Banking, Navigation, Robotics, and Asteroid Mining: Innovation and Agile AI Teamwork

Imagine driving in 1971 using an audio device for navigation—with no way to recover if you missed a turn. The BBC featured it and other technological prototypes, predictions, and demonstrations in its long-running (1965-2003) series, Tomorrow's World. Recurring themes include impressive ingenuity constrained by rigidity, lack of scalability, over-optimism about timelines, and the critical need for adaptability. These vintage reports offer timeless insights for today's technology landscape, including AI. My new article shares five standout examples with direct parallels to modern innovations. https://lnkd.in/gUBTH5S6 Hope you like it. ❤️ Scott #AI #Innovation #Prototypes #Technology cc: BBC Studios, BBC News

𝟐𝟎𝟐𝟔 𝐢𝐬 𝐡𝐞𝐫𝐞 𝐚𝐧𝐝 𝐰𝐞’𝐫𝐞 𝐛𝐚𝐜𝐤 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠. For several years, we have built our approach to AI around value, product, and real-world usage, built on architectures designed to last. In 2026, we continue on this path: turning AI into measurable impact, not demonstrations. This means making deliberate architectural choices, maintaining a demanding product mindset, and designing AI that integrates seamlessly into real-world operational environments. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐰𝐡𝐚𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐟𝐨𝐫 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐢𝐦𝐩𝐚𝐜𝐭 𝐥𝐨𝐨𝐤𝐬 𝐥𝐢𝐤𝐞 𝐢𝐧 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞. A new year begins. The vision remains unchanged. #QUANTAILab #NewYear #BackToWork #ArtificialIntelligence #VisionProduct #AIArchitecture #RealWorldAI QUANT AI Lab Bertrand K. Hassani, PhD Julien Doutremépuich Laurent LUCIEN, Ph.D. Manuel Revuelta Francisco V. Sandra S. Dayanne Vieira

2026 marks the shift from spectacle to practical deployment in AI As 2026 unfolds, AI is transitioning from large-scale demonstrations to specialised, deployable systems that integrate seamlessly into business operations, devices and environments, emphasising efficiency and real-world utility. After two years defined by spectacle and grand promises, 2026 is shaping up as the year artificial intelligence stops showing off and starts doing the heavy lifting. The conversation is shifting from “how big can a model be?” to “how does this fit into a company, a team or a device?” According to TechCrunch, that transition emphasises deployability: lower latency, predictable costs, easier maintenance, tighter security, and closer alignment with human workflows....

What to expect in 2026: Ten sports broadcasting predictions From a decisive move towards centralised production to continued growth in the use of robotics and AI- and ML-based tools, 2026 looks set to be the year that emerging technologies will prove their worth in real-world scenarios. Here, industry leaders share how they expect the coming year to develop, and the trends and technologies they see having the biggest impact. Featuring Tim Banks, Eamonn Curtin, Francis Williams, Benoit Dentan, Francesco Scartozzi, Roger Heiniger, Deidre Joubert, Henry Goodman, Guillaume Arthuis, and Miheer Walavalkar #SportsBroadcasting #SportsProduction #FanEngagement #MachineLearning #AI The link to the article is in the comments.

International CES - Consumer Electronics Show is great for tech demonstrations, but the enterprise reality is different as AI tools that look impressive on stage often don’t deliver real-world results. Before committing  serious budget for the latest AI tools and technologies, ask the following questions: ❓ First: what state is your data really in? Most projects don’t stall because of bad technology, they stall because the data is bad. ❓ Second: does it work inside your existing systems? Usage drops when people have to jump between platforms. ❓ Third: who will look after it in six months’ time? If it needs specialist talent you don’t have, the long-term cost creeps up quietly. Forrester has warned repeatedly that weak data foundations sink more AI projects than bad models. The best enterprise AI doesn’t feel flashy, it quietly makes work easier and that’s usually where the return is. #AI #CTO #EnterpriseTech

I’ve been working on an XAI healthcare project that uses an LLM to interpret the driving features of ML predictions in natural language, essentially explaining what the model’s picked up on and what that suggests clinically. Getting anywhere near usable has required massive prompts of 3000+ tokens with extensive restrictions. It works, but general-purpose LLMs fundamentally lack the domain-specific accuracy healthcare demands. In this context, accuracy is non-negotiable. Next phase is fine-tuning a model and embedding it in a RAG architecture with curated clinical examples. That should give us the reliability and precision we actually need. Interested in what Anthropic are doing in this space, their healthcare and life sciences briefing on 12th January looks relevant. Claude’s RAG project system is the best implementation I’ve used, and their focus on precise, contextually-aware AI aligns well with healthcare requirements. Has anyone else been tackling similar XAI challenges in clinical settings?

Looking forward to Anthropic's livestream on AI and healthcare! Having spent two years managing contracts and compliance at Spring Fertility, I'm curious to see how AI can support patients and healthcare providers while maintaining the rigorous standards healthcare requires. What I'm most interested in learning: ✨ How Claude's healthcare-specific capabilities could help patients better understand tests, procedures, and diagnoses—while protecting patient privacy and avoid discouragement for professional attendance. ✨ How providers will adapt to these new features and address real-world operational challenges through responsible AI implementation in HIPAA-regulated environments ✨ How AI can strengthen communication between patients and providers, helping both sides better understand each other's needs and priorities I'm eager to see how thoughtful AI deployment can transform healthcare without compromising the trust and care standards our industry demands. Who else is tuning in? Would love to connect with others interested in the intersection of AI, healthcare, and responsible innovation. #AIinHealthcare #HealthTech #ResponsibleAI #Anthropic #HealthcareInnovation

Banner Health is tackling physician burnout with AI with real results—powered by Claude on Amazon Bedrock. By automating administrative tasks and streamlining clinical workflows through AWS's managed AI service, they're giving doctors more time with patients and less time on paperwork. This AWS Bedrock implementation demonstrates how enterprise-grade AI infrastructure can address healthcare's most pressing challenge: keeping physicians focused on healing rather than documentation. The result? Reduced burnout, better patient care, and a scalable blueprint for health systems worldwide looking to deploy AI responsibly and effectively. #HealthcareInnovation #AWSBedrock #AIinHealthcare #PhysicianBurnout

Meta World Model promising step towards AGI. An LLM is a model trained mainly to predict and generate language (text) from patterns in data, while a Meta world model is trained to build an internal, predictive simulation of how the world works (states → actions → outcomes) so it can plan and act beyond just producing words. https://ai.meta.com/vjepa/