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The integration of artificial intelligence (AI) and machine learning (ML) into stealth and radar technologies represents a key element of the race to the top of defense technologies currently taking place. These offensive and defensive capabilities are constantly evolving with AI/ML serving as the next step in their evolution.

Integrating AI/ML into low-observable technology presents a promising avenue for enhancing stealth capabilities, but it also comes with its own set of challenges. ML algorithms rely on large volumes of high-quality data for training and validation. Acquiring such data for low-observable technology is challenging due to the classified nature of military operations and the limited availability of real-world stealth measurements.

ML algorithms analyze vast amounts of radar data to identify patterns and anomalies that were previously undetectable. This includes the ability to track stealth aircraft and missiles with greater accuracy and speed. These advancements have significant implications for deterrence strategies as traditional stealth technology may diminish in its effectiveness as AI/ML-powered radar becomes more sophisticated, potentially undermining the deterrent value of stealth aircraft and missiles.

Stealth technology remains a cornerstone of deterrence, allowing military assets to operate relatively undetected. Radar, on the other hand, is the primary tool for detecting and tracking these assets. However, AI/ML are propelling both technologies into new frontiers. AI algorithms can now design and optimize stealth configurations that were previously impossible. This includes the development of adaptive camouflage that dynamically responds to changing environments, making detection even more challenging.

Furthermore, stealth technology encompasses a multitude of intricately designed principles and trade-offs, including radar cross-section (RCS) reduction, infrared signature management, and reduction of acoustic variables. Developing ML algorithms capable of comprehensively modeling and optimizing these complex interactions poses a significant challenge. Moreover, translating theoretical stealth concepts into practical design solutions that can be effectively learned by ML models requires specialized domain knowledge and expertise.

As ML-based stealth design techniques become more prevalent, adversaries may employ adversarial ML strategies to exploit vulnerabilities and circumvent the defenses afforded to stealth aircraft. Adversarial attacks involve deliberately perturbing input data to deceive ML models and undermine their performance. Mitigating these threats requires the development of robust countermeasures and adversarial training techniques to enhance the resilience of ML-based stealth systems.

Additional complexities are inherent in the fact that ML algorithms often operate as black boxes, making it challenging to interpret their decision-making processes and understand the underlying rationale behind their predictions. In the context of stealth technology, where design decisions have significant operational implications, the lack of interpretability and explainability poses a barrier to trust and acceptance. Ensuring transparency and interpretability in ML-based stealth design methodologies is essential for fostering confidence among stakeholders and facilitating informed decision-making.

Implementing ML algorithms for stealth optimization involves computationally intensive tasks, including data preprocessing, model training, and simulation-based optimization. As low-observable technology evolves to encompass increasingly sophisticated designs and multi-domain considerations, the computational demands of ML-based approaches may escalate exponentially. Balancing computational efficiency with modeling accuracy and scalability is essential for practical deployment in real-world military applications.

Integrating AI and ML into military systems raises complex regulatory and ethical considerations, particularly regarding autonomy, accountability, and compliance with international laws and conventions. Ensuring that ML-based stealth technologies adhere to ethical principles, respect human rights, and comply with legal frameworks governing armed conflict is paramount. Moreover, establishing transparent governance mechanisms and robust oversight frameworks is essential to addressing concerns related to the responsible use of AI in military applications.

Addressing these challenges requires a concerted interdisciplinary effort, bringing together expertise from diverse fields such as aerospace engineering, computer science, data science, and ethics. By overcoming these obstacles, AI/ML has the potential to revolutionize low-observable technology, enhancing the stealth capabilities of military aircraft and ensuring their effectiveness in an increasingly contested operational environment. On the other hand, AI/ML has the potential to significantly impact radar technology, posing challenges to conventional low-observable and stealth aircraft designs in the future.

AI/ML algorithms can enhance radar signal processing capabilities by improving target detection, tracking, and classification in cluttered environments. Analyzing complex radar returns and discerning subtle patterns indicative of stealth aircraft, these algorithms can mitigate the challenges posed by low-observable technology, making it more difficult for stealth aircraft to evade detection.

