The Surprising Ways AI is Actually Helping

Añadido: 2026-05-12

[00:00:00]You know that stretch where you hit every single green light perfectly? That feels like magic. Like the traffic gods are smiling on you. Well, depending on where you live, that might not be luck anymore. There's an AI system that watches every single car, bike, and pedestrian approaching an intersection in real time, then lines up the lights so that you can cruise right through.

[00:00:19]And the numbers on how much faster people are getting home, they're kind of ridiculous. Speeding up your commute is just the beginning. There's a city in Tennessee where AI flagged so many near crashes on one block that planners made a single change and the results still blow my mind. And when I saw how much safer AI made bike lanes in Vancouver, Canada, I thought maybe I was the one hallucinating. The catch, for any of this to work, the city has to see you.

[00:00:43]Cameras, radar, the phone in your pocket, the car in your driveway. Kind of creeps me out. So, what do we actually get back? And is there a way to get all of this without cameras watching our every single move? I'm Matt Ferrell.

[00:00:55]Welcome to Undecided.

[00:01:00]This video is brought to you by MicroEnter. Cameras already surround us.

[00:01:04]They might be on your neighbor's front porch recording you, wishing them good morning from the sidewalk. They're watching you while you shop and as you pull out of the parking lot. It's gotten so bad, it's been in the news. How is all of this data used and by who? Some of the first cameras that got us used to being watched were at intersections. You probably noticed them while you were sitting at a red light. No cross traffic in sight, waiting forever for a green.

[00:01:25]Steven Smith, a robotics professor at Carnegie Melon University, he noticed them, too. But where many of us see surveillance, he saw an opportunity to build a traffic system so smart it could see everyone arriving in real time and line up the lights to let you cruise right through. It worked. Smith's AI traffic system called Sher Track sped cars through Pittsburgh 25% faster. Not because they were speeding, but because they weren't stopped at red lights. He cut idling by 40%. That's about half as much time waiting for a light to change.

[00:01:54]And by cutting down weight times, Smith system cut tailpipe emissions around 21%. Absolutely wild. It's more than just better air quality. Faster commutes mean more time off for breakfast and less money at the pump. Still, it's not a reason to stop worrying about the cameras watching us. But I think I might have found a technology that gives us all the upsides of intelligent traffic systems without the heebie-jebies. In systems like Sherek, each intersection uses computer vision to track what's happening and adjust in real time.

[00:02:21]Computer vision algorithms detect patterns and images the way chatbots recognize patterns in words. To do that, they train on massive video data sets where the cars, bicycles, and people are labeled by hand. One system I found trained on over 4 million labeled objects from 800 different locations.

[00:02:36]That lets computer vision see how many cars are coming and how fast, whether cars are waiting, and if there are bikes and pedestrians in the mix. All this data gets crunched streetside on advanced chips from companies like Nvidia. It's called edge AI and it means many calculations like the ones where time delays matter, they happen right in place instead of in the cloud or on a central server. A bad connection doesn't crash the lights. When deciding who gets the green, each intersection doesn't just look at its own data. Intersections are interconnected locally and they give each other a heads up on the traffic that's coming their way. That's what lets them line up those perfectly timed green waves. Compare that to the older traffic control like Los Angeles automated traffic surveillance and control system or ATSAC. I think it's called ATSAC. I'm just going to call it ATASSAC. The cameras at LA intersections don't identify cars and people. They're just there for when city traffic want to see why traffic jammed up. Instead, ATSAC relies on inductive loops, which are wire coils buried beneath the asphalt that detect a hunk of metal that's sitting on top. That means they can detect cars and trucks, but not motorcycles or bicycles like the camerabased AI systems can. It can't see what's coming next either. That means it can adjust the lights to relieve a traffic jam, but it can't predict the jam and adapt the lights to prevent it.

[00:03:48]It's the difference between calling in extra cashiers as soon as a crowd walks in and waiting until the checkout line stretches to the deli. It's no wonder dropped travel times 25% were at sack average just 10%. That's a massive win if you're okay with cameras watching every intersection in your town. Cameras don't just see the traffic, they see everything. So, we've got AI that speeds up your commute 25%. Cuts emissions, saves time. Sounds like a win, right?

[00:04:16]There's a catch, and it's it's a big one. But before I get into that catch, you just heard how Edge AI runs street side instead of in the cloud. The same idea is why I run AI on my own hardware at home, not someone else's servers. My home server is a Mac Studio, and I've got Llama and Quen models running on it locally, open source, private, it's mine. They handle voice assistants and home assistant and they help me write custom automations. It's genuinely useful and none of my data leaves my home. And a resource I love for all of this is today's sponsor, MicroEnter.

