The Convergence of Blockchain, AI, and Quantum Computing

Image source: YOLO2 object detection via YouTube

Last week, we spoke with Woody Preucil, senior managing director at 13D Global Strategy and Research, to discuss the convergence of Blockchain, AI, and Quantum computing—otherwise known as the "BLAIQ-NET"—and how this will lead to dynamic pricing of real-time events all around us.

Given the vast scale to which more and more of our world is being automatically priced, calculated, and influenced by artificially intelligent software systems running invisibly in the background, we titled this podcast "The Machine-Driven Economy" (see here for audio), though this only captures a small portion of the futuristic picture he painted for listeners.

Training the Darknet

To get an idea of how this will work, Preucil cited a recent video showing how an open source neural network ominously referred to as Darknet has been trained to identify people and objects in real-time.

To show just how effective the software can work, the programmers applied the algorithm to a fast-paced scene in a Bond movie, which you can watch on YouTube here.

For Preucil, this is just one component of a larger machine-ecosystem that is very quickly being developed where algorithms are pricing all available information in the world around us 24/7.

“A person that’s jumping off a bridge, before he hits the ground, the ambulance has already been called and a health claim already filed... Insurance for the surrounding cars in this scene instantly go up, real estate prices instantly drop 20 percent. I mean it sounds like science fiction but in many respects, this is the world where we are heading over the next decade.”

Self-Learning Algorithms

“AlphaZero is a self-learning algorithm developed by DeepMind, a subsidiary of Google, and...effectively in just a few hours, it blew past a level of chess mastery that took humans over 1,500 years to attain.

That was late last year and, since then, there's been a number of advancements in self-learning algorithms and this is key technology because most AI today train on large datasets...but AlphaZero does not require large datasets—it trains by itself—and it also requires less processing power.

So AlphaZero, the latest version, uses almost ten times less hardware processing power than the first those two factors...will help drive commercialization of AI.”

Stanford Test and AI Collaboration

“Another example is earlier this year Microsoft and Alibaba independently developed algorithms to take a Stanford test and this is signficant because the algorithm had to read the paragraph, understand three abstract concepts, and then put it all together to answer the question accurately. And the two algorithms...both scored higher than the average human score...

Another example was developed by the OpenAI foundation—OpenAI Five—to play a game...with a constantly changing environment that requires strategic thinking. In this particular algorithm, it was completely self-learning...they did not program the rules, they just threw the algorithm into the game and let it figure it out on its own. Also, this was signficant because most self-learning algorithms work independently, but OpenAI Five is basically a team of five different bots collaborating on strategy to win this multiplayer game against humans.

Bill Gates said this is a huge deal because this is a situation where AI required teamwork and collaboration, which is a huge, huge milestone for artificial intelligence.”

Listen to this full podcast with Woody Preucil by clicking here. For a free trial to our FS Insider podcast, click here. For more information about Financial Sense® Wealth Management and our current investment strategies, click here.

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