It's logical for humans to feel anxious about artificial intelligence. After all, the news is constantly reeling off job after job at which the technology seems to outperform us. But humans aren't yet headed for all-out replacement. And if you do suffer from so-called AI anxiety, there are ways to alleviate your fears and even reframe them into a motivating force for good. In one recent example of generative AI's achievements, AI programs outscored the average human in tasks requiring originality, as judged by human reviewers.

Standing around an empty parking lot in Arizona on a sunny, 100 F summer day was not a pleasant way to spend an afternoon. But excitement and anticipation overpowered our fears of heat exhaustion as we waited for Waymo's self-driving taxi. I was there with Kelly Funkhouser, CR's manager of vehicle technology. We looked back and forth wondering which direction the minivan would arrive from. "What if it gets stuck on the way here and never shows up?" Kelly was a little more optimistic.

Three distinct classes of aircraft piloted by artificial intelligence have emerged as options to fly alongside current and future U.S. Air Force fighters. The candidates range from expendable to exquisite systems, with a potential middle tier of attritable aircraft that leverage modular design features inspired by the automotive industry. All of these concepts were on full display inside the exhibit hall of the Air Force Association's annual Air, Space and Cyber Conference, which celebrated the 75th anniversary of the founding of the Air Force as an independent branch of the military. On the high end, Northrop Grumman's booth featured a concept model of the SG-101, the latest example of the company's long line of advanced flying-wing aircraft. Lockheed Martin, meanwhile, showed off the Skunk Works' concept for the Speed Racer, an expendably cheap uncrewed aircraft system (UAS) that will soon be teamed with F-35s for a demonstration called Project Carrera.

Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing. I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set of predictive signals (firm characteristics, systematic risks and macroeconomics). I demonstrate high performance of all kinds of state-of-the-art (SOTA) deep learning methods, and figure out that RNNs with memory mechanism and attention have the best performance in terms of predictivity. Furthermore, I demonstrate large economic gains to investors using deep learning forecasts. The results of my comparative experiments highlight the importance of domain knowledge and financial theory when designing deep learning models. I also show return prediction tasks bring new challenges to deep learning. The time varying distribution causes distribution shift problem, which is essential for financial time series prediction. I demonstrate that deep learning methods can improve asset risk premium measurement. Due to the booming deep learning studies, they can constantly promote the study of underlying financial mechanisms behind asset pricing. I also propose a promising research method that learning from data and figuring out the underlying economic mechanisms through explainable artificial intelligence (AI) methods. My findings not only justify the value of deep learning in blooming fintech development, but also highlight their prospects and advantages over traditional machine learning methods.

The Covid-19 pandemic brought about rapid change that demonstrated the range of human resilience and propelled digitization forward at unprecedented rates. According to a McKinsey Global Survey of executives, in only months, the digitization of customer and supply-chain interactions at companies accelerated by up to four years, while digitally enabled products accelerated by seven years. But digitization itself is not a new concept. Historically, digitization was primarily motivated by replacing manual tasks with robotic automation, but that wasn't the result. Instead, digitization elevated the value of work performed.

It's been a difficult year for shoppers looking for cars, electronics and anything that requires a computer chip. A global semiconductor shortage has left many companies unable to fill orders or even finish products they've started assembling, clogging up warehouses and leaving a lack of inventory across the nation. Buying a new PlayStation 5 console remains nearly impossible. Several automakers have slowed down production in their factories, delaying shipments of new vehicles. It's even impacted more obscure products--just try to find an affordable dog washing booth these days.

On July 3rd, Tom Cruise will be sixty years old. The fact that he does not look it, at all, even in IMAX closeups so tight you can study the grain of his tooth enamel, adds a note of cognitive dissonance to "Top Gun: Maverick," the long-aborning sequel in which he's called back to mentor a squad of younger stick-jockeys who address him as Pops and Old-Timer until he wins their respect in the air. Even for a physical performer like Cruise, sixty is no longer an expiration date. Mick Jagger blew by that milestone in 2003, as did Sylvester Stallone in 2006, and, thanks presumably to healthy habits and/or medical technology dreamt of only by science fiction, they're both still out there, doing a version of the kind of thing they've always done. But the level of performance expected of a Rolling Stone or an Expendable is one thing, and the work that Tom Cruise appears to demand of himself is something else entirely.

U..S. employment statistics hit a new milestone last year, but not a positive one. In August 2021, almost 4.3 million workers quit their jobs, according to the U.S. Department of Labor. That's the highest number since the department began tracking voluntary resignations. Their reasons for leaving their jobs vary--the numbers track people who quit for a different position, as well as those who quit without having another job lined up. While the reasons for quitting vary, one thing is clear: Businesses are having a tough time getting employees to come back.

Voting rules are powerful tools that allow multiple agents to aggregate their preferences in order to reach joint decisions. A common flaw of some voting rules, known as the no-show paradox, is that agents may obtain a more preferred outcome by abstaining from an election. We study strategic abstention for set-valued voting rules based on Kelly's and Fishburn's preference extensions.

We explore replacing the declarative memory system of the ACT-R cognitive architecture with a distributional semantics model. ACT-R is a widely used cognitive architecture, but scales poorly to big data applications and lacks a robust model for learning association strengths between stimuli. Distributional semantics models can process millions of data points to infer semantic similarities from language data or to infer product recommendations from patterns of user preferences. We demonstrate that a distributional semantics model can account for the primacy and recency effects in free recall, the fan effect in recognition, and human performance on iterated decisions with initially unknown payoffs. The model we propose provides a flexible, scalable alternative to ACT-R's declarative memory at a level of description that bridges symbolic, quantum, and neural models of cognition. Our intent is to advance toward a cognitive architecture capable of modeling human performance at all scales of learning.