Gary Marcus: COVID-19 should be a wake-up call for AI

The global pandemic has been cited as a “wake-up call” for many things — the environment, economic and social rights, and general global inequalities. However, scientist, author, and entrepreneur Gary Marcus thinks that the COVID-19 crisis should also be considered a wake-up call for AI.

Speaking at the virtual Intelligent Health AI conference yesterday, Marcus lamented decades of missed opportunities to build a more robust artificial intelligence, arguing that too much AI power has been put toward technologies that don’t really help the world in any meaningful way.

“We would like AI that could read and synthesize the vast, quickly growing medical literature, for example, about COVID-19,” he said. “We want our AI to be able to reason causally, we want it to be able to weed out misinformation. We want to be able to guide robots to keep humans out of dangerous situations, care for the elderly, deliver packages to the door. With AI having been around [for] 60 years, I don’t think it’s unreasonable to wish that we might have had some of these things by now. But the AI that we actually have, like playing games, transcribing syllables, and vacuuming floors, it’s really pretty far away from the things that we’ve been promised.”

One of the underlying issues, according to Marcus, is that we’re putting too much focus on deep learning.

“To understand how to bring AI to the next level, we first need to understand where we are, and where we are right now is in the era of deep learning, where deep learning is the best technique, and the dominant technique, and maybe one that’s getting too much attention,” Marcus said.

Marcus has a PhD in cognitive science from MIT and has been a professor of psychology and neural science at New York University for the past 20 years. Throughout that period, he has also written several books, and in 2015 he cofounded Geometric Intelligence, a stealth AI startup which was swiftly snapped up by Uber to serve as the foundation of its new AI Labs. Marcus stepped down as head of Uber’s new unit after just a few months, and he later went on to found Robust.ai to build an “industrial-grade cognitive engine” for robots.

The problem

Deep learning is a branch of machine learning based on artificial neural networks that try to mimic how the human brain works. Deep learning isn’t short of critics, and the inherent weaknesses are well understood. Large swaths of data (images, audio, text, consumer actions, etc) train the deep learning system to recognize patterns, which can be used to help Netflix recommend video content or aid autonomous cars in identifying pedestrians and road signs. But slight changes to the data input, changes that a human may (or may not) be able to spot, can confuse even the most advanced deep learning systems. An example that Marcus uses is that you can train a deep learning system to identify elephants — but show it a silhouette of an elephant, one that a human would easily recognize, and the AI would likely fail.

“The reality is that deep learning works best in a regime of big data, but it’s worse in unusual cases … so if you have a lot of routine data then you’re fine,” Marcus said. “But if you have something unusual and important, which is everything about COVID since there is no historical data, then deep learning is just not a very good tool.”

Marcus also reiterated points from his Rebooting AI book that was published last year, noting that the AI world needs to refocus its efforts on a more hybrid “knowledge-driven” approach. One that incorporates deep learning, which is good at some types of learning but “is terrible for abstraction,” and classical AI, systems capable of reasoning and encoding knowledge.

Whatever the best path forward is, Marcus’ main takeaway as far as COVID-19 is concerned is that the pandemic should serve to motivate the AI world to rethink the problems that they’re ultimately trying to solve. “COVID-19 is a wake up call, it’s motivation for us to stop building AI for ad tech, news feeds, and things like that, and make AI that can really make a difference,” he said.

“With better AI, we might have computers that can read, digest, filter, and synthesize all the vast growing literature [around COVID-19]. Robots could take on a lot of the risks that human health care workers are facing. To get to that level of AI, that can operate in trustworthy ways even in a novel environment, we’re going to need to work towards building systems with deep understanding not just deep learning.”

Published at Fri, 11 Sep 2020 19:03:49 +0000

Finance Sector Benefits from Machine Learning Development and AI

Banking and finance rely on “experts” but the new expert on the scene is your AI/ML combo, able to do far more, do it fast and do it accurately.

