On today’s show we are talking about accelerating training by orders of magnitude and the impact that this can have on the global arms race for the best artificial intelligence.

The key to learning fast is intensity. If you follow the human paradigm the key to learning is practice and repetition across a wide range of conditions. The problem is that humans learn very slowly if you think about it.

Think about the experience required to become an expert at driving a car. The vast majority of miles driven are completely uneventful and contribute next to zero in terms of driving experience. The same boring freeway driving mile after mile doesn’t make you a better driver. When you do improve it is the result of exceptional conditions. It’s when a cyclist comes into your lane without checking over their shoulder. It’s when a driver opens the door to a parked vehicle and you need to swerve to avoid hitting them.

But still, these events are relatively infrequent.

The way to accelerate the learning process is the same way that pilots train. They use a simulator. The simulator can artificially generate exception events that are rare and difficult to recreate in the real world. Engine failure, hydraulic failure, fire, avionics failure, frozen pitot tube, ice build up on the wings. So many cases that a pilot needs to train on repetitively to handle the real world situations when they occur.

If you are a user of AI, you’re probably thinking about whether the tool is giving you good results or not. If the results are less than you would accept, then the natural reaction is to abandon the use of AI for that particular function. But what if you could train the AI with a minimum amount of effort? A well crafted prompt to generate a series of simulation use cases could change the quality of the output, not just for your single query. The simulation use cases will train the AI forever.

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