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All in the mind’s AI

Araya is tackling concepts of conciousness and how they can help researchers improve artificial intelligence. © MF3d/Getty Images

Araya’s research in unusually academic for a commercial entity, particularly its focus on seemingly esoteric ideas about how consciousness operates. But the Tokyo-based company’s founder and CEO, Ryota Kanai, believes that manufacturers of artificial intelligence (AI), need to be experts in consciousness.

“Consciousness is no longer something mysterious and magical,” explains Kanai, a neuroscientist. “We are seeing AI researchers getting closer to architectures relevant to consciousness.” He believes big advances in these areas are the key to the future of AI.

“In the AI business we currently spend a huge amount of time creating a very specialized neural network to solve only one task, which is very computationally inefficient. We want to combine existing models, so we can keep improving,” explains Kanai. “These, more flexible, multi-purpose learning models are closer to some concepts of consciousness.”

Model business

Araya produces AI neural networks, which are similar to software, but have the ability to learn. Roughly 40% of the company’s income comes from custom research produced for other companies or institutions, or providing such organizations with support.

For example, for the Okinawa Institute of Science and Technology, Araya has been developing software to analyse calcium imaging data from microscopes, which can be used to measure electrical activity in neurons. Next, Araya is launching a service called Research DX, which will use AI to help speed up research.

About 30% of the company’s revenue comes from AI focused on industry. For a maker of precision parts, for example, Araya has developed an algorithm to detect and then classify defects, helping to improve processes. And for a media publisher, Araya developed a neural network to help narrow selections of sports images for photo editors.

The remainder of the revenue is generated by government grants and blue-sky funding.

Araya’s image-recognition products can narrow image selections for photo editors, inspect products for quality (pictured), and monitor crop growth.© Araya Inc.

Conscious of consciousness

The theory of consciousness that Kanai subscribes to is called Global Workspace Theory1. In this framework, the key to human consciousness is thought to involve prioritizing and amplifying key cognitive tasks.

For example, the human brain usually processes many things simultaneously, but the brain must select which among the tasks is the most important. For example, if sounds are identified as originating from a possible threat, the brain devotes more cognitive resources to processing these.

In a 2022 paper2, Araya researchers examined three theories of consciousness: Global Workspace Theory; Attention Schema Theory, which builds on Global Workspace Theory, adding new concepts; and, Kanai’s own Information Generation Theory3, which borrows from the ideas about mental simulations from the evolutionary biologist, Richard Dawkins.

In his book The Selfish Gene, Dawkins suggested consciousness was born out of mental simulations. And an animal that can include itself in its vision of the world may be better at planning ahead or outwitting opponents, which, Dawkins says, explains the evolutionary benefit of consciousness. Similarly, Information Generation Theory suggests that information that is consciously accessible is not simply based on sensory input, but the result of a holistic model or world view, held within the brain.

In their 2022 examination of these theories, the researchers believe that they demonstrated that all three must work together in humans2. They argue that AI models should also attempt to address all three theories to produce more generally intelligent AI systems.

How are such theories practically applied to AI? Araya’s researchers are already building in ideas and code around both self-perception and adaptability, says Kanai.

Model-based reinforcement learning has been shown to be important in simulations of crane lifting/shoveling robots, because, only by including itself in its framework, could the robot make predictions about what might happen if it performs certain actions. © Araya Inc.

For example, in an Araya paper published in August 20214, two approaches to reinforcement learning — model-based and model-free — were examined using simulations of crane lifting/shoveling robots. Model-based reinforcement learning is seen to be more reflective of consciousness, as it requires the agent to include itself in a model it constructs of the world. Only by doing this can it make predictions about what might happen if it performs certain kinds of actions. This informs the predictive coding and active inference systems that enable AI to reduce the training sample sizes needed, says Kanai.

Adaptability is also built into Araya’s ‘transfer learning AI’, a type of neural network that has been trained to complete one task and apply what it has learnt to a new related task. This type of learning is now used in some of Araya’s image-recognition products that do everything from product counting to monitoring crop growth.

While some of the topics in these papers may seem academic, these ideas are considered crucial to progress in the field of AI by pioneering technologists, says Kanai. A portion of the company’s revenue comes from funders who are AI pioneers, such as the entrepreneur, Marek Rosa, founder of GoodAI, a company devoted to rapidly developing safe general AI.

