The last piece ...

AI, no longer controlled by us rubes in many disciplines given the fact real-time analysis of incoming data requires software able to update itself in real-time, cannot be done by humans in any way, shape or fashion, could not process symbolic math, until now.

More than 70 years ago, researchers at the forefront of artificial intelligence research introduced neural networks as a revolutionary way to think about how the brain works. In the human brain, networks of billions of connected neurons make sense of sensory data, allowing us to learn from experience. Artificial neural networks can also filter huge amounts of data through connected layers to make predictions and recognize patterns, following rules they taught themselves.

By now, people treat neural networks as a kind of AI panacea, capable of solving tech challenges that can be restated as a problem of pattern recognition. They provide natural-sounding language translation. Photo apps use them to recognize and categorize recurrent faces in your collection. And programs driven by neural nets have defeated the world’s best players at games including Go and chess.

But a problem remained.

However, neural networks have always lagged in one conspicuous area: solving difficult symbolic math problems. These include the hallmarks of calculus courses, like integrals or ordinary differential equations. The hurdles arise from the nature of mathematics itself, which demands precise solutions. Neural nets instead tend to excel at probability. They learn to recognize patterns — which Spanish translation sounds best, or what your face looks like — and can generate new ones.

The situation changed late last year when Guillaume Lample and François Charton, a pair of computer scientists working in Facebook’s AI research group in Paris, unveiled a successful first approach to solving symbolic math problems with neural networks. Their method didn’t involve number crunching or numerical approximations. Instead, they played to the strengths of neural nets, reframing the math problems in terms of a problem that’s practically solved: language translation.

Soon, he and Lample plan to feed mathematical expressions into their networks and trace the way the program responds to small changes in the expressions. Mapping how changes in the input trigger changes in the output might help expose how the neural nets operate.

Zaremba sees that kind of understanding as a potential step toward teaching neural nets to reason and to actually understand the questions they’re answering. “It’s easy in math to move the needle and see how well [the neural network] works if expressions are becoming different. We might truly learn the reasoning, instead of just the answer,” he said. “The results would be quite powerful.”

An understatement to say the least.

Read the rest of the excellent Quanta's Magazine piece to see why the last piece ... of developing truly intelligent AI is now in place to enable this tech to become ... truly intelligent.