We discuss the adequacy of tests for intelligent systems and practical problems raised by their implementation. We propose the replacement test as the ability of a system to replace successfully another system performing a task in a given context. We show how it can characterize salient aspects of human intelligence that cannot be taken into account by the Turing test. We argue that building intelligent systems passing the replacement test involves a series of technical problems that are outside the scope of current AI. We present a framework for implementing the proposed test and validating the properties of the intelligent systems. We discuss the inherent limitations of intelligent system validation and advocate new theoretical foundations for extending existing rigorous test methods. We suggest that the replacement test, based on the complementarity of skills between human and machine, can lead to a multitude of intelligence concepts reflecting the ability to combine data-based and symbolic knowledge to varying degrees.

We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular doubly robust or double machine learning estimators combine outcome modeling with balancing weights -- weights that achieve covariate balance directly in lieu of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients that combine the coefficients from the original outcome model coefficients and coefficients from an unpenalized ordinary least squares (OLS) fit on the same data; in many real-world applications the augmented estimator collapses to the OLS estimate alone. We then extend these results to specific choices of outcome and weighting models. We first show that the augmented estimator that uses (kernel) ridge regression for both outcome and weighting models is equivalent to a single, undersmoothed (kernel) ridge regression. This holds numerically in finite samples and lays the groundwork for a novel analysis of undersmoothing and asymptotic rates of convergence. When the weighting model is instead lasso-penalized regression, we give closed-form expressions for special cases and demonstrate a ``double selection'' property. Our framework opens the black box on this increasingly popular class of estimators, bridges the gap between existing results on the semiparametric efficiency of undersmoothed and doubly robust estimators, and provides new insights into the performance of augmented balancing weights.

Justice systems around the world are using artificial intelligence (AI) to assess people with criminal convictions. These AI technologies rely on machine learning algorithms and their key purpose is to predict the risk of reoffending. They influence decisions made by the courts and prisons and by parole and probation officers. This kind of tech has been an intrinsic part of the UK justice system since 2001. That was the year a risk assessment tool, known as Oasys (Offender Assessment System), was introduced and began taking over certain tasks from probation officers. Yet in over two decades, scientists outside the government have not been permitted access to the data behind Oasys to independently analyse its workings and assess its accuracy – for example, whether the decisions it influences lead to fewer offences or reconvictions. Lack of transparency affects AI systems generally. Their complex decision-making processes can evolve into a black box – too obscure to unravel without advanced technical knowledge. Proponents believe that AI algorithms are more objective scientific tools because they are standardised and this helps to reduce human bias in assessments and decision making. This, supporters claim, makes them useful for public protection. But critics say that a lack of access to the data, as well as other crucial information required for independent evaluation, raises serious questions of accountability and transparency.

Bucharest, Romania – Last Wednesday, a Russian drone attack on Ukraine's grain port infrastructure shook Romania, a NATO member. The force of the attack on the Izmail port, across the Danube River from the Eastern European nation, was so intense that the windows of some village homes in southeastern Romania shattered. Even though she lives far from the county of Tulcea, where the impact was felt, 28-year-old Alexandra, a paralegal from the capital Bucharest, is concerned. "We share a border with Ukraine and the conflict could expand at any moment," she told Al Jazeera. Russia has launched several attacks on Danube ports since pulling out of the wartime Black Sea grain deal.

An MQ-9 Reaper drone with Customs and Border Protection awaits a mission over the U.S.-Mexico border on Nov. 4, 2022 at Fort Huachuca, Arizona. Florida Gov. Ron DeSantis said using drone strikes against the cartels is on the table if he is elected president.

AI researcher Connor Leahy advises whether humans should be fearful of artificial intelligence and where the technology is expected to be in the future. TOKYO -- The Japanese government announced last week that it will be adding 23 new technologies, including specific AI technologies, to its list of "Specified Key Technologies," according to the Cabinet Office's website. This designation means that the government will fund public and private research institutions to develop AI technologies for "active cyber defense" to prevent cyberattacks and technologies that can be used for detecting disinformation. The newly added technologies cover four areas, including cyberspace, maritime, aerospace and biotechnology. This is part of the government's Fostering Key Technologies for Economic Security strategy under the Economic Security Promotion Act.

Jets can be flown by A.I. and can even take off, land and participate in dogfights. It's no secret at this point that AI is taking over many industries fast, and it certainly has its positives and negatives. Some are concerned with how using this technology will impact jobs for humans, while others are thrilled to see how tasks will get done much more efficiently. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S TO MAKE YOU SMARTER One field that is using AI to its fullest capabilities is the U.S. Navy. Our military's defense mechanisms have improved enormously in the 21st century; however, they have never used technology quite like this.

Canopy CMO Yaron Litwin discusses how criminals are using deepfake technology to blackmail teens and generate child pornography. The Defense Advanced Research Projects Agency (DARPA) has announced a competition for companies to provide new artificial intelligence (AI) platforms to help identify and seal holes in national cybersecurity. "In the AI Cyber Challenge, our goal is to again create this kind of new ecosystem with a diverse set of creative cyber competitors, empowered by the country's top AI firms, all pointed at new ways to secure the software infrastructure that underlies our economy," DARPA Outreach told Fox News Digital. "Ultimately, we want to see the best and the brightest cybersecurity, computer science, program analysis and AI and machine learning from across industry and academia come together to participate in this challenge." DARPA announced the challenge at Black Hat USA 2023, calling the competition the AI Cyber Challenge (AIxCC), which will last two years and involve multiple rounds of qualification and competition for a $4 million prize.

Canopy CMO Yaron Litwin discusses how criminals are using deepfake technology to blackmail teens and generate child pornography. Most Americans don't have a clue about artificial intelligence and what it means to the world's inhabitants. For those who are in this fog, the person who is a heartbeat away from the presidency has added her clarity to the mix. "I think the first part of this issue should be articulated is AI is a kind of a fancy thing, first of all, it's two letters, it means artificial intelligence but ultimately… it's machine learning." Now that Vice President Harris has defined artificial intelligence for us, she further enlightens our minds by elaborating, "And so, the machine is taught, and part of the issue here is what information is going into the machine that will then determine, and we can predict then if we think about what information is going in, what then will be produced in terms of decisions and opinions that may be made through that process."

Get ready, San Francisco: The state government on Thursday approved a major expansion of driverless robotaxi service throughout the city. And get ready, Los Angeles: The industry is planning to push for driverless rides here as soon as it gets permits to do so. The state's green light, on a 3-1 vote by the California Public Utilities Commission, signals a historic turning point for the robotaxi business as it evolves from fascinating experiment to commercial reality. It also marks the beginning of a grand experiment in public safety as thousands of multi-ton vehicles operated via artificial intelligence attempt to safely negotiate the hills and narrow streets of San Francisco. It highlights California's messy multiagency regulation of new automobile technology: Two agencies are in charge of the robotaxi business, the CPUC and the California Department of Motor Vehicles.