Artificial Intelligence continues to become more common in everyday lives. The introduction of LLMs (Large Language Models) like ChatGPT placed AI in everyone’s hands. However, the technology was already well on its way to changing lives far before LLMs ever hit the market. Here’s a glimpse into what AI can’t do currently and where it will end up in the future.
AI Evolution
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.
Artificial intelligence as it’s seen today can be traced back to Alan Turing’s Universal Turing Machine in 1935. Only 15 years later, he created the Turing test. The Turing test uses human conversation to determine if an AI system has reached human-like capabilities and beyond. This test is still used today as a benchmark for AI interactions.
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 Today
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.
Help People Build Better Cities
AI systems can help city planners create more effective and efficient communities. AI systems can track traffic and other vital data that can be of great value when determining locations for new services, highways, and other community needs.
Infrastructure Insights
One way in which AI systems are already being used to create safer and more comfortable neighborhoods is through insights. A study1 published in the Journal of Smart Cities and Society delves deep into electrical security and stability.
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.
Building an AI Model to Rank Power Loss Risk
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.
Save Earth
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.
Whale Conservation
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.
The lead authors of the study2, Ahmed Aziz Ezzat and Josh Kohut, created the whale migration and tracking model using a variety of data designed to protect the rare North Atlantic Right Whale species. Sadly, data from the National Oceanic and Atmospheric Administration reveals fewer than 400 of these majestic creatures remain alive in the wild. Of the remaining population, only 70 are reproductive females, leading to heightened concerns by conservationists.
Source – 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.
Artificial Intelligence of Things (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.
Smart Economy
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.
Morality Advice
It seems that no matter how advanced AI technology gets, there will always be a little distrust for these systems. A recent study4 published in the journal Cognition highlights how humans don’t trust AI systems regarding moral advice.
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.
What Can’t AI Do? There’s Still a Lot
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 Now
Study Reference:
1. Majowicz, A., Popli, C., & Odonkor, P. (2025). Quantifying household vulnerability to power outages: Assessing risks of rapid electrification in smart cities. 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). Machine learning for modeling North Atlantic right whale presence to support offshore wind energy development in the U.S. Mid-Atlantic. 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). An AIoT framework with multimodal frequency fusion for WiFi-based coarse and fine activity recognition. 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). People expect artificial moral advisors to be more utilitarian and distrust utilitarian moral advisors. Cognition, 256, 106028. https://doi.org/10.1016/j.cognition.2024.106028