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Chinese creator Tianran Mu went viral for mimicking the eerie, unsettling aesthetic of AI videos, but his work is 100 percent human.

With recent advances in artificial intelligence (AI), synthetic voices have become increasingly prevalent in our everyday soundscape, raising the question: Can AI voices still be distinguished from human voices, and how attractive do they sound?

The Australian names HBO drama the best TV show of the past 25 years. Plus: reporters in the spotlight at the White HouseWant to get this in your inbox every Friday? Sign up for the Weekly Beast media newsletter hereTo mark the launch of a new culture section, The Australian has produced a list of the top 25 TV shows of the past 25 years.The best TV show in the last quarter of a century, as curated by the national broadsheet’s writers, is HBO’s Succession – widely seen, of course, as being heavily inspired by the Murdoch family, the proprietor of News Corp Australia’s flagship newspaper.Sign up to get Guardian Australia’s weekly media diary as a free newsletter Continue reading...

Beneath the free-market rhetoric, Washington actually intervenes to control the building blocks of AI systemsAt first glance, today’s artificial intelligence policy landscape suggests a strategic retreat from regulation. As of late, AI leaders such as the US have doubled down on this messaging. JD Vance champions AI policy with a “deregulatory flavor”. Congress considered a 10-year ban on state AI legislation. On cue, the Trump administration’s “AI action plan” warns against smothering the technology “in bureaucracy at this early stage”.But the deregulatory narrative is a critical misconception. Though the US federal government takes a hands-off approach to AI applications such as chatbots and image generators, it is heavily involved in the building blocks of AI. For example, both the Trump and the Biden administrations have been hands-on when it comes to AI chips – a crucial component of powerful AI systems. Biden restricted chip access to competing nations such as China as a matter of national security. The Trump administration has sought deals with countries such as the UAE. Continue reading...

Anne Keast-Butler says government and business must to work together to tackle future attacks as AI makes cybercrime easierCompanies need to do more to mitigate the potential effects of cyber-attacks, the head of GCHQ has said, including making physical, paper copies of crisis plans to use if an attack brings down entire computer systems.“What are your contingency plans? Because attacks will get through,” said Anne Keast-Butler, who has headed GCHQ, the British government’s cyber and signals intelligence agency, since 2023. Continue reading...

Like humans, artificial intelligence learns by trial and error, but traditionally, it requires humans to set the ball rolling by designing the algorithms and rules that govern the learning process. However, as AI technology advances, machines are increasingly doing things themselves. An example is a new AI system developed by researchers that invented its own way to learn, resulting in an algorithm that outperformed human-designed algorithms on a series of complex tasks.

