Page 28 – Advance Aii

3 stages of an agile process...

In our previous blog post, we’ve discussed the importance of content agility. But how to arrive at agile content? The road towards it is rather agile in itself. Today, we will walk you through the three major stages of an agile process: production, management, and distribution. 1. Production: creating individual content units Authors tend to write books from A to Z. Even if they work on chapters in a non-chronological order, the process is linear — they deliver the book after they’ve written all pages. When creating agile content, it’s crucial to adopt a brand-new perspective. Content agility implies that you should consider each piece of content as a standalone learning object or content unit. This modular view will allow you to divide longer works into separate parts, which you can mix up and re-use in accordance with students’ needs — at some point, chapter two from book A and chapter four from book B might be the combination an individual student is looking for. This approach affects the production stage. Creating content now resembles building a software product in an agile manner: you write and publish content in iterations, delivering individual pages or chapters. You don’t wait until a...

In artificial intelligence, machine learning, decision intelligence, statistics, and science, we use the word “decision” to mean a lot of things. Let’s tease out some distinctions: Decision Type Name Question answered Primary information Source Typical success criterion Typical method A ML classification “Decisions That”: “What is this picture?” “What disease does this person have?” “Is this a cat?” Data True positive, true negative Supervised learning B ML regression “Decision about a prediction”: “What will be the Covid-19 incidence next month?” “What will be this security’s price next month?” Data Mean squared error, R^2 Supervised learning C Decision intelligence (forward model) Decision to take an action, action-to-outcome mapping: “If I take this action, in this context, what will be the outcome?” Humans (causal model), ML, economics, complex systems models, much more (causal model links) Correct mapping of actions to outcomes Complex systems simulation D Decision intelligence (optimization) “Given my set of possible actions, what is the best set of actions to take to meet my goals” (same as above) Best set of decisions to reach multi-objective outcomes Complex systems simulation, optimization E Reinforcement learning Policy creation: “For each state that I can be in, what is the best next action?” (policy)...

AI is advancing at breakneck speed, but trust, accountability, and oversight still lag behind. As artificial intelligence systems are increasingly used to make decisions that impact jobs, health, credit, education, and civil rights, a growing chorus of leaders is calling for responsible AI governance that keeps pace with innovation without stifling it. The central question: How do we move fast and build trust? “If we’re using AI to make choices that affect people like their access to services, jobs, or fair treatment then we need to be clear about how it works and who’s responsible when it doesn’t,” says Sanjay Mood. “Maybe the answer isn’t one big rule for everything, but smart checks based on how risky the system is.” Below, we’ve synthesized key insights from industry leaders, researchers, and AI governance experts on how to responsibly scale AI while safeguarding public trust. Not One Rule—But Many Smart Ones Blanket regulations won’t work. Instead, experts advocate for risk-tiered frameworks that apply stronger guardrails to higher-impact AI systems. As Mohammad Syed explains, “Tailoring oversight to potential harm helps regulation adapt to rapid tech changes.” The EU’s AI Act, Canada’s AIDA, and China’s sector-specific enforcement models all point toward a future of...

Exciting news ahead! With an incredible surge of enthusiasm, we’re rolling out an exclusive Online Only option for this year’s Chatbot Conference, kicking things off with an absolutely phenomenal launch! Today kicks off an incredible flash sale, showcasing a limited selection of tickets—only 18 passes available at this unbeatable price. Prepare to grab this incredible opportunity with a massive 50% off your exclusive Online Only Virtual Pass! Exclusive Offer: Take advantage of a fantastic 30% discount on all in-person ticket options! Limited Opportunity: Just 18 Tickets Up for Grabs at This Rate Grab this chance to plunge into the innovative realm of AI and chatbot technology without emptying your wallet. Secure your spot now and join an exhilarating community of trailblazers in the AI arena! See you there!

Nvidia’s VD Jensen Huang har nyligen framfört en viktig uppmaning till Sverige om att utveckla en ”nationell AI” för att säkerställa kontrollen över sin teknologiska framtid. Han betonar att Sverige måste ta kontrollen över sin intelligens istället för att vara beroende av stora aktörer som USA och Kina. Jensen Huang har uttryckt vikten av att varje land bör utveckla sina egna ”nationella AI-system”. Detta koncept syftar till att länder ska ha kontroll över den intelligens och de data som produceras inom deras gränser. Enligt Huang är det avgörande för nationer att säkerställa att de har lämpliga verktyg och resurser för att utveckla ”sovereign AI” vilket innebär att AI-systemen ska vara självständiga och skräddarsydda för nationella behov och säkerhetskrav. Vilka risker är förknippade med att överlita på utländsk AI-teknologi? Det finns flera risker kopplade till att överlita på utländsk AI-teknologi, särskilt i en tid där utvecklingen av artificiell intelligens accelererar. En central risk är att sådana teknologier om de utformas med fel intentioner kan användas på ett skadligt sätt. Det handlar om möjligheten att AI-systemen kan implementeras för att undertrycka rättigheter eller diskriminera mot specifika befolkningsgrupper, vilket har visat sig vara ett problem i flera fall som exempelvis det svenska Försäkringskassans...

