AI Model Development

𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗮𝘁 𝗔𝗜 𝗶𝘀 𝗮 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁 𝗽𝗮𝘁𝗵 𝗳𝗿𝗼𝗺 𝗱𝗮𝘁𝗮 𝘁𝗼 𝘃𝗮𝗹𝘂𝗲. The assumption: 𝗗𝗮𝘁𝗮 → 𝗔I → 𝗩𝗮𝗹𝘂𝗲 But in real-world enterprise settings, the process is significantly more complex, requiring multiple layers of engineering, science, and governance. Here’s what it actually takes: 𝗗𝗮𝘁𝗮 • Begins with selection, sourcing, and synthesis. The quality, consistency, and context of the data directly impact the model’s performance. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 • 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Exploration, cleaning, normalization, and feature engineering are critical before modeling begins. These steps form the foundation of every AI workflow. • 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: This includes model selection, training, evaluation, and tuning. Without rigorous evaluation, even the best algorithms will fail to generalize. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 • Getting models into production requires deployment, monitoring, and retraining. This is where many teams struggle—moving from prototype to production-grade systems that scale. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 • Legal regulations, ethical transparency, historical bias, and security concerns aren’t optional. They shape architecture, workflows, and responsibilities from the ground up. 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰. 𝗜𝘁’𝘀 𝗮𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 𝘄𝗶𝘁𝗵 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗿𝗶𝗴𝗼𝗿 𝗮𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗮𝘁𝘂𝗿𝗶𝘁𝘆. Understanding this distinction is the first step toward building AI systems that are responsible, sustainable, and capable of delivering long-term value.

Every board is betting big on AI. Almost none are asking the question that actually protects them. I’ve been in boardrooms across industries, from finance to healthcare, and I keep seeing the same thing: Board members ask: - “What’s the AI budget?” - “What’s the timeline?” - “What’s the ROI?” But almost no one asks the most important question: “How do we even know this is AI?” Here’s the problem… Most boards are approving AI initiatives without a clear definition of what qualifies as AI because the lines are blurry. Vendors show up with polished demos and pitch tools labeled “AI-powered.” But without clarity, boards end up greenlighting: ✗ Rule-based systems dressed up as intelligence ✗ Traditional software relabeled with buzzwords ✗ Proof-of-concept demos, not scalable AI infrastructure ✗ “AI-washed” features that don’t actually learn or adapt Before the next AI contract crosses your desk, ask leadership: → Where exactly does machine learning happen in this system? → How does it improve over time with use? → What data powers it, and who owns that data? → How much human intervention is required for results? Because the companies truly win with AI? They’re not the ones with the flashiest tools. They’re the ones whose boards can differentiate real intelligence from noise. What’s your take - have you seen “AI” claims fall apart under scrutiny?

What makes Chinese #tech different? After a week travelling across the country, it feels like the Chinese tech sector uses the same ingredients as elsewhere, but in different proportions. And the result makes #China tech look both familiar and unique.   Let me give you three examples.   Europe has strong climate ambition, but China’s rapid rollout of #greentech is truly eye-opening. In Shenzhen, it felt like 80% of cars were electric — easily spotted by their green plates. I visited a tech provider installing hundreds of thousands of solar panels to power their AI. This is in fact backed up by data: in the past year alone, China installed more than 210GW of solar production capacity, which is more than the whole solar production capacity of the US. And carbon emissions of China reduced by 1% in H1 2025 🌳   Another example is the focus on #robotics. From street-cleaning robots to a mini-choreography of four dog-shaped robots and a humanoid from Huawei, automation is everywhere. In airports, malls, and even restaurants, service robots are now part of daily life — I even had a robot make a cocktail for me! Chinese factories installed four times more robots than Europe in 2024 (nine times more than the US), and China now accounts for over half of all new industrial robots worldwide 🤖🤖🤖   Finally, China’s approach on #AI is distinctive, with a clear focus on open-weight models, small language models, and deep verticalization. Alibaba Group’s Qwen LLM is now one of the world’s top open models. Companies are full-speed building specialized AI for sectors like finance, manufacturing, healthcare and public services. And while the US talks a lot about AI infrastructure, China is quietly building at scale — the city of Shanghai alone is developing a 500MW AI gigafactory.   China’s tech scene is unique, but it comes with its own set of challenges. Local habits, language barriers, and regulations mean that tech stacks in China are often completely separate from the rest of the world. For international companies, this creates real technical and compliance hurdles. But operating in and out of China should be seen as an opportunity for digital transformation, not a barrier. At Orange Business, we’re helping customers seamlessly connect their Chinese operations to their global networks — and China has become our fastest-growing market. Thank you so much to the partners and customers who welcomed us with Jacques Aschenbroich, Rob Willcock, Nick Lambert and Jack Zhang.

