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This is a rush transcript of'Fox News Sunday' on September 24, 2023. This copy may not be in its final form and may be updated. The chaos at the border grows by the day, as the pressure to take greater action builds yet again on the White House. We need people from the top. HEMMER (voice-over): A border city mayor and Democrat declaring a state of emergency as thousands upon thousands of migrants flow into the country. JOE BIDEN, PRESIDENT OF THE UNITED STATES: Republicans in Congress and my predecessor spent four years gutting the immigration system -- under my predecessor. They continue to undermine our border security today. HEMMER: We'll get reaction from border state Democrat, Texas Congressman Henry Cuellar. President Biden says he'll join the picket line in Michigan on Tuesday, just a day before Donald Trump will be there, too. Meanwhile, another presidential hopeful pushes back. TIM SCOTT (R-SC), PRESIDENTIAL CANDIDATE: We need a president who says we are not going to subsidize unions, period. HEMMER: We'll discuss with a man whose eyes are on the White House, South Carolina Senator Tim Scott. We'll ask Republican National Committee chairwoman Ronna McDaniel what voters can expect to see on stage Wednesday night. JAMES LANKFORD (R-OK): It's a symbol of respect for the country when you dress respectfully when you're doing this responsibility. JOHN FETTERMAN (D-PA): I think there are more important things we should be talking about rather if -- if I dressed like a slob. The number of illegals crossing our border hit another new record. We want to show you our FOX News drone camera from Eagle Pass, Texas. We've been watching remarkable images today of a human flood that shows no sign of receding. And today, a new survey shows how displeased Americans are with the president's border policies. In a moment, we'll speak with border state Democrat, Texas Congressman Henry Cuellar, on that. But, first, to Griff Jenkins who has been in Eagle Pass for what seems like several years now. Well, there's a humanitarian crisis playing out along our southern border in places like here in Eagle Pass, Texas, where migrants have traveled thousands of miles in hopes of reaching the U.S. in numbers far greater than what border officials are able to handle. Actions include sending active duty troops to the border, increasing deportations and granting temporary protective status to nearly half a million Venezuelans, making it easier for them to find work in cities like New York, where officials are struggling to find room for them. Meanwhile, Texas Governor Greg Abbott trying to deter the migrants from entering his state, with miles of dense razor wire, Humvees manning the riverbank and guardsmen in rafts attempting to turn them back.

John C. Herman, of Herman Jones LLP in Atlanta, told Fox News Digital that Google's control of wide swaths of digital content, powered with the emergence of artificial intelligence, gives it the potential for '"terrifying" power. The attorney behind a major class-action lawsuit against Google claims that advances in artificial intelligence give the digital monopoly almost unlimited power to control lives, influence thought and shape society. "When the average person interacts with the internet, Google monitors and controls everything," John C. Herman, of Herman Jones LLP in Atlanta, told Fox News Digital. "From the search results, to the advertisements, to the web pages themselves, Google controls it all," he said. INVISIBLE AI'S'INTELLIGENT AGENT' CAMERAS CAN SEE WHAT AUTOWORKERS AND MACHINES ARE DOING WRONG He also said, "Adding in an AI component, we now have a single company that dictates what news people see, what products they buy and even how they vote."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Miami Dolphins have been revamping their offense under first-year head coach Mike McDaniel. On Tuesday, they made two deals that have many believing this could be a Super Bowl contending team. Tua Tagovailoa is one of the believers.

Whilst adversarial attack detection has received considerable attention, it remains a fundamentally challenging problem from two perspectives. First, while threat models can be well-defined, attacker strategies may still vary widely within those constraints. Therefore, detection should be considered as an open-set problem, standing in contrast to most current detection strategies. These methods take a closed-set view and train binary detectors, thus biasing detection toward attacks seen during detector training. Second, information is limited at test time and confounded by nuisance factors including the label and underlying content of the image. Many of the current high-performing techniques use training sets for dealing with some of these issues, but can be limited by the overall size and diversity of those sets during the detection step. We address these challenges via a novel strategy based on random subspace analysis. We present a technique that makes use of special properties of random projections, whereby we can characterize the behavior of clean and adversarial examples across a diverse set of subspaces. We then leverage the self-consistency (or inconsistency) of model activations to discern clean from adversarial examples. Performance evaluation demonstrates that our technique outperforms ($>0.92$ AUC) competing state of the art (SOTA) attack strategies, while remaining truly agnostic to the attack method itself. It also requires significantly less training data, composed only of clean examples, when compared to competing SOTA methods, which achieve only chance performance, when evaluated in a more rigorous testing scenario.

