How Neuroscience is Revolutionizing Artifical Intelligence

Amazon Alexa, computerized chess players, and personalized social media feeds are all examples of how Artificial Intelligence (AI) is being integrated into our lives. Recently, scientists are investigating how they can combine human-like technology, with the real human brain.

The combination if neuroscience and AI work both ways. For one, scientists can utilize the technology to enhance neuroscience. For example, the technology could improve mental health hotlines, identify skin cancer, or monitor police officer’s levels of respect when communicating with citizens. However, before these technologies can be made, scientists need to better understand the human brain and social behavior. One goal of AI is to be human-like. That means the technology needs to respond in a way that makes you feel like you are speaking with a real person, not a robot. In order to make smarter AI, scientists need to further investigate social sciences and how users respond to the technology.

One of the pionners of AI, Demis Hassabis, agrees that AI can be improved by seeking inspraration from the human brain. Hassabis is the founder of British based AI startup DeepMind,which Google purushed in 2014 for 650 million dollars. In an article Hassabis and his colleuges published in journal Neuro agree with the statement above:

“We begin with the premise that building human-level general AI intelligent systems; is a daunting task, because the search space of possible solutions is vast and likely only very sparsely populated. We argue that this therefore underscores the utility of scrutinizing the inner workings of the human brain— the only existing proof that such an intelligence is even possible. Studying animal cognition and its neural implementation also has a vital role to play, as it can provide a window into various important aspects of higher-level general intelligence.


Gary Marcus, professor of psychology at New York University, believes that childhood development holds the key to improving machine learning. In a profile in with MIT Media Review, Marcus claimed if computer scientists want to revolutionize AI, they need to develop machines that learn in the ways toddlers do. For example, the more neurons fire, the stronger the connection becomes. The same principle can be applied to AI, the more a machine sees an image a pattern, the more likely they will be able to skew out a desired response.

The profile goes onto to explain how modern day AI algorithms are modeled human neuron and synapses. While the technology may be able to recognize faces from photos better then humans, it still functions differently. Machines need to learn to recognize thousands of examples in order to learn, while humans can shift through information very efficiently, as of two years old.

In other words, if AI wants to learn better, it needs to learn like humans.

In short, some version of AI is being modeled about the human brain. This finding confirms how powerful the human brain is. Even when we are trying to create superior modes of thinking, we still model them after ourselves.


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