May Habib, co-founder and CEO of Writer AI, delivered one of the bluntest assessments of corporate AI failures at the TED AI conference on Tuesday, revealing that nearly half of Fortune 500 executives believe artificial intelligence is actively damaging their organizations — and placing the blame squarely on leadership's shoulders.The problem, according to Habib, isn't the technology. It's that business leaders are making a category error, treating AI transformation like previous technology rollouts and delegating it to IT departments. This approach, she warned, has led to "billions of dollars spent on AI initiatives that are going nowhere.""Earlier this year, we did a survey of 800 Fortune 500 C-suite executives," Habib told the audience of Silicon Valley executives and investors. "42% of them said AI is tearing their company apart."The diagnosis challenges conventional wisdom about how enterprises should approach AI adoption. While most major companies have stood up AI task forces, appointed chief AI officers, or expanded IT budgets, Habib argues these moves reflect a fundamental misunderstanding of what AI represents: not another software tool, but a wholesale reorganization of how work gets done."There is something leaders are missing when they compare AI to just another tech tool," Habib said. "This is not like giving accountants calculators or bankers Excel or designers Photoshop."Why the 'old playbook' of delegating to IT departments is failing companiesHabib, whose company has spent five years building AI systems for Fortune 500 companies and logged two million miles visiting customer sites, said the pattern is consistent: "When generative AI started showing up, we turned to the old playbook. We turned to IT and said, 'Go figure this out.'"That approach fails, she argued, because AI fundamentally changes the economics and organization of work itself. "For 100 years, enterprises have been built around the idea that execution is expensive and hard," Habib said. "The enterprise built complex org charts, complex processes, all to manage people doing stuff."AI inverts that model. "Execution is going from scarce and expensive to programmatic, on-demand and abundant," she said. In this new paradigm, the bottleneck shifts from execution capacity to strategic design — a shift that requires business leaders, not IT departments, to drive transformation."With AI technology, it can no longer be centralized. It's in every workflow, every business," Habib said. "It is now the most important part of a business leader's job. It cannot be delegated."The statement represents a direct challenge to how most large organizations have structured their AI initiatives, with centralized centers of excellence, dedicated AI teams, or IT-led implementations that business units are expected to adopt.A generational power shift is happening based on who understands AI workflow designHabib framed the shift in dramatic terms: "A generational transfer of power is happening right now. It's not about your age or how long you've been at a company. The generational transfer of power is about the nature of leadership itself."Traditional leadership, she argued, has been defined by the ability to manage complexity — big teams, big budgets, intricate processes. "The identity of leaders at these companies, people like us, has been tied to old school power structures: control, hierarchy, how big our teams are, how big our budgets are. Our value is measured by the sheer amount of complexity we could manage," Habib said. "Today we reward leaders for this. We promote leaders for this."AI makes that model obsolete. "When I am able to 10x the output of my team or do things that could never be possible, work is no longer about the 1x," she said. "Leadership is no longer about managing complex human execution."Instead, Habib outlined three fundamental shifts that define what she calls "AI-first leaders" — executives her company has worked with who have successfully deployed AI agents solving "$100 million plus problems."The first shift: Taking a machete to enterprise complexityThe new leadership mandate, according to Habib, is "taking a machete to the complexity that has calcified so many organizations." She pointed to the layers of friction that have accumulated in enterprises: "Brilliant ideas dying in memos, the endless cycles of approvals, the death by 1,000 clicks, meetings about meetings — a death, by the way, that's happening in 17 different browser tabs each for software that promises to be a single source of truth."Rather than accepting this complexity as inevitable, AI-first leaders redesign workflows from first principles. "There are very few legacy systems that can't be replaced in your organization, that won't be replaced," Habib said. "But they're not going to be replaced by another monolithic piece of software. They can only be replaced by a business leader articulating business logic and getting that into an agentic system."She offered a concrete example: "We have customers where it used to take them seven months to get a creative campaign — not even a product, a campaign. Now they can go from TikTok trend to digital shelf in 30 days. That is radical simplicity."The catch, she emphasized, is that CIOs can't drive this transformation alone. "Your CIO can't help flatten your org chart. Only a business leader can look at workflows and say, 'This part is necessary genius, this part is bureaucratic scar tissue that has to go.'"The second shift: Managing the fear as career ladders disappearWhen AI handles execution, "your humans are liberated to do what they're amazing at: judgment, strategy, creativity," Habib explained. "The old leadership playbook was about managing headcount. We managed people against revenue: one business development rep for every three account executives, one marketer for every five salespeople."But this liberation carries profound challenges that leaders must address directly. Habib acknowledged the elephant in the room that many executives avoid discussing: "These changes are still frightening for people, even when it's become unholy to talk about it." She's witnessed the fear firsthand. "It shows up as tears in an AI workshop when someone feels like their old skill set isn't translated to the new."She introduced a term for a common form of resistance: "productivity anchoring" — when employees "cling to the hard way of doing things because they feel productive, because their self-worth is tied to them, even when empirically AI can be better."The solution isn't to look away. "We have to design new pathways to impact, to show your people their value is not in executing a task. Their value is