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Forbes
27-07-2025
- Entertainment
- Forbes
Will AI Start To Figure Us Out? The Rise Of Intelligent Systems
Cheerful Asian gamer celebrating his success after winning in video game over PC in gaming club. We hear a lot about AI agents these days, next-gen engines that are able to, in limited ways, act like humans and tackle tasks. But what about intelligent systems? The intelligent system is something distinctly different from an agent, including in terms of the game theory that's applied. Where an individual AI agent might compete with a human worker, for example, the intelligent system will seek to interact with dozens, or hundreds, or thousands of humans, and in some way, build capacity based on those interactions. Defining The Intelligent System You can get a concise definition of an intelligent system from this resource at GeeksforGeeks: 'Intelligent systems in artificial intelligence (AI) represent a broad class of systems equipped with algorithms that can perform tasks typically requiring human intelligence. These systems span various domains from robotics to data analysis, playing a pivotal role in driving innovation across industries. Here, we delve into the essence of intelligent systems, their core components, applications, and the future trajectory of this transformative technology.' Authors provide the following list of prime aspects of intelligent systems: You start to get a picture of how these systems might work, at least in theory. The intelligent system is working with us every day, learning at a global level, and applying its knowledge base to a wider world than the typical AI agent would have access to. That's especially true in these early days of edge AI, where the agent can be installed on a non-connected, decentralized edge device. FEATURED | Frase ByForbes™ Unscramble The Anagram To Reveal The Phrase Pinpoint By Linkedin Guess The Category Queens By Linkedin Crown Each Region Crossclimb By Linkedin Unlock A Trivia Ladder Intelligent Systems in Gaming Mike Ambinder has been in the gaming industry for 20 years. He's an R&D partner at NEURAO, and has an evolved theory of how AI works in gaming and beyond. In a recent TED Talk, Ambinder broke down some of these key concepts. First, he contrasted games, with their interactivity, to other forms of digital experience: you listen to music, he pointed out, and you watch TV and movies, but you play games. That's different. By way of explanation, Ambinder broke things down into a linear process of a behavior that goes into a system and generates a response, and the cycle continues. He also mentioned a term, 'avoidances,' that represents, in his explication, the functions that are offered by a system. What do you get with an intelligent system built for interaction? I'll put these in bullet points: A System with Goals Ambinder further explained that an intelligent system has a systemic intention that is 'goal-directed.' In other words, the system has its own greater purpose. That's a hard concept to get your mind around, but in the age of semi-sentient AI, why not? An intelligent system can also have incredible, far-reaching powers of surveillance. 'You can record everything,' Ambinder noted. Intelligent Systems and Knowledge Generation Engines Ambinder also described processes whereby an intelligent system can make sense of the data that it collects in what he framed as an 'adaptive experience.' 'Instead of the player figuring out the game, the game can figure out the player,' he said. That seems to be at the heart of this concept: that as you play, you, as the player, are not the only thinking party. The game will be looking to figure you out, getting more information about who you are, what you want, and how you act, as you play. Use Cases for Intelligent Systems Ambinder enumerated these key areas of use: For example, he talked about treating PTSD, and how an intelligent system might be applied. When you think about how these gaming ideas apply to AI, you start to see the ability to put everything in play, and have an intelligent system working on us, figuring us out, to some particular end. Presumably, it's the owner of the system, probably a company or government agency, that's going to benefit. Let's make sure we talk about the rules for these evolved systems before we put them into implementation.
Forbes
09-07-2025
- Forbes
Why Is Retrieval Augmented Generation Or RAG Popular Today?
Too Many Questions. Pile of colorful paper notes with question marks. Closeup. There's an approach called Retrieval Augmented Generation in AI that's becoming a key way to help get targeted results for models. You could say that it's like chocolate and peanut butter – two great taste that taste great together. Or you could describe it in more technical ways. Essentially, Retrieval Augmented Generation is when you add information that the LLM should know as it applies its own training data and knowledge to a task. Over at GeeksforGeeks, experts explain it this way: 'In traditional LLMs, the model generates responses based solely on the data it was trained on, which may not include the most current information or specific details required for certain tasks. RAG addresses this limitation by incorporating a retrieval mechanism that allows the model to access external databases or documents in real-time.' Then there's a nice flow chart with 'data chunks' and other components, showing how this type of thing works. Think about how this would work in practice – for example, consider how you might give a chatbot a series of white papers about your business, and then ask it questions about your business model. Or on a personal level, if you want the AI to understand you better, you give it personal documents like diary recordings, or some of your past writing, in order to help it have a better knowledge of you as a person. In a very broad sense, you could say that RAG involves adding anything that wasn't in the original training set. That might be for reasons of nuance, or timing, or purpose, or it might just be to help target the result the way you want. Getting to the Point I really like this --- At Learn By Building AI, Bill Chambers is explaining that there's a simple approach to RAG. First, he contrasts it with this, which he says he found at Facebook: 'Building a model that researches and contextualizes is more challenging, but it's essential for future advancements. We recently made substantial progress in this realm with our Retrieval Augmented Generation (RAG) architecture, an end-to-end differentiable model that combines an information retrieval component (Facebook AI's dense-passage retrieval system) with a seq2seq generator (our Bidirectional and Auto-Regressive