ELUTHRO

Putting “Intelligence” Into Artificial Intelligence

Augmented Enterprise / Introducing AI 3.0 / Natural Language Intelligence

By Jack Porter

August 25, 2019

In both AI 1.0 and AI 2.0, when creating an AI model, the intelligence came from the human data scientist doing the engineering or math. That means it not only required a very smart data scientist to create these sophisticated models, but they were limited to the capabilities of the human. As data sets went from thousands of rows of data to trillions of rows, the capabilities of the human became the cinch point. Big Data and Deep Learning got us pretty close to “what should be” predictions, but are barely scratching the surface of “what could be” predictions.

The human brain is amazing. With one hundred billion neurons and hundreds of trillions of synapses, our human brain can calculate 38 thousand trillion operations a second. Only our fastest supercomputers can come anywhere close to that. And where our supercomputers use 10–20 megawatts of electricity, our brain does this with about 20 watts of energy. But it is not just the calculations that are impressive in our brain. It is the creativity and innovation that is most fascinating. We are great at thinking outside of the box, applying learning from one area to another area and innovating remarkable new ideas.

The way AI 1.0 and AI 2.0 work, they will never be able to do that. To take AI to the next level, we need a new way. And that new way for AI is going to look a lot like the way of our brains. Our new models need to be much more generalized, more about the patterns and less about the math.

Our brains do three things that our AIs are going to need to master. First, our brain builds a “model of the world”. It stores many patterns across our cerebral cortex. Second, when our perceives something new, it stores that too. It sees these patterns across time in what is called a temporal model. Time is critical to the brain, and all patterns are stored in this context. Finally, our brain is constantly making predictions of what is going to happen next. If the prediction is correct, the pattern is enforced. If there is an anomaly, is captures this difference as a new pattern.

For our AIs to reach the next level, they are going to need to do this same thing. We are going to need to build sophisticated models of the world that rely on time, patterns and constant predictions. Like the brain, these models will probably be hierarchical. Instead of neurons being simply binary (on and off), they will project several states. They will also update with not only feed forward data paths, but also feedback and related neural context.

Then, there is consciousness. For many years, most scientists believed that when our computers reached a certain number of operations per second, consciousness would simply emerge from calculation. However, our fastest supercomputers are now more than 200 quadrillion operations per second. Not only has this prediction not happened yet, but there is no sign that this is a cogent assumption. New discoveries have suggested that consciousness is a quantum effect. If that is the case, we probably have a long way to go before our computers are conscious.

In the next few blogs, we are going to provide our thinking about where AI is going, how it is going to get there and how long it is going to take. We are going to be quite transparent about what is going on in our research labs and what strategies we find are working and what are not working. We hope you will enjoy this journey as much as we do.