“Q star” is very badly named, in my view, given the “Q anon” craze it has absolutely nothing to do with. Then again, the idea of an AI building an online cult with human followers does not seem all that far fetched.

Anyway, Gizmodo has an interesting article. Gizmodo does not restrict itself to traditional journalistic practices, such as articles free of profanity.

Some have speculated that the program might (because of its name) have something to do with Q-learning, a form of machine learning. So, yeah, what is Q-learning, and how might it apply to OpenAI’s secretive program? …

Finally, there’s reinforced learning, or RL, which is a category of ML that incentivizes an AI program to achieve a goal within a specific environment. Q-learning is a subcategory of reinforced learning. In RL, researchers treat AI agents sort of like a dog that they’re trying to train. Programs are “rewarded” if they take certain actions to affect certain outcomes and are penalized if they take others. In this way, the program is effectively “trained” to seek the most optimized outcome in a given situation. In Q-learning, the agent apparently works through trial and error to find the best way to go about achieving a goal it’s been programmed to pursue.

What does this all have to do with OpenAI’s supposed “math” breakthrough? One could speculate that the program that managed (allegedly) to do simple math operations may have arrived at that ability via some form of Q-related RL. All of this said, many experts are somewhat skeptical as to whether AI programs can actually do math problems yet. Others seem to think that, even if an AI could accomplish such goals, it wouldn’t necessarily translate to broader AGI breakthroughs.

Gizmodo

My sense is that AI breakthroughs are certainly happening. At the same time, I suspect the commercial hype has gotten ahead of the technology, just like it did for every previous technology from self-driving cars to virtual reality to augmented reality. Every one of these technologies reached a fever pitch where companies were racing to roll out products to consumers ahead of competitors. Because they rush, the consumer applications don’t quite live up to the hype, the hype bubble bursts, and then the technology seems to disappear for a few years. Of course, it doesn’t disappear at all, but rather disappears from headlines and advertisements for a while. Behind the scenes, it continues to progress and then slowly seeps back into our lives. As the real commercial applications arrive and take over our daily lives, we tend to shrug.

So I would keep an eye out on the street for the technologies whose hype bubbles burst a handful of years ago, and I would expect the current AI hype to follow a similar trend. Look for the true AI takeover in the late 2020s (if I remember correctly, close to when when Ray Kurzweil predicted 30-odd years ago???)