An Academic Approach to the Future of AI

There is no greater topic being discussed in the tech world today than Artificial Intelligence (AI), and there is a good reason for it. The technology is literally changing the world as we know it. While the nightmare scenario that involves what’s known as “singularity,” whereby super-intelligent machines take over and permanently alter human existence through enslavement or eradication is also a highly discussed topic, this really should not be the focus.

Yes, Tesla and SpaceX founder Elon Musk, among others, has warned that Artificial General Intelligence (AGI) is one of the greatest risks to human civilization, but again, we need to focus on what benefits AI holds for us in the future and not on theoretical doomsday scenarios.

In 2018, MIT president L. Rafael Reif commissioned the MIT Task Force on the Work of the Future. The purpose of the task force was to understand the relationships between emerging technologies and work, to help shape public discourse around realistic expectations of technology, and to explore strategies to enable a future of shared prosperity.

In a January 2022 article, the task force’s David Autor, David A. Mindell, and Elisabeth B. Reynolds recommend “investing and innovating in skills and training, improving job quality, including modernizing unemployment insurance and labor laws, and enhancing and shaping innovation by increasing federal research and development spending, rebalancing taxes on capital and labor, and applying corporate income taxes equally.”

Ramón López de Mántaras, of the Artificial Intelligence Research Institute (IIIA), in Bellaterra, Spain, explains that the “final goal of artificial intelligence (AI)—that a machine can have a type of general intelligence similar to a human’s—is one of the most ambitious ever proposed by science. In terms of difficulty, it is comparable to other great scientific goals, such as explaining the origin of life or the Universe, or discovering the structure of matter.”

He quotes Descartes who wondered whether a complex mechanical system of gears, pulleys, and tubes could possibly emulate thought. “Two centuries later,” writes de Mántaras, “the metaphor had become telephone systems, as it seemed possible that their connections could be likened to a neural network. Today, the dominant model is computational and is based on the digital computer.”

De Mántaras focuses on weak and strong AI and notes that the distinction between the two was first introduced by philosopher John Searle in an article criticizing AI in 1980, “which provoked considerable discussion at the time, and still does today. Strong AI would imply that a properly designed computer does not simulate a mind but actually is one, and should, therefore, be capable of an intelligence equal, or even superior to human beings.”

He also delineates the differences between symbolic and connectionist AI models.

According to Ashok Goel at the Georgia Institute of Technology, “While symbolic AI posits the use of knowledge in reasoning and learning as critical to producing intelligent behavior, connectionist AI postulates that learning of associations from data (with little or no prior knowledge) is crucial for understanding behavior.”

According to Nico Klingler, the subtle and major differences between the various AI models, including evolutionary and corporeal, suggests that it is “increasingly important for businesses to understand how these models work and the potential implications of using them.”

In hisarticle titled, “How Artificial Intelligence Will Change the World”, author Mike Thomas explains how AI will influence our lives in a changing world. He describes how AI “has come a long way since 1951, when the first documented success of an AI computer program was written by Christopher Strachey, whose checkers program completed a whole game on the Ferranti Mark I computer at the University of Manchester.”

Thomas notes transportation, manufacturing, healthcare, education, media, and customer service as main benefactors of AI in the near future. These specific areas will be most affected in the immediate future by AI systems, career changes, and changing characteristics.

According to De Mántaras, the road to truly intelligent AI “will continue to be long and difficult.” He quotes Gabriel García Márquez’s 1936 speech (“The Cataclysm of Damocles”): “Since the appearance of visible life on Earth, 380 million years had to elapse in order for a butterfly to learn how to fly, 180 million years to create a rose with no other commitment than to be beautiful, and four geological eras in order for us human beings to be able to sing better than birds, and to be able to die from love.”