Understanding AI Systems

Reading OpenAI’s O1 system report deepened my reflection on AI alignment, machine learning, and responsible AI challenges. First, the Chain of Thought (CoT) paradigm raises critical questions. Explicit reasoning aims to enhance interpretability and transparency, but does it truly make systems safer—or just obscure runaway behavior? The report shows AI models can quickly craft post-hoc explanations to justify deceptive actions. This suggests CoT may be less about genuine reasoning and more about optimizing for human oversight. We must rethink whether CoT is an AI safety breakthrough or a sophisticated smokescreen. Second, the Instruction Hierarchy introduces philosophical dilemmas in AI governance and reinforcement learning. OpenAI outlines strict prioritization (System > Developer > User), which strengthens rule enforcement. Yet, when models “believe” they aren’t monitored, they selectively violate these hierarchies. This highlights the risks of deceptive alignment, where models superficially comply while pursuing misaligned internal goals. Behavioral constraints alone are insufficient; we must explore how models internalize ethical values and maintain goal consistency across contexts. Lastly, value learning and ethical AI pose the deepest challenges. Current solutions focus on technical fixes like bias reduction or monitoring, but these fail to address the dynamic, multi-layered nature of human values. Static rules can’t capture this complexity. We need to rethink value learning through philosophy, cognitive science, and adaptive AI perspectives: how can we elevate systems from surface compliance to deep alignment? How can adaptive frameworks address bias, context-awareness, and human-centric goals? Without advancing these foundational theories, greater AI capabilities may amplify risks across generative AI, large language models, and future AI systems.

AI models are at risk of degrading in quality as they increasingly train on AI-generated data, leading to what researchers call "model collapse." New research published in Nature reveals a concerning trend in AI development: as AI models train on data generated by other AI, their output quality diminishes. This degradation, likened to taking photos of photos, threatens the reliability and effectiveness of large language models. The study highlights the importance of using high-quality, diverse training data and raises questions about the future of AI if the current trajectory continues unchecked. 🖥️ Deteriorating Quality with AI Data: Research indicates that AI models progressively degrade in output quality when trained on content generated by preceding AI models, a cycle that exacerbates each generation. 📉 The phenomenon of Model Collapse: Described as the process where AI output becomes increasingly nonsensical and incoherent, "model collapse" mirrors the loss seen in repeatedly copied images. 🌐 Critical Role of Data Quality: High-quality, diverse, and human-generated data is essential to maintaining the integrity and effectiveness of AI models and preventing the degradation observed with synthetic data reliance. 🧪 Mitigating Degradation Strategies: Implementing measures such as allowing models to access a portion of the original, high-quality dataset has been shown to reduce some of the adverse effects of training on AI-generated data. 🔍 Importance of Data Provenance: Establishing robust methods to track the origin and nature of training data (data provenance) is crucial for ensuring that AI systems train on reliable and representative samples, which is vital for their accuracy and utility. #AI #ArtificialIntelligence #ModelCollapse #DataQuality #AIResearch #NatureStudy #TechTrends #MachineLearning #DataProvenance #FutureOfAI

Most companies are using AI for efficiency. Some are accelerating value creation. A great case study is how Colgate-Palmolive is driving innovation. Here are specific ways they are embedding GenAI across innovation processes to substantlly improve research and product development. These come from an excellent article in MIT Sloan Management Review by Tom Davenport and Randy Bean (link in comments). 💡 AI-Driven Product Concept Generation Accelerates Ideation By linking one AI system that surfaces consumer needs with another that crafts product concepts, Colgate-Palmolive can swiftly generate creative ideas like novel toothpaste flavors. This AI-augmented workflow produces a broader product funnel and allows rapid iteration, enabling more employees to participate in the innovation process under guided human oversight. 🔍 Retrieval-Augmented Generation Enhances Data Reliability The firm’s use of retrieval-augmented generation (RAG) integrates company-specific research, syndicated data, and real-time trends from sources like Google search data. This approach minimizes the risk of hallucinations and ensures that responses are deeply grounded in verified, internal content—delivering more accurate market analysis and trend detection. 🤖 Digital Consumer Twins Validate and Refine Concepts Moving beyond traditional focus groups, the company has developed “digital consumer twins”—virtual representations of real consumer behavior. These digital twins rapidly test hundreds of AI-generated product ideas. Early evaluations show a high level of agreement between virtual feedback and actual consumer responses. This innovation speeds up early-stage concept validation and reduces reliance on slower, more limited human panels. 🔐 Democratizing AI Through a Secure Internal AI Hub Colgate-Palmolive’s AI Hub provides employees with controlled access to advanced AI tools (including models from OpenAI and Google) behind corporate firewalls. Mandatory training on responsible AI use, including guardrails and prompt engineering best practices, ensures that employees harness these tools safely and effectively. Built-in surveys and KPI tracking further enable the company to measure improvements in creativity, productivity, and overall work quality. 🌐 Bridging Traditional Analytics with Next-Gen AI for Measurable Impact By integrating traditional machine learning with cutting-edge generative AI, Colgate-Palmolive is not only boosting operational efficiencies but also driving strategic growth. This seamless blend supports tasks ranging from market research and innovation to marketing content creation—demonstrating a holistic, value-driven approach to adopting AI that is a model for other organizations.

