The AI landscape is rapidly evolving, with Americas $500 billion Stargate Project signaling massive infrastructure investment while Chinas DeepSeek emerges as a formidable competitor. DeepSeeks advanced AI models, rivaling Western capabilities at lower costs, raise significant concerns about potential cybersecurity threats, data mining, and intelligence gathering on a global scale. This development highlights the urgent need for robust AI regulation and security measures in the U.S.As the AI race intensifies, the gap between technological advancement and governance widens. The U.S. faces the critical challenge of not only accelerating its AI capabilities through projects like Stargate but also developing comprehensive regulatory frameworks to protect its digital assets and national security interests. With DeepSeeks potential to overcome export controls and conduct sophisticated cyber operations, the U.S. must act swiftly to ensure its AI innovations remain secure and competitive in this rapidly changing technological landscape.We have already seen the first wave of AI-powered dangers. Deepfakes, bot accounts, and algorithmic manipulation on social media have all helped undermine social cohesion while contributing to the creation of political echo chambers. But these dangers are childs play compared to the risks that will emerge in the next five to ten years. During the pandemic, we saw the unparalleled speed with which new vaccines could be developed with the help of AI. As Mustafa Suleyman, founder of DeepMind and now CEO of Microsoft AI, has argued, it will not be long before AI can design new bioweapons with equal speed. And these capabilities will not be confined to state actors. Just as modern drone technology has recently democratized access to capabilities that were once the sole province of the military, any individual with even a rudimentary knowledge of coding will soon be able to weaponize AI from their bedroom at home.The fact that U.S. senators were publicly advocating the shooting down of unmanned aircraft systems, despite the lack of any legal basis for doing so, is a clear sign of a systemic failure of control. This failure is even more concerning than the drone sightings themselves. When confidence in the governments ability to handle such unexpected events collapses, the result is fear, confusion, and conspiratorial thought. But there is much worse to come if we fail to find new ways to regulate novel technologies. If you think the systemic breakdown in response to drone sightings is worrying, imagine how things will look when AI starts causing problems.Seven years spent helping the departments of Defense and Homeland Security with innovation and transformation (both organizational and digital) has shaped my thinking about the very real geopolitical risks that AI and digital technologies bring with them. But these dangers do not come only from outside our country. The past decade has seen an increasing tolerance among many U.S. citizens for the idea of political violence, a phenomenon that has been cast into particularly vivid relief in the wake of the shooting of United Healthcare CEO Brian Thompson. As automation replaces increasing numbers of jobs, it is entirely possible that a wave of mass unemployment will lead to severe unrest, multiplying the risk that AI will be used as a weapon to lash out at society at large.These dangers will be on our doorsteps soon. But even more concerning are the unknown unknowns. AI is developing at lightning speed, and even those responsible for that development have no idea exactly where we will end up. Nobel laureate Geoffrey Hinton, the so-called Godfather of AI, has said there is a significant chance that artificial intelligence will wipe out humanity within just 30 years. Others suggest that the time horizon is much narrower. The simple fact that there is so much uncertainty about the direction of travel should concern us all deeply. Anyone who is not at least worried has simply not thought hard enough about the dangers.The regimented regulation has to be risk-basedWe cannot afford to treat AI regulation in the same haphazard fashion that has been applied to drone technology. We need an adaptable, far-reaching and future-oriented approach to regulation that is designed to protect us from whatever might emerge as we push back the frontiers of machine intelligence.During a recent interview with Senator Richard Blumenthal, I discussed the question of how we can effectively regulate a technology that we do not yet fully understand. Blumenthal is the co-author with Senator Josh Hawley of the Bipartisan Framework for U.S. AI Act, also known as the Blumenthal-Hawley Framework. Blumenthal proposes a relatively light-touch approach, suggesting that the way the U.S. government regulates the pharmaceutical industry can serve as a model for our approach to AI. This approach, he argues, provides for strict licensing and oversight of potentially dangerous emerging technologies without placing undue restrictions on the ability of American companies to remain world leaders in the field. We dont want to stifle innovation, Blumenthal says. Thats why the regimented regulation has to be risk-based. If it doesnt pose a risk, we dont need a regulator.This approach offers a valuable starting point for discussion, but I believe we need to go further. While a pharmaceutical model may be sufficient for regulating corporate AI development, we also need a framework that will limit the risks posed by individuals. The manufacturing and distribution of pharmaceuticals requires significant infrastructure, but computer code is an entirely different beast that can be replicated endlessly and transmitted anywhere on the planet in a fraction of a second. The possibility of problematic AI being created and leaking out into the wild is simply much higher than is the case for new and dangerous drugs. Given the potential for AI to generate extinction-level outcomes, it is not too far-reaching to say that the regulatory frameworks surrounding nuclear weapons and nuclear energy are more appropriate for this technology than those that apply in the drug industry.The announcement of the Stargate Project adds particular urgency to this discussion. While massive private-sector investment in AI infrastructure is crucial for maintaining American technological leadership, it also accelerates the timeline for developing comprehensive regulatory frameworks. We cannot afford to have our regulatory responses lag years behind technological developments when those developments are being measured in hundreds of billions of dollars.However we choose to balance the risks and rewards of AI research, we need to act soon. As we saw with the drone sightings that took place before Christmas, the lack of a comprehensive and cohesive framework for managing the threats from new technologies can leave government agencies paralyzed. And with risks that take in anything up to and including the extinction of humanity, we cannot afford this kind of inertia and confusion. We need a comprehensive regulatory framework that balances innovation with safety, one that recognizes both AIs transformative potential and its existential dangers.That means:Promoting responsible innovation. Encouraging the development and deployment of AI technologies in critical sectors in a safe and ethical manner.Establishing robust regulations. Public trust in AI systems requires both clear and enforceable regulatory frameworks and transparent systems of accountability.Strengthening national security. Policymakers must leverage AI to modernize military capabilities, deploying AI solutions that predict, detect, and counter cyber threats while ensuring ethical use of autonomous systems.Investing in workforce development. As a nation, we must establish comprehensive training programs that upskill workers for AI-driven industries while enhancing STEM (science, technology, engineering, and math) education to build foundational AI expertise among students and professionals.Leading in global AI standards. The U.S. must spearhead efforts to establish global norms for AI use by partnering with allies to define ethical standards and to safeguard intellectual property.Addressing public concerns. Securing public trust in AI requires increasing transparency about the objectives and applications of AI initiatives while also developing strategies to mitigate job displacement and ensure equitable economic benefits.The Stargate investment represents both the promise and the challenge of AI development. While it demonstrates Americas potential to lead the next technological revolution, it also highlights the urgent need for regulatory frameworks that can match the pace and scale of innovation. With investments of this magnitude reshaping our technological landscape, we cannot afford to get this wrong. We may not get a second chance.

