How AI Can Complement the Human Mind – Not Replace It
AI is officially here. Machine and deep learning have become powerful and functional enough that businesses, governments, and more can use them in their respective fields. But any attempts so far at artificial general intelligence (also known as strong AI) – the kind of AI that can think or decide like humans – has been an abject failure. Each round of excitement and investment into new developments in strong AI is followed by an equal amount of hand-wringing over a movie-esque dystopian future, then inevitable disappointment when the technology doesn’t live up to the hype.
As tantalizing as the possibilities for strong AI may be, narrow AI (also known as weak AI, defined as AI that is limited to a few specific tasks) represents an immense opportunity for innovation. Artificial intelligence is better understood as augmented intelligence – a complement to, not a replacement for, the human mind. Narrow AI is best at automating the boring, repetitive aspects of jobs, freeing up humans to perform tasks they are better suited for. Used in conjunction with human beings, narrow AI can achieve desirable results more efficiently than ever before.
Customer service is one area already using narrow AI to great effect, with now-ubiquitous chat bots being a natural use case. Chatbots can use language processing and language generation to answer the simple, everyday queries from most customers – when a question is more nuanced and complicated, human customer service representatives can step in. Customers are ultimately less frustrated, wait times are shorter, and employees have used their time more efficiently.
Healthcare and education are other fruitful use cases. Doctors possess intuition, an ability to think in the abstract, and expertise in a variety of areas. Their talent for deciding what is best for a patient that is difficult for machines to replicate. Deep learning algorithms, however, can be taught via images to quickly and efficiently spot kinds of cancer in ways humans can’t. Using AI in this way would allow doctors to help more patients in shorter amounts of time, deploying their talents where best suited. Teachers can use AI algorithms in similar ways – identifying problems in their curriculum or problem areas for their students based on data. They can then put their very human training and intuition to use to combat those issues in a way a machine could not compete with.
The human brain is a complex but flawed place. Humans can accomplish amazing things – they also process information slowly, forget facts, and make incorrect assumptions. Narrow AI can ultimately automate the repetitive, data-driven tasks where humans fall short and machines reign supreme. Rather than reproduce very human flaws within a strong AI algorithm, experts can focus on developing tools that enhance our weak spots, partnering with machines to do things that are not possible on their own.
If You’re Wondering When A.I. Will Start Making Market Predictions…
Guess what – it already is. Hedge funds and large institutional investors have been using Artificial Intelligence to analyze large data sets for investment opportunities, and they have also unleashed A.I. on charts to discover patterns and trends. Not only can the A.I. scan thousands of individual securities and cryptocurrencies for patterns and trends, and it generates trade ideas based on what it finds. Hedge funds have had a leg-up on the retail investor for some time now.
Not anymore. Tickeron has launched a new investment platform, and it is designed to give retail investors access to sophisticated AI for a multitude of functions:
- Finding stock patterns in the market
- Finding trends in the stock market
- Testing portfolios to see if they are well-diversified
- Back-testing statistics to see how different stock patterns generated trading results
- Making Predictions for price movements in the future, with “A.I. Rank” and level of confidence in the trade.
And much more. No longer is AI just confined to the biggest hedge funds in the world. It can now be accessed by everyday investors. Learn how on Tickeron.com.
Contributor
Sergey Savastiouk, Ph.D. has a degree in Applied Mathematics from Moscow University and has extensive experience as an entrepreneur, investor, manager, and mathematician. His professional expertise is in applied mathematics, mathematical modeling, system and pattern analysis, and software and hardware system integration. He has served as the CEO of several hi-tech start-up companies and nonprofit organizations, which has given him proven capabilities in business strategy for high-tech start-up companies, market assessment, company formation, team building, product development, marketing, and sales. He has published numerous articles in journals and magazines on related fields. As a retail investor, he spent 15 years developing his proprietary trading and quantitative algorithms (now Tickeron’s A.I.), which brought him significant returns in trading the stock market. His current work and goal in founding Tickeron is to bring professional, sophisticated stock market analysis capabilities to retail investors via an easy-to-use interface.
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