AI News, Machine Reasoning and the Rise of Artificial General Intelligences: An Interview With Bart Selman

Machine Reasoning and the Rise of Artificial General Intelligences: An Interview With Bart Selman

From Uber’s advanced computer vision system to Netflix’s innovative recommendation algorithm, machine learning technologies are nearly omnipresent in our society.

The argument, in its most basic sense, centers on the fact that machine learning evolved from theories of pattern recognition and, as such, the capabilities of such systems generally extend to just one task and are centered on making predictions from existing data sets.

however, before it is possible to understand the feasibility of machine reasoning across different categories of cognition, and the path that artificial intelligences will likely follow as they continue their evolution, it is necessary to first define exactly what is meant by the term “reasoning.”

To this end, Selman notes that machine reasoning is not about making predictions, it’s about using logical techniques (like the abductive process mentioned above) to answer a question or form an inference.

Since humans do not typically reason through pattern recognition and synthesis, but by using logical processes like induction, deduction, and abduction, Selman asserts that machine reasoning is a form of intelligence that is more like human intelligence.

He continues by noting that the creation of machines that are endowed with more human-like reasoning processes, and breaking away from traditional pattern recognition approaches, is the key to making systems that not only predict outcomes but also understand and explain their solutions.

Selman states that attempting to make blanket statements about when and how machines will surpass humans is a difficult task, as machine cognition is disjointed and does not draw a perfect parallel with human cognition.

“In some ways, machines are far beyond what humans can do,” Selman explains, “for example, when it comes to certain areas in mathematics, machines can take billions of reasoning steps and see the truth of a statement in a fraction of a second.

The human has no ability to do that kind of reasoning.” However, when it comes to the kind of reasoning mentioned above, where meaning is derived from deductive or inductive processes that are based on the integration of new data, Selman says that computers are somewhat lacking.

As Johns Hopkins’ Leslie Hall notes, “broadly stated, the computational complexity of an algorithm is a measure of how many steps the algorithm will require in the worst case for an instance [of a problem] of a given size.” Second, it is a method of classifying tasks (computational problems) according to their inherent difficulty.

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