AI rivalry heats up: Glean CEO Arvind Jain replies to Sam Altmans caution to investors.

The recently opened Nokia Design Archive revealed the depth of the brands design culture. It showcased not only the various thought experiments they did, some of which ended up as weird designs, but also the process behind them. Some of these designs did feel like they were way too ahead of their time, given how some have returned to the market in one way or another.Although HMD is also retiring its run of Nokia-branded phones, that doesnt stop others from carrying the torch, at least unofficially. Like this gaming handheld coming out of China that almost looks like a slider phone, except it swaps out the small QWERTY keyboard for gaming controls. This unusual design makes it look like a cross between the Nokia N97 and the PSP Go, both of which were also ahead of their times.Designer: Super Cubes (via Liliputing, NITTRX)Even back in 2009 when it launched, the Nokia N97 was hardly the first phone with a slider mechanism. It actually followed the design of the Nokia N series of that period, most of which shared that same form factor. What made it different, however, was how it raised the screen at an angle when sliding out to create a more ergonomic position, a design that inspired devices like the Planet Computers Astro Slide 5G phone and, more recently, the AYANEO Slide gaming handheld.In the same vein, the Sugar Cubes N97 Elite adopts that sliding mechanism and angled screen and applies it to a gaming handheld, one powered by Android rather than Windows. The name doesnt seem to be too shy about hinting at inspiration, which is probably also a potential lawsuit waiting to happen. Its an interesting design, no doubt, especially given the saturation of designs inspired by the Nintendo Switch or even the older Game Boy. Its a fresh new look, relatively speaking, though it does come with its own flaws.Given that its still trying to stick to a small phone size, around 4.6 inches, to be precise, there isnt much space for controls as well. The button layout will undoubtedly feel cramped, and theres no chance of fitting an analog stick inside. Its practically the same situation with the PSP Go, so its not entirely unusable. It just wont be that comfortable or efficient, especially for button-mashing games. Despite that small surface area, the phone slash gaming handheld is still a chunky beast.The Sugar Cubes N97 Elite is supposed to be an actual product, but considering its origin, its no surprise that there are already clones out there. The latter group seems to prefer more colorful finishes over plastic shells, in contrast to the block of aluminum that the brand teased. Theres no word on an international release, but that might not be much of an issue as there will probably be very little interest in the global market for an Android gaming device with 2019 specs.The post Nokia N95 slider phone returns as an Android gaming handheld first appeared on Yanko Design.

