Two dates from 2022 are destined to echo in geopolitical history. The first, Russia's invasion of Ukraine on February 24, hardly needs further elaboration. The second is October 7, 2022, when the United States enacted a new set of export controls designed to cripple China's future progress in AI technology. Rather than target AI software, the export controls choke off China's access to the advanced (and almost exclusively American-designed) computer chip hardware that powers AI. More than a decade of breakthrough after breakthrough in AI technology has convinced policymakers in both Beijing and Washington that leadership in AI technology is foundational to the future of economic and military power.

This has been a rough year for tech stocks--but there could be reason to hope for long-term growth. Market volatility, supply-chain issues and rising inflation have all contributed to the selloff. Morningstar research also suggests that big tech companies could see a significant hit to third-quarter earnings as a strong dollar eats into profits from abroad. Many exchange-traded funds that focus on tech-stock themes have had an equally rough go. The two largest semiconductor ETFs, iShares Semiconductor ETF (SOXX) and VanEck Semiconductor ETF (SMH), were trading near 52-week lows at quarter's end.

Software has been the star of high tech over the past few decades, and it's easy to understand why. With PCs and mobile phones, the game-changing innovations that defined this era, the architecture and software layers of the technology stack enabled several important advances. In this environment, semiconductor companies were in a difficult position. Although their innovations in chip design and fabrication enabled next-generation devices, they received only a small share of the value coming from the technology stack--about 20 to 30 percent with PCs and 10 to 20 percent with mobile. But the story for semiconductor companies could be different with the growth of artificial intelligence (AI)--typically defined as the ability of a machine to perform cognitive functions associated with human minds, such as perceiving, reasoning, and learning. Many AI applications have already gained a wide following, including virtual assistants that manage our homes and facial-recognition programs that track criminals. What will this development mean for semiconductor sales and revenues?

While AI-powered devices and technologies have become essential parts of our lives, machine intelligence may still contain areas wherein drastic improvements could be made. To fill these metaphorical gaps, non-AI technologies can come in handy. Artificial intelligence (AI) is an'emerging computer technology with synthetic intelligence.' It is widely accepted that the applications of AI we see in our daily lives are just the tip of the iceberg with regards to its powers and abilities. The field of artificial intelligence needs to constantly evolve and keep developing to eliminate the common AI limitations.

Company acquires assets from Twenty Billion Neurons GmbH to bolster its AI Team. Qualcomm Technologies (QTI) is running a series of webinars titled "The Future of...", and the most recent edition is on AI. In this lively session, I hosted a conversation with Ziad Ashgar, QTI VP of Product Management, Alex Katouzian, QTI SVP and GM Mobile Compute and Infrastructure, and Clément Delangue, Co-Founder and CEO of the open source AI model company, Hugging Face, Inc. I've also penned a short Research Note on the company's AI Strategy, which can be found here on Cambrian-AI, where we outline some impressive AI use cases. Qualcomm believes AI is evolving exponentially thanks to billions of smart mobile devices, connected by 5G to the cloud, fueled by a vibrant ecosystem of application developers armed with open-source AI models.

The adoption of artificial intelligence (AI) and machine learning (ML) in different sectors has transformed the conventional approach for different applications. Although the terms AI and ML are used interchangeably, the former aims at the success of a task, whereas the latter ensures accuracy. From marketing and retail to healthcare and finance, adoption of artificial intelligence (AI) and machine learning (ML) in these sectors is drastically transforming the conventional approach for different applications. AI makes it possible for systems to sense, comprehend, act, and learn for performing complex tasks such as decision making that earlier required human intelligence. Unlike the regular programming, where action needs to be defined for every situation, AI in conjunction with ML algorithms can process large data sets, be trained to choose how to respond, and learn from every problem it encounters to produce more accurate results.

A century after automakers showed the world the value of assembly-line manufacturing, a shortage of semiconductors is teaching the industry a painful new lesson in what it takes to build a car. For most of its history, the industry has relied on a distinct approach to buying car parts, procuring components from suppliers right at the moment they're needed. It's referred to as just-in-time manufacturing and is designed to streamline production and eliminate the costs of keeping warehouses stocked with parts waiting to be used. But the shortcomings of that system were made starkly clear this year as the automakers confronted a dearth of the chips they need to build advanced functions into their vehicles, and found themselves near the bottom of chipmakers' customer lists because of their just-in-time approach. That shortage is threatening to cut $110 billion in sales from the industry, and forcing auto manufacturers to overhaul the way they get the electronic components that have become critical to contemporary car design.

Artificial intelligence/machine learning (AI/ML) has the potential to generate huge business value for semiconductor companies at every step of their operations, from research and chip design to production through sales. But our recent survey of semiconductor-device makers shows that only about 30 percent of respondents stated that they are already generating value through AI/ML. Notably, these companies have made significant investments in AI/ML talent, as well as the data infrastructure, technology, and other enablers, and have already fully scaled up their initial use cases. The other respondents--about 70 percent--are still in the pilot phase with AI/ML and their progress has stalled. We believe that the application of AI/ML will dramatically accelerate in the semiconductor industry over the next few years. Taking steps to scale up now will allow companies to capture the full benefits of these technologies. This article focuses on device makers, including integrated device manufacturers (IDMs), fabless players, foundries, and semiconductor assembly and test services, or SATS (for more information on our research, see sidebar, "Our methodology").

Edge AI is here to stay! Artificial intelligence (AI) is powering many real-world applications which we see in our daily lives. AI, once seen as an emerging technology, has now successfully penetrated into every industry (B2B & B2C) Banking, logistics, healthcare, defence, manufacturing, retail, automotive, consumer electronics. Smart Speaker like Echo, Google Nest, is one such example of Edge AI solutions in the consumer electronics sector. AI technology is powerful, and human-kind has set its eye on the path of harnessing its potential to the fullest. Intelligence brought to the device can be very useful and creative.

NVIDIA is sitting pretty in AI (artificial intelligence) right now. For the next few years, most AI systems will continue to be trained on NVIDIA GPUs and specialized hardware and cloud services that incorporate these processors. However, NVIDIA has been frustrated in its attempts to become a dominant provider of AI chips for deployment into smartphones, embedded systems, and other edge devices. To address that strategic gap, NVIDIA this past week announced that it is acquiring processor architecture firm Arm Holdings from SoftBank Group and the SoftBank Vision Fund. Once the acquisition closes in the expected 18 months, NVIDIA will retain Arm's name, brand identity, management team, and base of operations in Cambridge, United Kingdom.