Generating Value from AI Systems
Essential Components
Feedback Loops
Stated Benefits of Open Source Systems
Focus on Projects that Benefit Humanity
Mitigate Power of Single Entity
Benefit From and Improve the Technology
Attract Elite Researchers
Why Do I Think OpenAI Was Established As Open Source?
The More Obvious/Discussed Justifications
The Less Obvious/Discussed Justification
In Part 1 of this analysis, I provided some background information on AI as a foundation for the discussion. In this part of the analysis I continue on to discuss why I think Elon Musk designated OpenAI as an open source entity.
A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.
Generating Value from AI Systems
Essential Components
I’ve mentioned several times throughout the analysis that AI technology involves three essential components: IA algorithms (software), AI platforms (hardware), and big data. In this section I describe the nature and use of these components in more detail.
I like the way Neil Lawrence describes the AI system in “OpenAI won't benefit humanity without data- sharing.” He uses the analogy of cooking, where AI algorithms are the recipes, the data are the ingredients, and the platform is the stove or oven.
Anyone who has tried to come up with an original recipe will tell you that it generally needs to be tweaked before you come out with the ideal output. Similarly, researchers design AI algorithms, test and train them by running data through them, then tweak them to improve their performance.
Generally speaking, the better cooks are those with more experience, and they tend to be the ones who come up with the best recipes. Of course, occasionally unknown or unpracticed chefs come up with excellent recipes, but that’s not the norm. Similarly in AI, the better, more experienced researchers are the ones who will probably generate most of the advancements in AI. However, that does not preclude the possibility that some unknown savants will be able to come up with advanced solutions on their own.
Also, in cooking, better ingredients produce better dishes. Similarly, in AI, higher quality data lead to better results – as the saying goes, garbage in, garbage out. At the same time, AI algorithms become more accurate (trained) as they run more data. This means that having access to larger volumes of data will generate more accurate algorithms. So when it comes to data, both volume and quality are important.
Finally, when cooking, the sizes of the ovens constrain the volume of food that can be produced. Similarly, with AI algorithms that need to run through large volumes of data to become properly trained, larger, more efficient hardware systems produce results much more quickly than do smaller systems.