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Dont let fear hold you back; take a leap of faith and see where it leads. Curious George

We are at the start of an incredible technological advance called Generative Artificial Intelligence (GAI). We dont know where it will lead us. Every day brings us more enhancements of this technology and more stories about how it is good or bad. Sometimes, it feels like we are on a precipice. It is an exciting time to be leading you get to shape the future of the organization you are leading and take advantage of all that GAI has to offer while minding the challenges that come with it.

Leaders need to understand what it is and how it can be used in decision-making.

What is GAI?

Generative artificial intelligence is artificial intelligence capable of generating text, images, videos, or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics. Wikipedia.

If you are a novice, there are a few excellent resources you can get started with:

GAI in Business Operations and Decision-Making

AI has the potential to automate mundane tasks, freeing us for work that requires uniquely human traits such as creativity and critical thinking or, possibly, managing and curating the AIs creative output. Ethan Mollick, Co-Intelligence

In the early 2000s, Big Data gained momentum in business decision-making, noting the ability of decision-making tools to handle large amounts of data available from information systems. For example, the company I cofounded, Retail Solutions, provided analytics to retailers and CPG companies based on retail data such as point-of-sale, distribution, and inventory, which helped them make informed decisions about what to promote, how much inventory to carry, and how to prevent out-of-stock.

In the last decade, machine learning, a subset of artificial intelligence, became part of the arsenal of tools businesses use. Their use has allowed enterprises to harness the power of the data to make operations more efficient and derive valuable insights about customer behavior. An example of the use of machine learning can be found in the recommendation engines used by businesses like Netflix. The algorithms learn from the vast amount of customer data to understand each customers watching behavior and be able to suggest what to watch next based on it, and also based on customers whose tastes are similar.

Today, businesses can mine even more data with GAI, such as call center interactions, email texts, and financial reports. GAI affords businesses quick summarization of the vast amount of internal and external data. A semantic search of information available across documents, product catalogs, and knowledge bases has been made possible by the power of the large language models (LLMs) which enable GAI. My previous article, GenAI Unleashed: A Leaders Guide for Maximizing Global Impact in Talent Management, Content Creation, and Customer Support, described several business areas that can benefit from GAI.

All this power comes with some downside. The technology is in the early stages, and the LLMs tend to hallucinate and makeup falsehoods. Leaders also need to be mindful of the bias in the underlying data (which, by the way, reflects the bias of humans who generated the data). The accuracy of the GAI solutions needs improvement. However, as I mentioned in a previous article, Riding the Wave of Generative AI: Tips for Enterprise Leaders, there are three things a leader can do to get started, namely, understand where the technology is, identify how GAI can help your business, and set up experiments.

Collaboration and Augmentation

The key to success in the AI era will be to understand how to leverage AI to augment human capabilities. Unknown

Keep the words augment and collaborate in your mind in the many ways you can use GAI. Approach GAI as a tool that can work alongside humans to increase productivity.

Today, GAI is reasonably capable of generating some decisions, but humans must decide whether and how to use it.

A 2023 research paper, Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence, found that using ChatGPT for mid-level professional writing tasks substantially increased productivity. It says,

ChatGPT could increase workers productivity in two ways. On the one hand, it couldsubstitute for worker effort by quickly producing output of satisfactory quality that workersdirectly submit, letting them reduce the time they spend on the task. On the other hand, itcould complement workers skills: humans and ChatGPT working together could producemore than the sum of their parts, for example if ChatGPT aids with the brainstorming process,or quickly produces a rough draft and humans then edit and improve on the draft.

It is essential to consider the collaboration parameters when using GAI in decision-making. Richard Benjamins, former Chief Responsible AI Officer at Telefonica and founder of its AI for Society and Environment area, proposed a Choices Framework for considering ethical and responsible choices when using GAI. He defines an Ethics Continuum and Impact on Society. It has Use AI for good at one end of the continuum and Malicious use of AI at the other end, with Do not use AI if effects cannot be mitigated, Best Effort to avoid the negative impact of AI, and Negative effect of AI is considered collateral damage in between. He says organizations need to decide, based on their norms and values, where they want to be in a continuum of ethics.

Embrace GAI with Caution

The advent of General Artificial Intelligence (GAI) can be compared to historical technological and scientific breakthroughs that transformed society, such as the Industrial Revolution. Generative AI is not a panacea for all problems; therefore, understanding what it is, its benefits, and its shortcomings would be tremendously advantageous for an enterprise. The practice of holding opposable ideas in mind is precious in understanding the continuously changing world of GAI. With many voices expressing opposing views on the advances in GAI, one has to think for oneself. Understand the diverse points of view, and then decide for yourself. And, as Curious George said, dont let fear hold you back.

