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When you set off to become a developer, you probably dreamed of working on self-driving cars, artificial intelligence, world-changing apps, or any number of other amazing products. However, there’s a lot of learning and beginner developer work you need to do before you can get there. This can be draining and can make you lose sight of what got you excited to be a developer in the first place.
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If you’re anything like me, AI used to evoke futuristic images of human-like androids and supercomputer hive-minds. Pop culture, especially in the 80s-2000s, has lead us to believe that “artificial intelligence” is synonymous with “artificial sentience”.
In practice, AI is used for much more than world domination and now touches every aspect of our lives. Artificial intelligence is just the process of getting computers to process things more as humans do. The most obvious way to do that is to get computers to learn from past experiences with training data or by analyzing past processing logs. There are many subfields of AI that go about learning in different ways, such as machine learning, neural networks, natural language processing, computer vision, and many more.
The more we discover about AI, the more applications we can see for it. After all, most businesses and users repeat similar tasks throughout their day-to-day lives. AI offers a way for our devices to remember those tasks and optimize for when we do them again.
But what about the uses of AI that most people would never think of? Let’s take a look at some unusual uses of AI and how they may develop in the future.
Traffic is a growing concern in cities across the world. As cities become more densely populated, traffic systems have to get smarter to correctly funnel traffic and avoid blockages. Traditional timed light systems are no longer enough, so many cities in Asian countries such as India, China, and Malaysia are adopting AI-powered traffic management systems.
These systems take data from traffic cameras, GPS systems, and built-in sensors to track traffic build-up. An AI algorithm then runs computations using these different data sources and references data of past blockages to alleviate traffic before it starts. All lights in the city are connected to this system, meaning traffic flow can be changed in an instant to funnel traffic away from a crash or to clear certain streets for emergency vehicles.
Alibaba’s City Brain is one of the most used systems right now and has been adopted in 23 cities across China. Other Intelligent Traffic Management Systems (ITMS) has been adopted in the United States, Saudi Arabia, and are gaining traction in parts of Europe. Initial results of these implementations have been positive, reducing travel times by 26% and vehicle emissions by 21%.
AI-powered Traffic Systems are becoming increasingly popular and may hold the secrets to the transportation issues of tomorrow.
Honeybee populations are dwindling due to climate change and the destruction of habitat. However, the exact reason for the decline is unknown. This has many environmental experts worried because flowers rely on cross pollination from bees to reproduce. Without bees, the number of flowers in our ecosystems will fall sharply. Scientists are trying to find a way to stimulate the honeybee population but have found limited success due to a lack of standardized data on the causes.
This is where AI comes in. An organization named the World Bee Project has set out to record all the data they can from isolated beekeepers. They’re also setting up real-time cameras and audio sensors around the world to track hive health by listening to its sound and watch for rises in predator populations like hornets. These help track a myriad of different data types, allowing experts to see which regions need help and how bee populations differ across climates.
The sensors on each hive then periodically send this data to a huge Oracle Cloud network. Machine learning algorithms use this wealth of information to find correlations across different regions. This allows scientists to warn beekeepers if their hives are declining and to contact government officials if there is a larger reason, such as deforestation efforts.
They can also predict hive behavior based on previous data, such as if a hive is about to swarm or go dormant. This information allows beekeepers to provide the optimal care for their bees at whatever stage they’re in.
Overall, the World Bee Project’s AI is helping beekeepers and scientists share real-time information and resources to help experts understand this threat and preserve our bees before it’s too late.
Gastronomy is the art of combining flavors, either in a dish of food or in pairings. Many would say the science of AI and the arts are fundamentally separate but that line has begun to blur.
Chefs are now using AI to enhance their craft and discover new recipes, pairings, and optimize their menus.
The idea is to create an algorithm that takes training data of current recipes as well as data on ingredients like texture, flavor notes, and chemical composition. The algorithm then suggests new recipes by combining ingredients in previously untouched ways. While not every dish will work, it allows an interesting new perspective and can lead to surprisingly good discoveries. For example, IBM’s Chef Watson recently created dishes looking specifically at chemical composition and was able to find 10,000 novel dishes for chefs to try.