When we began studying jailbreak evaluations, we found a fascinating paper claiming that you could jailbreak frontier LLMs simply by translating forbidden prompts into obscure languages. Excited by this result, we attempted to reproduce it and found something unexpected.

Podcast: Play in new window | Download | Embed Subscribe: In this episode of Singularity.FM, I sit down with renowned author and philosopher Donald J. Robertson to explore his latest book, How to Think Like Socrates: Ancient Philosophy as a Way of Life in the Modern World. As we navigate the crossroads of ancient wisdom and modern challenges, Donald shares timeless insights from Socrates that remain profoundly relevant in today’s age of rapid technological transformation and AI. We dive into the art of critical thinking, the value of questioning assumptions, and the ethical considerations of integrating philosophy with cutting-edge innovation. About halfway through our conversation, Donald J. Robertson turns the tables and starts asking me Socratic questions, sparking a dynamic and thought-provoking exchange that takes the discussion to new depths. Together, we tackle big questions: What can Socrates teach us about living wisely in a world dominated by AI? How do we balance our pursuit of knowledge with the necessity of wisdom? And how do ancient philosophical techniques help us confront the uncertainties of our time? Whether you’re a fan of philosophy, curious about the future, or looking to deepen your understanding of critical thinking in the digital age, this...

It is well-established that Vision Transformers (ViTs) can outperform convolutional neural networks (CNNs), such as ResNets in image recognition. But what are the factors that cause ViTs’ superior performance? To answer this, we investigate the learned representations of pretrained models. In this article, we will explore various topics based on high-impact computer vision papers: The texture-shape cue conflict and the issues that come with supervised training on ImageNet. Several ways to learn robust and meaningful visual representations, like self-supervision and natural language supervision. The robustness of ViTs vs CNNs, as well as highlight the intriguing properties that emerge from trained ViTs. Adversarial Attacks are well-known experiments that help us gain insight into the workings of a classification network. They are designed to fool neural networks by leveraging their gradients (Goodfellow et al. ). Instead of minimizing the loss by altering the weights, an adversarial perturbation changes the inputs to maximize the loss based on the computed gradients. Let’s look at the adversarial perturbations computed for a ViT and a ResNet model. Fig. 1: ViTs and ResNets process their inputs very differently. Source As depicted in the above figure, the adversarial perturbations are qualitatively very different. Even though both models may...

Your inbox is overflowing, and dozens, if not hundreds, of customer inquiries are pouring in. This scenario is especially noticeable during the peak of Black Friday sales or the holiday season rush. Each message represents a customer waiting for a response, and the clock is ticking. In these high-stakes moments, the pressure to respond promptly is immense. Delays can not only cause frustration but also risk losing customers to competitors who are just a click away. So, how do you manage this deluge of inquiries effectively? How do you ensure each customer feels well-served, even when your team is stretched? The solution lies in embracing the power of Artificial Intelligence. AI is transforming the customer service landscape, offering innovative ways to respond faster and smarter. In this article, we’ll explore some of the most effective AI solutions that can help your business navigate the tidal wave of customer inquiries not only during busy periods but every day, saving your team masses of time. Unresponsiveness & Long Response Times: Two Prevalent Business Challenges Responding promptly to customer inquiries is universally acknowledged as crucial in business. Yet, there’s a stark contrast between this recognition and the reality reflected in the research. The...

You’re not short on tools. Or models. Or frameworks. What you’re short on is a principled way to use them — at scale. Building effective generative AI workflows, especially agentic ones, means navigating a combinatorial explosion of choices. Every new retriever, prompt strategy, text splitter, embedding model, or synthesizing LLM multiplies the space of possible workflows, resulting in a search space with over 10²³ possible configurations. Trial-and-error doesn’t scale. And model-level benchmarks don’t reflect how components behave when stitched into full systems. That’s why we built syftr — an open source framework for automatically identifying Pareto-optimal workflows across accuracy, cost, and latency constraints. The complexity behind generative AI workflows To illustrate how quickly complexity compounds, consider even a relatively simple RAG pipeline like the one shown in Figure 1. Each component—retriever, prompt strategy, embedding model, text splitter, synthesizing LLM—requires careful selection and tuning. And beyond those decisions, there’s an expanding landscape of end-to-end workflow strategies, from single-agent workflows like ReAct and LATS to multi-agent workflows like CaptainAgent and Magentic-One. Figure 1. Even a simple AI workflow requires selecting and testing multiple components and hyperparameters. What’s missing is a scalable, principled way to explore this configuration space. That’s where syftr comes...