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Generative AI: Taking the Leap While Navigating Its Risks - CEOWORLD magazine

In an address to the G7 summit, Pope Francis discusses the threat and promise of artificial intelligence, the techno-human condition, human vs algorithmic decision-making, AI-written essays, and the necessity of political collaboration on technology.

By Joseph Tulloch

On Friday afternoon, Pope Francis addressed the G7 leaders summit in Puglia, Italy.

He is the first Pope to ever address the forum, which brings together the leaders of the US, UK, Italy, France, Canada, Germany, and Japan.

The Pope dedicated his address to the G7 to the subject of artificial intelligence.

He began by saying that the birth of AI represents a true cognitive-industrial revolution which will lead to complex epochal transformations.

These transformations, the Pope said, have the potential to be both positive for example, the democratization of access to knowledge, the exponential advancement of scientific research, and a reduction in demanding and arduous work and negative for instance, greater injustice between advanced and developing nations or between dominant and oppressed social classes.

Noting that AI is above all a tool, the Pope spoke of what he called the techno-human condition.

He explained that he was referring to the fact that humans relationship with the environment has always been mediated by the tools that they have produced.

Some, the Pope said, see this as a weakness, or a deficiency; however, he argued, it is in fact something positive. It stems, he said, from the fact that we are beings inclined to what lies outside of us, beings radically open to the beyond.

This openness, Pope Francis said, is both the root of our techno-human condition and the root of our openness to others and to God, as well as the root of our artistic and intellectual creativity.

The Pope then moved on to the subject of decision-making.

He said that AI is capable of making algorithmic choices that is, technical choices among several possibilities based either on well-defined criteria or on statistical inferences.

Human beings, however, not only choose, but in their hearts are capable of deciding.

This is because, the Pope explained, they are capable of wisdom, of what the Ancient Greeks calledphronesis(a type of intelligence concerned with practical action), and of listening to Sacred Scripture.

It is thus very important, the Pope stressed, that important decisions must always be left to the human person.

As an example of this principle, the Pope pointed to the development of lethal autonomous weapons which can take human life with no human input and said that they must ultimately be banned.

The Pope also stressed that the algorithms used by artificial intelligence to arrive at choices are neither objective nor neutral.

He pointed to the algorithms designed to help judges in deciding whether to grant home-confinement to prison inmates. These programmes, he said, make a choice based on data such as the type of offence, behaviour in prison, psychological assessment, and the prisoners ethnic origin, educational attainment, and credit rating.

However, the Pope stressed, this is reductive: human beings are always developing, and are capable of surprising us by their actions. This is something that a machine cannot take into account.

A further problem, the Pope emphasised, is that algorithms can only examine realities formalised in numerical terms:

The Pope then turned to consider the fact that many students are increasingly relying on AI to help them with their studies, and in particular, with writing essays.

It is easy to forget, the Pope said, that strictly speaking, so-called generative artificial intelligence is not really generative it does not develop new analyses or concepts but rather repeats those that it finds, giving them an appealing form.

This, the Pope said, risks undermining the educational process itself.

Education, he emphasised, should offer the chance for authentic reflection, but instead runs the risk of being reduced to a repetition of notions, which will increasingly be evaluated as unobjectionable, simply because of their constant repetition.

Bringing his speech to a close, the Pope emphasised that AI is always shaped by the worldview of those who invented and developed it.

A particular concern in this regard, he said, is that today it is increasingly difficult to find agreement on the major issues concerning social life - there is less and less consensus, that is, regarding the philosophy that should be shaping artificial intelligence.

What is necessary, therefore, the Pope said, is the development of an algor-ethics, a series of global and pluralistic principles which are capable of finding support from cultures, religions, international organizations and major corporations.

If we struggle to define a single set of global values, the Pope said, we can at least find shared principles with which to address and resolve dilemmas or conflicts regarding how to live.

Faced with this challenge, the Pope said, political action is urgently needed.

Only a healthy politics, involving the most diverse sectors and skills, the Pope stressed, is capable of dealing with the challenges and promises of artificial intelligence.

The goal, Pope Francis concluded, is not stifling human creativity and its ideals of progress but rather directing that energy along new channels.

You can find the full text of the Pope's address to the G7 here.

Read the rest here:
Pope to G7: AI is neither objective nor neutral - Vatican News - English

Catch up on select AI news and developments from the past week or so (in no particular order):

Apple Unveils 'Apple Intelligence' with OpenAI Partnership. Apple announced a partnership with OpenAI to integrate ChatGPT into its devices, unveiling a new AI system called "Apple Intelligence." This system aims to enhance Siri's capabilities and offer more personalized features on Apple devices. Despite investor concerns about Apple's AI competitiveness, the partnership with OpenAI is seen as a significant move. Apple emphasized user privacy, with AI features running on devices and in the cloud without collecting personal data. The announcement was made at Apple's annual developers conference, where other AI features and product updates were also showcased.

