Technology

What if the most important AI device at the FIFA World Cup isn’t a camera—but the ball itself? ⚽🤖 Modern FIFA match balls contain embedded sensors that capture hundreds of data points every second. Combined with AI-powered computer vision, they help determine the exact moment the ball is played, improving offside decisions, enhancing VAR, and generating rich real-time match analytics. But this is bigger than football. It’s a powerful example of sensor fusion—where edge devices, AI models, and high-performance computing work together to deliver insights in milliseconds. The same principles are transforming industries far beyond sport: 🏭 Smart manufacturing with connected sensors. 🚗 Autonomous vehicles combining cameras, radar, and LiDAR. 🏥 Healthcare devices delivering real-time diagnostics. 🏙️ Smart cities optimizing traffic, energy, and public safety. As AI moves from the cloud to the edge, every connected device becomes a source of intelligence. Turning that data into instant, reliable decisions requires powerful compute infrastructure. This is where AMD is helping drive the next era of AI—from Ryzen AI PCs at the edge, to EPYC processors powering modern data centers, and Instinct accelerators enabling large-scale AI inference and training. The future of AI isn’t just about bigger models. It’s about connecting billions of intelligent devices with the compute needed to make every decision count—in real time. From the football pitch to the factory floor, AI is changing how the world works. #AI #AMD #EdgeAI via @untoldoddities #SportsTech #ComputerVision #SensorFusion #HighPerformanceComputing #DataCenter #DigitalTransformation #Innovation #FIFA

𝗧𝗵𝗲 𝗽𝗮𝗿𝗮𝗱𝗼𝘅 𝗼𝗳 𝗺𝗼𝗱𝗲𝗿𝗻 𝗵𝗲𝗮𝗹𝘁𝗵 𝘁𝗲𝗰𝗵: 𝗧𝗵𝗲 𝗺𝗼𝗿𝗲 𝘄𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿, 𝘁𝗵𝗲 𝗺𝗼𝗿𝗲 𝗮𝗻𝘅𝗶𝗼𝘂𝘀 𝘄𝗲 𝗯𝗲𝗰𝗼𝗺𝗲. We track our bodies 24/7. Count every calorie. Measure sleep, HRV, glucose, stress. From Apple Watch. To Oura Ring. To the latest “temple” device. Somewhere along the way, awareness turned into obsession. Here’s the paradox no one talks about: We have the best health-tracking tools in history, and some of the worst health outcomes. Something doesn’t add up. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗵𝗼𝘄𝘀 𝗦𝗹𝗲𝗲𝗽 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗰𝗮𝗻 𝘄𝗼𝗿𝘀𝗲𝗻 𝘀𝗹𝗲𝗲𝗽 Studies on orthosomnia (an obsession with “perfect” sleep metrics) show that people who fixate on sleep scores experience more sleep anxiety, lighter sleep, and poorer recovery—even when objective sleep doesn’t improve. Trying to optimize sleep can literally break it. 𝗛𝗥𝗩 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝘀 𝘀𝘁𝗿𝗲𝘀𝘀 𝗳𝗼𝗿 𝗺𝗮𝗻𝘆 𝘂𝘀𝗲𝗿𝘀 HRV is a useful trend marker—but daily fluctuations are normal. Research shows that constant HRV checking can heighten health anxiety and perceived stress, especially when users don’t understand variability or context. Ironically, stressing about HRV often lowers HRV. 𝗠𝗼𝗿𝗲 𝗱𝗮𝘁𝗮 ≠ 𝗯𝗲𝘁𝘁𝗲𝗿 𝗵𝗲𝗮𝗹𝘁𝗵 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 Behavioral science research consistently finds that excessive self-monitoring leads to hypervigilance, loss of bodily trust, and decision fatigue. When every sensation becomes a data point, people stop listening to internal cues and start deferring to dashboards. In short: 𝗢𝘃𝗲𝗿-𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝘀 𝗮𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 𝘄𝗶𝘁𝗵 𝗮𝗻𝘅𝗶𝗲𝘁𝘆. So what actually creates health? The same fundamentals that worked 5,000 years ago: • Deep, peaceful sleep • Regular sunlight • Real, nourishing food • Daily movement • Time with people you love These don’t need algorithms. They need presence. Use wearables if they serve you—I do, occasionally. But don’t let them become your master. Your life isn’t an algorithm waiting to be optimized. It’s a system meant to be felt, explored, and course-corrected. The best health coach you’ll ever have is already inside you. Trust it.