Grants also come from Japanese government sources — including the Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research (Kakenhi), the Moonshot Research and Development Program at the Japan Science and Technology Agency (JST) (see sidebar, Neuroscience or science fiction?), and, in the past, JST's Core Research for Evolution Science and Technology programme.

Neuroscience or science fiction?

Araya’s neuroscience is partly funded by Japan’s Moonshot Research and Development Program. Launched in 2019, this is a national industrial initiative with 100 billion yen (roughly US$741 million) for disruptive innovation aimed at finding solutions to address Japan’s biggest challenges: most pressingly climate change and an ageing population.

Ryota Kanai, founder and CEO of Araya.© Araya Inc.

Among the nine goals set by the programme, Araya’s founder and CEO, Ryota Kanai, has been appointed to lead a team to achieve the first goal, which is “the realization of a society in which humans can be free from limitations of body, brain, space and time by 2050”. The effort brings together researchers from Araya, Sony and Japanese universities. Together they are developing the Internet of Brains, a virtual world that will be harnessed using brain-machine interfaces, doing away with the need for keyboards and computer monitors. The earliest beneficiaries will probably be people with disabilities, says Kanai. But to do this the team needs to develop, not just machinery for picking up brain signals, but AI to translate those signals into network code.

The ultimate goal of the project is to create user-friendly brain-machine interface technologies for brain-to-brain communication, aiming to create a society where these technologies are trusted and widely used, says Kanai.

Araya’s neurotech

By focusing on different graphic patterns while wearing an electroencephalogram headset, this player was able to move their avatar to the left or right. © Araya Inc.

As for Araya’s own neurotech, they have been developing several products utilizing devices such as electroencephalography (EEG), a technology that uses scalp electrodes to record brain electrical activity.

In one gaming demonstration, for example, company researchers showed how, using EEG, a player could move their avatar to the left on a screen by focusing his or her attention on a particular graphic pattern, while focusing on a different pattern would move the on-screen character to the right. The problem with EEG headsets is that they are bulky and impractical in real-life situations, notes Shuntaro Sasai, Araya’s Chief Research Officer.

Araya has also developed a technology called Face2Brain using AI software. By analysing camera images of a subject’s facial expressions, eye activity and pupil dilation, Face2Brain can accurately infer brain activity associated with concentration or fatigue, and more. It will probably be first used by car manufacturers, as a vehicle feature that can spot when motorists are feeling drowsy, and advise them to take a break, says Sasai.

Another way to pick up and interpret brain signals is via functional magnetic resonance imaging (fMRI), which measures blood flow to pinpoint areas of activity in the brain. But to harness fMRI, Araya has to develop research that moves away from the idea that individual brain regions are focused on specific cognitive tasks, says Kanai.

Research groups are today trying to understand how constellations of regions work together to achieve cognitive function. In a January 2022 study5, a collaborative team of researchers, including from Araya, demonstrated how groups of brain regions were working constantly in tandem while study subjects were at rest.

Discoveries such as these will be key to developing commercial AI, Kanai says. “We’re getting industrial clients, because of our reputation in research,” he adds.


1. VanRullen, R. & Kanai, R. Deep learning and the Global Workspace Theory Trends in Neuroscience 44(9), p692-704 (2021) doi: 10.1016/j.tins.2021.04.005

2. Juliani, A., Arulkumaran, K, Sasai, S. & Kanai, R. On the link between conscious function and general intelligence in humans and machines Transactions on Machine Learning Research (2022)

3. Kanai, R., Chang, A., Yu, Y., Magrans de Abril, I., Biehl, M., & Guttenberg, N. Information generation as a functional basis of consciousness Neuroscience of Consciousness 2019(1), niz016 (2019) doi: 10.1093/nc/niz016

4. K. Matsumoto, K., Tamai, S. & Kanai, R. 2021 Goal-Directed Planning by Predictive-Coding based Variational Recurrent Neural Network from Small Training Samples IEEE International Conference on Development and Learning (ICDL) 1-6 (2021)

5. Matsui, T., Pham, T.Q., Jimura, K. & Chikazoe, J. On co-activation pattern analysis and non-stationarity of resting brain activity NeuroImage 249, 118904 (2022) doi: 10.1016/j.neuroimage.2022.118904

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