To say that Apple's WWDC 2023 keynote was packed would be an understatement. The company introduced the Vision Pro, its first foray into mixed reality headsets, as well as a 15-inch MacBook Air. There was also an updated Mac Studio and Mac Pro, both of which use the equally new M2 Ultra chip. As you'd expect Apple announced significant upgrades to all its software platforms. It's safe to say the Vision Pro was Apple's marquee device at WWDC.

Microsoft and Google both unveiled their AI products for the masses, with Microsoft holding a whole event this week to show off the new Edge and Bing. Google also had an event in Paris and unveiled the first Android 14 developer preview, while OnePlus launched its first-ever tablet alongside a new phone. Cherlynn is joined this week by guest co-host Sam Rutherford to tear into the week's onslaught of news, and check in to see how we feel about Samsung's Galaxy S23 Ultra while reviewing it. Listen below or subscribe on your podcast app of choice. If you've got suggestions or topics you'd like covered on the show, be sure to email us or drop a note in the comments!

NVIDIA JetPack SDK is the most comprehensive solution for building end-to-end accelerated AI applications. JetPack SDK provides a full development environment for hardware-accelerated AI-at-the-edge development. All Jetson modules and developer kits are supported by JetPack SDK. JetPack SDK includes Jetson Linux Driver Package with bootloader, Linux kernel, Ubuntu desktop environment, and a complete set of libraries for acceleration of GPU computing, multimedia, graphics, and computer vision. It also includes samples, documentation, and developer tools for both host computer and developer kit, and supports higher level SDKs such as DeepStream for streaming video analytics, Isaac for robotics, and Riva for conversational AI.

Today, customer exchanges with Alexa are generally either one-shot requests, like "Alexa, what's the weather?", or interactions that require multiple requests to complete more complex tasks. An Alexa customer planning a family movie night out, for example, must interact independently with multiple skills to find a list of local theaters playing a particular movie, identify a restaurant near one of them, and then purchase movie tickets, book a table, and perhaps order a ride. The cognitive burden of carrying information across skills -- such as time, number of people, and location -- rests with the customer. "We envision a world where customers will converse more naturally with Alexa: seamlessly transitioning between skills, asking questions, making choices, and speaking the same way they would with a friend, family member, or co-worker," says Rohit Prasad, Alexa vice president and head scientist. "Our objective is to shift the cognitive burden from the customer to Alexa."

TensorFlow is a symbolic math software library for dataflow programming across a range of tasks. It's typically used for machine learning applications such as neural networks. The software library was developed by the Google Brain team for internal use at first, but then the company released it under the Apache 2.0 open-source license back in late 2015. This required resource heavy hardware to perform anything meaningful, but back in May of last year, the company launched TensorFlow Lite for mobile machine learning and just recently they added support for GPUs on Android. We're getting to the point where AI, machine learning, and neural networks are virtually everywhere in our daily lives.

Running inference on compute-heavy machine learning models on mobile devices is resource demanding due to the devices' limited processing and power. While converting to a fixed-point model is one avenue to acceleration, our users have asked us for GPU support as an option to speed up the inference of the original floating point models without the extra complexity and potential accuracy loss of quantization. We listened and we are excited to announce that you will now be able to leverage mobile GPUs for select models (listed below) with the release of developer preview of the GPU backend for TensorFlow Lite; it will fall back to CPU inference for parts of a model that are unsupported. In the coming months, we will continue to add additional ops and improve the overall GPU backend offering. Today, we are releasing a precompiled binary preview of the new GPU backend, allowing developers and machine learning researchers an early chance to try this exciting new technology.

Facebook today announced the release of deep learning framework PyTorch 1.0 in developer preview, which includes a series of tools and integrations to make it more compatible with popular services from Google Cloud, Amazon Web Services, and Microsoft's Azure Machine Learning. Arm, Nvidia, Qualcomm, and Intel are also adding PyTorch support for things like kernel library integrations and tools to track inference runtime. PyTorch was released to the public in January 2017 and has been downloaded more than 1 million times. PyTorch 1.0 was first announced in May at the F8 developer conference, and includes deeper integration with Facebook's Caffe2 and ONNX. Back in May, Facebook VP Bill Jia and CTO Mike Schroepfer promised PyTorch 1.0 would launch with new pretrained models, tools, and libraries to give developers more flexibility and options.

Facebook Inc. today launched a developer preview of its long-awaited PyTorch artificial intelligence software framework, which helps accelerate the deployment of AI-based applications. Announced in May, PyTorch was created by Facebook's AI research group as a machine learning library of functions for the programming language Python. It's primarily designed for use with deep learning, which is a branch of machine learning that attempts to emulate the way the human brain functions, and has led to major breakthroughs in areas such as language translation, image and voice recognition. PyTorch is supposed to help speed up the development of these kinds of AI capabilities, and has previously been used to build more realistic avatars for Facebook's Oculus virtual reality headset. Researchers at UC Berkeley have also used PyTorch to speed up their work on image-to-image transformation, for example.

For a company as big and sprawling as Google, an annual developer conference can feel overwhelming. Google offers a dizzying number of services and dabbles in almost every consumer tech industry under the sun, including seven core products with more than a billion users each. But the company has a measured tempo when it comes to Google I/O, and we've come to understand how it prioritizes certain products over others come May every year. At this year's I/O, which will be held again at the Shoreline Amphitheater in Mountain View, California starting Tuesday, May 8th, we know we'll be hearing about the future of Android and Google's artificial intelligence efforts. But there will also be news on everything from its new wearable platform, Wear OS, and Google Assistant to Android TV, Google Home, Google Play, and Search. This is the time of the year when Google pulls out all the stops to showcase how its software is smarter and more forward-looking than the products from its rivals Amazon, Apple, Facebook, and Microsoft.

Google IO 2018 is just weeks away, with the event happening in early May, and it's sure to be used to unveil a bunch of Android software goodies and potentially even some new hardware. We don't know much about IO 2018 yet, but Google has begun teasing the event, giving us a few clues as to what we might see – and when. We've also come up with a wish list for what we want from Google's annual developer conference, which you'll find at the bottom of this page. Google took to Twitter in January to tease IO 2018, and that tweet led users to the google.com/io That's not particularly surprising, as Google IO is usually held in May, but this would be slightly earlier in the month than usual.