Google New AI Solves Impossible Problems WITHOUT Instructions
DeepMind’s Breakthrough: Solving the Real-World Delivery Puzzle with AI
Okay, here’s something crazy. It turns out that most keys are terrible when it comes to making decisions in real life. I’m talking about the things that are really important.
Planning routes, keeping deadlines, delivering deliveries on time, without wasting fuel. We always hear that the key changes the world, doesn’t it? Even with 50 drivers or a city delivery, it fails like a cheap GPS. Until now.
Because what DeepMind has just achieved could actually fix one of the biggest blind spots in the key. If it works extensively, the food delivery, the operation planning of your doctor and the traffic avoidance in cities will change. You give the key real street knowledge.
And yes, that’s a big deal. So let’s talk about it. Okay, here’s the clue.
Planning delivery routes, assigning employees or bringing deliveries to stores is incredibly difficult. These are so-called combinatory problems. This means that there are a lot of possibilities and you have to choose the best one without violating the rules.
Delivery trucks need space and time windows, you have to make sure of that. Let’s take, for example, a delivery driver has to drive 50 houses in a city, each of which needs its package at a certain time. The truck can only transport a certain amount.
Finding the perfect route is like solving a puzzle with a billion pieces and that quickly. Good luck. In the tech world, such problems are called NP-difficult.
Why Traditional AI Fails at Logistics—and How DeepMind’s “Intelligent Discoverer” Changes the Game
They are so complex that even the fastest computers need forever for the perfect solution. Now you might think, hey, we have key, right? Can they just find it out? Well, here it gets tricky. Key, especially neural networks, are excellent at recognizing patterns.
Z. B. To recognize faces on photos or to say based on their course of action which film you might like. They work best with continuous data such as images, language or text. When planning truck routes with strict time and capacity requirements, rigid, binary decisions fail.
Such tasks require clear logic and the handling of restrictions that neural networks simply are not made for. On the other hand, conventional methods that can make such decisions, such as advanced mathematical solvers, are incredibly slow and require perfect information, which is not realistic when you try to move a mountain of packages in real time. To improve the key for difficult logistical puzzles, the researchers looked for a way.
They developed something ingenious. Key, which thinks like a human planner, only much faster. They developed a trick called MCMMC-Levels.
Don’t be alarmed by the name. It’s just another way of saying that you gave the key a tool in your hand to explore different options and choose the best one. Similar to how you try different routes on Google Maps to avoid traffic jams.
MCMC stands for Marov Chain Monte Carlo, by the way. But let’s just call it an intelligent discoverer. It’s like a GPS that doesn’t need a perfect map.
A Smarter Way to Plan: How DeepMind’s AI Learns Delivery Logistics Like a Human—But Faster
It checks options such as changing two stops or converting a truck and decides on clever rules. That’s how it works in a way that we can all understand. Imagine you’re planning a delivery route and you have a good plan, but it’s not perfect.
The clever explorer optimizes earlier stops, house changes to time and gasoline with small changes. He uses a method that is inspired by something called simulated cooling, where you slowly cool a hot piece of metal to make it firmer. Key decisions are cooled to choose the best ones in the long run.
The researchers turn this into a level that fits exactly into a neural network, so that the key can learn from its mistakes and choose better routes while remaining fast and flexible at the same time. The cool thing about this is that it doesn’t need a perfect solution to work. In the past, exact solutions were used to check a billion-puzzle piece by piece.
This takes forever, especially with big problems. This new approach uses so-called local search heuristics. Fast, intelligent estimates quickly lead to a great solution without perfectionism.
The researchers have ensured that these assumptions can be differentiated. This means that the key can learn from them, just as you learn from trial and error when planning a party. They also used something called fangirl young losses.
Okay, another technical term, but stay tuned. It’s like a scorecard that shows the key how close its plan is to the best possible plan. Even if the key only makes a quick attempt, this scorecard keeps the learning process going, which is enormous, as it means faster training and less computing power.
Winning in Real Time: How DeepMind’s AI Dominated a Major Logistics Challenge with Millisecond Decisions
They tested various starting methods for the key assumptions, a well-known good plan, ground truth, or a slightly improved one, using heuristics as a kind of rough route design as a prelude. Now let’s get to the interesting part, the results. They tested this using an extremely difficult problem called Dynamic Vehicle Routing Problem with Time Windows, or DVR-PTW from a major technology competition called Euro-Meets-Nu-IPS-2022.
Imagine a city where you have to go through delivery orders all day and have to assign the truck routes, while at the same time ensuring that the trucks reach every stop on time and are not overloaded. It’s like a real-time strategy game. Constant challenges, seconds to decide.
The researchers use a setup in which the route of each truck is planned in waves, where new orders are added and old ones are cleaned up one after the other. Their key based on this MCMC level was a blast. With only a millisecond to decide, the key routes, which were only 7.8 to 8% worse than the perfect one, took into account all future requests, end of plan, anticipatory baseline.
Let’s compare this with the old method, which used something called interference, so basically added random noise to shake things up, it was a whopping 65.2% worse. One hour compared to next week is so different with the package. Even when you gave it more time, for example 1000 milliseconds, your method reached 5.9% compared to 5.5% for the old approach, which shows that it is head-to-head with the best, but is much more practical for use in practice.
They also found out that the key became even more efficient when it started with a good plan, for example the perfect route from the baseline of the competition or a slightly adapted version. At 100 milliseconds, for example, the adapted start reached 5.9% relative costs. That was very close to ideal.
From Deliveries to Healthcare: How DeepMind’s AI Could Revolutionize Real-World Planning
They tested the setting temperature, which controls how much the key explores new ideas instead of using assessments. A temperature of 100 degrees Celsius was the sweet spot when you started with a good plan, but lower temperatures helped when you started at zero. For the test, they tested the method on simpler problems, such as the best combination of elements.
They found that their intelligent explorer of the perfect answer came very close after a few attempts. Performing several explorers at the same time was like the cooperation of a whole team of planners, which accelerated things without losing accuracy. They also proved that their method is absolutely solid mathematically, by using terms such as convergence guarantees to show that it reliably learns the best solutions over time, even for extremely complex problems.
In the vehicle route tests, optimizations such as the exchange of stops, the relocation of deliveries or the reversal of route sections were made, while the rules for loading and the time window were observed. They conducted these tests on a single CPU with 30 problem settings for the training and 25 for the tests with up to 100 requests per wave. The results were averaged over 50 runs to ensure that they were not just lucky.
A graph neural network, almost a super-organized table, processed the data. Why should you be interested in this? This technique brings faster, cheaper deliveries and reduces your prices, the stress for companies. Imagine that your food arrives hot, your packages arrive on time, or even hospitals plan operations more efficiently.
It’s not just about trucks. It could help with anything that requires intelligent planning, such as Z, B, with the organization of events or traffic control. The catch? It’s not perfect yet.
The researchers had to work around the internal processes of the key, so that it works, which is not always easy. They are already thinking about how they can improve it with smarter abbreviations for larger options. Okay.
So that’s how it starts. Key calculates delivery routes today and decides who gets health care tomorrow? Hmm. I would like to know what you think about it.
And hey, if you liked this analysis, then click on the subscribe button, to the blog for more wild tech stories and thanks for reading. See you next time.
- Google New AI Solves Impossible Problems WITHOUT Instructions
- Google New AI Solves Impossible Problems WITHOUT Instructions
- Google New AI Solves Impossible Problems WITHOUT Instructions
- Google New AI Solves Impossible Problems WITHOUT Instructions
- Google New AI Solves Impossible Problems WITHOUT Instructions
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