Artificial intelligence has made remarkable progress with still images. For months, services like Dall-E and Stable Diffusion have been creating beautiful, arresting and sometimes unsettling pictures. Now, a startup called Runway AI Inc. is taking the next step: AI-generated video. On Monday, New York-based Runway announced the availability of its Gen 2 system, which generates short snippets of video from a few words of user prompts. Users can type in a description of what they want to see, for example: "a cat walking in the rain," and it will generate a roughly 3-second video clip showing just that, or something close.

Qualcomm has unveiled its latest chipset that will power a wealth of mid-range phones starting later this month. Redmi and Realme are among the brands that will use the Snapdragon 7 Gen 2 chipset. As you might expect, the chipset isn't quite as powerful as the Snapdragon 8 Gen 2, but it appears to offer a notable upgrade over the Snapdragon 7 Gen 1. Qualcomm says the CPU will deliver a performance improvement of over 50 percent, with speeds of up to 2.91GHz. The company claims the Snapdragon 7 Gen 2 will offer improvements in GPU performance (by two times) and power efficiency (by 13 percent) as well. Moreover, Qualcomm says that "on-device AI is integrated across the entire platform."

Key additions include real-time hardware ray tracing, the ability to sense and optimize different "layers" in your photos, and massive connectivity upgrades whose throughput will likely outpace your home Internet connection. Specifically, AI will be used to power a number of new experiences in Snapdragon-powered phones, which will debut this fall. Smarter cameras will try to interpret what you're shooting and enhance it before you even take the picture, not afterwards, executives said. What Qualcomm now calls an "always sensing" camera will also remain in low-power mode, scanning the world around it; you'll be able to hold up the phone to scan an QR code even if the phone is in standby mode, they said. The phone will also use its AI capabilities to improve cellular connections, as previous phones have done.

Qualcomm has announced its latest flagship mobile chipset, the Snapdragon 8 Gen 2. Along with making it more powerful and efficient than Gen 1 chips, Qualcomm says it has packed more AI smarts into the new platform. The Snapdragon 8 will tap into the latest Qualcomm AI Engine and upgraded Hexagon processor to offer "faster natural language processing with multi-language translation and advanced AI camera features," the company claims. The processor has architectural upgrades that will enable up to 4.35 times the AI performance of Gen 1 chips, according to Qualcomm. There will be support for an AI precision format called Int4, which the company suggests will lead to a 60 percent performance/watt improvement over the previous-gen chipset for sustained AI inferencing. Meanwhile, the Sensing Hub will have dual AI processors, which can support features such as custom wake words.

Using information-theoretic principles, we consider the generalization error (gen-error) of iterative semi-supervised learning (SSL) algorithms that iteratively generate pseudo-labels for a large amount of unlabelled data to progressively refine the model parameters. In contrast to most previous works that {\em bound} the gen-error, we provide an {\em exact} expression for the gen-error and particularize it to the binary Gaussian mixture model. Our theoretical results suggest that when the class conditional variances are not too large, the gen-error decreases with the number of iterations, but quickly saturates. On the flip side, if the class conditional variances (and so amount of overlap between the classes) are large, the gen-error increases with the number of iterations. To mitigate this undesirable effect, we show that regularization can reduce the gen-error. The theoretical results are corroborated by extensive experiments on the MNIST and CIFAR datasets in which we notice that for easy-to-distinguish classes, the gen-error improves after several pseudo-labelling iterations, but saturates afterwards, and for more difficult-to-distinguish classes, regularization improves the generalization performance.

Qualcomm's next generation of mobile processors should help bring flagship features to more midrange devices. The chipmaker announced two new chips -- the Snapdragon 6 Gen 1 and the Snapdragon 4 Gen 1 -- that come bundled with AI enhancements and hardware to support 5G. The Snapdragon 6 Gen 1 is the more powerful of the pair, with the ability to shoot pictures at up to 200 megapixels and HDR video capture. It also comes with "intuitive AI assistance" that can help suggest apps and settings based on your activity. Qualcomm says the SoC's (system on a chip) CPU performs up to 40 percent better than the Snapdragon 695 it released last year, while its GPU performs up to 35 percent better.

Qualcomm launched the Snapdragon 8 Gen 1 mobile processor for smartphones at the Qualcomm Tech Summit in Hawaii late on Tuesday, adding substantially more performance and AI-powered features to 2022 smartphones. However, one of those may be controversial. While you may be used to your phone always listening for commands, are you ready for its camera to be always on, too? In an interesting twist, Snapdragon 8 phones will even be able to mint NFTs. Now the new Snapdragon 8 Gen 1 is poised to help launch even more, beginning in the fourth quarter of this year.

Every December for the last few years, Qualcomm has held an annual event in Hawaii to announce its latest flagship mobile chipset. This year was no different with the company taking the opportunity to unveil the Snapdragon 8 Gen 1. That's right, for the second year in a row, Qualcomm is moving away from the sequential numbering scheme that has defined its processors for years. Just as the Snapdragon 865 gave way to the 888, the company will now replace the 888 with the Gen 1. The company says it's capable of theoretical download speeds of 10Gbps. That's one of those specs that's impressive on paper, but won't mean much out in the real world since some of the fastest 5G networks can't deliver speeds greater than 4Gbps in ideal conditions.

We are roughly a decade removed from the beginnings of the modern machine learning (ML) platform, inspired largely by the growing ecosystem of open-source Python-based technologies for data scientists. It's a good time for us to reflect back upon the progress that has been made, highlight the major problems enterprises have with existing ML platforms, and discuss what the next generation of platforms will be like. As we'll discuss, we believe the next disruption in the ML platform market will be the growth of data-first AI platforms. It is sometimes easy to forget now (or, tragically, maybe it's all too real for some), but there was once a time when building machine learning models required a substantial amount of work. In days not too far gone, this would involve implementing your own algorithms, writing tons of code in the process, and hoping you make no crucial errors in translating academic work into a functional library.

In this paper the generations of artificial neural net- works (ANN) are surveyed. The assumptions present in Gen 1 and 2 ANNs are enumerated. In the pro- cess of reformulating the Gen 2 ANN an extension was observed that could increase the biological plausibility of the model. The resulting model makes use of the neurological interneuron structures that provide inhibi- tion and input gain control in the cortical regions of the brain. The resulting interneuron neural network (INN) is applied to the well know MNIST. The INN beats an identical ANN. The application of the model is used to validate the derivation of the model and associated backpropagation.