Time and space are fundamental to the existence of the universe, and human intelligence is our tool for navigating time and space in an appropriate manner. Our ability to see the future is critical. Through evolution, the human brain has evolved into a tool that perceives not only time, place, and things, but our neural network also predicts what will happen in the near future. What kind of path will the stone that you throw take? In which direction does the tree fall?

This article was published in the Spring 2021 issue of Litterae Populi. The full issue can be found here. The Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) was established as Japan's only research center specializing in hybrid research and education in the humanities, social sciences, neuroscience and AI. With the mission to create new knowledge on the nature of human existence, the Center conducts research and education taking advantage of the strengths and characteristics of Hokkaido University as a leading research university. In July 2019, the Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) was established as a university facility and it launched its activities. CHAIN's mission is to provide interdisciplinary research and education that integrate arts and sciences at the intersection of the humanities, social sciences, neuroscience and artificial intelligence (AI), i.e., to be a place where new knowledge is generated.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Deep learning has come a long way since the days it could only recognize hand-written characters on checks and envelopes. Today, deep neural networks have become a key component of many computer vision applications, from photo and video editors to medical software and self-driving cars. Roughly fashioned after the structure of the brain, neural networks have come closer to seeing the world as we humans do. But they still have a long way to go and make mistakes in situations that humans would never err.

The origin of machine and deep learning algorithms, which increasingly affect almost all aspects of our life, is the learning mechanism of synaptic (weight) strengths connecting neurons in our brain. Attempting to imitate these brain functions, researchers bridged between neuroscience and artificial intelligence over half a century ago. However, since then experimental neuroscience has not directly advanced the field of machine learning and both disciplines -- neuroscience and machine learning -- seem to have developed independently. In an article published today in the journal Scientific Reports, researchers reveal that they have successfully rebuilt the bridge between experimental neuroscience and advanced artificial intelligence learning algorithms. Conducting new types of experiments on neuronal cultures, the researchers were able to demonstrate a new accelerated brain-inspired learning mechanism.

Artificial Intelligence (AI) is more linked to dopamine-reinforced learning than you may think. That's a mouthful, so for now just think of Pavlov's dog study. DeepMind AI published a blog post on their discovery that the human brain and AI learning methods are closely linked when it comes to learning through reward. Their findings were also published in the journal Nature on Wednesday. It's been a well-known fact for a while now that we humans, and many animals, learn through reward. We are motivated by external and internal factors to learn more.

Despite their names, artificial intelligence technologies and their component systems, such as artificial neural networks, don't have much to do with real brain science. I'm a professor of bioengineering and neurosciences interested in understanding how the brain works as a system – and how we can use that knowledge to design and engineer new machine learning models. In recent decades, brain researchers have learned a huge amount about the physical connections in the brain and about how the nervous system routes information and processes it. But there is still a vast amount yet to be discovered. At the same time, computer algorithms, software and hardware advances have brought machine learning to previously unimagined levels of achievement.

Hokkaido University launched the Center for Human-Nature, Artificial Intelligence, and Neuroscience, or CHAIN, in July 2019. It will conduct interdisciplinary research and education at the intersection of humanities, artificial intelligence, and neuroscience. The inaugural symposium was held at the university's Sapporo Campus on July 23rd to unravel its vision and ambitious plans for research and graduate-level education. At the symposium, Professor Shigeru Taguchi, the Director of CHAIN, said, "Recent developments in neuroscience and artificial intelligence have made it possible for scientists to tackle problems that have been traditionally explored in humanities, such as consciousness, emotion, and self," explaining the ever-increasing demand for the integration of humanities and science. "We would like to open up new directions in understanding'what human beings are'."

In addition to discovering how the brain works, it's not at all clear which brain processes might work well as machine learning algorithms, or how to make that translation. One way to sort through all the possibilities is to focus on ideas that advance two research efforts at once, both improving machine learning and identifying new areas of neuroscience. Lessons can go both ways, from brain science to artificial intelligence – and back, with AI research highlighting new questions for biological neuroscientists.

Despite their names, artificial intelligence technologies and their component systems, such as artificial neural networks, don't have much to do with real brain science. I'm a professor of bioengineering and neurosciences interested in understanding how the brain works as a system – and how we can use that knowledge to design and engineer new machine learning models. In recent decades, brain researchers have learned a huge amount about the physical connections in the brain and about how the nervous system routes information and processes it. But there is still a vast amount yet to be discovered. At the same time, computer algorithms, software and hardware advances have brought machine learning to previously unimagined levels of achievement.

We have come a long way to building intelligent machines, in fact, the rate of progress in Deep Learning and Reinforcement Learning, the two corner stones of artificial intelligence, is unprecedented. Alan Turing would have been proud of our achievements in computer vision, speech, natural language processing and autonomous systems. However, there are still many challenges and we are still some distance from building machines that can pass the Turing test. In this paper, we discuss some of the biggest questions concerning intelligent machines and we attempt to answer them, as much as can be explained by modern AI. Turing choose to avoid answering this question directly, however, it is important to have a clear and concise meaning of thinking that incorporates lessons from neuroscience and Artificial Intelligence.