Qualcomm just unveiled the latest mobile chipset to join its armada, the Snapdragon 7 Gen 3. Obviously, this is a refresh of the mid-range Snapdragon 7 Gen 2 and brings some new features to the table. We've long known that Qualcomm chips were about to get on-device AI integration, and the Snapdragon 7 Gen 3 is no exception. Nearly every aspect of this chip seems to have been designed with artificial intelligence in mind, with Qualcomm saying that the components "deliver across-the-board advancements to ignite on-device AI." This should significantly speed up generative AI applications, with advertised benchmarks of just one second to create Stable Diffusion images from a text prompt. Of course, a mobile CPU is more than just AI, despite what marketing wants you to believe, and the 7 Gen 3 seems powerful for a mid-range chipset.

Artificial intelligence continues to gain momentum within business, government and society at large, but the world has yet to see its full potential. This is the conclusion from the 2022 AI Momentum Survey, which includes responses from more than 500 executives worldwide. Nearly all the conditions are in place for large-scale AI adoption – a major leap from when this survey was first conducted in 2018. The signals are clear: AI is poised to be an increasingly pervasive force in business, culture and society. The only question is how quickly it will happen.

Indika AI has been recognized with the Best AI company of the year award at the Global Artificial Intelligence Summit & Awards organized by the All India Council For Robotics & Automation. The awards recognize Indika AI for working towards real challenges adversely affecting AI acceleration and data privacy in India. Indika AI is an end-to-end data infrastructure company working closely with the Indian AI ecosystem comprising academia, corporates, startups, government department, and authorities to accelerate data-centric AI development. India AI took real efforts in building a synthetic data engine that enables anonymization as well as synthesizing the datasets with high quality to foster increased (while safe) collaboration for AI acceleration across Industries and all data types, a platform for programmatic data labeling as well as speech recognition/ transcription for Indic language data, and a platform to evaluate the dataset quality and fix poor quality issues, biasedness, etc. Indika AI's mission is to make India the global hub for AI data solutions leveraging state-of-the-art Technologies such as data-centric AI, synthetic data, and programmatic data labeling as well as the large workforce of India and generate sustainable earning opportunities for Indian Youth. About GAISA: An event organized by the All India Council For robotics & Automation is an effort to recognize & highlight the impact & importance of Artificial intelligence today.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Artificial Intelligence (AI) everywhere has the potential to transform every business and improve the life of every person on the planet. In fact, every day we hear about AI breaking new ground, from detecting cancer and playing Minecraft, to creating "sentient" chatbots and generating compelling art. The goal of AI is simple: To accelerate "data to insights."

The topic of AI is not new and each one of us is benefiting from AI every day, transforming many aspects of our lives. This trend is fueled by edge computing which is providing opportunities to move AI workloads from the Intelligent Cloud to the Intelligent Edge for improved response times and bandwidth savings. In combination with Digital Twins and IoT, there is a strong trend not only in manufacturing but also in other industries to leverage AI/ML analytics for getting better and faster insights for improved Predictive Maintenance and more. The benefit of edge deployments is especially strong when it comes to computer vision models that take large data streams like images or live video as input. With edge computing, these large data streams can now be processed locally at the device / client, eliminating the need for significant bandwidth or privacy concerns associated with streaming into a cloud data center. Edge video analytics systems can execute computer vision and deep-learning algorithms either directly integrated into the camera or with an attached edge computing system.

The exponential growth of data has fed artificial intelligence's voracious appetite and led to its transformation from niche to omnipresent. An equally important aspect of this AI growth equation is the ever-expanding demands it places on computer system requirements to deliver higher AI performance. This has not only led to AI acceleration being incorporated into common chip architectures such as CPUs, GPUs, and FPGAs but also mushroomed a class of dedicated hardware AI accelerators specifically designed to accelerate artificial neural networks and machine learning applications. While these hardware accelerators can deliver impressive AI performance improvements, software AI accelerators are required to deliver even higher orders of magnitude AI performance gains across deep learning, classical machine learning, and graph analytics, for the same hardware set-up. What's more is that this AI performance boost driven by software optimizations is free, requiring almost no code changes or developer time and no additional hardware costs.

Some of the most well-known examples of the use of machine learning technics in science applications are the detection and classification of gravitational-waves signals from LIGO and Virgo in astrophysics [1], the recent DeepMind Alpha-Fold2 capabilities outperforming classical methods in protein folding [2] or the winning team of the Gordon Bell 2020 with the Deep Potential Molecular Dynamics [3] which is opening new breakthroughs in the drug design process and could speed up future pandemic response efforts. Beyond these key examples, the convergence between HPC and AI is natural where DL-based surrogate modelling is more and more widely applied in research and recent advances in physics-informed neural networks such as HNN [4] bring physical properties and constraints to neural networks loss functions opening a great path towards a new generation of simulation. In Atos, we built a dedicated approach to support the scientific community and Industries by bringing data science and HPC expertise through the Atos Centers of Excellence. Each center is oriented towards a specific domain where our experts and our customers can jointly bring innovations and technologies with the support of some of our partners. Some of the first Atos Centers of Excellence are dedicated to weather forecast & climate changes [5] and life sciences [6].

A decade ago, edge computing meant delivering static content through a distributed content delivery network (CDN). Akamai, Limelight Networks, Cloudflare and Fastly are some of the examples of CDN services. They provide high availability and performance by distributing and caching the content closer to the end user's location. The definition of the edge has changed significantly over the last five years. Today, an edge represents more than a CDN or a compute layer.

Early this year, I detailed that Intel (INTC) was poised to lead the AI revolution over the coming decade. The widespread adoption of AI will contribute significantly to demand for (Intel) compute silicon, and hence, will be a growth driver for the company. Intel forecasts it will be about a $25 billion opportunity by 2025, compared to $3.8 billion revenue in 2019. On June 18, Intel launched its third-generation Xeon Scalable platform, codenamed Cooper Lake. This follows a bit over a year after the company's April 2019 data-centric portfolio launch, which included second-generation Cascade Lake, the 10nm Agilex FPGA and 800 series of 100G Ethernet adapters.

Semiconductor industry startups are usually founded by hardware engineers who develop a silicon architecture and then figure out how to map software for that specific hardware. Here is a tale of a chip startup founded in the age of artificial intelligence (AI) that has a software DNA. Groq was founded in 2016 by a group of software engineers who wanted to solve AI problems from the software side. When they approached the issue without any preconceptions of what an AI architecture may need to look like, they were able to create an architecture that can be mapped to different AI models. The company is focused on the inference market for data centers and autonomous vehicles, and its first product is a PCIe plug-in card for which Groq designed the ASIC and AI accelerator and developed the software stack.