クジラ保護から利便性向上まで、AIができないことは何か？

人工知能は日常生活でますます身近な存在になっています。ChatGPTのようなLLM（大規模言語モデル）の登場により、誰もがAIを利用できるようになりました。しかし、この技術はLLMが市場に登場するずっと以前から、人々の生活を変え始めていました。ここでは、AIが現在まだできないことと、将来どこまで発展する可能性があるのかを見ていきます。

AIの進化

The AI sector has undergone numerous iterations based on the technology’s capabilities and accessibility. The concept of human-like machines dates back to ancient times. The Renaissance revived these concepts, and famous inventors like Leonardo da Vinci demonstrated the concept with automatons in 1495.

現在の人工知能は、1935 年のアラン・チューリングの汎用チューリング機械にまでさかのぼります。わずか 15 年後に彼はチューリングテストを考案しました。チューリングテストは人間との会話を用いて、AI システムが人間に近い能力を持っているかどうかを判断します。このテストは現在でも AI の相互作用のベンチマークとして使用されています。

The 1990s computational breakthroughs helped to push AI systems further. Machine learning and other AI systems began to emerge during this time, leading to breakthroughs in the early 2000s. For example, IBM demonstrated Watson’s AI skills by beating out humans in a game of jeopardy. At this same time, Google started releasing data on its neural network systems.

AIの現在

This decade has seen some of the greatest leaps in AI technology so far. The introduction of self-learning AI technologies like torque clustering algorithms has made the sector a point of discussion.

These technological gains have been met with moral questions and environmental concerns as well. All of these factors have led to more scrutiny of the future of AI and its influence on daily lives. So what can’t AI do? These projects highlight just some of its current capabilities and limitations.

人々がより良い都市を築く手助け

AIシステムは、都市計画担当者がより機能的で効率的な地域社会をつくるうえで役立ちます。交通状況やその他の重要なデータを追跡できるため、新しい公共サービスや高速道路など、地域に必要な施設の場所を決める際に大きな価値をもたらします。

インフラストラクチャーの洞察

AIシステムは、データから得られる知見を活用して、より安全で快適な地域をつくるためにすでに利用されています。Journal of Smart Cities and Societyに掲載された研究¹では、電力網の安全性と安定性が詳しく検討されています。

Specifically, it examines energy distribution, house requirements, what type of energy source each house uses, and if that places the location at a higher risk of power loss. The study explains that as more houses move towards all electric options, some risks may have been overlooked.

In particular, the report highlights how solar panels are a great source of energy during the summer, but how they can leave entire communities without power during winter storms and other environmental conditions. Sadly this scenario has become more common as the push for all electrified homes continues.

停電リスクをランク付けする AI モデルの構築

Stevens researchers analyzed data from the Department of Energy (DOE) building stock, including the energy patterns of 129,000 single-family homes. These houses were located in eight different states, allowing the team to test the system in various environments.

The ML models successfully determined individual house energy system footprints. It then processed this data, cross-referenced it with its data models, and determined what type of energy sources the house relied on.

Furthermore, the system took this data and used it to decide the blackout risks to the house and community. Now, the team seeks to expand its testing to more communities to help planners build safer and more resilient electrical grids.

地球を守る

Environmental conditions, pollution, human expansion, and other factors have left many wildlife regions in tatters. Artificial intelligence has been one solution that environmentalists have turned to to help determine wildlife patterns.

These systems can help conservationists notice sudden changes in species, which is a valuable tool that can assist in protecting rare and endangered animals from extinction. Already, AI systems can track migration patterns, health, poacher activity, and other vital data.

クジラ保護

A team of innovative researchers from Rutgers University-New Brunswick introduced an AI model designed to help international shipping vessels avoid endangered species populations recently.

研究²の主著者であるAhmed Aziz EzzatとJosh Kohutは、希少なタイセイヨウセミクジラを保護するため、さまざまなデータを用いてクジラの回遊・追跡モデルを作成しました。残念ながら、米国海洋大気庁のデータによると、野生に残る個体は400頭未満です。さらに、残存個体のうち繁殖可能な雌はわずか70頭であり、保全関係者の懸念が高まっています。

ソース – Nature Communications

Specifically, the team integrated the Rutgers University Center for Ocean Observing Leadership data from as far back as the early 1990s, satellite imagery from the University of Delaware, and underwater glider info into the model. Notably, gliders are underwater craft that have an array of sensors. They traverse the ocean floor helping to map uncharted regions, track environmental changes, and search for valuable resources,

The AI predicted the location and time of whale populations based on the whale’s preferences, past locations, environmental conditions, and time of year. It enabled researchers to connect these dots and find the patterns that increased whale sighting possibilities. In the future, this system will accurately show ships where whale populations are, enabling them to rechart a course avoiding these natural habitats.

