Author: Nicos Kekchidis
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How Intelligent AI Really Is?
The forecast duties of meteorological service are adding up as we speak. We are hearing regular swift correlations between slightest weather fluctuations from the historical normal and global warming. Now we get bombarded every day with prognosis about the imminent arrival of AGI – Artificial General Intelligence. No day passes by without fear mongering or…
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AI Promise Land – Adapt Your Profession
The AI field, Generative AI specifically, is fledgling and quite promising. But it is a huge space, with abundant possibilities and professional opportunities. Previous two articles “Befriend AI” and “Make AI work for you. Not the other way around” went through what it takes to be mentally and practically prepared to live and thrive with…
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Make AI work for you. Not the other way around.
Last time we talked about how to befriend AI, explore the world of opportunities with it, while staying cognizant of dependency on it and asserting self as a true ruler in this symbiosis. It was about how we adjust to AI. This article continues the theme but with more emphasis on how we make AI…
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Befriend AI
In the previous article I took you on a brief excursion to the current AI landscape and its driving forces. Today I will talk about what is our individual role in the AI world and what should be our attitudes towards it. This will get us more prepared for the next set of articles dedicated…
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You Can Do Better than Silicon Valley
For the last few weeks I’ve been talking about the importance of gaining solid scientific intuition about AI in order to have a better grasp on our individual place in this fast paced space. The space that is quite promising yet over-hyped, confusing and at times scary. I alluded to the AI ethical implications that…
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Misuse of AI
In an earlier article about AI ethics I was addressing: I also emphasized the need for AI Researchers to make educated and responsible choices of: This article closes the two-part series about ethical considerations that every AI researcher should bear in mind when deploying AI products into the real world. It briefly covers: Malicious misuse…
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Be a Responsible AI Researcher
AI is reshaping society as we speak, offering vast potential for positive impact in many areas. Yet, there’s also the risk of misuse and unintended consequences. Admittedly, ethical debates often occur in response to issues rather than preemptively. It should not be the case. AI Ethics is a large and complex space. It is important…
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LLMs: Mystery, Misconceptions, Love and Hate
Space Age vs. accounting statistics I often hear “LLMs are nothing but statistical engines, which happen to be trained on a huge corpus of text”. Technically this is to a great extent a correct statement except for the tiny “nothing but” that creeped in it. The very fact that LLMs are statistics based is sometimes…
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LLM Training and Inference
Follow up on Manifolds and Representations This article is a culmination of gaining AI Intuition introductory series. The previous five posts explained scientific underpinnings and building blocks behind Generative AI magic: Here we will talk about what is achieved during Large Language Model training and inference. Main objectives of LLM Training We learn two main things…
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Manifolds and Representations
Follow up on LLM foundations and Brain-DNA analogy In my efforts to help you build convincing intuition about Large Language Models (LLMs) I mentioned in earlier posts two main scientific pillars that it is based on – Information Theory and Probability Theory. They help compressing the essence of knowledge on the one hand, and choosing from and correlating massive…