in orchestrating systems of execution, to ask the next great question," Habib said. She advocates replacing career "ladders" with "lattices" where "people need to grow laterally, to expand sideways."She was candid about the disruption: "The first rungs on our career ladders are indeed going away. I know because my company is automating them." But she insisted this creates opportunity for work that is "more creative, more strategic, more driven by curiosity and impact — and I believe a lot more human than the jobs that they're replacing."The third shift: When execution becomes free, ambition becomes the only bottleneckThe final shift is from optimization to creation. "Before AI, we used to call it transformation when we took 12 steps and made them nine," Habib said. "That's optimizing the world as it is. We can now create a new world. That is the greenfield mindset."She challenged executives to identify assumptions their industries are built on that AI now disrupts. Writer's customers, she said, are already seeing new categories of growth: treating every customer like their only customer, democratizing premium services to broader markets, and entering new markets at unprecedented speed because "AI strips away the friction to access new channels.""When execution is abundant, the only bottleneck is the scope of your own ambition," Habib declared.What this means for CIOs: Building the stadium while business leaders design the playsHabib didn't leave IT leaders without a role — she redefined it. "If tech is everyone's job, you might be asking, what is mine?" she addressed CIOs. "Yours is to provide the mission critical infrastructure that makes this revolution possible."As tens or hundreds of thousands of AI agents operate at various levels of autonomy within organizations, "governance becomes existential," she explained. "The business leader's job is to design the play, but you have to build the stadium, you have to write the rule book, and you have to make sure these plays can win at championship scale."The formulation suggests a partnership model: business leaders drive workflow redesign and strategic implementation while IT provides the infrastructure, governance frameworks, and security guardrails that make mass AI deployment safe and scalable. "One can't succeed without the other," Habib said.For CIOs and technical leaders, this represents a fundamental shift from gatekeeper to enabler. When business units deploy agents autonomously, IT faces governance challenges unlike anything in enterprise software history. Success requires genuine partnership between business and IT — neither can succeed alone, forcing cultural changes in how these functions collaborate.A real example: From multi-day scrambles to instant answers during a market crisisTo ground her arguments in concrete business impact, Habib described working with the chief client officer of a Fortune 500 wealth advisory firm during recent market volatility following tariff announcements."Their phone was ringing off the hook with customers trying to figure out their market exposure," she recounted. "Every request kicked off a multi-day, multi-person scramble: a portfolio manager ran the show, an analyst pulled charts, a relationship manager built the PowerPoint, a compliance officer had to review everything for disclosures. And the leader in all this — she was forwarding emails and chasing updates. This is the top job: managing complexity."With an agentic AI system, the same work happens programmatically. "A system of agents is able to assemble the answer faster than any number of people could have. No more midnight deck reviews. No more days on end" of coordination, Habib said.This isn't about marginal productivity gains — it's about fundamentally different operating models where senior executives shift from managing coordination to designing intelligent systems.Why so many AI initiatives are failing despite massive investmentHabib's arguments arrive as many enterprises face AI disillusionment. After initial excitement about generative AI, many companies have struggled to move beyond pilots and demonstrations to production deployments generating tangible business value.Her diagnosis — that leaders are delegating rather than driving transformation — aligns with growing evidence that organizational factors, not technical limitations, explain most failures. Companies often lack clarity on use cases, struggle with data preparation, or face internal resistance to workflow changes that AI requires.Perhaps the most striking aspect of Habib's presentation was her willingness to acknowledge the human cost of AI transformation — and insist leaders address it rather than avoid it. "Your job as a leader is to not look away from this fear. Your job is to face it with a plan," she told the audience.She described "productivity anchoring" as a form of "self-sabotage" where employees resist AI adoption because their identity and self-worth are tied to execution tasks AI can now perform. The phenomenon suggests that successful AI transformation requires not just technical and strategic changes but psychological and cultural work that many leaders may be unprepared for.Two challenges: Get your hands dirty, then reimagine everythingHabib closed by throwing down two gauntlets to her executive audience."First, a small one: get your hands dirty with agentic AI. Don't delegate. Choose a process that you oversee and automate it. See the difference from managing a complex process to redesigning it for yourself."The second was more ambitious: "Go back to your team and ask, what could we achieve if execution were free? What would work feel like, be like, look like if you're unbound from the friction and process that slows us down today?"She concluded: "The tools for creation are in your hands. The mandate for leadership is on your shoulders. What will you build?"For enterprise leaders accustomed to viewing AI as an IT initiative, Habib's message is clear: that approach isn't working, won't work, and reflects a fundamental misunderstanding of what AI represents. Whether executives embrace her call to personally drive transformation — or continue delegating to IT departments — may determine which organizations thrive and which become cautionary tales.The statistic she opened with lingers uncomfortably: 42% of Fortune 500 C-suite executives say AI is tearing their companies apart. Habib's diagnosis suggests they're tearing themselves apart by clinging to organizational models designed for an era when execution was scarce. The cure she prescribes requires leaders to do something most find uncomfortable: stop managing complexity and start dismantling it.