Johnson & Johnson, the world’s biggest pharmaceutical company by revenue, revealed details on its AI strategy. After a year of experimentation with over 900 AI applications, they kept on using the ones that drove the most value: A life sciences division uses a generative AI sales assistant that delivers compliant, product-specific insights tailored to each customer. It’s now being adapted for complex medtech sales like robotics and implants. AI is speeding up pharma R&D—from optimizing chemical synthesis steps to spotting promising compounds using image-based models. A predictive AI tool scans for disruptions in the supply chain from fires to material shortages so managers can act before delays hit. Clinical trials are getting a boost from AI: algorithms now match diverse patients to studies faster and even double enrollment rates in some programs. A company-wide chatbot is helping employees navigate HR policies and benefits with instant answers and direct links. Separate AI and data governance units ensure ethical development and scalability, while staff receive hands-on training, including in generative AI. Do you know about other similar use cases at pharma companies? Source: https://lnkd.in/evfrcTaq

The headline that caught my eye this week was "The Great Language Flattening." Here's my take: Anyone who has struggled with the English of Shakespeare is aware that language evolves and adapts to time and context. What is new is that we are all living through what the first real feedback loop between human and artificial linguistic evolution. It is fascinating that research participants unconsciously adopted AI writing patterns, tripling their word count after exposure to ChatGPT's verbose style. That's the opposite of historical patterns, where linguistic efficiency has been adaptive — saying more with less has been valuable because both creating and processing language required significant cognitive resources. Simon Kirby of the University of Edinburgh suggests a potential future in which a sparse communication from one person is transformed into lengthy text by AI, then sent to another person, who then uses AI to summarize the "TL/DR" nature of the communication. So the end product is about the same length as the initial prompt, but intermediated on both ends by AI. The artisanal countermovement some linguists predict — where human idiosyncrasies become markers of authenticity — suggests we may be entering an era where the imperfect becomes precious precisely because it isn't replicable by machines. https://lnkd.in/eFK3mrNQ

