Here Are 6 Trends and Takeaway Messages from 2018 AI World

AI World’s Jeff Orr moderates a panel on “Removing Bias and Explainable AI.”

Cold weather this week didn’t matter to the crowds at the AI World conference here, as activity around artificial intelligence continues to heat up. Over three days, more than 2,200 attendees learned about the latest advances in machine learning, deep learning, and the industries being affected by AI.

While most of the conference focused on AI’s impact on the healthcare, pharmaceutical, and enterprise software markets, a few sessions discussed industrial automation efforts, including the Industrial Internet of Things (IIoT), manufacturing, and autonomous vehicles.

Here are six themes from this year’s AI World, as observed by Robotics Business Review editors attending the event:

1. Adding humanity to AI

During several general plenary keynotes, speakers noted that in order for AI to advance, more “human traits” needed to be added to the algorithms. Andrew Lo, a professor at the MIT Sloan School of Management, noted that a student of his referred to this as “artificial stupidity,” but then softened it by saying he prefers the term “artificial humanity.”

In his AI World session covering algorithmic models of investor behavior, Lo noted that decision-making in humans relies a lot on emotions such as fear, greed, and anxiousness, and those traits would need to be factored into any AI algorithms.

In citing research about the psychophysiology of professional investors, he noted that the most successful trades would occur when skin conductivity was high, indicating tension on the part of the investors. Lo also noted that professional investors had the ability to “move on” from losses in comparison with amateur investors.

Danny Lange, vice president of AI and machine learning at Unity, talked about adding human traits like curiosity to reinforcement learning models to achieve more successful results.

When researchers programmed machine learning algorithms to achieve a specific goal — such as rewards when they found something in a maze of rooms — it wasn’t until the system was programmed to explore more that better results occurred.

However, Lange also noted that too much curiosity would be a problem, comparing it to someone watching Netflix on a TV and just continuing to watch show after show. He said that algorithms would need to add traits like impatience and boredom to offset an AI’s curiosity.

“There’s a lack of formal rigor in understanding deep neural networks,” observed Nicholas Roy, a professor at MIT’s Computer Science Artificial Intelligence Laboratory (CSAIL). MIT’s “Quest for Intelligence” combines the efforts of CSAIL students with the expertise of brain scientists, linguists, and social scientists to better understand intelligence itself, he said.

“It’s a core set of people looking at fundamental questions,” added Cynthia Breazeal, director of the personal robotics group at the MIT Media Lab and associate director of the Bridge for Strategic Initiatives in MIT’s Quest for Intelligence.

2. AI models will enhance software, back-office functions

At a session focusing on where investment funds are flowing, AI World speakers mentioned two specific areas of growth for the next few years. First, machine learning models will be used to enhance existing software services, making those more efficient and optimized. With lots of companies using cloud-based software services, efficiencies will improve as AI is added to the software.

Second, many back-office functions are being automated through the use of AI and machine learning. Routine tasks such as bookkeeping, accounting, and expense management will become automated. One panelist noted that 80% of a bookkeeper’s job is routine tasks or functions.

Robotic process automation (RPA) provider and exhibitor UiPath cited a McKinsey study predicting that automation will add the equivalent of 2.5 billion full-time workers to the global workforce.

Like many in the robotics space, AI World presenters didn’t say whether the AI will replace humans in those jobs. Instead, they claimed that those workers would be freed up to handle more tasks that couldn’t be automated.

“Medicine is likely to see the biggest transformation in the near future,” said CSAIL’s Roy.

“If we don’t find ways to use data and analytics in healthcare, we’ll go broke,” asserted Dale Kutnick, senior vice president emeritus at Gartner Inc., referring to the increasing demand from aging baby boomers.

Read the source article at Robotics Business Review.