Excitement over artificial intelligence is proving a powerful counterforce for a tech economy that had been slowing, lifting share prices and growth outlooks at many giants and igniting a wave of new startups.

Hey, party people, it's Kyle, continuing to step in for Greg to write Week in Review as he spends time with his newborn. Dunno about y'all, but it's been a week. But because the news never sleeps, I'm rallying with the help of a fourth cup of coffee. I've talked your ears off about it at this point, but I'm under contractual obligation (not really, but still) to mention TechCrunch's upcoming Early Stage 2023 event in Boston on April 20. The one-day summit on startups will include advice and takeaways from top experts, plus opportunities to meet fellow founders and share your own entrepreneurial experiences.

In a downturn, "it's very easy to cut things that are new, that others do not understand, that are yet to prove value," said Katia Walsh, chief strategy and artificial intelligence officer at Levi Strauss & Co. Five years ago, some companies made huge investments in AI without having enough high-quality foundational data to train and run the algorithms. That left executives underwhelmed by the results and disillusioned, according to Todd Lohr, KPMG LLP's U.S. technology consulting leader. Costly early projects failed to pay off, especially in sectors like healthcare, where the difficulties of coalescing and structuring data are complex, Mr. Lohr said. Additionally, many companies approached AI without a sense of what it could realistically do, said Andrew Ng, founder and chief of startup Landing AI and a former chief scientist of Baidu Inc.

CyberGuy shows you how to screencast on your Android phone so you can display the content from your phone to your TV screen. The future is right around the corner. Some wild new innovations like flying cars, kinder tech, and even more robots are being unveiled at the Consumer Electronics Show in Las Vegas. Last year's hyped focus on VR, AR and the Metaverse feels like it's falling flat this year for more gear worth living within the real world. Dodge is unveiling the Ram 1500 Revolution, a battery-electric vehicle concept.

Check out all the on-demand sessions from the Intelligent Security Summit here. As companies scramble to protect themselves against the economic downturn, all sorts of projects are being impacted. And applied artificial intelligence (AI) is no exception. Before the downturn, the AI industry was enjoying a gold rush, with companies pouring plenty of cash into machine learning (ML) talent, research and projects. While these efforts have borne fruit and can be seen in applications we use every day, much of this investment was prompted by unjustified hype surrounding AI.

Investors and analysts are starting to push beyond the hype about artificial intelligence and ask more questions about AI software companies' near-term growth prospects. Professional service and software providers including Palantir Technologies Inc., C3.ai Inc. and Veritone Inc. market themselves as AI companies with high growth potential, offering services to enhance enterprise analytical capabilities in sectors like cybersecurity and telecommunications. But amid a tech market downturn, these companies are struggling to convince Wall Street they can withstand the pressures of a weakened macroeconomic environment. "We believe chunky data analytics projects are more likely to be put on hold in a weaker growth environment," Goldman Sachs analysts said in a Nov. 8 note following Palantir's third-quarter 2022 earnings call. All three stocks have been hard-hit amid the broader sell-off in tech stocks in 2022, with Palantir and C3.ai both down about 59% year-to-date as of Nov. 23.

You can find many examples of Deep Neural Network (DNN) models that successfully forecast the stock market. Typically, these models are using a very short time frequency. As variables inputs, these DDN models use a number of other stock indices that correlate with the S&P 500. They often use autoregressive variables (most recent S&P 500 levels). The mentioned high-frequency trading DNN models use covariates, or variables that are absent any explanatory logic besides being correlated with the S&P 500 (or whatever stock they predict). Let's step back and differentiate between covariates and explanatory variables because this is at the essence of my effort to Deep Learn the stock market "honestly." The mentioned "successful" high-frequency trading DNN models use covariates, or variables that are absent any true exogenous explanatory logic regarding the behavior of the S&P 500. Stating that the S&P 500 moves in tandem with the Nikkei 225 is not explanatory per se. It just exploits a tautological correlation.

LAGUNA BEACH, Calif.--With their valuations and earnings down, and their guidance gloomy, America's tech companies have entered a phase when they have to be brutally honest with themselves about what really works. This means executives are trimming staff, moonshots and unprofitable distractions. They're also deciding what to focus on. It's a transition away from more than a decade of "gee-whiz" projects--think self-driving cars, flying cars, metaverses and crypto--all fueled by seemingly limitless cash and venture-backed meal-replacement slurries. The task at hand now: the sometimes-boring but always-important work of building and expanding businesses that actually make money, by delivering things people and companies want and need.

I am sure you've seen many doom and gloom stories. Tech companies that grew during the pandemic are now cutting back and laying off workers in various tech sectors. Tech companies' earnings are down. PC demand is off by 20% this year, and earliest estimates for tech growth looks like in early 2024.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Recently, I wrote a piece for VentureBeat distinguishing between companies that are AI-based at their very core and ones that simply use AI as a function or small part of their overall offering. To describe the former set of companies, I coined the term "AI-Native." As a technologist and investor, the recent market downturn made me think about the technologies poised to survive the winter for AI -- brought on by a combination of reduced investment, temporarily discouraged stock markets, a possible recession aggravated by inflation, and even customer hesitation about dipping their toes into promising new technologies for fear of missing out (FOMO).