ML algorithms can optimize radar waveforms in real time based on environmental conditions, target characteristics, and mission objectives. Dynamically adjusting waveform parameters such as frequency, amplitude, and modulation, radar systems can exploit vulnerabilities in stealth designsincreasing the probability of detection. This adaptive approach enhances radar performance against evolving threats, including stealth aircraft with sophisticated countermeasures.

Cognitive radar systems leverage AI/ML techniques to autonomously adapt their operation and behavior in response to changing operational environments. These systems learn from past experiences, anticipate future scenarios, and optimize radar performance adaptively. Continuously evolving their tactics and strategies, cognitive radar systems can outmaneuver stealth aircraft and exploit weaknesses in their low-observable characteristics.

AI/ML facilitates the coordination and synchronization of multi-static and distributed radar networks, comprising diverse sensors deployed across different platforms and locations. By fusing information from multiple radar sources and exploiting the principles of spatial diversity, these networks can enhance target detection and localization capabilities. This collaborative approach enables radar systems to overcome the limitations of individual sensors and effectively detect stealth aircraft operating in contested environments.

ML techniques can be employed to develop countermeasures against stealth technology by identifying vulnerabilities and crafting effective detection strategies. By generating adversarial examples and training radar systems to recognize subtle cues indicative of stealth aircraft, researchers can develop robust detection algorithms capable of outperforming traditional radar techniques. ML provides a proactive defense mechanism against stealth threats, potentially rendering conventional low-observable technology obsolete.

AI and ML enable the construction of data-driven models and simulations that accurately capture the electromagnetic signatures and propagation phenomena associated with stealth aircraft. By leveraging large datasets comprising radar measurements, electromagnetic simulations, and physical modeling, researchers can develop comprehensive models of stealth characteristics and devise innovative counter-detection strategies. These data-driven approaches provide valuable insights into the vulnerabilities of stealth technology and inform the design of more effective radar systems.

In the quest for technological superiority in modern warfare, the integration of AI and ML into radar technology holds significant promise with the potential to challenge conventional low-observable and stealth aircraft designs by enhancing radar-detection capabilities. AI and ML algorithms improve radar signal processing, optimize radar waveforms in real time, and enable radar systems to autonomously adapt their operation. By leveraging multi-static and distributed radar networks and employing adversarial ML techniques, researchers can develop robust detection algorithms capable of outperforming traditional radar systems. Moreover, data-driven modeling and simulation provide insights into the vulnerabilities of stealth technology, informing the design of more effective radar systems.

The rapid advancement of AI/ML is revolutionizing both stealth and radar technologies, with profound implications for deterrence strategies. Traditionally, deterrence has relied on the balance of power and the credible threat of retaliation. However, the integration of AI/ML into these technologies is fundamentally altering the dynamics of detection, evasion, and response, thereby challenging the established tenets of deterrence. Of further concern is the consideration that non-stealth assets become increasingly vulnerable to detection and targeting as ML-powered radar systems become more prevalent. This could lead to a greater reliance on stealth technology, further accelerating the arms race.

This rapid development of AI/ML-powered technologies could destabilize the existing balance of power, leading to heightened tensions and miscalculations. The changing technological landscape may necessitate the development of new deterrence strategies that incorporate AI and ML. This could include a greater emphasis on cyber warfare and the development of counter-AI and counter-ML capabilities.

The integration of AI/ML into stealth and radar technologies will be a game-changer for deterrence. To maintain stability and prevent conflict, policymakers and military strategists must adapt to this new reality of a continuous arms race, wherein both offensive and defensive capabilities are constantly evolving in pursuit of technological superiority. Continued investment in AI/ML research is essential to stay ahead of the curve and maintain a credible deterrent posture. International cooperation on the development and use of AI/ML technologies in military applications is crucial to limit the scope of a potential arms race that regularly shifts the balance of power and destabilizes global security.

