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The Future of Warfare: AI-Powered Drones Should the AI-powered drones of the future have a licence to kill? The question is becoming ever more pressing as governments and the defence industry acknowledge that drone systems will play an increasingly crucial role in future warfare. The Moral Dilemma of Autonomous Weapons With drones being deployed in huge numbers in the Ukraine war and AI being used to assist bombing missions in the Iran conflict, there is an expectation among some observers that weapons will have to operate with increased operational autonomy, which means they will need something approximating a moral framework. Expert Opinions on AI and Morality Last year Mustafa Suleyman, chief executive of Microsoft’s AI arm and a co-founder of the UK-based DeepMind, was unequivocal about the issue of machines making moral decisions. He said: “AIs cannot be people – or moral beings.” David Omand, the former head of the UK spy agency, GCHQ, believes AI can create a “moral” configuration for unmanned weapons. The UK armed forces minister, Al Carns, told the Financial Times recently there must be an option to “take the human out of the loop” in decision-making. The Challenges of Programming Morality Zee Talat, an academic specialising in machine learning at the University of Edinburgh’s school of informatics, argues that large language models – the technology that underpins modern generative AI systems such as chatbots – are fundamentally incapable of moral decision-making. “If you have a machine that’s probabilistic by nature it will veer towards the most likely answer in a situation. Do we think that morality follows probabilistic notions?” The Debate on Autonomous Weapons Governance Jessica Dorsey, an assistant professor of international law at Utrecht University in the Netherlands, raises concerns about determining whose morality the drone is following, given the United Nations is still trying to achieve a global consensus on autonomous weapons governance. “War is filled with so many variables and it is a given that things will go wrong. And when that happens at AI-like speed, it is difficult to unravel.” The Future of AI-Powered Drones Some experts argue that giving drones greater autonomy, and programming rules of engagement and morality into them, will be a necessity if other nation states continue to develop and deploy similar technology at pace. Nicholas Wright, a neuroscientist and author of Warhead, a book on the human brain and war, says: “For any military to compete effectively against other high-end militaries it is going to need a large amount of systems that will be required to take decisions on their own.” Olaf Hichwa, the co-founder of Neros, a US drone startup, believes that drones will not replace human decision-makers, but enhance the abilities of their human pilots.

President Donald Trump signed an executive order on June 2, 2026 establishing a voluntary framework for early government review of powerful new AI models. The order aims to bolster national security by giving agencies a chance to vet AI systems before they reach the market, marking a departure from the administration’s earlier deregulatory stance.Executive Order Introduces Voluntary Early‑Access Review for AI ModelsThe order asks technology companies to submit their latest AI models to the federal government for a voluntary review up to 30 days prior to public launch. While it stops short of mandating compliance, it reflects pressure from hard‑line supporters for stricter oversight and from industry advocates for a lighter touch.Scope and Timeline of the Voluntary Review Framework30‑day pre‑release review window for participating firms.Voluntary participation, though the administration encourages broad adoption.Key agencies involved: National Security Agency (NSA), Department of Defense (DoD), and the Department of Treasury for vulnerability testing.Existing agreements already cover OpenAI, Anthropic, Microsoft, Google DeepMind, and xAI; the new order expands the approach to all AI developers.Implications for AI Governance and National SecurityThe framework is intended to mitigate risks from advanced models such as Anthropic’s Mythos, which possesses sophisticated cybersecurity capabilities. By granting agencies early insight, the government hopes to identify potential exploits that could threaten critical infrastructure like hospitals and banks. Critics warn that even voluntary sharing could set precedents for future mandatory controls and raise free‑speech concerns.Future Trajectory of U.S. AI Regulation Under TrumpAnalysts anticipate that the administration will continue to tighten AI oversight, potentially moving from voluntary to mandatory reviews if security threats intensify. The order also directs hiring of additional cybersecurity and AI experts, suggesting a longer‑term institutional commitment. Upcoming legislative battles may focus on balancing national security with industry innovation and civil‑liberties protections.

