Rejecting Regulatory Entrepreneurship and Unethical Data Sourcing by OpenAI and GitHub Copilot The business model of Regulatory Entrepreneurship, i.e., they find a lucrative market, break the law, then get the law changed afterward is something I reject. As a result I reject all products from YCombinator, OpenAI and GitHub Copilot.
There are many better alternatives including AWS CodeWhisperer which includes attribution, doesn’t appear to be trained on Pirated Datasets and isn’t associated with a deeply unethical incubator YCombinator which pushes negative externalities on to the world.
Ethical Weighting What is an ethical weakling? Someone who says one of the following: “Everything is bad”, “Everything is the same” when confronted with unethical company or product. The counter to this is develop ethical muscles and weigh attributes again one product or company against another. We can select better solutions, no, everything isn’t equal. Additionally, things are not static, if your selected alternative gets worse, then swap.
A general heuristic is:
Ethical Sourcing of Datasets for LLMs One of the key issues with LLMS (Large Language Models) is ethical sourcing of datasets used for training. This topic is especially tough in light of the way Bay Area VC-funded startups operate via Regulatory Entrepreneurship, i.e., they find a lucrative market, break the law, then get the law changed afterward. We see this with Uber, Airbnb, other startups, and now with OpenAI.
The Coming Insurgency against LLMs An upcoming battle will fight against the unauthorized use of content. Here are some of the emerging ideas. This article will be updated frequently.
Background My background is unique to the LLM problem in that I currently work in the AI domain outside of Big Tech, so I can have any view I want without worrying about censorship. Additionally, I have film credits and spent years working on big-budget Hollywood films and live TV in the technical realm.
Data Science of a Post Covid-19 14-Day Fast With a Dislocated Elbow Disclaimer: Please consult your doctor about fasting. This article is not advice but an explanation of the data science around a 14-day fast. I did tell my general practitioner I was about to do a 14-day fast. Additionally, Dr. Jason Fung has a great book, The Complete Guide to Fasting, about fasting that spells out a safe 14-day protocol.
Running and Fasting *Disclaimer: Please consult your doctor about fasting and running. This article is not advice but an explanation of the data science running and fasting.
My genetic lottery ticket needs moderate to intense cardio daily to maintain body weight. A recent foot surgery caused weight gain since I was severely limited in what I typically do for cardio, walking. The result is that I need to drop 20-25 lbs (or more if I feel grate) again.
Organic Technology The general public widely knows the concept of negative externalities in the broader scope of capitalism; a great example is a factory that sells products but emits black soot into the air, hurting people’s health in their neighborhoods. A negative externality is a cost to a third party from consuming a product. One example is the recent case of PCB, or polychlorinated biphenyl, pollution by Monsanto in Anniston, Alabama, beginning in 1929.
Back in the early 1990’s I attended Cal Poly San Luis Obispo and majored in Nutritional Science. I picked this degree because I was obsessed with being a professional athlete. I felt like studying Nutritional Science could give me an extra edge. I was first exposed to research about calorie restriction and aging.
I was also involved in self-experimentation in my Nutritional Biochemistry class. We centrifuged our own blood and calculated LDL, HDL, and total cholesterol levels.
One of the startling facts about the United States is the ability for the country to be a global leader in many facets of modern countries yet fall behind remarkably in others, such as health care. A great example of this phenomenon is the fact that of the top twenty companies in the world by market cap, 15 out of 20 are from the U.S. On the other hand, the U.
Disruption is always easy to spot in hindsight. How did paying one million dollars for a taxi medallion make sense as a mechanism to facilitate public taxi service?
File:CALIFORNIA, SAN FRANCISCO 1996 -TAXI MEDALLION SUPPLEMENTAL LICENSEPLATE - Flickr - woody1778a.jpg
What were the problems that companies like Lyft and Uber solved?
Lower price Push vs. Pull (Driver comes to you) Predictable service Habit building feedback loop Async by design Digital vs analog Non-linear workflow Current State of Higher Education That Will Be Disrupted A similar disruption is underway with education.