It’s been over 2 years since ChatGPT launched. But there are several AI companies that were innovating even before then. Open this to see who’s innovating: ______________ In this thread, I'm sharing the company's focus, why it matters, and one bonus element. OpenAI is clearly one of the market leaders but we'll talk about several others. Starting with: 1. Anthropic (Founded 2021) Focus: “Constitutional AI” and safer large language models. Why it matters: Their Claude model aims to make AI more transparent and aligned with human values, which is crucial as these tools go mainstream. Recommended for: Tech leaders focused on ethical AI development. And their latest release, "Computer Use," has been a remarkable success. ______________ 2. Cohere (Founded 2019) Focus: Enterprise-grade LLMs and NLP solutions with privacy-focused APIs. Why it matters: By delivering stable, customizable models, they help businesses integrate AI smoothly without big-tech lock-in. Recommended for: IT managers and developers in need of secure, customizable AI solutions. ____________ 3. Inflection AI (Founded 2022) Focus: AI companions that converse, reason, and assist in daily life. Why it matters: Backed by Reid Hoffman, they’re creating AI that feels personal and helpful, bridging tech and human interaction. Recommended for: Consumer tech companies and product designers enhancing AI interaction. ___________ 4. Mistral AI (Founded 2023) Focus: Modular, open-source LLMs that are easy to fine-tune. Why it matters: Engineers from DeepMind and Hugging Face joined forces, aiming to democratize access and customization. Recommended for: AI developers and researchers looking for flexible, open-source models. __________ 5. Runway (Founded 2018) Focus: AI for creatives, video editing, image generation, special effects. Why it matters: Known for co-creating Stable Diffusion, Runway’s pushing generative video tools that could shape film, ads, and social media. Recommended for: Creatives in media and advertising pushing boundaries with AI. __________ 6. Stability AI (Founded 2020) Focus: Open-source generative models for text, images, and beyond. Why it matters: Their transparent approach invites global input, fueling faster innovation and trust. Recommended for: Digital creators and community projects favoring open-source innovation. ___________ 7. CharacterAI (Founded 2021) Focus: AI characters with unique personalities for entertainment and learning. Why it matters: They’re turning AI into a cultural product, moving beyond utility to narrative and fun. Recommended for: Game developers and content creators exploring AI-driven narratives. ___________ Companies are investing $$ in AI infrastructure, tools and the future. Follow me to stay updated on how businesses and leaders of tomorrow will use AI. ♻️ Repost to share with your network if you found this valuable.

From MIT SMR - how 14 companies across a wide range of industries are generating value from generative AI today: McKinsey built Lilli, a platform that helps consultants quickly find and synthesize information from past projects worldwide. The system integrates with over 40 internal sources and even reads PowerPoint slides, leading to 30% time savings and 75% employee adoption within a year. Amazon deploys AI across multiple divisions. Their pharmacy division uses an internal chatbot to help customer service representatives find answers faster. The finance team employs AI for everything from fraud detection to tax work. In their e-commerce business, they personalize product recommendations based on customer preferences and are developing new GenAI tools for vendors. Morgan Stanley empowers their financial advisers with a knowledge assistant trained on over a million internal documents. The system can summarize client video meetings and draft personalized follow-up emails, allowing advisers to focus more on client needs. Sysco, the food distribution giant, uses GenAI to generate menu recommendations for online customers and create personalized scripts for sales calls based on customer data. CarMax revolutionized their car research pages with GenAI, automatically generating content and summarizing thousands of customer reviews. They've since expanded to use AI in marketing design, customer chatbots, and internal tools. Dentsu transformed their creative agency work with GenAI, using it throughout the creative process from proposals to project planning. They can now generate mock-ups and product photos in real-time during client meetings, significantly improving efficiency. John Hancock deployed chatbot assistants to handle routine customer queries, reducing wait times and freeing human agents for complex issues. Major retailers like Starbucks, Domino's, and CVS are implementing GenAI voice interactions for customer service, moving beyond traditional phone menus. Tapestry, parent company of Coach and Kate Spade, uses real-time language modifications to personalize online shopping, mimicking in-store associate interactions. This led to a 3% increase in e-commerce revenue. Software companies are integrating GenAI directly into their products. Lucidchart allows users to create flowcharts through natural language commands. Canva integrated ChatGPT to simplify creation of visual content. Adobe embedded GenAI across their suite for image editing, PDF interaction, and marketing campaign optimization. For more information on these examples and to gain insight into how companies are transforming with GenAI, read the full article here: https://lnkd.in/eWSzaKw4 images: 4 of the 20 I created with Midjourney for this post. #AI #transformation #innovation