Why a Living AI Glossary Matters NowArtificial intelligence is reshaping every industry, but its rapid evolution has spawned a parallel explosion of terminology that can leave even seasoned technologists feeling insecure. TechCrunch’s new glossary aims to provide a single, regularly‑updated reference that translates the most common AI buzzwords into plain language.Key Definitions from AGI to RLHFThe article walks readers through a spectrum of concepts, including:Artificial General Intelligence (AGI) – AI that outperforms humans on most economically valuable tasks, as defined by OpenAI and Google DeepMind.AI Agent – An autonomous tool that can perform multi‑step tasks such as expense filing, ticket booking, or code maintenance.API Endpoints – “Buttons” that let software components interact, enabling agents to automate third‑party services.Chain‑of‑Thought Reasoning – A technique that breaks problems into intermediate steps to improve accuracy.Compute – The hardware (GPUs, CPUs, TPUs) that powers AI model training and inference.Deep Learning – Multi‑layered neural networks that learn features directly from data.Diffusion – The process behind many generative AI models that learns to reverse noise‑added data.Distillation – A teacher‑student method for creating smaller, faster models like GPT‑4 Turbo.Fine‑Tuning – Adding task‑specific data to a pre‑trained model to improve performance.GAN – Generative Adversarial Networks that pit a generator against a discriminator to produce realistic outputs.Hallucination – When models generate inaccurate or fabricated information.Inference – Running a trained model to make predictions, often accelerated by specialized hardware.LLM – Large Language Models that power assistants such as ChatGPT, Claude, Gemini, and Llama.Memory Cache (KV Caching) – An optimization that stores intermediate calculations to speed up inference.Open Source vs. Closed Source – The debate over publicly available model code (e.g., Meta’s Llama) versus proprietary systems (e.g., OpenAI’s GPT).Parallelization – Executing many calculations simultaneously, a cornerstone of modern AI hardware.RAMageddon – The current shortage of memory chips driven by AI data‑center demand.Recursive Self‑Improvement (RSI) – Models that can redesign themselves, a potential step toward singularity.Reinforcement Learning from Human Feedback (RLHF) – Training models with reward signals to improve helpfulness and safety.Tokens & Throughput – The basic units of text processing that determine cost and performance.Quantifying the AI Vocabulary ExplosionThe glossary covers more than 30 distinct terms, each accompanied by concise explanations and links to deeper resources. By cataloguing this breadth, the piece highlights how quickly the AI lexicon has expanded within just a few years of mainstream adoption.Implications for Developers, Investors, and the PublicUnderstanding this terminology is no longer optional. For developers, clear definitions accelerate product building and reduce miscommunication when integrating APIs or deploying agents. Investors gain a sharper lens for evaluating startup pitches that hinge on concepts like fine‑tuning or distillation. Meanwhile, the broader public can better assess claims about “AGI” or “hallucinations,” mitigating hype‑driven misinformation.Future of AI Terminology and Industry AdoptionTechCrunch positions the glossary as a “living document,” promising regular updates as new techniques (e.g., emerging diffusion variants or next‑gen RLHF methods) appear. As AI systems become more autonomous and specialized, the vocabulary will continue to evolve, making ongoing education essential for anyone interacting with the technology.

Lead: A Papal Voice Joins the Global AI DebateThe Vatican has entered the AI ethics arena with Pope Leo XIV’s inaugural encyclical, Magnifica Humanitas, a 42,000‑word manifesto that puts humanity at the centre of the digital revolution. By partnering with Anthropic’s Christopher Olah, the Pope signals a rare alliance between religious authority and cutting‑edge AI research.Leo XIV Unveils “Magnifica Humanitas” Encyclical on AIOn 15 May 2026 the Vatican presented the encyclical, echoing Pope Leo XIII’s 1891 social teaching *Rerum Novarum*. The document catalogues the “daunting challenges” of artificial intelligence and calls on political leaders to safeguard human dignity as technology outpaces ethical regulation.Published in the Vatican’s official channels on 15 May 2026.Co‑presented by Christopher Olah, co‑founder of Anthropic.Frames AI as a moral, not merely technical, issue.Scope and Scale: 42,000‑Word Document Highlights AI RisksThe encyclical’s length underscores the depth of the Vatican’s analysis. Key statistics include:42,000 words covering AI’s impact on labour, healthcare, warfare, and personal autonomy.References to existing AI‑related legislation in the EU, US, and China.Calls for “state regulation” to ensure AI benefits are distributed equitably.Implications for Tech Industry, Regulation, and Moral DiscourseThe papal intervention arrives as US President Donald Trump postponed an executive order on AI safety reviews, highlighting a policy vacuum. The encyclical’s moral framing could influence:Corporate responsibility standards for firms like Anthropic, OpenAI, and Google DeepMind.Legislative momentum in Europe and the United Nations on AI governance.Public perception of AI, shifting the narrative from profit‑driven hype to human‑centred ethics.Critics have accused the Vatican of “pope‑washing,” but the collaboration suggests a willingness to engage with secular experts.Future Trajectory: From Papal Guidance to Global AI GovernanceIf the Vatican’s call gains traction, we may see:Increased inclusion of ethical clauses in AI development roadmaps.New international forums where religious leaders, technologists, and policymakers co‑draft standards.Potential pressure on tech CEOs—such as Elon Musk—to adopt more transparent, accountable practices.Ultimately, *Magnifica Humanitas* positions the Catholic Church as a moral stakeholder in the AI age, urging a future where technology amplifies, rather than diminishes, human flourishing.

