Predictive Analytics: How AI Can Run Future Market Growth
From helping consumers compare insurance quotes to fighting back against telemarketing phone dialers, artificial intelligence has slowly but surely established a home in the public eye that has become increasingly difficult to miss.
To say that the stage is set for artificial intelligence to take the world over might still be a bit of an overstatement, but if the context were changed from “world” to “financial market”, then that statement just might be better-founded than some have yet to discover.
Money management and AI
Artificial intelligence has, to say the least, proven to be far more than just a notion of science fiction on the distant horizon. The rise of tangible AI solutions in medicine and computer markets, and the upward-trending investments in their further development, has forced many experts to both critically consider the future and reexamine the past.
The meteoric development of modern AI technology has restored some of the attention given to older AI concepts that had more or less fallen by the wayside, such as neural networks. These supposed automatic trading algorithm solutions that investors might have glanced at skeptically before are now being looked at with measured curiosity today.
What investors have observed is just the same as what those in any other market have realized; that the potential for these AI solutions to be refined and deepened is far greater than many would have expected.
With these many implications of the AI’s ceiling being higher and more attainable than previously believed, many investors have found it difficult not to wonder if there just may be potential yet for the concept of AI-calculated market predictions.
Deep learning and the financial market’s future
What has given the most encouragement for a reinvestment of interest in the role of AI in financial algorithm generation is the “deep learning” concept. With deep learning, the DeepMind AI subsidiary of Google was able to swiftly achieve mastery in Go, a game with so many different possible arrangements that they’re nearly impossible to quantify.
Deep learning came to be as a natural progression of the foundation laid by neural network concepts introduced two decades prior. DeepMind’s unique composition enables it to operate with the powers of two distinct yet complementary neural networks; one calibrated for long-term permutations, and the other dedicated to those in the short term.
DeepMind is also capable of refining its knowledge through self-inculcation, in which its own hypothetical calculations are used as “challenges” to learn from and develop better insight into more possible scenarios; this was what enabled the capability to master Go in such a dramatically short window of time.
Ultimately, what this sophistication of neural networking implies is the potential for AI solutions to master the art of predicting things beyond complicated games. If DeepMind is capable of swiftly consolidating a body of data with more permutations than could be counted in several lifetimes, the then significance of that predictive power in the financial market is easy to envision.
Closing thoughts
At this moment in time, it remains yet unseen just what the extent of these potential impacts on the financial market could lead to if the implications hold water. The AI world remains one that is still far from realizing its full potential, and yet has already become too much strong of a presence to be written off.
Zealous hedge funds dedicated to AI have already come into being, and regardless of whether or not they have a chance at succeeding, their emergence has been noted. The current buzz surrounding AI’s potential is bound to contain some sensationalism, but it’s the rare kind of sensationalism that comes from a place of observed results. The true test of AI’s potential to change the market even further will come after the dust has settled.