Importance for marketers: This partnership highlights Apple's commitment to integrating advanced AI while prioritizing user privacy, making it a crucial development for marketers to monitor. Understanding these enhancements can help marketers leverage new AI-driven capabilities for personalized customer experiences on Apple devices.

Meta to Use European Social Media Posts for AI Training. Meta Platforms announced plans to use publicly shared social media content from Europe to train its generative AI models. This move aligns Meta's data usage approach in Europe with its global practices, despite stringent EU privacy regulations. Meta assured that private posts and messages will not be used. The company will notify European users about this data usage. Advocacy groups have filed complaints, arguing that Meta should obtain opt-in consent from users as per EU privacy laws.

Importance for marketers: Meta's strategy could enhance its AI capabilities, improving targeted advertising and content recommendations. Marketers should stay informed about regulatory changes and user consent requirements to ensure compliance and maintain trust with their audience.

Arm-Qualcomm Legal Dispute Threatens AI-Powered PC Launch. A legal dispute between Arm Holdings and Qualcomm may disrupt the release of new AI-powered PCs. The conflict arises from a disagreement over licensing technology acquired by Qualcomm from Nuvia. An Arm victory could halt shipments of new AI-driven laptops from Qualcomm and its partners, including Microsoft. The litigation could impact the anticipated market growth for these AI-powered PCs.

Importance for marketers: The outcome of this legal battle could influence the availability of cutting-edge AI technology in the PC market, affecting marketing strategies for tech products. Marketers should monitor this situation to anticipate potential disruptions in product launches and technology adoption.

Mistral AI Secures 600M Funding to Expand AI Capabilities. French AI startup Mistral AI raised 600 million in a Series B funding round, led by General Catalyst and other prominent investors. The company's valuation surged to 5.8 billion. Mistral plans to use the funds to enhance computing capacity, hire more staff, and expand its presence, particularly in the US. This funding highlights the growing investor interest in AI.

Importance for marketers: Mistral's expansion signifies the increasing competition and innovation in the AI industry. Marketers should explore potential collaborations with emerging AI companies like Mistral to leverage cutting-edge technology for improved marketing strategies and customer engagement.

Colorado Introduces Legislation to Regulate AI and Data Privacy. Colorado lawmakers are pushing for new laws to regulate AI, social media, and data privacy. The proposed measures include public disclosure of AI-generated content, oversight for high-risk AI systems, and enhanced protections for minors' data on social media. These regulations aim to address algorithmic discrimination and ensure user privacy. The legislation would position Colorado as a leader in AI regulation in the US.

Importance for marketers: These regulations could impact how marketers use AI and data for targeting and personalization. Marketers should prepare for stricter data privacy laws and consider the ethical implications of AI in their strategies to stay compliant and maintain consumer trust.

Study Warns AI Training Data Supply Could Be Exhausted by 2032. A study by Epoch AI predicts that the supply of publicly available data for training AI language models could be depleted by 2032. This shortage may hinder the progress of AI development, pushing companies to seek alternative data sources or rely on synthetic data. The study highlights the importance of high-quality data for improving AI systems and the potential challenges of data scarcity.

Importance for marketers: The potential scarcity of training data could affect the quality and effectiveness of AI-driven marketing tools. Marketers should explore diverse data sources and innovative approaches to ensure the continued advancement of AI applications in marketing, while also considering ethical data usage practices.

OpenAI Bolsters Leadership, Partners With Apple for ChatGPT Integration. OpenAI hired Sarah Friar as CFO and Kevin Weil as chief product officer to strengthen its leadership team. The company also announced a partnership with Apple to integrate ChatGPT into iOS, iPadOS, and macOS. This integration aims to enhance Apple's AI capabilities and user experience. OpenAI's leadership expansion and high-profile partnership underscore its growing influence in the AI sector.

Importance for marketers: OpenAI's leadership changes and partnership with Apple reflect its strategic growth and focus on expanding AI applications. Marketers should consider how these developments can enhance AI-driven customer interactions and explore opportunities for integrating advanced AI solutions into their marketing efforts.

Apple Assures Data Privacy With 'Apple Intelligence' AI System. Apple emphasized its commitment to data privacy at WWDC 2024, highlighting that its new "Apple Intelligence" AI system processes data on-device or on secure cloud servers without storing or accessing personal data. The system uses Apple silicon and cryptographic measures to ensure privacy. This approach aims to build trust among users concerned about data security in AI applications.

Importance for marketers: Apple's focus on data privacy can enhance consumer trust and loyalty. Marketers should leverage this assurance to promote privacy-focused AI solutions and communicate the value of secure, personalized experiences to customers, aligning with growing privacy concerns.

