Asilla, a developer of security systems with world-class behavior recognition AI at its core, launched the Human Science AI Research Team (HSAR) in February 2023 to further improve the accuracy of its AI. In this article, we interviewed Mr. Masahiro Wakasa, CTO of Asilla, the founder of HSAR, about the research efforts and future development of "Generative AI.
" Generative AI", as the name implies, refers to "AI that generates data based on certain inputs". For example, it is an AI that generates images, text, music, etc., through its own learning based on past data or human instructions. It is truly a creative AI. Generative AI has been rapidly advancing and further research has been conducted since the announcement of the image-generating GAN in 2014. AI has become a hot topic in Japan as well. Generative AI has matured technologically and is now in the process of being put to practical use in multiple fields. We are now in the phase of "how to connect AI-generated products to new value". We are now at the stage of using this technology in terms of behavior recognition AI.
HSARでは、人間の行動を基とした行動データを生成AIが自動生成するという試みを行っています。行動データというのは、例えば「喧嘩をしている」「倒れて動かなくなった」というような人の行動に関するデータのことを指します。このような行動データを基にAIの研究・開発を日々行っているのですが、従来は私たちが実際に行動し記録するというアナログ要素が強い方法でデータ取得を行っていました。カメラを設置してカメラの前で人が実際に何百回、何千回も行動をし、行動データとして地道にデータの蓄積を続けていたという感じですね。AIの精度向上は学習するデータ量と比例します。現在は更なるデータ収集を目的に、生成AIを活用し、過去蓄積した膨大なデータを基にAI自らが新たな行動データを生成できないか、と考えています。そして、生成したデ��タを用いてまたAIが自ら学んでいく。このような好循環の実現に向け、現在研究を進めています。
It is very difficult to determine how to correctly identify the data that " the human behavior data generated by AI is really human-like". After all, computer science techniques alone cannot answer the question, "Is this behavioral data human-like? Or is it a mechanical movement? I feel that it is very difficult to properly discern whether this behavioral data is truly human or mechanical. I believe that human science research is necessary for this determination. Since behavioral data is the foundation of AI research, utilizing only truly human-like behavioral data will further improve the accuracy of AI's prediction models, which will lead to new learning. We hope to see a cycle in which an AI that can truly infer human behavior is created. Although we are still in the research phase, we are beginning to see some very good results. We have been steadily accumulating data on human behavior for many years before we began to use generative AI. It is precisely because we have a vast amount of human-like behavioral data that we feel we are entering the next phase of our research.
We are not quantifying this in a strictly definite way, but you have dramatically improved the speed of data generation. For example, let's say you have to create more than 10,000 pieces of data for a single action in order to correctly detect it. With conventional data acquisition methods, this can take up to two weeks. On the other hand, with generative AI, data generation can be completed in as little as 1-2 hours. Moreover, the conventional method requires a person to be involved in the process, which requires labor costs, whereas with generation AI, all you have to do is press a keyboard button and wait. Therefore, no human labor is required for data generation. Not only does this dramatically improve the speed of data generation, but it also has the advantage of reducing costs.
この生成AIの活用に関しては、この先研究を行う上で一つの転換点であると捉えています。引き続きHSA研究チームでは、更なるAI精度の向上のため、生成AIの活用に関しても研究を進めてまいります。AIの精度向上には、その基となるデータが必ず必要となります。しかし、活用するデータは量だけでなく質も求められます。そのため、データ取得には時間もコストも必要です。データ取得を���っては、安い労働力をめぐる倫理的な問題も最近問題となっています。もし、AIが自ら正しいデータを作ることができるようになれば、こうした倫理的な問題の解消にも繋がると考えています。
Generative AI, which has the characteristic of " AI creates itself," has evolved remarkably over the past year or two. I have the impression that most generative AI is currently focused on the entertainment domain, such as the generation of beautiful images and illustrations. This trend is expected to continue in the entertainment domain, and at the same time, it is also expected to expand to solve problems in the business domain. One of these problem-solving areas is the area of "behavior-aware AI," which is one of our strengths. With such a future vision in mind, the entire team at Asilla is working together to advance research and development toward that future.
After completing his postgraduate studies at Tokyo Institute of Technology, he worked for JGC Corporation where he was involved in plant design IT work on overseas construction projects. He then joined Asilla Corporation, where he was in charge of proof-of-concept and product development projects related to behavior recognition AI, and became the company's executive officer and CTO in 2022. He continues to focus on product development using AI technology and research and development of new technologies.