Cynthia Erivo was committed to the visual effects spectacle.

(Too far? Maybe a bit too far.)

The iconic jewelry vibrator is back, smaller than ever.

The Chinese start-up used several technological tricks, including a method called mixture of experts, to significantly reduce the cost of building the technology.

MacworldBy the time you read this, Apple may have launched the 4th-gen iPhone SE or it may not. Who can say? Unlike a fall iPhone launch, which takes place at a special media event and is therefore flagged a week in advance by a clue-filled invite, were told that the new SE will follow the less glamorous route of an emailed press release. Press releases drop into our inboxes with zero notice, often in the window between writing an article and posting it on the website. A really well-timed email can send tech journalists scurrying to the nearest content management system like panicking meerkats.They may give Apple the element of surprise (and save money on video production and acting coaches), but press releases lack a certain something when it comes to excitement. I complained recently about the injustice of a product as important as the new SE being treated like a coupon offer at your local burrito spot. Events mean hype, and if anything deserves a bit of hype, its this strategically vital entryway into the Cupertino ecosystem.Hype, however, may be a commodity we will have to learn to live without. It may be time to recognize that big Apple events are on borrowed time, and that the days of tech product announcements as must-watch appointment TV may be over.In 2024, admittedly, we got a generous slate of events. But the odd thing was that none of them seemed necessary. The Let Loose event last May ran long on gaffes and short on substantive reveals, and I saw nothing to make me revise my pre-event prediction of a meeting that could have been an email. WWDC was all software, and the iPhone event was heavy on Apple intelligence. And the one event that mattered didnt happen. Instead of a Mac event in late fall to announce the redesigned M4 Mac mini, Apple chose instead to hold a week of quieter announcements on three consecutive days in October.By doing so, the company held the attention of the tech media for multiple news cycles at a fraction of the cost of an event, and because it deployed the whisper rather than the megaphone, fans were less inclined to be disappointed by the new kit. Its also worth bearing in mind that, as big events become rarer, the amount of attention and interest each one can harvest becomes greater. The boy who cries wolf too often may find that customers stop bothering to tune into his wolf live streams.The warning signs for the big Apple event appeared, as with the decline of so many beloved cultural institutions, when normal life shut down for the pandemic. WWDC 2020 was the first of a series of virtual-only Apple events, with risky but exhilarating on-stage demos replaced by safe and dull pre-recorded videos: glorified adverts, really. When restrictions lifted the company understandably decided to retain many aspects of the visual events, blending canned presentations with a few hands-on elements. The benefits of virtual were simply too appealing to leave behind.The problem is that with the live stage sections removed, events start to lose their original purpose and meaning. A pre-recorded video will be sharper and faster than a live performance, but it wont communicate the same excitement to the audiencethere will be no sense of being present at a historic moment when anything could happen. And once you get to that point, it feels quaint to sit through 80 minutes of joyless skits and robotic marketing spiel in order to get to the headline announcements. Why not save everyones time and just send out an email? Why not just post the advert on the website?Once you add in the comparatively dull nature of many recent Apple product updates, and Tim Cooks obvious lack of interest in being the same kind of showman as his predecessor, theres really only one conclusion: there will be fewer Apple events in the future, and the ones we get simply arent going to be as exciting as they once were. There will be fewer mistakes (at least of the whoops, this feature didnt work type) and fewer moments of authentic wonder. Imagine if Steve Jobs hadnt unveiled the iPhone in front of a live audience. But maybe, given how artificial the whole thing has become, its all for the best. Its supposed to be about the products, not the event. The steak, not the sizzle. And the new iPhone SE will live and die by its qualities, not its press release.