Inside Apple's Privacy-First Approach to AI with 'Apple Intelligence'. Apple detailed its dual approach to AI privacy, combining on-device processing with secure cloud servers to handle more complex tasks. The "Apple Intelligence" system uses Apple's own AI models and public data, ensuring user data is not used for training. The system's design emphasizes minimal data sharing and transparency, with public inspection of server code to verify privacy claims.

Importance for marketers: Apple's privacy-centric AI approach can serve as a model for ethical AI practices in marketing. Marketers should adopt similar principles to ensure data security and transparency, fostering trust and demonstrating a commitment to protecting consumer privacy in AI-driven marketing strategies.

LinkedIn Enhances Job Search and Learning with AI Tools. LinkedIn introduced new AI-powered features to streamline job searching and application processes, including tools for generating cover letters and personalizing learning content. The platform aims to enhance user experiences and maintain relevance amid growing AI adoption. LinkedIn's focus on AI builds on its long-standing use of the technology for connecting users and ensuring security.

Importance for marketers: LinkedIn's AI enhancements offer new opportunities for targeted job ads and personalized content delivery. Marketers should explore these tools to improve recruitment marketing strategies and leverage AI-driven insights for more effective audience engagement on the platform.

Yahoo Revamps News App with AI-Powered Personalization from Artifact. Yahoo launched a revamped version of its news app, incorporating AI technology from the acquired news reader app Artifact. The new app uses AI algorithms to deliver personalized content and generate summaries of news articles. This move aims to enhance user engagement and position Yahoo as a leader in AI-driven news delivery.

Importance for marketers: Yahoo's AI-powered news app offers marketers new avenues for content distribution and targeted advertising. Understanding how AI personalization can improve user engagement can help marketers develop more effective strategies for reaching their target audience through AI-enhanced platforms.

Adobe Faces Backlash From Employees Over AI Content Use Concerns. Adobe faced internal criticism after suggesting it might use customer content to train AI models, prompting employee demands for better communication and long-term strategy. Adobe clarified that it does not train AI on customer content, but the controversy highlights ongoing concerns about data usage in AI development. Employees called for improved transparency and customer engagement to address these issues.

Importance for marketers: The controversy underscores the need for clear communication and ethical practices in AI development. Marketers should prioritize transparency in how customer data is used and engage openly with their audience to build trust and mitigate concerns about AI and data privacy.

Luma AI Launches 'Dream Machine' for High-Quality AI-Generated Videos. Luma AI introduced Dream Machine, a new AI system capable of generating realistic videos from text descriptions. This technology is accessible to the public, allowing users to create video content quickly and easily. Dream Machine's open approach aims to foster a community of creators and developers, positioning Luma AI as a leader in AI-powered video generation.

Importance for marketers: Dream Machine's capabilities offer marketers a powerful tool for creating engaging video content at scale. By using this technology, marketers can produce high-quality, customized videos efficiently, enhancing their content strategies and driving audience engagement.

Memory Constraints Prevent Apple Intelligence on iPhone 15. Apple's new AI system, Apple Intelligence, is likelyincompatible with the iPhone 15 and probably iPhone 15 Plus due to insufficient memory. The system requires a minimum of 8GB of RAM, available in the iPhone 15 Pro and newer devices. This limitation is attributed to the large language model used by Apple Intelligence, which demands significant memory resources.

Importance for marketers: Understanding device limitations is crucial for marketers developing AI-driven apps and services. Marketers should account for hardware constraints when targeting users with AI-powered features, ensuring compatibility and optimal performance across different devices to maximize user experience.

Pope Francis Joins G7 Summit to Discuss AI Ethics. Pope Francis will join G7 leaders to discuss the ethical implications of artificial intelligence. His participation underscores the importance of developing AI technologies that benefit humanity and promote peace. The pope's involvement follows the Rome Call for AI Ethics, a set of principles for responsible AI development.

Importance for marketers: Ethical AI practices are increasingly important in technology development. Marketers should stay informed about global discussions on AI ethics to align their strategies with responsible AI principles, fostering trust and ensuring that AI applications benefit society while minimizing risks.

Elon Musk Withdraws Lawsuit Against OpenAI. Elon Musk has unexpectedly dropped his legal case against OpenAI, which accused the company of deviating from its mission to benefit humanity. The lawsuit's withdrawal comes amid recent tensions following Apple's partnership with OpenAI. Musk's decision to drop the case remains unexplained, though it leaves open the possibility of future legal action.

Importance for marketers: The resolution of legal disputes can impact business relationships and market dynamics. Marketers should monitor such developments to understand potential shifts in industry alliances and competitive strategies, ensuring they remain agile and informed in their marketing efforts.

You can find last week's AI Update here.

Editor's note: GPT-4o was used to help compile this week's AI Update.

Read the original post:
AI Update, June 14, 2024: AI News and Views From the Past Week - MarketingProfs.com

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