Today in Cell, we published new research showing how AI can help accelerate cancer discovery. With GigaTIME, we can now simulate spatial proteomics from routine pathology slides, enabling population-scale analysis of tumor microenvironments across dozens of cancer types and hundreds of subtypes.   Developed in partnership with Providence and the University of Washington, our hope is that this work helps scientists move faster from data to insight, revealing new links between genetic mutations, immune activity, and clinical outcomes, and ultimately improving health for people everywhere. https://lnkd.in/dSpPdtzz

She was rejected by 100+ investors and was told she would never succeed. Today, she's a billionaire and Canva is worth $40B. Melanie Perkins was 19. Living in Perth. And frustrated that design software was too complex. So she did what most wouldn’t: She decided to fix it. Started from her mom’s living room. And heard 100+ rejections before someone finally said “yes.” At one point, she even learned kitesurfing, just to build credibility in the VC world. Because sometimes, as a woman, brilliance isn’t enough. You have to fit in just to get your foot in the door. But she kept showing up. She kept pitching. She kept building. Today, Canva is used by 60M+ people and supports 25,000+ nonprofits. But Melanie? Still journals. Still reads user feedback. Still shows up to welcome every new team member. Still wears a $30 engagement ring. Still plans to give away most of her wealth. She’s not just building software. She’s building a legacy. The next time you question your big idea, your timing, your lack of credentials, please remember this: The world doesn’t reward qualification. It rewards commitment.

Last week, China barred its major tech companies from buying Nvidia chips. This move received only modest attention in the media, but has implications beyond what’s widely appreciated. Specifically, it signals that China has progressed sufficiently in semiconductors to break away from dependence on advanced chips designed in the U.S., the vast majority of which are manufactured in Taiwan. It also highlights the U.S. vulnerability to possible disruptions in Taiwan at a moment when China is becoming less vulnerable. After the U.S. started restricting AI chip sales to China, China dramatically ramped up its semiconductor research and investment to move toward self-sufficiency. These efforts are starting to bear fruit, and China’s willingness to cut off Nvidia is a strong sign of its faith in its domestic capabilities. For example, the new DeepSeek-R1-Safe model was trained on 1000 Huawei Ascend chips. While individual Ascend chips are significantly less powerful than individual Nvidia or AMD chips, Huawei’s system-level design to orchestrate how a much larger number of chips work together seems to be paying off. For example, Huawei’s CloudMatrix 384 system of 384 chips aims to compete with Nvidia’s GB200, which uses 72 higher-capability chips. Today, U.S. access to advanced semiconductors is heavily dependent on Taiwan’s TSMC, which manufactures the vast majority of advanced chips. Unfortunately, U.S. efforts to ramp up domestic semiconductor manufacturing have been slow. I am encouraged that one fab at the TSMC Arizona facility is operating, but issues of workforce training, culture, licensing and permitting, and the supply chain are still being addressed, and there is still a long road ahead for the U.S. facility to be a viable substitute for Taiwan manufacturing. If China gains independence from Taiwan manufacturing significantly faster than the U.S., this would leave the U.S. much more vulnerable to possible disruptions in Taiwan, whether through natural disasters or man-made events. If manufacturing in Taiwan is disrupted for any reason and Chinese companies end up accounting for a large fraction of global semiconductor manufacturing capabilities, that would also help China gain tremendous geopolitical influence. Despite occasional moments of heightened tensions and large-scale military exercises, Taiwan has been mostly peaceful since the 1960s. This peace has helped the people of Taiwan to prosper and allowed AI to make tremendous advances, built on top of chips made by TSMC. I hope we will find a path to maintaining peace for many decades more. But hope is not a plan. In addition to working to ensure peace, practical work lies ahead to multi-source, build more fabs in more nations, and enhance the resilience of the semiconductor supply chain. Dependence on any single manufacturer invites shortages, price spikes, and stalled innovation the moment something goes sideways. [Original text: https://lnkd.in/gxR48TK8 ]