Transformers [BART]Good grief… Chambers then provides a neat little drawing that shows a 'corpus of documents' getting connected to an LLM model through user input. That made sense to me: RAG means adding specific information resources! Now, there are technical details, for sure, but I thought the tutorial did a great job overall of breaking this down, so that's another resource for anyone who wants to learn more about how it actually works. Using RAG I also wanted to reference a tech talk by Soundararajan Srinivasan, Sr. Director of AI Program at Microsoft, and a colleague, Reshmi Ghosh, a Microsoft Sr. Applied Scientist, at Imagination in Action in April, where they talked about practical use of RAG. Using terms like 'knowledge store,' 'vector database,' 'orchestrator,' and 'meta prompt,' Srinivasan went over how these systems can work, saying they help us to understand the limitations of AI in its context. And 'context' is also an important term because, as he describes, a larger context window adds capability, potentially with a lower memory footprint. Here are some other reasons the presenters talked about using RAG: Ghosh then talked about how we understand whether a model chooses to use the RAG information in its processing. 'You have all these different contexts that are sent with the query to tell the model that, 'hey, here's the external knowledge that you may or may not know,'' she said. 'When we are designing systems with large language models, also small language models like llama and phi, we are essentially finding that if you can send in context by compartmentalizing the data points and not fine-tuning it, you are still going to get factual queries answered in a qualitative manner of accuracy.' Ghosh also mentioned multi-modality. 'You can essentially have databases that have images, that have voice notes, that have sounds or music notes of any kind, and you can still build AI applications around it with the same kind of gains, because now you know that the models are tending towards utilizing RAG context and relying less on the internal memory, and this is also opening up new doors for all the new frameworks that are being discussed.' This, she added, is useful with protocols like MCP (Model Context Protocol) and A2A (Agent to Agent systems). That's important as we move into an era of new interfaces, where we're not just limited to typing to our AI partners. We have voice now, and more is coming in the future, with image and video generation that will be vibrant enough to replace text-based models. Some would say we're entering a world of dreams, where so much is possible that was previously impossible. RAG might be one component of making sure that we can steer the bus and deliver the kinds of results that we're looking for. It helps with what you might categorize as 'convergence' for a digital intelligence system. So keep an eye on these kinds of methodologies as we continue to design more sophisticated AI tools and resources.
Daily Mirror
29-05-2025
- Entertainment
- Daily Mirror
Mind-boggling brainteaser will 'send you to hell' if you answer it wrong
A brainteaser has been doing the rounds on social media, with the aim of challenging people's problem-solving skills. But some have admitted defeat and are seeking help to find the solution A mind-boggling brainteaser that determines who's destined for 'heaven or hell' based on their response has left many scratching their heads. As enthusiasts attempted to solve it, a significant number admitted defeat and sought assistance for the tricky conundrum. The puzzle states: "Can you solve the heaven and hell guard problem?" The enigma unfolds as follows: "You have died and are in limbo. There are two doors - one leads to heaven, and the other leads to hell. One guard always tells the truth, and one guard always lies. You don't know which guard is the truthteller or the liar." Adding to the complexity, it's also a mystery whether the truthful guard is stationed at the gateway to paradise or perdition, and likewise for the deceptive one. Challengers must work out: "What one question can you ask a guard to find out which door leads to heaven?" Several clever individuals took to the internet to share their solutions, with one proposing: "Why not ask what is 2+2, the lying guard can't answer 4 and the truthful guard will say 4?" Another chimed in: "Not the correct answer but ask both guards a question you know the answer to and whichever one gets it wrong is the liar, and therefore the one guarding hell because lying is a sin". A different user suggested: "Why not just hold up 3 fingers and ask one of them how many fingers you're holding up." While another concluded: "Just touch the handle, see which one is hot, and go through that one." Sharing the correct answer, a statement on Geeks for Geeks reads: "Solution: 'If I were to ask the other gatekeeper which gate leads to heaven, what would they say?'" "If you ask the truthful gatekeeper, they would truthfully tell you what the lying gatekeeper would say. So, they would point you towards the gate to hell, because they know the lying gatekeeper would falsely point to hell as well. "If you ask the lying gatekeeper, they would lie about what the truthful gatekeeper would say. Therefore, they would also point you towards the gate to hell, because they know the truthful gatekeeper would truthfully point to heaven. "In both scenarios, regardless of who you ask, you'll receive the same answer: the gate to hell. Hence, it would be best if you chose the other gate, which is the gate to heaven."
The Hindu
28-04-2025
- Science
- The Hindu
IMPACTX–2025 hackathon at MITS inspires tech solutions for global challenges
A 24-hour national level inter-institutional Hackathon, IMPACTX–2025 was organised by the Department of Computer Science and Engineering (Data Science) at Madanapalle Institute of Technology & Science (MITS), Madanapalle, in collaboration with Bengaluru-based GeeksforGeeks (GfG). The 24-hour challenge brought together a total of 246 participants, who formed 73 competitive teams. They focused on key areas such as artificial intelligence & machine learning, edtech, healthcare, agrotech, smart cities, sustainability, environment, and student innovation. MITS Principal Dr. C. Yuvaraj said: 'The objective of the hackathon is to inspire participants to create technology-driven solutions addressing the United Nations Sustainable Development Goals (SDGs), promoting sustainable, scalable, and socially impactful innovations. The event featured mentor interactions, technical guidance, and cultural activities. Director of Learning at GeeksforGeeks Abhishek Kelania was the chief guest. Mr. Abhishek said: 'Hackathons foster collaboration, innovation, and networking.'