Joshua Thibert is a Contributing Senior Analyst at the National Institute for Deterrence Studies (NIDS) and doctoral candidate at Missouri State University. His extensive academic and practitioner experience spans strategic intelligence, multiple domains within defense and strategic studies, and critical infrastructure protection. The views expressed in this article are the authors own

Joshua Thibert is a Contributing Senior Analyst at theNational Institute for Deterrence Studies (NIDS)and doctoral candidate at Missouri State University. His extensive academic and practitioner experience spans strategic intelligence, multiple domains within defense and strategic studies, and critical infrastructure protection.

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The Double-edged Sword of Artificial Intelligence Global Security Review - Global Security Review

Prime Minister Justin Trudeau is headed into the second day of the G7 leaders' summit, which will feature a special appearance by Pope Francis.

The pontiff is slated to deliver an address to leaders about the promises and perils of artificial intelligence.

He is also expected to renew his appeal for a peaceful end to Russia's full-scale invasion of Ukraine and the Israel-Hamas war in the Gaza Strip.

Leaders of the G7 countries announced on Thursday that they will deliver a US$50-billion loan to Ukraine using interest earned on profits from Russia's frozen central bank assets as collateral.

Canada, for its part, has promised to pitch in $5 billion toward the loan.

Trudeau met with European Commission President Ursula von der Leyen on Friday morning and is scheduled to meet with the Pope and Japanese Prime Minister Fumio Kishida later in the day.

Trudeau was in a working session on migration in the morning while leaders will hold a working luncheon on the Indo-Pacific and economic security.

Migration is a priority for summit host Italy and its right-wing Prime Minister Giorgia Meloni, whos seeking to increase investment and funding for African nations as a means of reducing migratory pressure on Europe.

This report by The Canadian Press was first published June 14, 2024.

- With files from The Associated Press

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Pope Francis to meet with Trudeau, lead session on artificial intelligence - Central Alberta Online

We are interacting with artificial intelligence (AI) online not only more than ever but more than we realize so researchers asked people to converse with four agents, including one human and three different kinds of AI models, to see whether they could tell the difference.

The "Turing test," first proposed as "the imitation game" by computer scientist Alan Turing in 1950, judges whether a machine's ability to show intelligence is indistinguishable from a human. For a machine to pass the Turing test, it must be able to talk to somebody and fool them into thinking it is human.

Scientists decided to replicate this test by asking 500 people to speak with four respondents, including a human and the 1960s-era AI program ELIZA as well as both GPT-3.5 and GPT-4, the AI that powers ChatGPT. The conversations lasted five minutes after which participants had to say whether they believed they were talking to a human or an AI. In the study, published May 9 to the pre-print arXiv server, the scientists found that participants judged GPT-4 to be human 54% of the time,

ELIZA, a system pre-programmed with responses but with no large language model (LLM) or neural network architecture, was judged to be human just 22% of the time. GPT-3.5 scored 50% while the human participant scored 67%.

Read more: 'It would be within its natural right to harm us to protect itself': How humans could be mistreating AI right now without even knowing it

"Machines can confabulate, mashing together plausible ex-post-facto justifications for things, as humans do," Nell Watson, an AI researcher at the Institute of Electrical and Electronics Engineers (IEEE), told Live Science.

"They can be subject to cognitive biases, bamboozled and manipulated, and are becoming increasingly deceptive. All these elements mean human-like foibles and quirks are being expressed in AI systems, which makes them more human-like than previous approaches that had little more than a list of canned responses."

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The study which builds on decades of attempts to get AI agents to pass the Turing test echoed common concerns that AI systems deemed human will have "widespread social and economic consequences."

The scientists also argued there are valid criticisms of the Turing test being too simplistic in its approach, saying "stylistic and socio-emotional factors play a larger role in passing the Turing test than traditional notions of intelligence." This suggests that we have been looking in the wrong place for machine intelligence.