AI just helped a couple get pregnant - after 19 years and 15 failed IVF cycles. The breakthrough came with an AI tool built by a team at Columbia University. It’s called STAR - the world’s first AI system trained to find sperm that embryologists can’t. The husband had azoospermia - a condition where no sperm is visible under the microscope. Dozens of attempts, surgeries, and even overseas experts had failed. But the team at Columbia didn’t give up. They spent 5 years building STAR (Sperm Track and Recovery). The system scans 8 million images per hour using a chip and computer vision, then gently isolates viable sperm missed by even the most experienced lab techs. And it worked. ▶︎ STAR found 44 sperm in a sample that had been manually searched for two full days. ▶︎ That one breakthrough led to a pregnancy that had felt impossible for nearly two decades. ▶︎ And it did so without chemicals, donor samples, or invasive extraction methods. For millions of couples dealing with infertility, this is a glimpse of what AI-assisted reproductive medicine could unlock. But more importantly - this shows us what AI in healthtech should be aiming for: Not just more data. Not just smarter models. But real clinical results that change lives. And as a healthtech investor, this is what I look for in AI-driven care: → A clear pain point → A targeted intervention → And a story no one can ignore What’s your take - could AI reshape fertility care the way it’s starting to reshape diagnostics and mental health? #entrepreneurship #healthtech #innovation

Using light as a neural network, as this viral video depicts, is actually closer than you think. In 5-10yrs, we could have matrix multiplications in constant time O(1) with 95% less energy. This is the next era of Moore's Law. Let's talk about Silicon Photonics... The core concept: Replace electrical signals with photons. While current processors push electrons through metal pathways, photonic systems use light beams, operating at fundamentally higher speeds (electronic signals in copper are 3x slower) with minimal heat generation. It's way faster. While traditional chips operate at 3-5 GHz, photonic devices can achieve >100 GHz switching speeds. Current interconnects max out at ~100 Gb/s. Photonic links have demonstrated 2+ Tb/s on a single channel. A single optical path can carry 64+ signals. It's way more energy efficient. Current chip-to-chip communication costs ~1-10pJ/bit. Photonic interconnects demonstrate 0.01-0.1pJ/bit. For data centers processing exabytes, this 200x improvement means the difference between megawatt and kilowatt power requirements. The AI acceleration potential is revolutionary. Matrix operations, fundamental to deep learning, become near-instantaneous: Traditional chips: O(n²) operations. Photonic chips: O(1) - parallel processing through optical interference. 1000×1000 matmuls in picoseconds. Where are we today? Real products are shipping: — Intel's 400G transceivers use silicon photonics. — Ayar Labs demonstrates 2Tb/s chip-to-chip links with AMD EPYC processors. Performance scales with wavelength count, not just frequency like traditional electronics. The manufacturing challenges are immense. — Current yield is ~30%. Silicon's terrible at emitting light and bonding III-V materials to it lowers yield — Temp control is a barrier. A 1°C change shifts frequencies by ~10GHz. — Cost/device is $1000s To reach mass production we need: 90%+ yield rates, sub-$100 per device costs, automated testing solutions, and reliable packaging techniques. Current packaging alone can cost more than the chip itself. We're 5+ years from hitting these targets. Companies to watch: ASML (manufacturing), Intel (data center), Lightmatter (AI), Ayar Labs (chip interconnects). The technology requires major investment, but the potential returns are enormous as we hit traditional electronics' physical limits.

