Once considered a remote possibility for a futuristic tomorrow, the advances in technology over the past 20 years have accelerated the development and integration of AI in multiple private and public sectors. Governmental agencies, ranging from the Department of Defense to the Treasury, are active in exploring and implementing AI for use in the public sector. Within the private sector, well-known companies such as Google, Facebook, Apple and Uber, as well as start-ups across the country, are active in the research and development of innovative AI technology-based products.
They filter our emails, personalize our newsfeeds, update our GPS systems, and drive our personal assistants. 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.
AI researchers like Rodney Brooks, a former professor of Robotics at MIT, argue that true reasoning, and true intelligenceis several steps beyond these kinds of learning systems.
But if we already have machines that are proficient at learning through pattern recognition, how long will it be until we have machines that are capable of true reasoning, and how will AI evolve once it reaches this point?
His research is dedicated to understanding the evolution of machine reasoning. According to his methodology, reasoning is described as taking pieces of information, combining them together, and using the fragments to draw logical conclusions or devise new information.
Sports provide a ready example of expounding what machine reasoning is really all about. When humans see soccer players on a field kicking a ball about, they can, with very little difficulty, ascertain that these individuals are soccer players.
However, humans can also see a person in a soccer outfit riding a bike down a city street, and they would still be able to infer that the person is a soccer player. This process— of taking information that is known, uniting it with background knowledge, and making inferences regarding information that is unknown or uncertain — is a reasoning process.
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.
However, Selman notes that making human-level AI is also the first step to attaining super-human levels of cognition.
And due to the existential threat this could pose to humanity, it is necessary to understand exactly how this evolution will unfold.
The Making of a super Mind It may seem like truly intelligent AI are a problem for future generations. Yet, when it comes to machines, the consensus among AI experts is that rapid progress is already being made in machine reasoning.
In fact, many researchers assert that human-level cognition will be achieved across a number of metrics in the next few decades. Yet, questions remain regarding how AI systems will advance once artificial general intelligence is realized.
A key question is whether these advances can accelerate farther and scale-up to super-human intelligence. This process is something that Selman has devoted his life to studying. Specifically, he researches the pace of AI scalability across different categories of cognition and the feasibility of super-human levels of cognition in machines.
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. The human has no ability to do that kind of reasoning.
Given these variances, how can we determine how AI will evolve in various areas and understand how they will accelerate after general human level AI is achieved?
For his work, Selman relies on computational complexity theory, which has two primary functions. First, it can be used to characterize the efficiency of an algorithm used for solving instances of a problem.
These two features provide us with a way of determining how artificial intelligences will likely evolve by offering a formal method of determining the easiest, and therefore most probable, areas of advancement. It also provides key insights into the speed of this scalability.
Ultimately, this work is important, as the abilities of our machines are fast-changing. About 25 years ago, the best reasoning engines could combine approximately or facts and deduce new information from that.
The current reasoning engines can combine millions of facts. As Selman explains, given the present abilities of our AI systems, it may seem like machines with true reasoning capabilities are still some ways off; however, thanks to the excessive rate of technological progress, we will likely start to see machines that have intellectual abilities that vastly outpace our own in rather short order.
Anticipating exactly when this transition will occur will help us better understand the actions that we should take, and the research that the current generation must invest in, in order to be prepared for this advancement.For more about artificial intelligence, watch: All this takes us back to the rise of machine learning and its ability to learn from data and make predictions based on the information.
Incorporating artificial intelligence (AI) and machine learning (ML) into business processes creates an intriguing prospect. With finance being one of the most critical functions of an enterprise, CFOs should understand and leverage AI and ML to provide real time insights, inform decision making and drive efficiency across the enterprise.
Bart Selman is a professor of Computer Science at Cornell University. His research is dedicated to understanding the evolution of machine reasoning. According to his methodology, reasoning is described as taking pieces of information, combining them together, and using the fragments to draw logical conclusions or devise new information.
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