Global enterprise technology spending is set to grow by 5.6% in 2025, reaching $4.9 trillion, as enterprises continue to prioritize investments in cybersecurity, cloud computing, generative AI, and digital transformation.North America and the Asia-Pacific region are projected to be the fastest-growing markets, while software and IT services are projected to account for 70% of all global technology spending by 2029, according to a Forrester report.Despite geopolitical instability and a softening IT and telecom services market in 2024, technology investments remain resilient, said the report titled Global Tech Market Forecast, 2024-2029.While certain sectors of the IT and telecom services market are showing signs of slowing down, businesses are accelerating their adoption of AI-driven tools and cloud-based solutions to enhance productivity and efficiency.Over the next five years, technology investments will reshape industries at an unprecedented pace, Michael OGrady, principal forecast analyst at Forrester said in the report. GenAI, cloud technologies, and cybersecurity will take center stage, transforming how businesses operate and deliver value.OGrady further said that companies that prioritize these investments will not only strengthen their competitive edge but also achieve sustainable growth, but its important that they also balance their rapid tech investments with ongoing efforts to manage legacy systems and reduce technical debt.Software and AI investments fuel growthForrester projects that software spending will grow by 10.5% in 2025, making it the fastest-growing category within the global tech market. Enterprise investments in AI, cloud computing, and cybersecurity are expected to drive long-term expansion, with businesses increasingly shifting toward SaaS-based models.Software will comprise 37% of global technology spending by 2029, nearly doubling its share from 2016, the report noted.The balance between AI hype and enterprise adoption is stabilizing as businesses focus on practical, ROI-driven applications, said Charlie Dai, VP and principal analyst at Forrester.He noted that while AI spending continues to grow, its measurable benefits are now evident in areas such as document automation, customer service, and employee augmentation. Success depends on clear use cases, integration, and managing expectations, ensuring investments align with tangible business outcomes, Dai said.The report further added that with the demand for AI-driven infrastructure rising rapidly, AI server and storage markets are expected to see a 13% annual growth rate through 2030. OpenAIs annualized revenue has already surged to $3.4 billion, up from $1 billion in mid-2023, highlighting the increasing adoption of generative AI solutions in enterprise environments.This trend reflects broader corporate interest in AI-powered automation, which is transforming industries ranging from healthcare and finance to manufacturing and retail.Cloud transformation on the riseThe IT services sector is expected to grow by 3.6% in 2025, as businesses continue to rely on consulting, IT outsourcing, and infrastructure-as-a-service (IaaS) to modernize their operations. The shift from traditional capital expenditures to operating expenditures through cloud-based services is accelerating, as enterprises seek more scalable and cost-effective solutions.Dai noted that while many industries are shifting to an opex model for flexibility and scalability, a hybrid approach will persist. He explained that sectors such as manufacturing and utilities will likely continue investing in capex for critical, long-term infrastructure, whereas tech-driven industries will favor opex-based cloud solutions.Cost control, regulatory requirements, and strategic asset ownership will drive this decision, he said.IaaS is poised for substantial growth, with a projected compound annual growth rate of 16% through 2028. This expansion is being driven by enterprises migrating workloads to major cloud providers, including Microsoft Azure, AWS, and Google Cloud, the report added.These investments are expected to improve operational agility, reduce infrastructure costs, and enhance security resilience in an increasingly digital business landscape.Europe faces challengesAccording to the report, technology spending trends vary significantly by region, with North America and Asia-Pacific expected to see the strongest growth.North America is projected to experience a 6.1% increase in tech spending, with AI investments in financial services, retail, and media leading the way. Businesses in the US and Canada are accelerating cloud migration and cybersecurity initiatives, positioning the region at the forefront of enterprise IT innovation.The Asia-Pacific region is forecasted to grow by 5.6%, with China, India, Japan, and Malaysia emerging as key drivers of expansion. India, in particular, is expected to have the regions fastest-growing tech spending CAGR of 9.6% from 2024 to 2029, fueled by investments in AI, cloud, and digital transformation initiatives. The regions investments in AI and semiconductor technologies continue to support enterprise adoption of next-generation computing solutions.Meanwhile, Europes tech market is expected to grow at a slower rate of 5%, as economic challenges in Germany and Italy dampen enterprise spending.Charlie Dai pointed to fragmented regulations, stricter data privacy laws, and higher operational costs as key barriers to faster enterprise IT growth in Europe.He explained that cultural diversity and varying levels of digital maturity across countries further complicate scaling efforts for technology providers. Europe faces slower enterprise IT growth due to fragmented regulations, stricter data privacy laws, and higher operational costs, he said.Latin America and the Middle East are also witnessing steady growth, with tech spending in these regions projected to rise between 5.2% and 5.4%. Governments and telecom operators are leading digital transformation efforts, with cloud adoption and AI integration playing a crucial role in modernizing public services and business operations.Enterprise takeaways and strategic considerationsFor enterprises, the forecast highlights several key takeaways that will shape IT investment strategies in the coming years. The growing dominance of cloud and AI-driven technologies is compelling organizations to rethink their approach to IT spending. Cybersecurity remains a top priority, with leading firms such as Palo Alto Networks forecasting a 16% revenue increase in 2024, reflecting heightened enterprise demand for advanced security solutions.Enterprises should prioritize a zero-trust strategy, integrating cybersecurity and compliance into every stage of AI and cloud adoption, Dai said, adding that enterprises must rethink their IT investment strategies by integrating cybersecurity and compliance into every stage of AI and cloud adoption.At the same time, organizations must navigate the challenges of modernizing legacy IT infrastructure. While cloud adoption is accelerating, two-thirds of global IT budgets are still allocated to maintaining existing systems. This underscores the complexity of balancing innovation with managing costs, ensuring compliance, and mitigating security risks. The evolving regulatory landscape around AI and data protection is further influencing how enterprises deploy new technologies while safeguarding customer trust and operational integrity. With enterprises expected to spend nearly $5 trillion on technology in 2025, the decisions made today will have a lasting impact on business resilience and digital competitiveness.