Decades of science fiction have primed people to conjure a very specific image when the subject of artificial intelligence arises: The malevolent robotic overlord that has evolved beyond the need for humans, or concern for their safety. And with that image in mind, it's easy to imagine that it's still a long way off. But in actuality, AI is already here, and its most common form is a lot closer to home than many people realize. "It's already hard to interact with a modern technology that doesn't in some way benefit from AI, whether it's obvious or not," said Mason McDaniel, chief technology officer at the Bureau of Alcohol, Tobacco, Firearms and Explosives, said during a Sept. 4 Meritalk webinar. "And I'd say that's going to continue as we move forward. And AI is going to increasingly be embedded and hidden behind the scenes simply as part of the applications; you're not going to go specifically to an AI app." "And I see that as those mature, as those get better, those are going to become more of a primary interface, whether it's spoken or using natural language text typing, to ask the devices for what you want," McDaniel said.

Smart speakers such as Amazon's Echo and Alphabet Inc.'s Google Home products can handle a growing array of tasks from playing music to adjusting the thermostat to arming a security system. They are also sometimes freaking people out, seeming to drop into conversations uninvited, playing music unprompted in the middle of the night, turning on other gadgets at random and acting generally, well, possessed. Companies say there are reasonable explanations, such as the device mishearing its "wake word"--which it recognizes to start listening to commands. But such episodes can leave owners shaken and unsure of what to do next. Put the device in time out?

A few times each year, senior digital executives from around the world assemble at Forrester's Digital Transformation Summit to check in with each other and Forrester analysts to discuss the current state of digital evolution. I was pleased to be invited as a guest to the sold-out event, held in early May in Chicago. For those who couldn't spare the two days, here is a summary of key insights and trends. While there were updates on familiar themes from prior years, such as Agile practices, the Internet of Things (IoT) and cloud computing, the major shift this year has been a central focus on Artificial Intelligence (AI) across industries and a need for an even more aggressive approach to digital transformation in order to compete. Get weekly insights by signing up for our CIO Leader newsletter.

Machine learning has advanced radically over the past 10 years, and machine learning algorithms now achieve human-level performance or better on a number of tasks, including face recognition,31 optical character recognition,8 object recognition,29 and playing the game Go.26 Yet machine learning algorithms that exceed human performance in naturally occurring scenarios are often seen as failing dramatically when an adversary is able to modify their input data even subtly. Machine learning is already used for many highly important applications and will be used in even more of even greater importance in the near future. Search algorithms, automated financial trading algorithms, data analytics, autonomous vehicles, and malware detection are all critically dependent on the underlying machine learning algorithms that interpret their respective domain inputs to provide intelligent outputs that facilitate the decision-making process of users or automated systems. As machine learning is used in more contexts where malicious adversaries have an incentive to interfere with the operation of a given machine learning system, it is increasingly important to provide protections, or "robustness guarantees," against adversarial manipulation. The modern generation of machine learning services is a result of nearly 50 years of research and development in artificial intelligence--the study of computational algorithms and systems that reason about their environment to make predictions.25 A subfield of artificial intelligence, most modern machine learning, as used in production, can essentially be understood as applied function approximation; when there is some mapping from an input x to an output y that is difficult for a programmer to describe through explicit code, a machine learning algorithm can learn an approximation of the mapping by analyzing a dataset containing several examples of inputs and their corresponding outputs. Google's image-classification system, Inception, has been trained with millions of labeled images.28 It can classify images as cats, dogs, airplanes, boats, or more complex concepts on par or improving on human accuracy. Increases in the size of machine learning models and their accuracy is the result of recent advancements in machine learning algorithms,17 particularly to advance deep learning.7 One focus of the machine learning research community has been on developing models that make accurate predictions, as progress was in part measured by results on benchmark datasets. In this context, accuracy denotes the fraction of test inputs that a model processes correctly--the proportion of images that an object-recognition algorithm recognizes as belonging to the correct class, and the proportion of executables that a malware detector correctly designates as benign or malicious. The estimate of a model's accuracy varies greatly with the choice of the dataset used to compute the estimate.

A few times each year, senior digital executives from around the world assemble at Forrester's Digital Transformation Summit to check in with each other and Forrester analysts to discuss the current state of digital evolution. I was pleased to be invited as a guest to the sold-out event, held in early May in Chicago. For those who couldn't spare the two days, here is a summary of key insights and trends. While there were updates on familiar themes from prior years, such as Agile practices, the Internet of Things (IoT) and cloud computing, the major shift this year has been a central focus on Artificial Intelligence (AI) across industries and a need for an even more aggressive approach to digital transformation in order to compete. Tyler McDaniel, VP Data Insights explained Forrester's model for the empowered customer, which divides customers into five tiers, from Progressive Pioneers to Reserved Resisters.