Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart's actions. No existing modeling theories and tools, including Markovian decision processes, user and behavior modeling, deep sequential modeling, and personalized sequential recommendation, can quantify such complex decision-making on a personal level. We take a data-driven approach to learn the next-best actions for personalized decision-making by a reinforced coupled recurrent neural network (CRN). CRN represents multiple coupled dynamic sequences of a customer's historical and current states, responses to decision-makers' actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker). Next-best actions are then recommended on each customer at a time point to change their state for an optimal decision-making objective. Our study demonstrates the potential of personalized deep learning of multi-sequence interactions and automated dynamic intervention for personalized decision-making in complex systems.

We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fair, transparent and trustworthy ML methods. The purpose of this article is to introduce a new information-theoretic learning framework (admissible machine learning) and algorithmic risk-management tools (InfoGram, L-features, ALFA-testing) that can guide an analyst to redesign off-the-shelf ML methods to be regulatory compliant, while maintaining good prediction accuracy. We have illustrated our approach using several real-data examples from financial sectors, biomedical research, marketing campaigns, and the criminal justice system.

Modern evolvements of the technologies have been leading to a profound influence on the financial market. The introduction of constituents like Exchange-Traded Funds, and the wide-use of advanced technologies such as algorithmic trading, results in a boom of the data which provides more opportunities to reveal deeper insights. However, traditional statistical methods always suffer from the high-dimensional, high-correlation, and time-varying instinct of the financial data. In this dissertation, we focus on developing techniques to stress these difficulties. With the proposed methodologies, we can have more interpretable models, clearer explanations, and better predictions.

It's been nearly two years since Tesla's first "Autonomy Day" event, at which CEO Elon Musk made numerous lofty predictions about the future of autonomous vehicles, including his infamous claim that the company would have "one million robotaxis on the road" by the end of 2020. This time, the event will be called "AI Day," and according to Musk, the "sole goal" is to persuade experts in the field of robotics and artificial intelligence to come work at Tesla. The company is known for its high rate of turnover, the latest being Jerome Guillen, a key executive who worked at Tesla for 10 years before recently stepping down. Attracting and retaining talent, especially top tier names, has proven to be a challenge for the company. The August 19th event is scheduled to start at 5PM PT / 8PM ET at Tesla's headquarters in Palo Alto, California.

When the House of Representatives returns early from summer recess next week to vote on a blueprint for Democrats' eventual multi trillion-dollar spending bill, the Democratic majority will quickly have to resolve a high-stakes standoff. In the other: Nine House moderates, led by New Jersey Rep. Josh Gottheimer, co-chair of the bipartisan but not necessarily accurately named Problem Solvers Caucus. So which side would you put your money on? Which makes the most pressing question for our nation's lawmakers: What, precisely, is Gottheimer's endgame here? Gottheimer, a former speechwriter for Bill Clinton representing a swingy, but Democrat-trending, northern New Jersey district, was elected to Congress in 2016 and has regularly raised the ire of the left.

Google CEO Sundar Pichai has suggested--more than once--that artificial intelligence (AI) will affect humanity's development more profoundly than humanity's harnessing of fire. He was speaking, of course, of AI as a technology that gives machines or software the ability to mimic human intelligence to complete ever more complex tasks with little or no human input at all. You may laugh Pichai's comparison off as the usual Silicon Valley hype, but the company's dealmakers aren't laughing. Since 2007, Google has bought at least 30 AI companies working on everything from image recognition to more human-sounding computer voices--more than any of its Big Tech peers. One of these acquisitions, DeepMind, which Google bought in 2014, just announced that it can predict the structure of every protein in the human body from the DNA of cells--an achievement that could fire up numerous breakthroughs in biological and medical research.

Tesla could face further federal scrutiny over its Autopilot feature. Senators Ed Markey and Richard Blumenthal have called on the Federal Trade Commission to investigate the company over "misleading advertising and marketing" of the Autopilot and Full Self-Driving (FSD) systems. "Tesla and [CEO Elon] Musk's repeated overstatements of their vehicle's capabilities -- despite clear and frequent warnings -- demonstrate a deeply concerning disregard for the safety of those on the road and require real accountability," the senators wrote in their letter to FTC chair Lina Khan. "Their claims put Tesla drivers -- and all of the travelling public -- at risk of serious injury or death." It's not yet clear whether the FTC will heed the senators' call and investigate the company. Along with several examples of Tesla and Musk seemingly overselling Autopilot and FSD functions, Markey and Blumenthal cited an investigation that the National Highway Traffic Safety Administration opened this week.

NASA has marked the Curiosity rover's ninth anniversary on Mars by unveiling a new panoramic image of the Martian landscape, a locale that may explain why the Red Planet became dry. The panoramic image, which was put together on July 3 by stitching 129 individual images together, shows Curiosity's current home, Mount Sharp, a 5-mile-tall mountain inside Mars' Gale Crater. NASA marked the Curiosity rover's ninth anniversary on Mars by unveiling a new panoramic image. The image was created by the rover's Mast Camera, or Mastcam. Upon arrival at Mount Sharp in 2014, Curiosity has been traveling up the rock formation for the past several years.

Two US senators requested a federal investigation of Tesla's statements about its Autopilot driver assistance system Wednesday, asserting in the wake of multiple crashes that the automaker's exaggerations have put consumers at risk. The letter to the Federal Trade Commission concerning "Tesla's misleading advertising" comes two days after another federal agency launched a probe into Autopilot. Tesla and Chief Executive Elon Musk's "repeated overstatements of the vehicles' capabilities -- despite clear and frequent warnings -- demonstrate a deeply concerning disregard for the safety of those on the road and require real accountability," Democratic senators Richard Blumenthal and Ed Markey wrote. "We urge you to swiftly open an investigation into Tesla's repeated and overstated claims about their Autopilot and Full Self-Driving features and take appropriate enforcement action to prevent further injury or death as a result of any Tesla feature," they said in the letter to FTC Chair Lina Khan. The Autopilot system assists with steering and automatic braking and can be employed to help drivers navigate past slow cars, according to Tesla's website, which says the system's features "require active driver supervision and do not make the vehicle autonomous."

Around 70% of 96 strategic goals under Vision 2030 are related to data and AI. - The growing investment toward smart cities in Saudi Arabia results in massively increasing adoption of AI solutions along with 5G and software, such as predictive analytics. In 2021, Saudi Arabian Crown Prince Mohammed bin Salman announced plans to build The Line, a 105-mile-long belt of hyper-connected communities in the kingdom's northeast that will feature no cars, no streets, and carbon emissions but will have smart infrastructure costing up to USD 200 billion.