Its become increasing clear thatquality plunges when moving from English to non-English-based large language models(LLMs). Theyre less accurate and theres a serious lack of transparency around data training, both in terms of data volume and data quality.The latter has long been aproblem for generative AI (genAI) tools and platforms.But enterprises arent paying less for less-productive models, even though the value they offer is diminished. So,why arent CIOs getting a price break for non-English models? Because without any data transparency, they rarely know theyre paying more for less.There are a variety of reasons why model makers dont disclose their data training particulars. (Lets not even get into the issue of whether they have legal rights to do whatever training they did though its tempting to do so, if only to explore the hypocrisy ofOpenAI complainingaboutDeepSeeknot getting permission before training on much of its data.)Speaking of DeepSeek, dont read too much into the lower cost of its underlying models. Yes, its builders cleverly leveraged open source to find efficiencies and lower pricing, but theres been little disclosure of how much the Chinese government helped with DeepSeeks funding, either directly or indirectly.That said, ifDeepSeek is the cudgel that puts downward pressure on genAI pricing, Im all for it and IT execs should be, too. But until we see evidence of meaningful price cuts, they should use the lack of data transparency in non-English models to try and get model maker pricetags out of the stratospheric.The non-English issue isnt really about the language, per se. Its more about the training data that isavailablewithin that language.(By some estimates,the training datasets for non-English models could be just 1/10 or even 1/100 the size of their English counterparts.)Hans Florian, whose title is a distinguished research scientist for multilingual natural language processing at IBM, said he uses a trick to guesstimate how much data is available in various languages. You can look at the number of Wikipedia pages in that language. That correlates quite well with the amount of data available in that language, he said.To further complicate the issue, sometimes its not about the language or the available data in that language. It can logically enough be about data related to activities in theregionwhere a particularlanguage is dominant.If model makers start seeing meaningful pricing pushback from a lot of enterprises concerned about model quality, they have only a couple of options. They can selectively and secretly negotiate lower prices for non-English models for some of their customers or they can get serious about data transparency.Because LLM makers have invested billions of dollars in genAI, they arent going to like the idea of lower pricing. That leads to that second option: deliver full transparency to all customers about all models both in terms of quantity and quality and price their wares accordingly.Given that quality is almost impossible to represent numerically, that will mean disclosing all training data details so each customer can make their own determination of quality for the topics, verticals and geographies they care about.The pricing disparity between what a model can deliver and what an enterprise is forced to pay is at the heart of whyCIOs are still struggling to deliver genAI ROI.Obviously, lower pricing would be the best way to improve the ROI for genAI investments. But if thats not going to happen anytime soon, full data transparency is the next best thing.There is a catch: model makers almost certainly realize that full data-training transparency will likely force them to lower prices, since it would showcase how low quality their data is.Note: I say that their data is low-quality as if its a given; it is absolutely a given. If model makers believed they were using lots of high-quality data, far from resisting transparency, they would embrace it. It would be a selling point. It might even be useful for propping up prices. High quality usually sells itself.Their refusal to deliver any kind of data-training transparency tells you everything you need to know about their quality beliefs, and about the state of the market at the moment.