In a striking act of self-critique, one of the architects of the transformer technology that powers ChatGPT, Claude, and virtually every major AI system told an audience of industry leaders this week that artificial intelligence research has become dangerously narrow — and that he's moving on from his own creation.Llion Jones, who co-authored the seminal 2017 paper "Attention Is All You Need" and even coined the name "transformer," delivered an unusually candid assessment at the TED AI conference in San Francisco on Tuesday: Despite unprecedented investment and talent flooding into AI, the field has calcified around a single architectural approach, potentially blinding researchers to the next major breakthrough."Despite the fact that there's never been so much interest and resources and money and talent, this has somehow caused the narrowing of the research that we're doing," Jones told the audience. The culprit, he argued, is the "immense amount of pressure" from investors demanding returns and researchers scrambling to stand out in an overcrowded field.The warning carries particular weight given Jones's role in AI history. The transformer architecture he helped develop at Google has become the foundation of the generative AI boom, enabling systems that can write essays, generate images, and engage in human-like conversation. His paper has been cited more than 100,000 times, making it one of the most influential computer science publications of the century.Now, as CTO and co-founder of Tokyo-based Sakana AI, Jones is explicitly abandoning his own creation. "I personally made a decision in the beginning of this year that I'm going to drastically reduce the amount of time that I spend on transformers," he said. "I'm explicitly now exploring and looking for the next big thing."Why more AI funding has led to less creative research, according to a transformer pioneerJones painted a picture of an AI research community suffering from what he called a paradox: More resources have led to less creativity. He described researchers constantly checking whether they've been "scooped" by competitors working on identical ideas, and academics choosing safe, publishable projects over risky, potentially transformative ones."If you're doing standard AI research right now, you kind of have to assume that there's maybe three or four other groups doing something very similar, or maybe exactly the same," Jones said, describing an environment where "unfortunately, this pressure damages the science, because people are rushing their papers, and it's reducing the amount of creativity."He drew an analogy from AI itself — the "exploration versus exploitation" trade-off that governs how algorithms search for solutions. When a system exploits too much and explores too little, it finds mediocre local solutions while missing superior alternatives. "We are almost certainly in that situation right now in the AI industry," Jones argued.The implications are sobering. Jones recalled the period just before transformers emerged, when researchers were endlessly tweaking recurrent neural networks — the previous dominant architecture — for incremental gains. Once transformers arrived, all that work suddenly seemed irrelevant. "How much time do you think those researchers would have spent trying to improve the recurrent neural network if they knew something like transformers was around the corner?" he asked.He worries the field is repeating that pattern. "I'm worried that we're in that situation right now where we're just concentrating on one architecture and just permuting it and trying different things, where there might be a breakthrough just around the corner."How the 'Attention is all you need' paper was born from freedom, not pressureTo underscore his point, Jones described the conditions that allowed transformers to emerge in the first place — a stark contrast to today's environment. The project, he said, was "very organic, bottom up," born from "talking over lunch or scrawling randomly on the whiteboard in the office."Critically, "we didn't actually have a good idea, we had the freedom to actually spend time and go and work on it, and even more importantly, we didn't have any pressure that was coming down from management," Jones recounted. "No pressure to work on any particular project, publish a number of papers to push a certain metric up."That freedom, Jones suggested, is largely absent today. Even researchers recruited for astronomical salaries — "literally a million dollars a year, in some cases" — may not feel empowered to take risks. "Do you think that when they start their new position