"Raw intellect only goes so far. What really matters is being sufficiently intelligent to understand a situation, the skills of others and to have the empathy to plug those elements together. Capabilities are only a small part of AI's value their ability to understand the values, preferences and boundaries of others is also essential. It's these qualities that will let AI serve as a faithful and reliable concierge for our lives."

Watson added that the study represented a challenge for future human-machine interaction and that we will become increasingly paranoid about the true nature of interactions, especially in sensitive matters. She added the study highlights how AI has changed during the GPT era.

"ELIZA was limited to canned responses, which greatly limited its capabilities. It might fool someone for five minutes, but soon the limitations would become clear," she said. "Language models are endlessly flexible, able to synthesize responses to a broad range of topics, speak in particular languages or sociolects and portray themselves with character-driven personality and values. Its an enormous step forward from something hand-programmed by a human being, no matter how cleverly and carefully."

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GPT-4 has passed the Turing test, researchers claim - Livescience.com

I didnt much care for AI Artificial Intelligence when it came out, in 2001. The films origin story a decades-long, endlessly reworked Stanley Kubrick project picked up by Steven Spielberg and put into production within months of Kubricks death was, it seemed at the time, probably responsible for its many flaws. I agreed with the San Francisco Chronicle when it wrote that we end up with the structureless, meandering, slow-motion endlessness of Kubrick combined with the fuzzy, cuddly mindlessness of Spielberg. Its a coupling from hell.

But a couple of decades and several technological leaps forward later, the film looks a more convincing version of where we are heading than it did at the start of this century. Most of that is a question of pure form; the phenomenon known in English as the uncanny valley was coined in 1978 by the robotics professor Masahiro Mori to describe the sense of unease generated by machines that look, sound or behave almost but not quite like humans.

In AI the same queasiness is generated not by a robot (although that is one of the films supposed themes) but by the very contrary world views and obsessions of its two creators. The movie is itself a sort of uncanny valley. As Tim Greiving pointed out in a 20th-anniversary appreciation for the Ringer, when you cut AI open, you find cold Kubrick machinery underneath warm Spielberg skin.

Kubrick spent almost 30 years trying to develop Brian Aldisss short story Supertoys Last All Summer Long. By the early 1980s it had been reconfigured as a Pinocchio allegory, with David, an artificial boy, rejected by his human mother and going on a quest with his Jiminy Cricket-like friend Teddy in search of a Blue Fairy who will explain the mystery of his existence. Having hired and fired several screenwriters, as was his wont, Kubrick showed it to his friend Spielberg, who described it as the best story youve ever had to tell.

Kubrick was an obsessive genius with a bleak view of the human condition expressed through a canon of unique films that he managed to finance by pretending they were in mainstream genres such as historical drama or horror. Spielberg is a populist master of commercial cinema with a humanist sensibility that seeks a transcendent redemption to every narrative arc. Kubrick, who never had a blockbuster hit on the scale of Jaws or ET: The Extra-Terrestrial, thought AI could be his shot at topping the box office. But as the years wore on, the gaps between his films became longer and longer. In the final 20 years of his life he only made three and died of a heart attack while completing post-production on the last of those, Eyes Wide Shut. With Minority Report delayed by Tom Cruises unavailability, Spielberg jumped in.

Kubricks long-time confidant and collaborator Jan Harlan insists the director truly believed Steven would be the better director for this film and I think he was right.

He wasnt. The film has the ho-hum competence we associate with middling Spielberg. An 11-year-old Haley Joel Osment, fresh from his Oscar nomination for The Sixth Sense, is at the core of everything as the lost robot boy. The set pieces in a 22nd-century dystopia scarred by climate change are unmemorable. There is no sense of the internet, much less of the intelligence explosion that IJ Good posited in 1965, four years before Aldiss wrote Supertoys Last All Summer Long and 35 years before the film AI was made. Good predicted a tipping point at which technology achieves sentience and autonomy from humans. In that sense, The Terminator is a more accurate vision of the future.

