Posts

Efficiency Improvements

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Everyone wants efficiency improvement from AI. Most of the talk lately is about how companies are deciding to spend on AI instead of headcount. This usually means spending on AI hardware, but what benefit does the hardware provide without the software to run on it? Who is going to code that software and how are they going to know what to code? We're going to see a lot of C*Os poking at hardare with sticks... Why? Because in order to make something better, you have to first understand it. Well. Anyone who has ever written automation code has come to this realization: A simple task isn't so simple when every little thing has to be included. Every corner case, every variation of the input data, every little naunce that the code might run into that could cause a failure. The kind of AI that is most like automation is reinforcement learning (RL) and requires expertise to model. It's not necessarily true that the agent (AI model) needs to be told exactly what to do...
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March Madness

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I recently met someone through LinkedIn that I had the pleasure of seeing in real life. He met me on the OSU campus, and we walked and talked for about an hour. His primary interest in data science is related to sports and we had a fascinating discussion about the use of AI models in sports betting, particularly the NCAA basketball tournaments. Every year, ESPN has a men's and women's tournament challenge. (There are $135k in prizes to be won, but ONLY for the men's tournament. Go figure.) What I learned from my new friend is that AI models were able to predict the winners in this year's tournament with an accuracy of 93%. Getting it 100% correct will win some cash, so there's definitely an incentive to get that last 7%! His comment that really struck me is that an upset is not *really* an upset. It's usually bad seeding. That is, when a #12 team beats a #5 team it's usually because the #12 team should have been seeded higher and the #5 team...
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"Eliminating Waste" is Meaningless

When someone says they're going to "eliminate waste" what does that mean? Whether it's a business executive, a political leader, or someone in between saying it, they usually mean that they're going to remove things that aren't adding value. It's usually said in such a way as to shut down disagreement. I mean, who would come out in support of waste? What it should do, though, is beg the question, "What do you mean by 'waste'?" Everyone's definition of waste is different, so claiming to be in favor of "eliminating waste" simply allows everyone hearing it to define "waste" in a way that is meaningful to them. It's a reflection of individual values: What one person sees as critical, another sees as waste. Consider corporate travel spending as an example. Is it a waste, or is it critical? It depends on who you ask and what it's for. The same goes for training, employee perks, and many other expendi...
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The Measure of Life

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This is an essay I wrote in 1995 for an RIT undergraduate school course titled "Science, Technology, and Values." Being a computer engineering student and a big fan of Star Trek, I am fascinated by the character Lt. Commander Data, who is an android. During several "Star Trek: The Next Generation" episodes, more than I could count, Data expresses desire to become more human. In one particular episode, Data's cooperation is sought in order that he take part in an experiment that would involve taking him apart for study. Some guy shows up on the Enterprise and has transfer orders ready for Commander Data so that he might be transferred to his lab and be shut down for study and experimentation, a process that could cause Data much harm, especially since this guy does not fully understand the work of Dr. Sung, Data's creator. Needless to say, Data does not want to participate and decides to resign so that he may avoid a transfer of duty; at thi...
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(Un)natural Language Processing

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I'm planning for some of my posts to get into the technical details of AI, some will just be my thoughts and opinions, and others will try to explain an aspect of AI in teams easy for everyone to understand. This is one of those posts. Natural language processing (NLP) attempts to understand, interpret, and generate human language. In my observation, it does fine at "generate," okay (I guess) at "interpret," but does not really "understand." NLP is based on statistics. It tries to guess what's next based on what word (or words) most often follow what you've typed. That is, most often in the training data. If the training data is really generic, then you might get one response, but if it's contextualized then you might get a different one. Here's an example: If I were to write "I had several bats in my garage, which..." the next few words could be "I would bring to baseball practice." or "I had to get ...
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My Love/Hate for GenAI

I have a love/hate relationship with Generative Artificial Intelligence (GenAI). This type of AI what most people think of when (if?) they think about AI. It's what's using large language models (LLMs) to build chatbots, create fake restaurant receipts, and help students cheat on their assignments. First, let me explain what I love about GenAI. It's great at helping people find information, the proverbial needle in a haystack. I love the idea of building an LLM from a company's HR policies and turning that into an internal-only chatbot that employees can use to get their questions answered. I love the idea of building an LLM based on my own writing so that I can generate content "in my own words" using GenAI. (Whether using it is against academic integrity standards is a different discussion.) I love the idea of artists using GenAI to customize their own work to make it more commercially viable. These are all ways that GenAI can be used to make pe...
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Welcome to my Blog!

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My goal for this blog is to share my thoughts on Artificial Intelligence. I started a Ph.D. program in AI at Oregon State University last fall and have been thinking about starting a new blog ever since. There's just too much on my mind and I need to get it out and organized! My first blog post was almost 19 years ago was about my martial arts training. I may have to sneak some martial arts training posts into this one, becuase the subject is never far from my mind but that won't be the main focus. The title of this blog, Actual Intelligence, and the URL, actualintel.blogspot.com , are a double entendre of a sort since I work at Intel Corporation . I *may* share some thoughts about Intel here, but probably not many. I don't follow the tech news very closely and therefore don't want to risk publishing anything that's not already public. I expect that most of my posts will be about what I'm doing at OSU. I may recycle some old writing and/or blog posts th...
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