they feel empowered to try their wild ideas and more speculative ideas, or do they feel immense pressure to prove their worth and once again, go for the low hanging fruit?" he asked.Why one AI lab is betting that research freedom beats million-dollar salariesJones's proposed solution is deliberately provocative: Turn up the "explore dial" and openly share findings, even at competitive cost. He acknowledged the irony of his position. "It may sound a little controversial to hear one of the Transformers authors stand on stage and tell you that he's absolutely sick of them, but it's kind of fair enough, right? I've been working on them longer than anyone, with the possible exception of seven people."At Sakana AI, Jones said he's attempting to recreate that pre-transformer environment, with nature-inspired research and minimal pressure to chase publications or compete directly with rivals. He offered researchers a mantra from engineer Brian Cheung: "You should only do the research that wouldn't happen if you weren't doing it."One example is Sakana's "continuous thought machine," which incorporates brain-like synchronization into neural networks. An employee who pitched the idea told Jones he would have faced skepticism and pressure not to waste time at previous employers or academic positions. At Sakana, Jones gave him a week to explore. The project became successful enough to be spotlighted at NeurIPS, a major AI conference.Jones even suggested that freedom beats compensation in recruiting. "It's a really, really good way of getting talent," he said of the exploratory environment. "Think about it, talented, intelligent people, ambitious people, will naturally seek out this kind of environment."The transformer's success may be blocking AI's next breakthroughPerhaps most provocatively, Jones suggested transformers may be victims of their own success. "The fact that the current technology is so powerful and flexible... stopped us from looking for better," he said. "It makes sense that if the current technology was worse, more people would be looking for better."He was careful to clarify that he's not dismissing ongoing transformer research. "There's still plenty of very important work to be done on current technology and bringing a lot of value in the coming years," he said. "I'm just saying that given the amount of talent and resources that we have currently, we can afford to do a lot more."His ultimate message was one of collaboration over competition. "Genuinely, from my perspective, this is not a competition," Jones concluded. "We all have the same goal. We all want to see this technology progress so that we can all benefit from it. So if we can all collectively turn up the explore dial and then openly share what we find, we can get to our goal much faster."The high stakes of AI's exploration problemThe remarks arrive at a pivotal moment for artificial intelligence. The industry grapples with mounting evidence that simply building larger transformer models may be approaching diminishing returns. Leading researchers have begun openly discussing whether the current paradigm has fundamental limitations, with some suggesting that architectural innovations — not just scale — will be needed for continued progress toward more capable AI systems.Jones's warning suggests that finding those innovations may require dismantling the very incentive structures that have driven AI's recent boom. With tens of billions of dollars flowing into AI development annually and fierce competition among labs driving secrecy and rapid publication cycles, the exploratory research environment he described seems increasingly distant.Yet his insider perspective carries unusual weight. As someone who helped create the technology now dominating the field, Jones understands both what it takes to achieve breakthrough innovation and what the industry risks by abandoning that approach. His decision to walk away from transformers — the architecture that made his reputation — adds credibility to a message that might otherwise sound like contrarian positioning.Whether AI's power players will heed the call remains uncertain. But Jones offered a pointed reminder of what's at stake: The next transformer-scale breakthrough could be just around the corner, pursued by researchers with the freedom to explore. Or it could be languishing unexplored while thousands of researchers race to publish incremental improvements on architecture that, in Jones's words, one of its creators is "absolutely sick of."After all, he's been working on transformers longer than almost anyone. He would know when it's time to move on.