Making the right decisions and grabbing opportunities in the fast moving world of finance can make a difference to your bottom line. This is where artificial intelligence and machine learning make a tangential difference. Engage machine learning development services in your finance segment and life will not be the same. Markets and Markets study shows that artificial intelligence in financial segment will grow to over $ 7300 million by 2022.

Data

The simple reason you need machine learning development company to help you make better decisions with the help of AI/ML is data. Data flows in torrents from diverse sources and contains precious nuggets of information. This can be the basis of understanding customer behaviors and it can help you gain predictive capabilities. Data analysis with ML can also help identify patterns that could be indicative of attempts at fraud and you save your reputation and money by tackling it in time.

The key

Normalize huge sets of data and derive information in real time according to specifiable parameters. Machine Learning algorithms can help you train the system to carry out fast analysis and deliver results based on algorithm models created for the purpose by Machine Learning Development Company for you. As it ages the system actually becomes smarter because it learns as it goes along.

To achieve the same result manually using standard IT solutions you would employ a team of IT specialists but even then it is doubtful if you could get outputs in time to help you take decisive action.

Fraud prevention

This is one case where prevention is better than cure. A typical bank may have hundreds of thousands of customers carry out any number of different transactions. All such data is under the watchful eye of the ML imbued system and it is quick to detect anomalies. In fact, ML has been known to cause misunderstanding because a customer not familiar with credit card operations repeatedly fumbled and that raised a false alarm. Still, it is better to be safe than sorry and carry out firefighting after the event.

Stock trading

Day trading went algorithmic quite a few years back and helped brokers profit by getting the system to make automatic profitable trades. Apart from day trading there are derivatives, forex, commodities and binary where specific models for ML can help you, as a trader or a broker, anticipate price movements. This is one area where price is influenced not just by demand-supply but also by political factors, climate, company results and unforeseen calamities. ML keeps track of all and integrates them into a predictive capability to keep you ahead of the game.

Investment decisions

Machine learning; image by Salvatore P, via Pexels, CC0.

Likewise, investments in other areas like bonds, mutual funds and real estate need to be based on smart analysis of present and future while factoring external influencers. No one, for example, foresaw the covid-19 devastation that froze economies and dried up sources of funds that have an impact on investments, especially in real estate. However, if you have machine learning based system it would keep track of developments and alert you in advance so that you can be prepared. Then there are more mundane tasks in finance sector where ML does help. Portfolio managers always walk a tight rope and rely on “experts” who can make false decisions and affect client’s capital. Tap into the power of ML to stay on top and grow wealth of wealthy clients. Their recommendations will get you more clients making the investment in ML solutions more than worthwhile. It could be the best investment you make.

Automation

Banks, private lenders, institutions and insurance companies routinely carry out repetitive and mundane tasks like attending to inquiries, processing forms and handling transactions. This does involve extreme manpower usage leading to high costs. Your employees work under a deluge of such tasks and cannot do anything productive. Switch to ML technologies to automate such repetitive tasks. You will have two benefits:

Your employees can attend to more critical and urgent tasks and do something productive, and,
ML powered systems can gather data and present actionable insights.

The second one alone is worth the investment. In the normal course of things you would have to devote considerable energies to identify developing patterns whereas the ML solution presents trends based on which you can modify services, design offers or address customer pain points and ensure loyalty.

Risk mitigation

Smart operators are always gaming the system such as finding ways to improve credit score and obtain credit despite being ineligible. Such operators would pass the normal scanning technique of banks. However, if you have ML for assessment of loan application the system delves deeper and digs to find out all relevant information, collate it and analyze it to help you get a true picture. Non-performing assets cause immense losses to banks and this is one area where Machine Learning solutions put in place by expert machine learning development services can and does prove immensely valuable.

Banking and finance rely on “experts” but the new expert on the scene is your AI/ML combo, able to do far more, do it fast and do it accurately.

Published at Fri, 11 Sep 2020 18:45:00 +0000