This technology is also being applied to optimize the taste of our favorite products. For example, New York startup Analytical Flavor Platform has created a Gastrograph that analyzes a food product and makes suggestions based on preferences data from hundreds of consumers.
Some companies are looking at how this could change the way we order food at restaurants by recommending food based on our preferences. A Los Angeles-based company named Halla I/O is currently experimenting with a “Netflix-style” recommendation system for food. It allows users to log their favorite foods and generates recommendations for restaurants or specific food items that the user may enjoy.
While still early in development, gastronomy seems to be a field that could greatly benefit from AI systems due to the number of variables chefs have to consider. Not all chefs will want to adopt this cooking-as-chemistry approach but it has ignited the imagination of many to seek out the yet undiscovered flavors our world has to offer.
The “unusual” frontier for AI isn’t just helping humans anymore—it’s acting independently in workflows, collaborating with other systems, and even making decisions. For example: a marketing campaign where an AI agent manages bidding, creative A/B testing, and optimizes budget across platforms, without constant human intervention.
These kinds of autonomous systems signal a shift: from “AI as assistant” to “AI as co-pilot or teammate.” When you’re exploring unusual uses of AI, include:
Agents that monitor processes end-to-end (e.g., supply-chain optimization) and respond in real time.
Multi-agent systems that coordinate with one another (AI + AI) to handle complex tasks.
Hybrid human-AI teams where the AI leads routine decisions and humans handle exceptions.
“Unusual” now also means AI taking on new roles, not just appearing in new domains.
Most coverage focuses on AI in business or science. But one of the most exciting spaces is creative expression and world-building. Consider how AI is now being used to:
Compose full songs, generate lyrics, and collaborate with human artists.
Create virtual actors or entirely synthetic “people” (avatars) in games, metaverses, and interactive entertainment.
Design immersive experiences (VR/AR) where the narrative, visuals, and interactions are dynamically generated by AI.
Generate “digital twins” of cultural artifacts or historical figures — used in museums, films, or tourism.
When we talk about unusual uses of AI, the most surprising ones often happen at the intersection of art and technology.
Some of the most unusual uses aren’t headline-grabbing — they’re subtle, embedded, and behind the scenes. Think of AI that:
Analyzes employee workflows and suggests optimizations in real time (like recommending better sequences of tools or automating bottlenecks).
Lives in enterprise tools silently helping decisions — like onboarding systems that generate tailored training paths or help-desk tools that triage tickets before humans touch them.
Exists as “shadow AI,” where employees bring in unofficial AI tools (chatbots, code assistants, prompt-engineering helpers) that reshape workflows without formal approval.
These hidden uses may not look flashy, but they show how deeply AI is being woven into everyday operations — often without anyone noticing.
Unusual uses of AI aren’t always about new applications — they’re also about new contexts. One growing area is AI for governance, compliance, and audit. For example:
AI systems designed to monitor other AI systems.
AI tools that audit code, detect bias, or flag problematic model behavior before it reaches production.
AI used for regulatory “what-if” simulations — predicting how new laws or policies might affect deployment.
Surveillance or organizational “digital twins” that map behavior and flag deviations.
These may sound less glamorous, but they’re some of the most impactful and unusual ways AI is being applied today.
To wrap up, it’s worth looking at where unusual AI applications are heading next:
Cognitive agents that self-improve and evolve over time.
AI ecosystems where multiple agents collaborate across domains without human coordination.
AI in synthetic biology, designing materials, enzymes, or even living systems.
Edge AI and tiny models embedded in everyday objects, from wearables to appliances, are opening up new, unexpected use cases.
The key takeaway? “Unusual” is a moving target. As AI matures, the most surprising applications will come from unexpected combinations—new roles, new contexts, and new frontiers we haven’t even imagined yet.
What are the uses of AI in everyday life?
What is the biggest advantage of AI?