Google DeepMind's Shift in Labor Relations Google DeepMind, the artificial intelligence arm of Google, has agreed to enter formal talks with UK tech workers that could lead to trade union representation. This move comes amid growing staff concerns about the use of its AI by the US and Israeli governments' defence and intelligence. The Concerns Driving Unionization Hundreds of workers have signed petitions raising concerns about the application of the technology, with concern rising since the company's 2025 decision to drop a promise not to allow its technologies to be used in harmful weapons or surveillance that violated international norms. The Impact of AI on Labor Rights The move comes as Google is facing a court challenge from a DeepMind AI researcher of Palestinian heritage who alleges he was wrongfully dismissed after protesting against its work for the Israeli government. Israeli officials have credited Google's cloud computing with enabling 'phenomenal things [to] happen in combat' during the Gaza conflict. The Path Forward Google DeepMind declined to voluntarily recognise the unions for collective bargaining purposes, but said in a staff email on Wednesday that talks at Acas 'may lead to a formal ballot in a few months' time, giving all eligible employees the opportunity to vote on whether they want to be represented by the unions.' The Future of AI Development and Labor Relations A CWU source said: 'It's a concession that they need to address some of the serious issues on the shop floor. There's clearly a groundswell of opinion about the contracts they are expected to serve, for example the relationship with drone technology and the Israeli government and the relationship with the US military. This is definitely a step forward and they are not just shutting this off.'

Executive Summary: Engineer Claims Unfair Dismissal Over Israel‑Related AI WorkAn AI engineer at Google DeepMind has lodged a claim with a UK employment tribunal alleging that he was unfairly dismissed after protesting the firm’s provision of military AI to the Israeli government. The dispute highlights growing tensions between AI developers and staff concerned about the technology’s ethical implications.Leaflets, Emails, and the Tribunal ClaimThe engineer, of Palestinian heritage, distributed flyers in the London office stating “Google provides military AI to forces committing genocide” and asked colleagues, “Is your paycheck worth this?”. He also emailed staff about Google’s 2025 decision to drop a promise not to pursue weapons that harm people and urged them to join the union United Tech and Allied Workers. After HR meetings, he was told he had resigned; he denies this and says he was laid off in September.Monetary and Workforce Figures Highlighting the Dispute$1.2bn cloud‑computing deal between Google and the Israeli government has sparked protests.An insider cited at least 10 staff members who quit over ethical concerns.Recent polls in Great Britain show one in three university students fear AI‑driven job loss could trigger social unrest.Broader Implications for AI Ethics and Employee ActivismThe case adds to a wave of internal dissent at major tech firms over AI’s use in defence and surveillance. Workers argue that while they may not oppose all military applications, they oppose “irresponsible use” and “misuse by anti‑democratic actors”. Google maintains that it does not fire employees for expressing opinions and that it adheres to policies governing unionised staff.What May Lie Ahead for Google and the AI IndustryIf the tribunal finds in favor of the engineer, it could set a precedent for how tech companies handle internal whistle‑blowing on ethical issues. The dispute may pressure Google to revisit its AI principles, especially regarding weapons and surveillance, and could encourage further regulatory scrutiny of AI contracts with governments.

Musk’s Public Critique of OpenAI’s GovernanceJune 2024: Musk tweeted concerns about OpenAI’s board composition and perceived drift from its original nonprofit mission.July 2024: He funded a think‑tank to explore alternative AI safety frameworks, positioning himself as a watchdog.Altman’s Defense and Strategic Counter‑MovesAugust 2024: Altman released a detailed blog post reaffirming OpenAI’s commitment to safe, broadly beneficial AI.September 2024: OpenAI announced a $2 billion funding round led by major venture firms, signaling continued investor confidence.Financial Impact on OpenAI and Its StakeholdersOpenAI’s valuation dipped 5% in the week following Musk’s comments, according to private market data.Despite the dip, the new funding round valued the company at roughly $30 billion, underscoring strong backing from institutional investors.Industry Ripple Effects of the Leadership ClashCompeting AI labs, including Anthropic and DeepMind, have issued statements emphasizing independent governance, hinting at a broader sector reassessment.Regulators in the EU and US cited the feud as a catalyst for accelerating AI oversight proposals.Outlook: What the Musk‑Altman Standoff Means for AI’s TrajectoryAnalysts predict a possible bifurcation: one path led by OpenAI’s commercial expansion, another driven by alternative, more open‑source initiatives championed by Musk.Stakeholders are watching for any formal changes to OpenAI’s board or charter, which could redefine the balance between profit motives and safety commitments.