But, with all its flaws (or maybe because of them), AI still feels a more plausible future than Arnold Schwarzenegger chasing us with a big gun. A decaying capitalist society. A climate disaster. The end of humanity. It just doesnt sound like a Spielberg movie. Spielberg was faithful to Kubricks preparatory notes and adjusted his shooting style to match the older mans visual sensibility. But that warm fuzziness is still there, encasing Kubricks far chillier vision. And despite what the San Francisco Chronicle said, theres none of the deadpan monotony of classic Kubrickian sequences in 2001: A Space Odyssey, Barry Lyndon or The Shining.

Viewed in 2024, though, AI Artificial Intelligence bears many of the qualities that are becoming familiar from the chatbots and generative products that are beginning to infiltrate our day-to-day lives courtesy of Google, Microsoft and soon, apparently, Apple. The humanlike touches. The ingratiating tone. And, beneath it all, the cold, dark void.

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Hugh Linehan: Even with Spielberg-style cuddliness, there's a cold, dark void at the heart of artificial intelligence - The Irish Times

Bill Gate poses for a portrait at NPR headquarters in Washington, D.C., June 13, 2024. Ben de la Cruz/NPR hide caption

Artificial intelligence may come for our jobs one day, but before that happens, the data centers it relies on are going to need a lot of electricity.

So how do we power them and millions of U.S. homes and businesses without generating more climate-warming gases?

Microsoft founder, billionaire philanthropist and investor Bill Gates is betting that nuclear power is key to meeting that need and hes digging into his own pockets to try and make it happen.

Gates has invested $1 billion into a nuclear power plant that broke ground in Kemmerer, Wyo., this week. The new facility, designed by the Gates-founded TerraPower, will be smaller than traditional fission nuclear power plants and, in theory, safer because it will use sodium instead of water to cool the reactors core.

TerraPower estimates the plant could be built for up to $4 billion, which would be a bargain when compared to other nuclear projects recently completed in the U.S. Two nuclear reactors built from scratch in Georgia cost nearly $35 billion, the Associated Press reports.

Construction on the TerraPower plant is expected to be completed by 2030.

Gates sat for an interview at NPR headquarters with Morning Edition host Steve Inskeep to discuss his multibillion dollar nuclear power investment and how he views the benefits and challenges of artificial intelligence, which the plant hes backing may someday power.

This interview has been edited for length and clarity.

Steve Inskeep: Let me ask about a couple of groups that you need to persuade, and one of them is long-time skeptics of the safety of nuclear power, including environmental groups, people who will put pressure on some of the political leaders that you've been meeting here in Washington. Are you convinced you can make a case that will persuade them?

Bill Gates: Well, absolutely. The safety case for this design is incredibly strong just because of the passive mechanisms involved. People have been talking about it for 60 years, that this is the way these things should work.

Meaning if it breaks down, it just cools off.

Exactly.

Something doesn't have to actively happen to cool it.

There's no high pressure on the reactor. Nothing that's pushing to get out. Water, as it's heated up, creates high pressure. And we have no high pressure and no complex systems needed to guarantee the safety. The Nuclear Regulatory Commission is the best in the world, and they'll question us and challenge us. And, you know, that's fantastic. That's a lot of what the next six years are all about.

Taillights trace the path of a motor vehicle at the Naughton Power Plant, Jan. 13, 2022, in Kemmerer, Wyo. Bill Gates and his energy company are starting construction at their Wyoming site adjacent to the coal plant for a next-generation nuclear power plant he believes will revolutionize how power is generated. Natalie Behring/AP/FR170146 AP hide caption

Let me ask about somebody else you need to persuade, and that is markets showing them that this makes financial sense. Sam Altman, CEO of OpenAI, is promoting and investing in nuclear power and is connected with a company that put its stock on the market and it immediately fell. Other projects that started to seem too expensive have been canceled in recent years. Can you persuade the markets?

