Relating Data, Recommendations, Boredom, and the ROI of AI
Similar Posts
Which Problems Is It Hard to Design AI for?
The less data there is, or the lower quality the data that is available, the more difficult it is to build AI based on statistical learning. For scarce data domains, the only way to design AI is to elicit knowledge from experts, design rules that represent that knowledge, parameterize them so that they apply to…
Machine/AI as Inventor? Notes on Thaler v. USPTO
Can “an artificial intelligence machine be an ‘inventor’ under the Patent Act”? According to the Memorandum Opinion filed on September 2, 2021, in the case 1:20-cv-00903, the US Patent and Trademark Office (USPTO) requires that the inventor is one or more people [1]. An “AI machine” cannot be named an inventor on a patent that…
Algorithmic Accountability Act for AI Product Managers: Section 3
This text follows my notes on Sections 1 and 2 of the the Algorithmic Accountability Act (2022 and 2023). When (if?) the Act becomes law, it will apply across all kinds of software products, or more generally, products and services which rely in any way on algorithms to support decision making. This makes it necessary…
Can Opacity Be Solved in an AI Derived from an LLM?
The short answer is “No”, and the reasons for it are interesting. An AI system is opaque if it is impossible or costly for it (or people auditing it) to explain why it gave some specific outputs. Opacity is undesirable in general – see my note here. So this question applies for both those outputs…
AI for the sake of AI :-) L’art pour l’art
Just like l’art pour l’art, or art for the sake of art was the bohemian creed in the 19th century, it looks like there’s an “AI for the sake of AI” creed now when building general-purpose AI systems based on Large Language Models. Let’s say that the aim for a sustainable business are happy, paying,…
