Does social media need more than attention?
For transformers and recommendation algorithms, it is all about attention. Social media platforms want to capture as much of your attention as possible in order to show you ads and collect metadata about you. LinkedIn recently notified users that it would utilize their content to train AI models unless they opt out. I’m curious if…
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A Wakeup Call for Tech Workers
Tech companies, and frankly, most of the knowledge work industry, have jumped at the opportunity to replace human workers with AI. This possibility was so enticing that they didn’t bother to wait to see if the technology’s promises were real. They may have shown their hand too soon. I believe that we are fortunate that…
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Are LLMs profitable?
Current LLMs are losing money. More and more reporting is suggesting that these frontier style models may in fact never be profitable. This certainly isn’t a consensus opinion, but it does seem to be gaining momentum. If LLMs don’t make money, it is going to put a damper on the AI-revolution. One potential path to…
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All about the context
Human conversations carry incredible amounts of implicit information. We decipher meaning from each other by 🗣️Tone 👺Facial Expressions 🙅Body Language We add more context based on our environment: 🤼Our relationship 🏴🏳️Our organizational affiliations 🧑🌾⛷️🤹Our roles There is an incredible amount of data in human conversations that is never spoken.AI systems don’t yet capture all of…
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Reading Recap: August 2025
Outlive by Peter Attia An approach to extend not just our lifespan, but more importantly our healthspan. Thoughts on how we can stave off the four horsemen of age-related decline and a death. A bit on the sciencey side at times, but overall, very accessible. Prisoners of Geography by Tim Marshall A look at how…
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John McPhee and More Personal Computing
In Draft No. 4, McPhee describes his pre-computer writing method. Handwritten notes and audio tapes are transcribed by typing on a typewriter. He manually rolled the paper after each note to leave space. Once all the notes were typed up, he photocopied them and then used scissors to cut out each individual note. Notes were…
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Business Continuity Planning for AI Agents
Contingency plans are nothing new for business operations. Workers get sick, take vacation, don’t perform as expected, etc. The business needs some slack capacity and/or a prioritization system when there isn’t enough capacity to meet demands. Process automation also isn’t particularly new. Again, you can build in slack capacity and/or task prioritization to manage outages…