The world will be pushed into a recession, but perhaps we can build something more promising from the piecesThe world economy hinges on the success or failure of artificial intelligence. It’s becoming apparent that we are probably doomed either way.Employment growth is stuck and wage growth is slowing, especially among low-paying jobs. Loan delinquencies are rising, driving an increase in bankruptcies. Consumer confidence has collapsed. And reckless policymaking is taking its toll. Donald Trump’s trade war is cutting farmers’ access to the Chinese market and manufacturers’ access to Chinese rare-earth magnets. His clampdown on migration is hitting access to labor, from agriculture to healthcare. The drawn-out government shutdown is starting to sap economic growth. Continue reading...

OpenAI will soon let adults create erotic content in ChatGPT. Experts say that could lead to “emotional commodification,” or horniness as a revenue stream.

One of the leading architects of the current generative AI boom — Microsoft CEO Satya Nadella, famed for having the software giant take an early investment in OpenAI (and later saying he was "good for my $80 billion") — published his latest annual letter yesterday on LinkedIn (a Microsoft subsidiary), and it's chock full of interesting ideas about the near-term future that enterprise technical decision makers would do well to pay attention to, as it could aid in their own planning and tech stack development.In a companion post on X, Nadella wrote, “AI is radically changing every layer of the tech stack, and we’re changing with it." The full letter reinforces that message: Microsoft sees itself not just participating in the AI revolution, but shaping its infrastructure, security, tooling and governance for decades to come.While the message is addressed to Microsoft shareholders, the implications reach much further. The letter is a strategic signal to enterprise engineering leaders: CIOs, CTOs, AI leads, platform architects and security directors. Nadella outlines the direction of Microsoft’s innovation, but also what it expects from its customers and partners. The AI era is here, but it will be built by those who combine technical vision with operational discipline.Below are the five most important takeaways for enterprise technical decision makers.1. Security and reliability are now the foundation of the AI stackNadella makes security the first priority in the letter and ties it directly to Microsoft’s relevance going forward. Through its Secure Future Initiative (SFI), Microsoft has assigned the equivalent of 34,000 engineers to secure its identity systems, networks and software supply chain. Its Quality Excellence Initiative (QEI) aims to increase platform resiliency and strengthen global service uptime.Microsoft’s positioning makes it clear that enterprises will no longer get away with “ship fast, harden later” AI deployments. Nadella calls security “non-negotiable,” signaling that AI infrastructure must now meet the standards of mission-critical software. That means identity-first architecture, zero-trust execution environments and change management discipline are now table stakes for enterprise AI.2. AI infrastructure strategy is hybrid, open and sovereignty-readyNadella commits Microsoft to building “planet-scale systems” and backs that up with numbers: more than 400 Azure datacenters across 70 regions, two gigawatts of new compute capacity added this year, and new liquid-cooled GPU clusters rolling out across Azure. Microsoft also introduced Fairwater, a massive new AI datacenter in Wisconsin positioned to deliver unprecedented scale. Just as important, Microsoft is now officially multi-model. Azure AI Foundry offers access to more than 11,000 models including OpenAI, Meta, Mistral, Cohere and xAI. Microsoft is no longer pushing a single-model future, but a hybrid AI strategy.Enterprises should interpret this as validation of “portfolio architectures,” where closed, open and domain-specific models coexist. Nadella also emphasizes growing investment in sovereign cloud offerings for regulated industries, previewing a world where AI systems will have to meet regional data residency and compliance requirements from day one.3. AI agents—not just chatbots—are now Microsoft’s futureThe AI shift inside Microsoft is no longer about copilots that answer questions. It is now about AI agents that perform work. Nadella points to the rollout of Agent Mode in Microsoft 365 Copilot, which turns natural language requests into multistep business workflows. GitHub Copilot evolves from code autocomplete into a “peer programmer” capable of executing tasks asynchronously. In security operations, Microsoft has deployed AI agents that autonomously respond to incidents. In healthcare, Copilot for Dragon Medical documents clinical encounters automatically.This represents a major architectural pivot. Enterprises will need to move beyond prompt-response interfaces and begin engineering agent ecosystems that safely take actions inside business systems. That requires workflow orchestration, API integration strategies and strong guardrails. Nadella’s letter frames this as the next software platform shift.4. Unified data platforms are required to unlock AI valueNadella devotes significant attention to Microsoft Fabric and OneLake, calling Fabric the company’s fastest-growing data and analytics product ever. Fabric promises to centralize enterprise data from multiple cloud and analytics environments. OneLake provides a universal storage layer that binds analytics and AI workloads together.Microsoft’s message is blunt: siloed data means stalled AI. Enterprise teams that want AI at scale must unify operational and analytical data into a single architecture, enforce consistent data contracts and standardize metadata governance. AI success is now a data engineering problem more than a model problem.5. Trust, compliance and responsible AI are now mandatory for deployment“People want technology they can trust,” Nadella writes. Microsoft now publishes Responsible AI Transparency Reports and aligns parts of its development process with UN human rights guidance. Microsoft is also committing to digital resilience in Europe and proactive safeguards against misuse of AI-generated content.This shifts responsible AI out of the realm of corporate messaging and into engineering practice. Enterprises will need model documentation, reproducibility practices, audit trails, risk monitoring and human-in-the-loop checkpoints. Nadella signals that compliance will become integrated with product delivery—not an afterthought layered on top.The real meaning of Microsoft’s AI strategyTaken together, these five pillars send a clear message to enterprise leaders: AI maturity is no longer about building prototypes or proving use cases. System-level readiness now defines success. Nadella frames Microsoft’s mission as helping customers “think in decades and execute in quarters,” and that is more than corporate poetry. It is a call to build AI platforms engineered for longevity.The companies that win in enterprise AI will be the ones that invest early in secure cloud foundations, unify their data architectures, enable agent-based workflows and embrace responsible AI as a prerequisite for scale—not a press release. Nadella is betting that the next industrial transformation will be powered by AI infrastructure, not AI demos. With this letter, he has made Microsoft’s ambition clear: to become the platform on which that transformation is built.

Self-driving taxis are sweeping the country and will likely start service in Colorado in the coming months. How many of us will be lining up to take a ride? That depends on our level of trust, says Amir Behzadan, a professor in the Department of Civil, Environmental and Architectural Engineering, and a fellow in the Institute of Behavioral Science (IBS) at CU Boulder.