Apple released its new MacBook Air on Monday, touting the updated device as being twice as fast as previous generations. The'world's most popular laptop' starts at 1,099 and features the tech giant's new M3 chip released in 2023. The upgraded chip increases speeds up to 60 percent faster than its M1 predecessor and makes it 13 times quicker than the Intel-based MacBook Air, Apple has claimed. The 13- and 1,299 15-inch screen options are currently available for pre-order starting today and deliveries are set for Friday, March 8, the company announced. 'MacBook Air is our most popular and loved Mac, with more customers choosing it over any other laptop.

Intel today is entering the "AI PC" era with the launch of its new Core Ultra notebook chips. Originally codenamed "Meteor Lake," these are Intel's first processors to include an NPU, or neural processing unit, for accelerating AI tasks. The launch comes a week after AMD revealed its upcoming Ryzen 8040 hardware, its second batch of chips to include NPUs. While Intel is playing a bit of AI catch-up, the Core Ultra chips still sound like a solid step forward -- at least according to the company's benchmarks. Intel claims the Core Ultra chips use up to 79 percent less power than AMD's last-gen Ryzen 7840U while idling in Windows, and they're also up to 11 percent faster than AMD's hardware for multithreaded tasks.

We've now tested every version of Apple's M1 processor, from the first M1 chip in the 13-inch Macbook Pro all the way up to the M1 Ultra in the new Mac Studio, and the chip's ability to scale performance is pretty incredible. The M1 Ultra fuses two M1 Max chips together to get you a processor with 20 CPU cores and 64 GPU cores, along with up to 128GB of RAM, and it's one of the fastest processors we've ever tested. We asked what tests you'd like to see run on the M1 Ultra and assembled quite a list, including Adobe Lightroom and Premiere Pro, Davinci Resolve and Fusion, 3D modeling in Blender, machine learning tests like TensorFlow and Pytorch, and even some gaming. Amazingly, the M1 Ultra really does seem to be around twice as fast as the M1 Max in most applications. Whatever overhead is required to shuffle data around such a large chip, it rarely impacts CPU performance.

Some people have updated other people's views and understanding of life with the new idea they presented. Darwin is undoubtedly one of these people. Darwin's difference from other biologists and researchers is that he explains the evolutionary process in an algorithmic way and bases it on the laws of nature. Darwin's dangerous idea began in biology but has spread from engineering to sociology. There is greatness in this idea to be able to conceive of infinite beauty and complexity.

Machine learning (ML) models have been deployed successfully across a variety of use cases and industries, but due to the high computational complexity of recent ML models such as deep neural networks, inference deployments have been limited by performance and cost constraints. To add to the challenge, preparing a model for inference involves packaging the model in the right format and optimizing the model for each target hardware such as CPU, GPU, or AWS Inferentia. ML acceleration technologies have evolved to close the gap between productivity-focused ML frameworks and performance-oriented and efficiency-oriented hardware backends. However, optimizing a model for target hardware still involves assembling a complex tool chain of framework-specific converters and hardware-specific compilers, each with their own dependencies and configuration choices that can be difficult to understand, and then using it to compile the model. Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy ML models at 50% lower total cost of ownership than self-managed deployments on Amazon Elastic Compute Cloud (Amazon EC2).

It's clear that NVIDIA's newly announced 20-series GPUs are incredibly powerful, but so far, it's been tough to tell how much faster they are than the previous generation. Today during a press briefing at Gamescom, the company gave us a bit more insight: The new RTX 2080 is up to twice as fast as the GTX 1080 when using the new DLSS (Deep Learning Super-Sampling) feature. That relies on the Turing GPU's Tensor Cores for AI-powered rendering. It's up to developers to implement DLSS, but so far major titles like Final Fantasy XV, PUBG and the upcoming Shadow of the Tomb Raider can take advantage of the feature. Without DLSS turned on, those same games are around 1.5 times faster on the RTX 2080 than the 1080, according to NVIDIA.

Xbox has developed a new'FastStart' tool that will let Xbox Game Pass subscribers jump into the service's digital roster "twice as fast." The new feature uses machine learning to understand how people play, allowing it to identify which files are essential when starting a new game. It then prioritizes the download of those all-important files, enabling Game Pass users to quickly jump into full-fidelity gameplay while rest of the download takes place in the background. "While FastStart does not speed up download times, by identifying which files are needed to begin gameplay and prioritizing the download of those files first, you can expect to jump into your game, on average, twice as fast as you did previously," explains a post on the Xbox Wire blog. "That means if a game previously took 30 minutes to download and play, you will now be able to begin gameplay after just 15 minutes. In addition, since FastStart takes advantage of machine learning, we will continue to improve our algorithm over time getting players into the fun as soon as possible."

A German technology company has built a robotic spider that looks like the stuff of nightmares. Called the BionicWheelBot, the robo-spider is outfitted with eight terrifyingly spindly legs that allow it to walk, run and transform into a rolling wheel that can chase after you at alarming speeds. Scientists from Festo modeled the somersaulting robot after the real-life flic-flac spider, which is known for its unusual movements. The flic-flac spider is found in the Moroccan desert and can walk like other spiders. But it also has the eye-popping ability to propel itself into the air by rolling and cartwheeling through the air, usually as a means of deterring predators.

Machine learning is part of the broader artificial intelligence (AI) market, where computers are given information and learn how interpret it on their own. Self-driving cars, for example, may be preprogrammed not to cross over a double yellow line when they drive, but machine learning within those driverless cars will learn that in certain situations -- like getting around a parked car on the street -- it may be necessary to cross over that line. Machine learning's market potential is often lumped together with the AI market, which is expected to be worth $47 billion in 2020, according to industry analytics company IDC. And three companies -- NVIDIA (NASDAQ:NVDA), Intel (NASDAQ:INTC), and International Business Machines (NYSE:IBM) -- are betting big on the market in 2017. NVIDIA's known for its graphic processing units (GPUs) for the gaming sector, but the company is increasingly using its chips to power machine learning systems in powerful servers.

Facebook is releasing the hardware design for a server it uses to train artificial intelligence software, allowing other companies exploring AI to build similar systems. Code-named Big Sur, Facebook uses the server to run its machine learning programs, a type of AI software that "learns" and gets better at tasks over time. It's contributing Big Sur to the Open Compute Project, which it set up to let companies share designs for new hardware. One common use for machine learning is image recognition, where a software program studies a photo or video to identify the objects in the frame. But it's being applied to all kinds of large data sets, to spot things like email spam and credit card fraud.