[00:04:46]They really are the AI destination right now. They've got AI workstations for every level. Want to start small? The HP ZBook Ultra runs LLM locally on a laptop. Going all in on fine-tuning your own models? The Nvidia DGX Spark is basically a Grace Blackwell supercomputer on your desk for around 4,500 bucks. They've even got a device comparison tool to help you find the right fit. A couple of quick store updates. Austin, Texas, you're getting a MicroEnter. Their 31st store opens later this year. Sign up using the link in the description and grab a free 128 gig flash drive when the doors open. Now, apparently people showed up during construction asking for theirs. So, wait for the actual opening. In New Jersey, your store is getting a full remodel.

[00:05:28]Whatever level you're at, the AI destination page rounds it all up. All the links are in the description, and thanks to MicroEnter and all of you for supporting the channel. Okay, about that catch. The cameras in the original Sherac system don't have high enough resolution to recognize faces or even read license plates, but that was back in 2012. Traffic sensors in today's market combine radar with 1080p HD cameras. They're not marketed for facial recognition, but the hardware could. And there's no guarantee a future software update or a new town mayor couldn't change that. The question is, would we even be told? In the United States today, thousands of AI powered cameras capture video clips and license plates from vehicles moving through town.

[00:06:07]They're paid for by both local law enforcement agencies and private businesses. It sounds like a smart choice for local safety, but data from any of those cameras operated by a single company called Flock is now searchable nationwide. Your car could be tracked across cities and even state lines. It's not just cameras. My Vision is the company that bought up the original SETAC technology. They collect MAC addresses from phones that are passing through intersections for traffic studies. A MAC address is the unique identifier your phone broadcasts when it's scanning for Wi-Fi. It's useful information. Myio vision can use it to figure out where cars and bicycles are entering the main drag and how long it took them to pass through downtown in Bryce Germany and I know I butchered that name. MAC addresses help determine which traffic lights were slowing things down leaded changes that nearly guaranteed a green wave on the town thoroughfare for measuring throughput or how long it would take any single person to pass through this town. This is relevant data. In order to do this, they need to track specific devices. Adding one car to the traffic as it enters downtown and subtracting one as it leaves tells you nothing about how long it will take any specific car to get through town. Tracking MAC addresses makes this possible. At first glance, this seems like it might not be a problem. MAC addresses are just strings of random numbers and letters. A city collecting data with this or any other system won't know its you specifically.

[00:07:28]However, it could know a device leaves Fifth and Vine at 8 a.m. and arrives at Montgomery Boulevard 20 minutes later every weekday. It kills me how little data you need to build up a pretty clear picture of someone's habits. But what if you didn't have to choose between green waves and giving out your data? There's another type of AI traffic system that doesn't track people and cars with cameras or cell phones at all. It can't identify a person or even read a license plate. That's because it uses LAR or light detection and ranging. Think of how bats navigate in the dark, sending out sounds and then listening for the echoes bouncing off nearby objects like lakes, trees, and even flying insects.

[00:08:05]The longer it takes for the sound to return, the further away the object is.

[00:08:08]With echolocation, the bat maps the world around it, even honing in on insects for dinner. LAR does kind of the same thing with laser light instead of sound. LAR sensors strapped to street poles reflect infrared light off of objects to build a 360° three-dimensional map of the intersection. But instead of tracking flying insects, LAR tracks speeding cars, bicycles, the odd jaywalker, and it does it rain or shine night or day.

[00:08:34]The LAR data looks like, well, clouds or points in space where light is reflected, but the shape of the car or cyclist moving northwest at 8 mph, not faces, not license plates. And unlike camera systems that promise not to identify you, LAR just can't. That limit is built right into the hardware. A neighborhood full of LAR sensors could track an individual trip, but it couldn't tie it to a person day after day the way MAC addresses could. These more anonymous LAR AI traffic systems are already happening like right now.

[00:09:05]Scion is based in California, but it's rolling out its LAR AI systems to intersections in Sweden and Finland.

[00:09:11]That makes sense given how simple LAR makes it to comply with GDPR, Europe's general data protection regulation. This regulation makes capturing and storing personal data like someone's face or license plate, a real compliance and data security headache, which it should be like everywhere. This is very sensitive stuff. Lighter is coming to America, too. Ster is another LAR AI traffic system out of California. And it's deploying to 100 intersections in Utah and another 120 in Chattanooga, Tennessee. And Chattanooga is that place with a crosswalk stat I can't get out of my head because AI traffic systems are about so much more than speeding up your commute. Getting home fast is great, but getting home alive is even better.