モノのインターネットと人工知能（AIoT）.

The melding of Artificial Intelligence and Internet of Things technologies created the now budding AIoT industry. This sector combines the reach and data gathering capabilities of IoT systems with pattern recognition, processing, and convenience AI systems provide.

AIoT Systems enable logistics companies to get their products across the globe faster, reduce counterfeiting, and provide a host of other high-level features that would be impossible. AIoT improves on the IoT concept in a couple of key ways including efficiency.

Traditional IoT systems operate as sensors. They gather and send data via the internet to another location that then processes that data.  AIoT eliminates the need to send the data over the internet. Instead, these systems can process the information in-house, reducing time, costs, and bandwidth requirements.

スマートエコノミー

AIoT will serve a crucial role in tomorrow’s smart economy. Today’s smart homes rely on various human activity recognition protocols to try and provide convenience to their operators. For example, imagine telling Alexa that you want to work out, and initiating a sequence of tasks designed to make the process more convenient.

These tasks could include adjusting the lighting, playing music, closing the blinds, and even starting up the after-workout smoothie maker. All of this is possible now using preset macros. However, the use of advanced human activity recognition AI systems will automate this process.

MSF-Net

Incheon National University engineers recently raised eyebrows after introducing³ their MSF-Net (multiple spectrogram fusion network) Wifi human activity tracking system. The system can leverage channel state information (CSI) to determine subtle human activity.

This technology will make smart homes more convenient. Soon AI will be able to determine if you are cooking, resting, watching TV, or about to leave for work and adjust the environment accordingly. This technology could see integration into multiple sectors including manufacturing, security, and healthcare.

道徳的助言

AI技術がどれほど進歩しても、こうしたシステムに対する不信感はある程度残り続けるようです。学術誌Cognitionに掲載された最近の研究⁴は、道徳的な助言に関して人々がAIシステムを信頼していないことを示しています。

The School of Psychology studies why there is a distrust of AI systems, how it has slowed adoption, and what it would take to overcome these roadblocks to adoption. The researchers looked at the rise of artificial moral advisors (AMAs) and how they could one day become common.

Artificial moral advisors are purpose-built algorithms that leverage pre-programmed ethical theories, principles, and guidelines to provide users with answers to difficult moral dilemmas.

Notably, the study found that people were wary of AI advice on moral issues, even when it was the same advice given by a human advisor. It also demonstrated that many people have misconceptions about AI and concerns regarding its ability to go rogue.

In the future, you may have an AMA by your side to help you make the right choice. At the very least, it will document and probably report any “wrong” choices you make. As such, it’s easy to see why humanity may be ready to give AI missile launch codes, but not let it run the sermon.

AIができないことは何か？まだまだたくさんある

The AI revolution is in full swing and major technological breakthroughs continue to drive AI system capabilities further. Despite the constant echo that AI is coming for your job, most people still have plenty of time before their robot overlords take control. However, the human touch is still a way out.

Many factors like distrust and misunderstandings will inevitably lead to skepticism regarding AI’s true capabilities and purpose. For now, this tech has infinite potential but remains limited in its ability to connect to humans. As LLMs and robotics improve, the line between humanity and AI may meld further, opening the door for new realities.

Learn about Other Cool AI Projects 今すぐ

研究参考文献：

^{1. Majowicz, A., Popli, C., & Odonkor, P. (2025). 電力停止に対する世帯の脆弱性の定量化：スマートシティにおける急速な電化のリスク評価. Journal of Smart Cities and Society, 0(0). https://doi.org/10.1177/27723577241306340}

^{2. Ji, J., Ramasamy, J., Nazzaro, L., Kohut, J., & Ezzat, A. A. (2024). 北大西洋ミンククジラの存在をモデル化する機械学習：米国中大西洋の洋上風力エネルギー開発を支援. Scientific Reports, 14, 29147. https://doi.org/10.1038/s41598-024-80084-z}

^{3. Chen, J., Xu, X., Wang, T., Jeon, G., & Camacho, D. (2024). WiFiベースの粗細活動認識のためのマルチモーダル周波数融合を備えた AIoT フレームワーク. IEEE Internet of Things Journal, 11(24), 39020-39029. https://doi.org/10.1109/JIOT.2024.3400773}

^{4. Myers, S., & Everett, J. A. C. (2025). 人々は人工道徳アドバイザーがより功利的であることを期待し、功利的な道徳アドバイザーを不信頼する. Cognition, 256, 106028. https://doi.org/10.1016/j.cognition.2024.106028}