For the past year, I’ve had the privilege of co-chairing together with Carme Artigas the UN’s High-level Advisory Body on AI,which included 38 members from 33 countries. We were tasked with developing a blueprint for sharing AI’s transformative potential globally, while identifying and addressing the risks and filling the gaps that limit participation.  Following our interim report in Dec 2023, today we’re sharing our final report which outlines our key findings and recommendations to enhance global cooperation on AI governance. The report was informed by extensive consultation, including more than 2000 participants from all regions, 18 deep dives with 500 expert participants, 250 written submissions, 100+ virtual discussions, as well as research and surveys. AI has the potential to assist people in everyday tasks to their productive and creative endeavors, enable entrepreneurs and small and large businesses, transformation of sectors from healthcare to agriculture, power economic growth, advance science in ways that benefit society, and contribute to achieving the UN’s Sustainable Development Goals. At the same time, as with any powerful technology, it poses risks, challenges and complexities ranging from bias, misapplication and misuse, impact on work, to potentially widening global inequities. Our work highlighted many of these themes as well as key gaps in governance and the capacity for all to fully benefit from AI.  To harness AI’s potential and mitigate its risks, we need a truly inclusive and international effort – and current governance structures are missing too many voices. Our recommendations focus on these and other findings and I encourage you to read the report. Thank you to the UN’s Tech Envoy Amandeep Gill and his team, my co-chair Carme Artigas, and my fellow members of the advisory body -- from whom I learned a lot -- for their expertise and diverse views and vantage points, partnership, persistence and commitment to governing and harnessing AI’s potential benefits for all of humanity. https://lnkd.in/gFhFWWEh Carme Artigas, Anna Christmann, Anna Abramova, Omar Sultan AlOlama, @Latifa Al-Abdulkarim, Estela Aranha, Ran Balicer, Paolo Benanti, Abeba Birhane, Ian Bremmer, Anna Christmann,Natasha Crampton, Nighat Dad, Vilas Dhar, Virginia Dignum, @Arisa Ema, @mohamed farahat, Wendy Hall, Rahaf Harfoush, Hiroaki Kitano, Haksoo Ko, Andreas Krause, Maria Vanina Martinez, Seydina M. Ndiaye, @Moussa Ndiaye, Mira Murati, Petri Myllymäki, Alondra Nelson, Nazneen Rajani, Craig Ramlal, @Ruimin He, Emma Ruttkamp-Bloem, Marietje Schaake, @Sharad Sharma, @Jaan Tallinn, Ambassador Philip Thigo, MBS, Jimena Viveros LL.M., Yi Zeng, @Zhang Linghan

China isn't replicating AI anymore - it's defining it. A decade ago, China was labeled a tech imitator. Today it's an AI superpower setting global trends. Massive government investments have sparked innovation: - Over $140 billion targeted for AI by 2030. - Giants like Baidu, Alibaba, and Tencent lead national AI projects. China's AI innovations are transforming industries: - Healthcare: AI accurately diagnosing cancers and rare diseases. - Manufacturing: Predictive AI cutting downtime; humanoid robots redefining factories. - Autonomous Vehicles: Baidu's Apollo rivals Tesla, aiming for over 50% market penetration by 2030. - Generative AI: DeepSeek & Manus AI deliver AI solutions 13-20x cheaper than Western counterparts. China also dominates global AI research and talent: - Leading worldwide in AI publications and patents. - Home to thousands of AI companies and a booming talent pool. We aren't just witnessing an AI shift - we're experiencing a new era shaped by China. Paradigm shift.

