Understanding AI's Realistic and Perceived Capabilities

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.

What most people think AI looks like vs. what AI actually is 🔹 Many people have misconceptions about AI, often influenced by media portrayals and popular beliefs! Let's talk about some common myths and the reality of AI: 1️⃣ Data:  What most people think: They believe that AI is primarily about collecting and analyzing massive amounts of data. ✅ What AI actually is: Data is indeed crucial for AI, but it's not just about quantity. Quality, relevance, and diversity of data, along with effective data management practices, are essential for accurate and meaningful AI-driven insights 2️⃣ Data Science: What most people think: They perceive AI as a field dominated solely by data scientists who crunch numbers and make predictions ✅ What AI actually is: Data science is vital to AI, but it's not the sole focus. AI encompasses a range of disciplines, including machine learning, natural language processing, and computer vision, working together to extract value from data 3️⃣ Value: What most people think: They expect AI to deliver tangible business value and maximize profits effortlessly instantly ✅ What AI actually is: While AI has the potential to generate significant value, it requires a strategic approach and careful implementation. Realizing the benefits of AI often involves incremental progress, continuous improvement, and aligning AI initiatives with specific goals 4️⃣ Data Engineering:  What most people think: They consider data engineering as a secondary concern compared to developing AI models ✅ What AI actually is: Data engineering plays a critical role in the AI journey. It involves collecting, storing, and preprocessing data, ensuring its quality and accessibility. Without proper data engineering practices, AI models may suffer from poor performance or biases 5️⃣ Modeling and Operationalizing:  What most people think: They see building AI models as the ultimate goal, often overlooking the challenges of deployment ✅ What AI actually is: Model development is just one aspect. Operationalizing AI models in real-world scenarios involves integrating them into existing systems, monitoring their performance, and ensuring ongoing maintenance and updates. To truly understand the potential of AI, it's crucial for most people to move beyond misconceptions and buzzwords. By recognizing the importance of data, data science, value generation, data engineering, modeling, and operationalizing, individuals can gain a deeper understanding of AI's true capabilities!

We often hear AI skeptics argue that Large Language Models such as ChatGPT, are merely “statistical inference machines” with no practical value. Stochastic parrots, they proclaim! A recent groundbreaking case study published in Harvard Business Review challenges this perspective by demonstrating the significant economic value that AI can bring when utilized strategically (link below). The study involved a unique experiment that compared the efficiency of AI to human intelligence in the field of business strategy. A team of INSEAD MBA students was pitted against an AI equipped with the Blue Ocean strategic framework. Their task was to develop a value proposition for a new business concept - a bagel bakery in Paris. Equipped with traditional tools and methodologies, the MBA students undertook a week-long strategic planning project. Their process involved extensive individual research, time-consuming meetings, in-depth value curve discussions, and thorough ecosystem mapping. Ultimately, their efforts culminated in a detailed PowerPoint presentation that required an estimated 150 collective man-hours to complete. In stark contrast, the AI, armed with the Blue Ocean framework and custom programming, generated a similar strategy in 60 minutes. The AI's suggestions were not only comparable to the MBA team's, but they also demonstrated originality. For example, the AI proposed transient product offerings inspired by fashion industry trends, initially deemed impractical but later recognized as innovative. This experiment highlights the amazing capabilities of AI in strategic thinking. The AI's performance proves its ability to generate original and efficient ideas, challenge conventional wisdom, and offer novel solutions. Skepticism surrounding the practical value of AI often stems from a misunderstanding of its evolving capabilities. As demonstrated by this experiment, AI has the potential to significantly enhance strategic planning by providing insights that human bias may overlook. For AI skeptics, this study highlights not just AI's strategic competence but also its ability to generate immense and tangible economic value in fields typically assumed to require human creativity and analysis. We are in the first days of this technology and based on what we’re already seeing, we would be wise to prepare ourselves for the coming wave rather than be caught off guard when it arrives. https://lnkd.in/gZAG2VEe #ai #artificialintelligence

In recent months, I have had the pleasure of contributing to the International Scientific Report on the Safety of Advanced AI, a project of the UK government's Department for Science, Innovation and Technology (DSIT) and AI Safety Institute. This report sets out an up-to-date, science-based understanding of the safety of advanced AI systems. The independent, international, and inclusive report is a landmark moment of international collaboration. It marks the first time the international community has come together to supports efforts to build a shared scientific and evidence-based understanding of frontier AI risks. The intention to create such a report was announced at the AI Safety Summit in November 2023 This interim report is published ahead of the AI Seoul Summit next week. The final report will publish before the AI Action Summit in France. The interim report restricts its focus to a summary of the evidence on general-purpose AI, which have advanced rapidly in recent years. The report synthesizes the evidence base on the capabilities of, and risks from, general-purpose AI and evaluates technical methods for assessing and mitigating them. Key report takeaways include: 1️⃣ General-purpose AI can be used to advance the public interest, leading to enhanced wellbeing, prosperity, and scientific discoveries. 2️⃣ According to many metrics, the capabilities of general-purpose AI are advancing rapidly. Whether there has been significant progress on fundamental challenges such as causal reasoning is debated among researchers. 3️⃣ Experts disagree on the expected pace of future progress of general-purpose AI capabilities, variously supporting the possibility of slow, rapid, or extremely rapid progress. 4️⃣ There is limited understanding of the capabilities and inner workings of general-purpose AI systems. Improving our understanding should be a priority. 5️⃣ Like all powerful technologies, current and future general-purpose AI can be used to cause harm. For example, malicious actors can use AI for large-scale disinformation and influence operations, fraud, and scams. 6️⃣ Malfunctioning general-purpose AI can also cause harm, for instance through biassed decisions with respect to protected characteristics like race, gender, culture, age, and disability. 7️⃣ Future advances in general-purpose AI could pose systemic risks, including labour market disruption, and economic power inequalities. Experts have different views on the risk of humanity losing control over AI in a way that could result in catastrophic outcomes. 8️⃣ Several technical methods (including benchmarking, red-teaming and auditing training data) can help to mitigate risks, though all current methods have limitations, and improvements are required. 9️⃣ The future of AI is uncertain, with a wide range of scenarios appearing possible. The decisions of societies and governments will significantly impact its future. #ResponsibleAI #GenerativeAI #ArtificialIntelligence #AI #AISafety