Europe's launch of a digital wallet is a game changer for #banking and #payments, far beyond than we can imagine. Let’s take a look.   What happened?   On 29 Feb 2024 the EU adopted regulation to launch a European Digital Identity Wallet (EUDIW) that will harmonize #digitalidentity across Europe.   Main provisions:   —   EUDIW is an app allowing citizens to digitally identify themselves, store and manage identity data and official documents in digital form   —   Many wallets in each member state with the same technical standards, UX and functionality   —   Addressing both online and offline public and private services across the EU —   Recognized throughout Europe   —   Voluntary   —   Free for natural persons, businesses may be subject to fees   —   User control over their personal data   —   E-signature   —   EUDIW Toolbox based on the Architecture and Reference Framework (ARF) defining common specifications, referenced in implementing acts (legislative texts) across all EU Member States   —   Pilot projects until 2025 - 360 private companies and public authorities across the EU - testing everyday scenarios   —   Successor of the eIDAS regulation (launched in 2014)   Example use cases:   —   Access or open a bank account —   Perform onboarding process (AML, KYC) —   Initiate a payment —   Apply for a loan —   Submit a tax declaration —   Enroll for university —   Rent a car or book a hotel online —   Strong Customer Authentication   Implications for the #finance industry:   —   EUDIWs will unify all physical documents (IDs, passports, driving licenses, etc) under a digital front layer   —   Financial institutions and online platforms with more than 45 mn users (i.e. Amazon, Facebook) will be obliged to accept EUDIW   —   Banks will not have to maintain anymore their own authentication mechanisms, however the wallet will largely complement and not replace banks’ solutions   —   Service providers, such as PSPs or credit card companies may have to pay for identification services (i.e. to onboard customers)   —   PSD2 authentication requirements will be met via EUDIWs paving the ground for an increase in payment initiation and account information calls and boosting POS-based use cases such as QR code payments or payment initiation at POS   —   A combination with the Digital Euro is almost certain   Players in #financialservices will be influenced across 4 directions:   —   User experience —   Compliance —   Reduction of fraud —   New use cases   Impact:   —   Europeans can save up to 855,000 hours of time and businesses more than €11 bn a year   —   80 % EU citizens' adoption expected by 2030   Timing:   —   Publication in the EU Official Journal – Mar 2024   —   6 - 12 months for Implementing Acts   —   Within 24 months after Implementing Acts, Member States must provide EUDIWs. Organizations must accept them as an authentication method in the following year   Opinions: my own, Graphic sources: European Commission, Innopay, Gataca

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

MAJOR AI LEGAL NEWS. The revised EU Product Liability Directive came into force yesterday, 8 December 2024. It represents a fundamental shift in how liability for AI systems and software is addressed. The Directive could directly impact organisations using and developing AI, and they may wish to consider if they need to reassess their contracts, policies, and operational approaches to liability management. Under the new framework, AI system providers (treated as manufacturers in the legislation) are liable for defects in AI systems and software that cause harm, potentially including defects that emerge after deployment. This potentially includes harm linked to updates, upgrades, or the evolving behaviour of machine-learning systems. Organisations should also consider the liability implications for failing to have sufficient AI literacy among their staff which is a requirement under the AI Act from 2 February. AI training may now be a business imperative for some organisations. The Directive’s approach to defectiveness considers not only when a product is placed on the market but also whether the manufacturer retains control over it post-market, such as through updates or connected services. This means manufacturers may be held liable for defects that arise after deployment if they could reasonably foresee and mitigate risks but fail to act. Organisations, particularly those providing software or AI systems, should look at ongoing compliance and risk management to meet evolving safety expectations. The Directive's coverage of potential liability for post-market defects could have big implications for contracts. Organisations should consider whether their agreements with suppliers, integrators, and distributors include clear terms governing responsibility for defects. The focus is on whether the product provides the safety consumers are entitled to expect. A proactive approach to risk management, extending beyond initial product deployment to encompass ongoing updates and system monitoring may be prudent. Software providers should take note that they potentially could be held liable even if their product operates as a component of a larger system. This liability regime incentivises stronger warranties, indemnities, and cooperation agreements to allocate risk effectively across supply chains. Companies should review existing contracts to confirm they reflect the Directive's requirements and renegotiate where necessary to close gaps in accountability. The Directive also works in tandem with EU regulations like the AI Act. Businesses that fail to meet mandatory product safety requirements under the likes of the AI Act risk facing presumptions of defectiveness under the Product Liability Directive. With the AI Liability Directive in progress, organisations should also prepare for further changes that will make it easier for claimants to bring AI-related liability claims.