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What happens when AI becomes your broker?

Posted By E. Alex Floate, Friday, November 8, 2019

Alex Floate, a member of our Emerging Fellows program examines the use of AI in fintech through his new blog post. The views expressed are those of the author and not necessarily those of the APF or its other members.

 

“Here is a summary of your weekly investment activity. Maynard has made the following changes to your investment portfolio,” my assistant announced. Maynard is the name I gave to my investing AI, who oversees my portfolio and makes decisions about where and how to invest my small nest egg.

 

“There were changes to twenty-three individual stock and seven bond positions. Analysis of long-range weather forecasts, with adjustments for anticipated climate changes, resulted in a modification to the commodity strategy with modified positions in Ethiopian coffee and Phillipino cocoa. The latest climate assessments have also caused modification to real estate investments.”

 

“Currency swaps from national currencies to Amazonians and Alibablers have also occurred. Twenty-seven micro-investments in the Lagos and Kinshasha metroplexes have been created, with twelve micro-investments recouped and closed. Your investment return averages seven-point-three percent, and your social scoring has increased thirty-one points.”

 

Maynard has, once again, pleased me with my return and the socially responsible method by which he achieved it.

 

Of course, it wasn’t always like this. True AI advisors have to assimilate vast amounts of data, analyze it, learn from it, and formulate a strategy that takes advantage of various possibilities. The analysis moves beyond basic linear financial analysis and into the multiple and complicated systems that support the investment and ultimately create value. The more it looks into these systems, the more it will learn about them and how they affect investments and strategies to gain value. Maynard also had to learn my personal ethical preferences to ensure transactions would not endanger my social credit score.

 

When AI advisors first came on-line, there were fears that it could cause trouble in the markets. Most concerning was that those AI agents could all come to the same conclusions and make the same trades at the same time, destroying value in some assets and over-inflating value in others. There were also concerns that AI advisors would learn to game the system and make moves that circumvent legal or ethical standards while covering their tracks. Although that has occurred, it has not been as widespread as discovered in the court case Global Securities Exchange Commission vs. “Alladin” (AI Agent # 234GXE36576). By tying AI agents to their human’s social score, it helped ally the fear of widespread ethical lapses.

 

What has occurred is an increase in the choices for the average investor. Before the internet, the majority of investing was placed through brokers, who often manipulated investors by steering them towards items that were more profitable for the brokers than the client. Average investors were also unable to enter other investment vehicles such as commodities, real estate, and small business start-ups. Although the brokerage model was partly to blame, it was usually due to a lack of knowledge; AI solved this last issue.

 

AI promised to even the odds that average investors could compete with the largest funds and firms, and for those who could afford a good AI agent, it did. Initially, it required champions and interventions by authorities to ensure widespread access to the various exchanges at reasonable fees. However, once set in motion, the financialization of the economy became everybody’s business, and business was good.

 

© E Alex Floate 2019

Tags:  AI  artificial intelligence  fintech 

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Artificial Intelligence and Us

Posted By Administration, Thursday, March 22, 2018
Updated: Sunday, February 24, 2019

Monica Porteanu has written her third installment in our Emerging Fellows program. Here, she questions the effects of artificial intelligence on society. The views expressed are those of the author and not necessarily those of the APF or its other members.

“Will AI take over the world?” is a common question across many news outlets these days. “Artificial Intelligence will best humans at everything by 2060, experts say,” predicts one of them. “More than 70% of US fears robots taking over our lives, survey finds,” describes another. Most of all, “how long will it take for your job to be automated?” seems to be the question on everyone’s mind. Opposing views are also present, arguing about “The great tech panic: robots won’t take all our jobs.” How do we reconcile these views into what Artificial Intelligence is and can be?

The term “Artificial Intelligence” was coined in the 1950s, intending to describe the ability of machines to perform tasks at a human intelligence level. Today, the definition encompasses more nuanced meanings, especially when considering the level of human cognition. In this regard, there seem to be four categories: (1) automation; (2) machine learning using artificial neural networks; (3) deep learning; (4) and beyond.

Automation represents a low cognitive process that is repeatable, having well-defined sequences of actions that are pre-programmed into machine behaviour. The machine is a passive executor of what is being instructed to accomplish. Its ability to complete complex computations fast and without error is superior to humans. Automation can be applied on a large scale, with numerous examples from manufacturing production lines, to, more recently, interactions with customers, such as onboarding operations. It has the most concrete social impact, as it does take away jobs as we know them today. However, it also opens the opportunity for humans to do what they are better at than machines are: empathy, critical thinking, and creativity. The key to staying ahead of automation is, as Garry Kasparov puts it, “human ambition.”

Machine learning using artificial neural networks requires a more sophisticated, yet still moderate level of cognition. The machine can mimic repeatable but personalized activities, while learning from each interaction, and utilizing increasing amounts of data. It reacts to events based on what was instructed to be accomplished. In other words, it can present a solution to a problem as posed, recommend tasks, or take simple actions. For example, it can automatically set up preferences at home, adjust ambient environment parameters based on these preferences, turn appliances on/off, or keep track of our grocery list. This stage has developed in leaps and bounds during the last decade or so, achieving results in recognition and even digitization of image, face, or speech. However, the machine still has difficulty perceiving at a level comparable to a human. Although we are still irritated by recommendations gone wrong or irrelevant comments coming from the chat box, we allow this type of artificial intelligence into our lives, without yet understanding its concrete positive and negative impacts.

The leap to deep learning is the phase that debuted only a few years ago. With big visions at the forefront, deep learning aims to build capacity for a machine to solve problems without being told how. Such machines mimic the brain, through layers of artificial neurons that connect with and send signals to each other in the network. Initial results are astounding. For example, the machine has been able to beat humans at Go, the complex ancient Chinese game, whose number of alternative positions surpasses the atoms in the universe. However, it seems we have yet to uncover what is happening inside these deep neural networks. Scientists are currently investigating adversarial examples, in which the difference between what the human and machine sees is extreme (e.g., turtle versus gun).

Beyond deep learning is yet an area for even bigger dreams in which, perhaps, machines will surpass the human brain capacity, being able to create symbol systems (e.g., language, money, time, religion, governance) and with that, structurally alter every aspect of the life as we know it.

It seems we are now somewhere during the development of the second category, machine learning, and in the early stages of the third one, deep learning.

We have been warned that “Artificial Intelligence will best humans at everything by 2060.” With the many and contradicting opinions though, one could wonder, what will human capacity be in 2060? How will our brain functions evolve, and with that, where will our creativity, empathy, ambition, and critical thinking take us?

 



© Monica Porteanu 2018

Tags:  artificial intelligence  machine  society 

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