Well, the current reactors are too expensive. There are companies working on fission and there's companies working on fusion. Fusion is further out. I hope that succeeds. I hope that in the long run it is a huge competitor to this TerraPower nuclear fission. Unlike previous reactors, we're not asking the ratepayers in a particular geography to guarantee the costs. So this reactor, all of the costs of building this are with the private company, TerraPower, in which I'm the biggest investor. And for strategic reasons, the U.S. government is helping with the first-of-the-kind costs.

The U.S. Department of Energy is funding half the costs of TerraPowers project, which includes the cost of designing and licensing the reactor, the AP reports.

I wonder if you can approach an ordinary investor and say, This is a good risk. It's going to pay off in a reasonable time frame?

You know, we're not choosing to take this company public, because understanding all of these issues are very complex. Many of our investors will be strategic investors who want to supply components, or they come from countries like Japan and Korea, where renewables are not as easy because of the geography. And so they want to go completely green. They, even more than the U.S., will need nuclear to do that.

What is the connection between AI and nuclear power?

Well, I suppose people want innovation to give us even cheaper electricity while making it clean. People who are optimistic about innovation in software and AI bring that optimism to the other things they do. There is a more direct connection, though, which is that the additional data centers that we'll be building look like they'll be as much as a 10% additional load for electricity. The U.S. hasn't needed much new electricity but with the rise in a variety of things from electric cars and buses to electric heat pumps to heating homes, demand for electricity is going to go up a lot. And now these data centers are adding to that. So the big tech companies are out looking at how they can help facilitate more power, so that these data centers can serve the exploding AI demand.

I'm interested in whether you see artificial intelligence as something that potentially could exacerbate income inequality, something that you as a philanthropist would think about.

Well, I think the two domains that I'm most involved in seeing how AI can help are health and education. I was in Newark, New Jersey, recently seeing the Khan Academy AI called Khanmigo being used in math classes, and I was very impressed how the teachers were using it to look at the data, divide students up to have personalized tutoring at the level of a kid who's behind or a kid who's ahead.

Whenever I get, like, a medical bill or a medical diagnosis, I put it in the AI and get it to explain it to me. You know, it's incredible at that. And if we look at countries like in Africa where the shortage of doctors is even more dramatic than in the United States, the idea that we can get more medical advice to pregnant women or anybody suffering from malaria, I'm very excited. And so driving it forward appropriately in those two domains I see as completely beneficial.

Did you understand what I was asking, about the concentration of power?

Absolutely. This is a very, very competitive field. I mean, Google is doing great work. Meta. Amazon. And it's not like there's a limited amount of money for new startups in this area. I mean, Elon Musk just raised $6 billion. It's kind of like the internet was in the year 2000. The barriers to entry are very, very low, which means we're moving quickly.

And the other thing about a concentration of power Do you worry about, you know, more money for investors and fewer jobs for ordinary people? Like they can get this wonderful AI technology, but they dont have a job?

I do worry about that. Basically, if you increase productivity, that should give you more options. We don't let robots play baseball. We're just never going to be interested in that. If robots get really good, and AIs get really good, are we in some ways going to want, in terms of job creation, to put limits on that, or tax those things? Ive raised that in the past. They're not good enough yet to be raising those issues. But you know, say in three to five years, they could be good enough.

But for now, your hope is the AI doesn't replace my job. It makes me more productive in the job that I already have.

Well, there are few jobs that will replace you just like computers did. In most things today, AI is a co-pilot, it raises your productivity. But if you're a support person, taking support calls and you're twice as productive, some companies will take that productivity and answer more calls and have more quality of answer. Some companies will need less people, now freeing up labor to do other things. Do they go and help reduce class size or help the handicapped or help with the elderly? If we're able to produce more, then the pie is bigger. But are we smart in terms of tax policies or how we distribute that, so we actually take freed-up labor and put into things wed like to have?

The Bill & Melinda Gates Foundation is an NPR funder.

The audio version of this story was produced by Kaity Kline and edited by Reena Advani. The digital version was edited by Amina Khan.

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Bill Gates on his nuclear energy investment, AI's challenges - NPR