[00:09:51]According to the National Highway Traffic Safety Administration, about 40,000 people die in traffic accidents in the US every single year. That's an entire crowd of a baseball stadium. Not just people dying in cars, but over 7,000 pedestrians and 1,000 bicyclists are struck and killed. That doesn't even include the other 50,000 bicyclists that are injured each year. For me, the most exciting part of AI traffic monitoring is that it detects not just trucks and cars, but bicyclists and pedestrians, too. That means it can help make the roads safer for everyone. AI can tell that there are people still in the crosswalk and just hold the light.

[00:10:24]That's every kindergarten teacher's dream. And honestly, pretty useful for Fremont Street, too. Las Vegas has announced it'll install 16 smart lights there this year to automatically detect pedestrians. So, no button, just the walk sign when you need one. But how about a whole crosswalk when you want it? In cities like San Francisco, pedestrian fatalities are often due to jaywalking. And jaywalking makes me think of desire paths, which are those trails through the grass that show where people actually walk. With AI, traffic planners can now see exactly where those paths are, even when they cut across asphalt on a busy city street. So, when ouster's AI LAR system flagged a bunch of close calls on a city block in Chattanooga, city planners were able to install a crosswalk that reduced near misses 100%. How many things in this world are 100%. This is the power of using AI data to design streets for people the way they actually use them.

[00:11:16]The reason those near misses are gold for improving the safety of our streets is that current systems only record the crashes or the fatalities in police reports. Accident studies have shown that for every major injury or fatality, 29 minor injuries and about 300 near misses also occur. With computer vision watching the roads, we don't have to wait for someone to get hurt or die.

[00:11:38]After studying near misses and bike lanes in Vancouver, Canada, Myio Vision says they were able to suggest a cheap safety improvement that reduced risks by nearly 55%. And also boosted bicycle ridership by 68%. That's close to doubling the number of cyclists using the path because they were able to make it more than twice as safe. And now that intersections can see everyone, we can program in our values, giving the green light to greener transportation methods.

[00:12:04]And it's not just intersections that are getting smarter. Massachusettsbased Cambridge Mobile Telematics launched a system called Street Vision that uses AI and data from drivers cell phones to identify places where people are breaking hard or even driving distracted, which in Massachusetts is pretty much everybody. They gave the example of aggressive braking, leading them to find a stop sign that was hidden by an overgrown bush. As the company put it, "What we're looking at is the accumulation of events." That brought me to the infrastructure problem. And the solution to the infrastructure problem was a pair of garden shears. garden shears. It's a simple fix to a problem found thanks to AI and millions of drivers across the US that have opted in to having their data used. The catch is that opting in can be a little bit squidgy. Sometimes your insurer says that they'll only give you the discount if you install an app that tracks your driving or your workplace requires it as part of your driver score. Or as the New York Times reported for General Motors and its telematic service OnStar, data sharing can be baked into your new car's cell phone app. The kind where you quickly scroll through the fine print looking for the accept button. It's not always clear what you've signed up for, who's getting all the data, or the ways it's going to be used. Regulators have started to respond. Just this past January, the Federal Trade Commission banned General Motors and OnStar from sharing consumer data for 5 years, but they didn't even find them. From where I stand, privacy creep, it's not creeping anymore. It's going double the speed limit through a school zone and I don't see US laws catching up anytime soon.

[00:13:30]Sure tracks Steven Smith pointed out that the more data AI systems have the better decisions they can make. On one hand, I want them to have that data. On the other hand, I want it to be anonymous whenever possible like LAR allows. I don't want any of it to be used in a way that was not originally intended. So, how far to take AI when the laws lag so far behind? It's a really tough call. When I learned that Smith developed a similar AI algorithm to optimize delivery routes for over 100,000 meals to Pittsburgh families during the pandemic, kids that lost their free school lunches and elderly people stuck at home. It's not a question of whether AI can help. It absolutely can and it is. The question is how to capture all of the upside without losing control of our data and our privacy. What do you think about AI being implemented in ways like this? Do you think there's a balance? Any other examples that you've seen? jump in the comments and let me know. You can also check out extended cuts of this video over on Patreon. And if human written and research videos matter to you, Patreon support helps a ton. And a big welcome to new supporter plus members Ryan High and Edwin Pile. And new producers Art Henry and Casey. Thanks so much. And the links in the description if you'd like to join, but just watching, subscribing, and turning on notifications is absolutely awesome. And check out my follow-up podcast still to be determined will keep this conversation going. Keep your mind open, stay curious, and I'll see you in the next