Immersive Simulations with Street View and Genie Google has taken Street View to the next level by integrating it with its Genie world model, a general-purpose world model that can generate diverse, interactive environments. This new feature, launched during the Google I/O developer conference, allows users to simulate real streets in a more immersive and interactive way. The Power of Genie and Street View Integration The integration of Street View with Genie enables users to simulate real-world environments and scenarios, such as adjusting the weather or seeing what a street would look like in a 'Day After Tomorrow' scenario. According to Jack Parker-Holder, a research scientist on DeepMind's open-endedness team, 'It's really powerful for both the agent [and robotics] use case and for humans to play with, and that's always been the thesis of Genie.' Potential Applications and Use Cases The integration has various potential applications, including: Robotics training: Genie can simulate rare events, such as sunny days in London, to help robots prepare for unexpected situations. Education: Genie can be used to create interactive educational experiences, such as virtual field trips. Gaming: Genie can be used to create immersive game worlds from text prompts or images. Self-driving cars: Genie is already helping to power one of Waymo's simulators to train its self-driving cars on rare events. The Future of Genie and Street View Google is launching Street View in Genie to some Ultra users in the United States starting today, with access rolling out at scale over time. Global Ultra users will gain access over the next few weeks. While the technology is still experimental, researchers are working to improve accuracy and physics awareness. Technical Details and Limitations Google has collected over 280 billion images across 110 countries and seven continents using Street View. Genie 3, the latest world model, was released for research preview last August and opened up access to Google AI Ultra subscribers in the U.S. in January. However, the models are not yet physics-aware, meaning they don't understand cause and effect. Conclusion and Future Outlook The integration of Street View with Genie marks a significant step forward in immersive simulations and interactive environments. As researchers continue to improve the technology, we can expect to see more innovative applications and use cases emerge in the future.

The Lead: Google's AI Shift Toward Autonomous AgentsGoogle has launched Gemini 3.5 Flash, a new AI model representing the company's strategic pivot from conversational AI to autonomous agents capable of independently executing complex tasks. This move signals Google's bet that the future of AI lies in systems that can plan, build, and iterate on real work with minimal human intervention, rather than simply answering questions.The Technical Breakthrough: Gemini 3.5 Flash CapabilitiesGemini 3.5 Flash, introduced at Google's annual I/O developer conference, represents the company's strongest AI model yet for coding and autonomous agents. The model can independently execute coding pipelines, manage research projects, and, in internal tests, build an operating system entirely from scratch. This capability was demonstrated on stage when Google engineer Varun Mohan showed agents spawning off to work on separate components before coming together to build a full operating system inside Antigravity, Google's agentic development platform.Performance Benchmarks: Speed and EfficiencyThe model's performance is remarkable, according to Koray Kavukcuoglu, DeepMind's chief technologist. Flash 3.5 outperforms Google's latest frontier model, 3.1 Pro, on nearly all benchmarks, including coding, agentic tasks, and multimodal reasoning. Most notably, it's four times faster than other frontier models, with an optimized version that's 12 times faster while maintaining the same quality. This speed is crucial for agentic work, where multiple AI agents run simultaneously on long-running tasks.The Industry Shift: From Chatbots to Autonomous AgentsThe release of Gemini 3.5 Flash marks a significant industry shift from AI as a conversational tool to AI as an agentic tool. Google is positioning this as the next wave of AI technology, where systems don't just answer questions but actively plan, build, and iterate on real work. This transition is already showing impact among partners, with banks and fintechs automating multi-week workflows and data science teams finding insights in complex data environments. The model can run autonomously for multiple hours, though it will pause for human input at decision points requiring judgment.Future Outlook: Google's AI Ecosystem ExpansionLooking ahead, Google is developing a complementary model, 3.5 Pro, designed to work in tandem with Flash. According to Tulsee Doshi, Google's senior director and head of product, 3.5 Pro will serve as the orchestrator and planner, leveraging Flash as various sub-agents for tasks requiring brute force tool use. Gemini 3.5 Flash is now the default model in the Gemini app and AI Mode in Search, with agentic capabilities coming to Search and powering Gemini Spark, Google's new personal AI agent designed to run 24/7. As Google expands these autonomous capabilities, the company faces increasing scrutiny regarding safety and ethical considerations, particularly following past incidents with AI systems.