If you're in the market for a new device, you can't really beat the MacBook Air. This sleek and powerful Apple computer is easy to bring anywhere, and right now you can get one for just 199.97-- 800 off the regular price--through July 20. It's hard to find the perfect laptop--they're often packed with serious perks but not very lightweight, or they're slim and portable but lacking in performance. The MacBook Air strikes the perfect balance--it packs a 1.8GHz Intel Core i5 processor and 8GB of RAM in a sleek 2.96-pound design. You can answer emails, stream content, or browse the web on a generous 13.3-inch widescreen display equipped with Intel HD Graphics 6000.

Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and wellchosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effect on the underlying loss landscape, are not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple "filter normalization" method that helps us visualize loss function curvature and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture affects the loss landscape, and how training parameters affect the shape of minimizers.

Each AI model has its own strengths, and it can be tough to keep up with their unique advantages. Whether you need to create an image for social media, whip up a blog post, or optimize your business's website, 1min.AI can do it all. This lifetime subscription lets you enjoy all the perks of these platforms at a one-time low price, with no subscription fees required. And aside from saving you money, it saves time since you won't have to switch between different AI services constantly. This Pro Plan gives you 1,000,000 monthly credits, with an unlimited prompt library and storage.

Breakthroughs, discoveries, and DIY tips sent every weekday. Weather forecasts are notoriously unreliable. Most people can relate to booking a trip or making plans expecting a sunny day, only to have it disappointingly rained out. While seven-day weather forecasts are accurate about 80 percent of the time, that figure drops to around 50 percent when extended to 10 days or more. Recent staffing cuts at the National Weather Service have already led to reduced weather balloon data collection, which experts warn could further degrade forecast accuracy.

OpenAI CEO Sam Altman sits down with Shannon Bream to discuss the positives and potential negatives of artificial intelligence and the importance of maintaining a lead in the AI industry over China. OpenAI has just made a move that's turning heads across the tech world. The company is acquiring io, the AI device startup founded by Jony Ive, for nearly 6.5 billion. It's a collaboration between Sam Altman, who leads OpenAI, and the designer responsible for some of Apple's most iconic products, including the iPhone and Apple Watch. Together, they want to create a new generation of AI-powered devices that could completely change how we use technology.

TL;DR: PicWish is an easy-to-use AI photo editor that lets you enhance, retouch, and batch edit images, and it's only 60 for life. Creating clean, professional-looking images can be time-consuming, especially if you're not experienced with photo editing software. PicWish offers a simpler solution. Designed for content creators, online sellers, and small business owners, this AI-powered image editor makes it easy to enhance, edit, and retouch photos in just a few clicks. While it's on sale, you can get PicWish for only 59.99 (reg.

I didn't expect it to change my entire computing setup. But here I am now, using the 300 Beelink SER5 Mini PC as my daily workhorse, powering an ultrawide 1440p 100Hz monitor and smoothly handling any productivity task I've thrown at it. This little computer has been so delightful to use that I've relegated my full-sized desktop tower PC to the basement television, where it's now serving exclusively as a gaming rig. Consider this a lesson on technological overkill. Outside of some specialized use cases, the required compute power for getting things done might be a lot less than you think.

This paper studies the problem of efficient exploration in finite episodic MDPs. They present a variant of optimistic initialization tuned learning rates for Q-learning that recover a UCB-style algorithm. The main contribution of this work is a polynomial regret bound for perhaps one of the most iconic "model-free" algorithms. There are several things to like about this paper: - Q-learning is perhaps the classic intro to RL algorithms, so it's nice to see that we can recover sample efficient guarantees for a variant of this algorithm. The computational time is also particularly appealing compared to existing model-free algorithms with sqrt{T} *expected* (Bayesian) regret (such as RLSVI), which have much higher computational and memory requirements.

Google unveiled a slew of new AI tools and features at I/O, dropping the term Gemini 95 times and AI 92 times. However, the best announcement of the entire show wasn't an AI feature; rather, the title went to one of the two hardware products announced -- the Android XR glasses. CNET: I hated smart glasses until I tried Google's Android XR. For the first time, Google gave the public a look at its long-awaited smart glasses, which pack Gemini's assistance, in-lens displays, speakers, cameras, and mics into the form factor of traditional eyeglasses. I had the opportunity to wear them for five minutes, during which I ran through a demo of using them to get visual Gemini assistance, take photos, and get navigation directions.

Tech expert Kurt Knutsson discusses LEVA, the autonomous robot that walks, rolls and lifts 187 pounds of cargo for all-terrain deliveries. Autonomous delivery robots are already starting to change the way goods move around cities and warehouses, but most still need humans to load and unload their cargo. That's where LEVA comes in. Developed by engineers and designers from ETH Zurich and other Swiss universities, LEVA is a robot that can not only navigate tricky environments but also lift and carry heavy boxes all on its own, making deliveries smoother and more efficient. Join the FREE "CyberGuy Report": Get my expert tech tips, critical security alerts and exclusive deals, plus instant access to my free "Ultimate Scam Survival Guide" when you sign up!