"How do we ensure that the rapid development of AI is more considerate of harms and the public interest? In our inaugural Responsible AI Impact Report, All Tech Is Human (ATIH) aims to reveal our most urgent risks, emerging safeguards, and public-interest solutions, and provide a roadmap for how we will shape how AI impacts society in the year ahead. We examine the state of Responsible AI (RAI) throughout 2025 and highlight what we consider to be some of the most impactful contributions made by civil society organizations this year to enrich this broad and dynamic field. We believe the Responsible AI field can only thrive if we effectively tackle the complex challenges at the intersection of technology and society. When we refer to “Responsible AI,” we mean AI that is well-regulated and guard-railed, governed and assured (documented, standardized, and benchmarked with relevant measurements), and assessed, evaluated, and red-teamed. As we outlined in our recent Responsible Tech Guide (2025), our organization believes in a human-centered future that values our agency in desired outcomes and rejects tech determinism. As such, we are focused on elevating AI models that do as little harm as possible, for use cases in which risks have been carefully considered and meaningfully mitigated; and ethically deployed AI, in which lofty principles are operationalized with grounded KPIs. This Responsible AI Impact Report highlights the growing focus on Public Interest AI that is of, by, for, and in service to the people. This Public Interest AI should be applied to humanity’s most pressing challenges and enable us to reimagine what a better tech future entails. This report also explores a future in which Public Interest AI is developed on public infrastructures for an AI-literate society. At the heart of the years ahead lies a defining question: who determines the purpose of AI and the kinds of lives it will shape?" Rebekah Tweed, with support from David Ryan Polgar, Sandra Khalil, and Sherine Kazim. 

"Inclusive AI" - a noble goal or a new front of AI geopolitics?   When we say #inclusiveAI, we generally mean AI systems designed to be non-discriminatory, unbiased and accessible (particularly for marginalised and underrepresented groups), via data cleaning techniques, diverse decision making process, human oversight, etc.   But what does "inclusive AI" mean on a global scale?   Typical answers include: ✅ Equitable access to AI and their benefits across different countries and regions. ✅ Incorporating diverse perspectives and cultural contexts into AI development and deployment. ✅ Using AI to address global challenges (e.g. climate change).   But recently, I've been thinking a lot about inclusive AI, especially as "inclusiveness" becomes increasingly mentioned in international forums. For example (just from November 2024 alone): ▪ the Joint Ministerial Statement of the recent APEC summit (at Peru) mentions "inclusive" 24 times ▪ the G20 Rio de Janeiro Leaders' Declaration (at Brazil) also mentions "inclusive" 23 times.   At this month's G20 summit, China President Xi delivered a speech, calling for the G20 to ensure AI is "for good and for all, not a game of the rich countries and the wealthy" (see news headlines).   I'd count this as another reference to inclusive AI, particularly around rebalancing access to AI between the "rich" Global North and the Global South.   That brings us to the #geopolitics of things.   The reality is that AI is only the tip of the iceberg. It's intertwined with other 'deeper' areas like cloud, telco infrastructure, cross-border data and funds transfer, chips, international payments, big tech presence, start-up environment etc. There would need to be "inclusiveness" in those areas for us to achieve some real level of globally inclusive AI. Yet each area has its own practical and geopolitical challenges. Also, it depends on whether you're looking at "inclusive AI" from the perspective of consumer access, development & innovation capabilities, smart infrastructure, etc. Each angle tells a different story.   Given the realities, do you ever wonder if "inclusive AI": ❓ is now just another codename for "sovereign AI" or "AI nationalism" (i.e. each nation adopts policies/laws that advance their own domestic AI capabilities while also hampering overseas competitors)? It's arguably "inclusive" because each nation promotes AI access for their own population... ❓will create a 'tokenistic' mindset to AI where efforts towards inclusive AI are superficial, focusing on surface-level diversity rather than addressing deeper systemic issues? ❓will promote a culture of data exploitation as more data is collected under the guise of diversity/inclusion? Related concepts include "technofeudalism", "AI/digital colonialism", etc.   What do you think? 👓 Want more? I track #AI laws and policies around the world in my Global AI Regulation Tracker (see link in the 'Visit my website' button above). #tech #aiethics

