If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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Broadcom Inc. has announced expanding opportunities for organizations to gain greater value from the company's advanced AI, security, and hybrid cloud solutions with "Day One" support for IBM's new z16. Broadcom's suite of software solutions, services, and unique "beyond code" programs provide clients an advantage to succeed in an increasingly challenging business environment. "Our strategic investments position clients to exploit the z16 along with advances in AI, cybersecurity, cloud integration, and agility," said Greg Lotko, senior VP and GM, Mainframe Software Division, Broadcom. What distinguishes Broadcom is our deep investment in technology and how we work side-by-side in partnership with our clients to overcome their unique challenges and create new opportunities." As a member of the z16 Early Ship Program, Broadcom collaborated with IBM to ensure clients can capitalize on the full range of our mainframe software solutions on the new platform to drive progress toward their innovation and business goals. "Nothing can match the transaction performance of a mainframe, and the way that we manage the platform using Broadcom technology is a real differentiator for us," said Johan Bosch, executive director for iOCO Infrastructure Services. "We can deliver our services at 25 percent of the cost when measured against standalone banking environments.
We all know when AI crosses an ethical line. What's less easy is understanding what each of these examples have in common, and drawing lessons that apply to early-stage companies. There are plenty of broad statements of AI ethics principles, but few tools for putting them into practice, especially ones tuned for the harsh realities of startups tight on money and time. That challenge extends to VCs too, who must increasingly attempt to assess whether founders have thought through how customers, partners and regulators might react to the ways they're using artificial intelligence. Even when founders have the best intentions, it's easy to cut corners.
Stephanie Condon is a senior staff writer for Red Ventures based in Portland, Oregon, covering business technology for ZDNet. Intel on Tuesday launched the latest generation of its deep learning processors for training and inference, Habana Gaudi2 and Habana Greco, making AI more accessible and valuable for its data center customers. At its Intel Vision event, the chipmaker also shared details about its IPU and GPU portfolios, all aimed business customers. "AI is driving the data center," Eitan Medina, COO of Habana Labs, Intel's data center team focused on AI deep learning processor technologies, said to reporters earlier. But different customers are using different mixes for different applications."
Samsung Electronics today released the second edition of its private 5G networks whitepaper, highlighting the architectures, features and benefits of private 5G networks for industrial scenarios--such as smart factories, smart hospitals, smart logistics and transportation, among others. With the growing interest in private networks, Samsung explores how enterprises can successfully deploy private 5G networks to meet business goals and service demands. The whitepaper outlines various architectural options for building private networks that enable 5G services -- such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC) and Massive Machine Type Communications (mMTC) -- which can bring new innovation to a range of sectors rapidly transitioning to Industry 4.0. The paper spotlights Samsung's complete set of private 5G network solutions, which enable enterprises to simplify network deployment and operation. With a portfolio and capability to build highly reliable private 5G networks, Samsung offers solutions for small, medium to large-scale enterprises.
Wish your network could predict its own problems and fix them automatically? Cisco believes it has the technology you need. The networking tech giant announced today what it said is the culmination of two years of work: an analytics engine that can predict network issues before they happen, and with enough integration and training even fix problems itself, Cisco said. Citing data from an in-house study, Cisco said that 45 percent of IT leaders it surveyed cited responding to disruptions as their biggest networking challenge of 2021. Predictive analytics technology, coupled with "enormous amounts of historical [networking] data," is a potential solution, Cisco said.
AMD plans to introduce processors next year that integrate AI engines from the company's recently acquired Xilinx FPGA business unit, which helped the chip designer deliver high sales growth in the first quarter along with the company's traditional PC and server businesses. CEO Lisa Su disclosed the plans for new AI-fueled CPUs during her company's first-quarter earnings call Tuesday, where she said the resulting microprocessors will "enable industry-leading inference capabilities" as part of broader plans to capitalize on AMD's $49 billion Xilinx acquisition. The AI engines are already being used in Xilinx's FPGA-based products for embedded and edge applications, including image recognition for cars, according to Victor Peng, Xilinx's former CEO who now leads AMD's Adaptive and Embedded Computing Group. Peng said AMD is working on developing "unified" software that will help developers take advantage of the new AI capabilities for both inference and training in datacenters and at the edge. Overall, Su said, Xilinx will allow AMD to have a "much broader set of offerings" in the AI hardware space that goes beyond the company's current capabilities with CPUs and GPUs.
It is no secret that everybody wants to predict recessions. Many economists and finance firms have attempted this with limited success, but by and large there are several well known leading indicators for recessions in the US economy. However, when presented to the general public these indicators are typically taken alone, and are not framed in a way that can give probability statements associated with an upcoming recession. In this project, I have taken several of those economic indicators and built a classification model to generate probabilistic statements. Here, the actual classification ('recession' or'no recession') is not as important as the probability of a recession, since this probability will be used to determine a basic portfolio scheme which I will describe later on.
It's tempting to see AI as a solution to building a super-success investment engine. After all, if AI can solve text-to-speech or self-driving cars or landing rockets vertically, couldn't an artificially intelligent investing engine with access to all stock market, economy, weather, and trends data vastly outpace human investors and guarantee massive returns? And won't we be able to simply ask Alexa to buy a stock that's going to triple in value in six months? Well, never say never, but it's unlikely. One is that investment AI engines are returning benefits right now, but not Everest-sized performance that will blow your financial socks off and make you fire your investment advisor.
The advances in computing capacity and artificial intelligence have long been hailed as a tremendous efficiency boost for wealth advisors and their clients. While almost all advisors recognize the implicit value of integrating AI into their daily practice, most AI adoption has either been via robo advisors (Wealthfront and Betterment) or "intelligent" customer service (think chatbots). The rise in model portfolios and manager selection has relieved advisors of the sole focus on individual equities, but clients still want insights on their favorite names (TSLA, AAPL, BTC). In addition, advisors with in-house research need to be able to provide the full context behind analyst upgrades/downgrades or price target changes and, therefore, have to dig through a mountain of PDFs and collate information from disparate sources in order to answer client questions and be seen as value-add. In the past, advisors with access to a dedicated information terminal could quickly access news and data their clients weren't seeing.