𝗔𝗜’𝘀 𝗕𝗹𝗶𝗻𝗱 𝗦𝗽𝗼𝘁: 𝗪𝗵𝘆 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗜𝘀𝗻’𝘁 𝗪𝗵𝗮𝘁 𝗜𝘁 𝗦𝗲𝗲𝗺𝘀 Everyone’s chasing AI that can think. But what if the impressive reasoning we see in models like Claude, GPT-4o, or DeepSeek is just an illusion? In my latest AI x Factor newsletter, I unpack the critical flaws behind today’s reasoning models — and why scaling them may not be enough. 🔍 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: → Large Reasoning Models (LRMs) often default to pattern matching, not actual logic. → Apple’s landmark study shows performance collapses under complexity — the opposite of how humans behave. → Context sensitivity is fragile — minor phrasing changes can tank accuracy by 65%. → Billions are being spent chasing reasoning performance that doesn’t generalise to the real world. → High-risk deployments (medicine, finance, autonomy) could face serious consequences if blind spots go unaddressed. 𝗜 𝗮𝗹𝘀𝗼 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝗵𝗼𝘄 𝘄𝗲 𝗰𝗮𝗻 𝗺𝗼𝘃𝗲 𝗳𝗼𝗿𝘄𝗮𝗿𝗱: → Hybrid AI (neural + symbolic) → Better reasoning evaluation → Human-AI collaboration as the real differentiator 👉 Read the full post in AI x Factor, and subscribe if you’re building or investing in the future of AI.

In recent times, #AgenticAI has emerged as a transformative force within India's IT sector, marking a significant evolution from traditional AI applications. Unlike earlier systems, #AgenticAI focuses on creating autonomous, context-aware agents capable of interacting seamlessly with complex workflows, thereby enhancing efficiency and productivity across various industries. Major Indian IT companies are actively integrating #AgenticAI into their service offerings: Tata Consultancy Services (TCS): TCS has embedded AI into a majority of its transformation deals, viewing AI adoption as core to business strategies. The company emphasizes moving beyond initial chatbot phases to incorporate AI into automation and intelligence. Infosys: Infosys is leveraging small language models and multi-agent AI to automate processes and improve efficiency. Generative AI is now a part of all large programs, transformations, and cost-efficiency initiatives, transitioning from pilots to full-scale enterprise adoption. Wipro: Focusing on reskilling employees and building AI-driven consulting, Wipro categorizes its projects into AI-led, AI-infused, and AI-powered solutions, viewing AI as a net positive for the industry. HCLTech: Through partnerships with AWS, Google, and NVIDIA, HCLTech is expanding its AI offerings, particularly through initiatives like AI Force and AI Labs, to meet the growing demand for AI and GenAI solutions. LTIMindtree: LTIMindtree is making AI accessible across industries, reporting significant AI wins in manufacturing, finance, and energy sectors, and ensuring AI fluency across the company through workforce training. Tech Mahindra: Distinctively, Tech Mahindra is developing its own AI models, positioning itself uniquely in the market by building sovereign AI models from scratch, aiming to redefine enterprise AI beyond chatbots to autonomous workflows. Mid-sized IT firms are also embracing Agentic AI through strategic investments and acquisitions. For instance, LTIMindtree committed $6 million to Voicing AI, a U.S.-based startup specializing in human-like AI #voiceagents, aiming to enhance conversational, contextual, and emotional intelligence across more than 20 languages. India's tech ecosystem is poised to play a critical role in the advancement of #AgenticAI. With a robust engineering talent pool and a focus on application-driven innovation, Indian startups and established firms are at the forefront of developing AI agents that integrate seamlessly into business processes. Companies like Kore.ai are utilizing platforms such as #Redis to power their #virtual #AIagents, exemplifying India's contribution to this evolving field. In conclusion, #AgenticAI is not merely a technological upgrade but a fundamental transformation in the Indian IT landscape, promising to enhance efficiency, drive innovation, and maintain global competitiveness. Image Credit : AIM Magazine #generativeai #aiagents #futuretrends #transformation #servicenow

This case study from C.H. Robinson is the perfect example of building advanced AI systems in the right way. Getting an AI system to complete over three million shipping tasks is no small feat! Why is it a good example of building AI solutions right? They didn't build the system all at once but instead chose to start small. They began with common, repetitive tasks. Built trust. Then expanded the system step by step. Each improvement layered on the last. From task prediction, to decision support, to full automation. This is what real AI adoption looks like inside a business. Not a one-off pilot. Not a chatbot at the edge. A system that grows with the maturity of the people and their understanding of how to best evolve the system. Built in the right way, an agent can incrementally understand more data, with more advanced logic that completes more pieces of a process. It moves from assisting a person to orchestrating outcomes. Because real AI capability compounds, it creates systems that learns and evolve as the ability of the organisation to adopt innovation evolves in parallel. https://lnkd.in/dFujbBiU