The hype surrounding artificial intelligence (AI) systems has grown in recent years, as technology advances at increasingly rapid rates, but machine learning could be more disruptive than predicted, writes
TechTarget’s Nicole Laskowski.
Erik Brynjolfsson, director of the Massachusetts Institute of Technology (MIT)
Initiative on the Digital Economy (IDE), and Andrew McAfee, principal scientist and co-director of the MIT IDE, explored this theory during the MIT Sloan CIO Symposium in Massachusetts on 23 May 2017. According to the pair, vast data sets and access to more computing power have allowed machine learning algorithms to develop at faster rates. This is set to lead to a highly disruptive “second wave of the second machine age”, where machines will be capable of learning on their own, removing the need for humans to codify knowledge for them.
“We think it's probably the most important thing that's affecting the economy and society over the coming decade,” says Brynjolfsson, quoted by Laskowski.
Developments in areas such as speech and image recognition – Google improved its speech recognition technology from an 8% error rate to approximately 4% in just ten months – are putting machine learning systems at the forefront of the software sector. These advancements represent a unique opportunity for chief information officers.
“There are so many opportunities that people haven't cashed in on yet,” says Brynjolfsson. “And the bottleneck now is actually identifying the problems and opportunities that these technologies can be applied to most effectively.” McAfee adds: “The main error that a lot of companies are going to make is to extrapolate from the past and keep doing what they were doing with a little bit better accuracy or a little bit better precision.”
TechTarget article
Falling behind the curveWhile IT giants such as Google, Facebook and Microsoft have invested billions in developing their AI assets, the financial sector is currently falling behind. Despite a number of firms announcing ambitious machine learning strategies, “customers are still waiting months later for these proposed products and services”, writes
Forbes’ Adelyn Zhou.
According to data from a National Business Research Institute and Narrative Science report,
The rise of AI in financial services, which is cited in the article, just 32% of the financial organisations surveyed said they are using AI techniques such as recommendation engines, predictive analytics, voice recognition and allied technologies. Of those who are not using AI, 12% are put off by the technology being new, untested and risky, while others name “siloed data sets, regulatory compliance, fear of failure, and unclear internal ownership of emerging technologies” as key reasons.
The requirements for technology adoption by banks are extensive. Providers must be able to support on-premise deployment, with banks’ internal servers running the software. Other challenges come in the form of penetration testing, disaster recovery and compliance.
Despite this, the adoption of AI technology is expected to grow exponentially over the next few years, with the retail and banking sectors out in front. Zhou cites
data from IDC Research, which shows that global revenues from the adoption of cognitive systems across industries will undergo a “near six-fold increase from $8bn in 2016 to over $47bn in 2020”. Further research from Accenture, entitled
Technology for people, finds that 79% of banking executives agree “AI will revolutionise the way they gain information from and interact with customers”, while 76% believe that the majority of organisations in the banking industry will deploy AI interfaces as their primary point for interacting with customers in the next three years.
Forbes article
Preparing for the futureWorkers in all areas of finance, ranging from analysts, portfolio managers and traders to chief investment officers, must make an effort to learn about machine learning techniques, says Sarah Butcher, writing for eFinancialCareers, or they will be left behind. Citing information from J.P. Morgan’s recently released
Big data and AI strategies: machine learning and alternative data approach to investing report, Butcher notes that machine learning will become integral to how markets function in the future.
High-frequency trading is a key area where machines are becoming more prevalent, and this is expected to increase over the medium term. According to Butcher, the report says: “Machines have the ability to quickly analyse news feeds and tweets, process earnings statements, scrape websites, and trade on these instantaneously.” As a result, demand for fundamental analysts, equity long-short managers and macro investors will fall.
$47bn
The global revenues expected in 2020 as a result of the adoption of cognitive systems across industries
Humans will retain an advantage in the long term, however. “Machines will likely not do well in assessing regime changes (market turning points) and forecasts which involve interpreting more complicated human responses such as those of politicians and central bankers, understanding client positioning, or anticipating crowding.”
Furthermore, the advancements in machine learning represent new employment opportunities for many. Data scientists will be indispensible for their ability to acquire and analyse data used in trading strategies, and to understand the economics behind the data. Quantitative researchers, too, will be desirable due to the similar skillsets required. This could offer financial sector opportunities for those with broader backgrounds in computer science, statistics, mathematics, financial engineering, econometrics or natural sciences.
The impact of AI on financial services careers and employment systems is hard to predict, so the sector will need to be astute when it comes to preparing its workforce for the future. As ever, knowledge and understanding will be crucial in navigating the evolving machine learning economy.
eFinancialCareers article
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