I’ve worked in data science for a decade, and I’ve seen the field evolve a lot. But nothing compares to what’s happened in the last three. Generative AI has completely reshaped our workflows. What used to take weeks of manual data prep and iteration now happens in days or even hours. The role of a data scientist is shifting fast: less about repetitive coding, more about designing intelligent workflows that solve real business problems. I recently came across Google's new Practical Guide to Data Science, and here are a few insights that stood out for me: ➝ The agentic shift Most of a data scientist’s day used to be cleaning data, tuning models, and writing the same pipelines again and again. Now AI agents automate those parts. The value we bring is moving to analysis, interpretation, and driving business outcomes. ➝ Multimodal data For years, our work was limited to structured tables. But most enterprise data is unstructured like images, PDFs, audio, and free text. With BigQuery, you can now analyze this directly with SQL. That means questions that used to be impossible, like combining sales data with call transcripts, are finally within reach. ➝ Blending external intelligence with enterprise data Foundation models bring real-world knowledge into the enterprise stack. Instead of writing rules for every scenario, you can ask nuanced questions like: Which of our products show high satisfaction based on quality? This type of reasoning used to take months of manual analysis. ➝ AI as a feature engineering engine Instead of just running basic sentiment analysis, you can extract structured insights at scale. For example, pulling out sentiment specifically around “battery life” or “user interface” and joining it with sales data. Raw text turns into powerful features that drive models. ➝ In-place model development Moving data around used to be the bottleneck. With BigQuery ML, you can now train and deploy models right where the data lives. Teams have seen deployment times cut by 10x, shifting the focus from infrastructure to speed of insight. ➝ Vector embeddings and semantic search Vector search used to mean adding another system. Now it’s built into BigQuery. That means semantic product discovery, document retrieval, and multimodal analysis all within your data warehouse. Data scientists role is changing, and now it's less about syntax, more about strategy. Less about writing every line of code, more about designing AI-powered workflows. If you want to dive deeper, I recommend checking out the full guide. It’s packed with practical examples that show just how much the landscape has shifted

👀 Look inside the new OpenAI-scale data center; here’s what’s actually powering AI. The story is what happens after training AI and the infrastructure that runs AI for billions of people every single day. Here’s what’s inside these next-generation AI factories: - GPU Superclusters Rows of racks filled with NVIDIA GPUs or custom accelerators, wired together into one giant machine. These are not web servers; they are purpose-built for AI. - Networking as the nervous system Custom fabrics like InfiniBand and NVLink move data between chips at lightning speed. Without this, a large model couldn’t even generate a single sentence in real time. - Power + Cooling One rack can draw as much energy as hundreds of homes. That’s why you’ll find these centers built next to dams, nuclear plants, or renewable hubs. Cooling isn’t air; it’s liquid, direct-to-chip, sometimes immersion. - Software orchestration Frameworks like vLLM and DeepSpeed split a single query across hundreds of GPUs, stitch the results, and return an answer in under a second. And here’s the key point most miss: ⚡ Inference is the business of AI Training is expensive but happens once in a while. Inference never stops; every chatbot reply, every co-pilot suggestion, every AI Agent, every enterprise RAG query is inference. This is where costs explode and where optimization is worth billions. These new AI data centers are the factories of intelligence. 👉 The build-out of AI data centers over the next 5 years will be massive; on the scale of the railroad system in the 19th century. The companies that control this infrastructure won’t just run AI. They’ll own the rails the future runs on.

With 30 years of experience in the technology sector, including in engineering & operations, I’ve developed my own best practices that help organizations build trust with the communities who will use their technology.  In this week’s special TIME Magazine Davos issue, I outlined a framework based on those hard-won lessons to help ensure AI development is responsible, thoughtful, and benefits humanity, including: - Embrace Early Collaboration: Bringing outside voices into the development process early helps to create technology that better reflects the breadth and depth of the human experience. Ensuring you partner with - and listen to - experts & local communities can help mitigate potential risks. - Operationalize Care: The success of AI projects often hinges on how well organizations implement systems that operationalize their commitment to care. For example, at Google DeepMind, we have developed frameworks that embed ethical considerations and safety measures into the fabric of any research and development process - as fundamental building blocks, not bolted-on afterthoughts. - Build Trust Through Real-World Impact: The antidote to apprehension around AI is to build products that solve real problems, and then highlight those solutions. When people understand how AI is adding clear value to their lives, the conversation can focus both on positive  opportunities and managing risk. I very much appreciated the opportunity to share my thoughts, and you can read more here: