🤖 Google’s New AI Solves “Impossible” Problems Without Instructions – A Leap Toward AGI? 🚀🧠 | AI Revolution - video Dailymotion

Ai Revolution

Google unveils a groundbreaking AI system that can solve complex problems without needing step-by-step instructions. This revolutionary leap in artificial intelligence brings us closer to Artificial General Intelligence (AGI) by showing unprecedented autonomous problem-solving capabilities. Experts say it could transform science, engineering, and decision-making forever. 🔍📈  #GoogleAI #ArtificialIntelligence #AGI #TechNews #MachineLearning #AIInnovation #FutureTech #AutonomousAI #DeepLearning #BreakthroughTechnology #AIRevolution #GoogleDeepMind #ImpossibleProblems #SmartTech #NextGenAI #AIWithoutInstructions #AIResearch #AIDevelopment #AIinScience #TechUpdate

Transcript

00:00All right, so here's something crazy. Turns out most AIs are horrible at real

00:06life decision-making. I'm talking about the stuff that actually matters. Planning

00:10routes, hitting deadlines, keeping deliveries on time without wasting fuel.

00:14We always hear that AI is changing the world, right? But ask it to schedule 50

00:19drivers or plan a delivery run across a city and it breaks down like a cheap GPS

00:23that is until now. Because what DeepMind just pulled off might actually

00:29fix one of AI's biggest blind spots. And if it works at scale, it could change how

00:35your groceries get to your door, how your doctor schedules surgeries, even how cities

00:39avoid traffic jams. It's like giving AI actual street smarts. And yeah, it's a

00:45big deal. So let's talk about it. Okay, so here's the deal. Planning delivery routes,

00:51scheduling workers, or even figuring out the best way to get supplies to a store

00:54is crazy hard. These are what tech folks call combinatorial problems, which

01:00just means there are a ton of possible choices and you need to pick the best one

01:04without breaking the rules. Like making sure a delivery truck doesn't run out of

01:09space or miss a time window. For example, let's say a delivery driver has to hit 50

01:14houses in a city, each with a specific time they need their package, and the truck

01:18can only carry so much. Finding the perfect route is like solving a puzzle with a

01:22billion pieces and doing it fast? Good luck. In the tech world, they call these

01:27problems NP-hard, which basically means they're so complex that even the fastest

01:33computers would take forever to find the perfect answer. Now, you might think, hey,

01:37we've got AI, right? Can it just figure this out? Well, here's where it gets tricky.

01:42AI, specifically neural networks, are excellent at spotting patterns like recognizing faces

01:47in photos or predicting what movie you might like based on your history. They work best with

01:52smooth, continuous data like images, speech, or text. But when it comes to rigid, all or

01:57nothing decisions, like planning a truck route with strict timing and capacity rules, they

02:03fall short. These kinds of tasks need clear-cut logic and constraint handling, which neural

02:08networks just aren't built for. On the other hand, traditional methods that can handle these

02:13decisions, like advanced mathematical solvers, are incredibly slow and demand perfect information,

02:19which isn't realistic when you're trying to move a mountain of packages in real time.

02:23So, these researchers were like, we need a way to make AI better at these tough logistical

02:29puzzles. And they came up with something awesome. A way to teach AI to think like a human planner,

02:35but way faster. They created a trick called MCMC Layers. Don't let the name scare you. It's

02:42just another way of saying they gave AI a tool to explore different options and pick the best one,

02:47kind of like how you'd try different routes on Google Maps to avoid traffic.

02:51By the way, MC, MC stands for Markov Chain Monte Carlo, but let's just call it a smart explorer.

02:57It's like a GPS that doesn't need a perfect map. It checks out nearby options, like swapping two stops

03:03or rerouting a truck, and decides if it's a good move based on some clever rules. Here's how it works

03:10in a way we can all get. Imagine you're planning a delivery route, and you've got a decent plan,

03:15but it's not perfect. The smart explorer looks at small tweaks, like moving one stop earlier,

03:20or swapping two houses, and checks if they save time or gas. It uses a method inspired by something

03:27called simulated annealing, which is like slowly cooling a hot piece of metal to make it stronger.

03:34In this case, it's cooling the AI's choices to focus on the best ones over time. The researchers

03:41turn this into a layer that fits right into a neural network, so the AI can learn from its mistakes and

03:46get better at picking routes, all while staying fast and flexible. What's really cool is that this

03:52doesn't need a perfect solution to work. Old school methods relied on something called exact solvers,

03:57which are like trying to solve that billion piece puzzle by checking every single piece. That takes

04:02forever, especially for big problems. This new approach uses what's called local search heuristics.

04:08Think of them as quick, smart guesses that get you close to a great solution without obsessing over

04:14perfection. The researchers made sure these guesses are differentiable, which just means the AI can

04:19learn from them, like how you learn from trial and error when planning a party. They also used

04:25something called Fenchel Young Losses. Okay, I know, another techie term, but stick with me. It's like a

04:30scorecard that tells the AI how close its plan is to the best possible plan. Even if the AI only tries one

04:37quick guess, this scorecard keeps the learning process on track, which is huge. Because it means

04:42faster training and less computing power, they tested different ways to start the AI's guesses,

04:48like starting with a known good plan, called ground truth, or a slightly improved one using a heuristic,

04:54which is like giving the AI a head start with a rough draft of the route. Now let's get to the juicy

05:00part, the results. They tested this on a super tough problem called the dynamic vehicle routing problem

05:07with time windows or DVRPTW from a big tech competition called Euro meets NUR IPS 2022.

05:15Picture a city where delivery requests keep popping up all day and you've got to assign routes to trucks

05:21while making sure they hit each stop on time and don't overload the trucks. It's like playing a real-time

05:28strategy game where new challenges keep coming and you've got seconds to decide. The researchers use

05:32a setup where each truck's route is planned in waves with new requests added and old ones cleared out as

05:39you go. Their AI, powered by these MCMC layers, was a rock star when they gave it just one millisecond

05:47to decide a blink of an eye. It came up with routes that were only 7.8 percent worse than a perfect plan

05:53that knows all future requests, called the anticipated baseline. Compare that to the old method, which used

05:59something called perturbation, basically adding random noise to shake things up, and it was a whopping

06:0565.2 percent worse. That's like the difference between getting your package in an hour versus next week.

06:11Even when they gave it more time, like 1,000 milliseconds, their method hit 5.9 percent compared to

06:185.5 percent for the old approach, showing it's neck and neck with the best, but way more practical for

06:24real-world use. They also found that starting the AI with a good plan, like the perfect routes from the

06:30competition's baseline or a slightly tweaked version, made it perform even better. For example, at 100

06:36milliseconds, the tweaked start got them to 5.9 percent relative cost, super close to ideal. They played with

06:44a setting called temperature, which is like telling the AI how much to explore new ideas, versus

06:50sticking to what's working. A temperature of 100 was the sweet spot when starting with a good plan,

06:56but lower temperatures helped when starting from scratch. To make sure this wasn't just a fluke,

07:01they tested it on simpler problems, like picking the best combination of items. They found that their

07:06smart explorer got super close to the perfect answer, even with just a few tries. Running multiple

07:11explorers at once was like having a team of planners working together, speeding things up without

07:16losing accuracy. They also proved that their method is rock-solid mathematically, using terms like

07:22convergence guarantees to show that it reliably learns the best solutions over time, even for super

07:28complex problems. In the vehicle routing tests, they used specific tweaks, like swapping two stops,

07:36moving a delivery to a different spot, or flipping part of a route, and made sure they followed the

07:41rules, like not overloading trucks or missing time windows. They ran these tests on a single CPU,

07:49using 30 problem sets for training and 25 for testing, with up to 100 requests per wave.

07:57The results were averaged over 50 runs to make sure they weren't just getting lucky, and they used a

08:02graph neural network, like a super organized spreadsheet, to handle the data. So why should you care?

08:08This tech could make deliveries faster and cheaper, which means lower prices for you and less stress

08:13for companies. Imagine your food arriving hot, your packages showing up on time, or even hospitals

08:20scheduling surgeries more efficiently. It's not just about trucks. It could help with anything that needs

08:25smart planning, like organizing events or managing traffic. The catch? It's not perfect yet. The researchers

08:31had to tinker with the AI's inner workings to make it work, which isn't always easy. But they're already

08:37thinking about ways to make it even better, like using smarter shortcuts to explore bigger options.

08:44All right now, is this how it starts? AI solving delivery routes today, deciding who gets healthcare

08:50tomorrow? Hmm, I want to know what you think. And hey, if you liked this breakdown, hit that like button,

08:56subscribe for more wild tech stories, and thanks for watching. Catch you in the next one.

🤖 Google’s New AI Solves “Impossible” Problems Without Instructions – A Leap Toward AGI? 🚀🧠 | AI Revolution

Category

Transcript

Recommended