And we’ve made a lot of progress with Optimus. And as you can see, we started up with someone in a robot suit, sort of that. And then we’ve progressed dramatically year after year. So if you extrapolate this, you’re really going to have something spectacular, something that anyone can own. So you can have your own personal R2D2C3PO. And I think at scale, this would cost something like, I don’t know, $20,000, $30,000. Probably less than a car is my prediction, long term. It’ll take us a minute to get to the long term. But fundamentally, at scale, the Optimus robot, you should be able to buy an Optimus robot for, I think, probably $20,000 to $30,000, long term.

So and what can it do? It can, it’ll be able to do anything you want. So it can be a teacher, babysit your kids, it can walk your dog, mow your lawn, get the groceries, just be your friend, serve drinks, whatever you can think of, it will do. And yeah, it’s gonna be awesome. And I think this will be the biggest product ever of any kind. Because I think everyone, of the 8 billion people of Earth, I think everyone’s going to want that they’re Optimus Buddy. And there’s going to be maybe two.

And then they’ll be producing products and services. I predict actually, provided we address risks of digital super intelligence, 80% probability of good outcome. Look at the right side, top is 80% full. The cost of products and services will decline dramatically. And basically anyone will be able to have any products and services they want. It will be an age of abundance, the likes of which people have almost no one has envisioned. It’ll be something special. Now one of the things we wanted to show tonight was that Optimus is not a canned video.

It’s not rolled up. The Optimus robots will walk among you. Please, please be nice to the Optimus robots. So you’ll be able to walk right up to them. And they’ll serve drinks at the bar. And you’ll directly, I mean, that’s just, it’s a wild experience just to have people who are robots. And they’re there, just in front of you. So yeah, with that, let’s party. But Tesla isn’t just using AI for robots, but also for their newest car. As Tesla just unveiled its robo taxi cybercab, a fully autonomous vehicle set to transform ride hailing and car ownership.

Drawing design inspiration from the Cybertruck, it features a futuristic look with aerodynamic enhancements to boost efficiency. Without the need for traditional driving controls, the interior offers a spacious lounge like experience with advanced amenities. The cybercab supports wireless charging via inductive pads, enabling autonomous recharging priced under $30,000. It aims to be a mass market solution appealing to consumers and ride hailing services. This pricing challenges Tesla’s manufacturing capabilities, but leverages its innovative production methods equipped with full self driving technology. The cybercab will undergo real world testing in California and Texas before its final release to consumers.

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Click the link below and secure a dot chat domain today at pork bun dot com for under $2 for the first year. Meanwhile, a new paradigm of gaming is coming as researchers just released an AI model named diamond, which is short for diffusion for world modeling, and it’s capable of simulating a version of counter strike global offensive at 10 frames per second, all on a single Nvidia RTX 3090 graphics card. Amazingly, the team trained diamond using only 87 hours of CS go gameplay data, a mere 0.5% of the data set size used in similar projects like Game and Gen.

And despite this limited data, the model still manages to create a notable simulation of the game with diamond originally demonstrating its potential with Atari games. But now it utilizes a transformer based method where player movements are treated as tokens, as if they were words in a sentence. And by predicting these tokens, the model learns to anticipate future movements based on past actions. In fact, researchers showcased diamonds capabilities on Twitter, where videos depicted players interacting with the simulated CS go environment using standard controls. The simulation includes intricate elements such as player interactions, weapon mechanics, and environmental physics.

But the diamond model is not without its challenges, because certain bugs still persist, such as allowing players to jump infinitely due to diamonds lack of consideration for the source engine’s gravity and collision detection, where straying from frequently used paths in the training data can cause the simulation to break down completely. However, the researchers suggest that increasing the scale of data and computational resources could enhance the model’s performance. Plus, they also see potential in developing AI systems capable of navigating complex real world scenarios. And for those interested in exploring diamond further, the model is already available for download on GitHub.

Anyways, like and subscribe for more breakthrough AI news, and thanks for watching. Thanks for watching. [tr:trw].