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Diagram of Thought (DoT): An AI Framework that Models Iterative Reasoning in Large Language Models (LLMs) as the Construction of a Directed Acyclic Graph (DAG) within a Single Model https://flip.it/Mh_Mhu

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Interesting insight from Santosh Rao on Marketplace by APM My research talks about metrics Tech/SaaS Leaders should measure depending on your scale https://lnkd.in/gGfBWgn5

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AI pricing is hard. Customers (esp enterprise) need to do annual budgeting. They won’t sign up for “who knows what it will cost???” But the AI provider (whether OpenAI or something built on it) have highly variable costs based on usage. In addition, if AI increases productivity, it may **reduce the number of user accounts a company buys**. As you provide more value, you (as the AI provider) can see less revenue. No one has entirely solved this.

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Google DeepMind's FACTS Grounding is a new benchmark to check how well large language models stick to real facts instead of “hallucinating.” Another step on the path to more trustworthy #AI. Article: https://lnkd.in/eBjndhfk The full paper: https://lnkd.in/eA8vtPw3

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Explore AI agents and their capabilities, from what they are to real-world applications in this new virtual, live training from Ai+–Free with an Ai+ paid subscription. Signup Now. https://hubs.li/Q02BNkrQ0

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Generative AI: miracle worker or sophisticated guesser? While AI can now write, code, and create, it's essentially still a prediction machine. This revelation challenges how we view and utilize AI in business. What does this mean for your AI strategy? Let's discuss. #ArtificialIntelligence #BusinessStrategy #TechInnovation #AILimitations #FutureOfWork

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AI is navigating a very tricky transition from "novel" to "useful" at the moment, and while the wild creative swings and the (now-maligned) wearables are capturing the attention, there's a deeper discussion happening about what it means to get generative models ready for reliable, robust, enterprise-grade deployment. More in our breakdown of this week's top #fashiontechnology news: interli.net/4aYFUhn

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What do Aravind Srinivas (CEO of Perplexity.ai), Yann Lecun (Chief AI scientist, Meta), Steve Case (founder of AOL), Dinesh Maheshwari (CTO Groq), and Ramin Hasani (CEO Liquid AI) have in common? They all participated in the Imagination in Action summit at Massachusetts Institute of Technology (MIT) yesterday. and this is just a short list of the amazing speakers and participants at this amazing event. Here are a few key takeaways: 1. Human relationships, human collaboration, and human innovation are going to be even more important in the age of AI. It was important for me to start with that, as many people believe AI is taking over the world and that is not the case. which leads me to: 2. The smartest people in the AI world today are concerned about the potential negative impacts of AI, and are investing significant resources to reduce those risks. That being said - they are running forward at an astonishing speed, so I am not sure what they are doing is enough. 3. Several breakthroughs are required in order to allow us to get to AGI, and to make this technology scalable to every process and every device. the current compute requirements are just to high. companies like Groq with their LPU innovative chip design that runs 10-1000 times more efficient than NVIDIA JPUs, and companies like Liquid AI with their innovative AI architecture, are spearheading some of these efforts. 4. There is a huge number of REALLY SMART people who are working on using AI to solve some of the biggest problems of the human race and our planet, such as curing cancer, fighting viruses, supporting the elderly, and much more. It was truly inspiring to see. 5. The entrepreneurial spirit is alive and well. over 100 startup founders participated in the event. 80 of them came to give a 90 seconds pitch (yes, 90 seconds). 6. Future models will have significantly less hallucinations, they would be able to understand our world (which they do not today), and hence, will be able to reason and plan. 7. While the number one concern about AI at the enterprise level is Data Security, there are more and more solutions that allow you to benefit from AI benefits while maintaining 100% control over your data. 8. The open source world of AI, is a critical component to the successful future of wide AI implementation 9. The future of code writing and interacting with computers is going to change dramatically. Natural language, voice, and vision, will allow us to program and use computers in a much more intuitive way. Computers will adapt to humans and not humans to computers 10. The conversations outside of the 4 presentation halls were as fascinating as the speakers and I met some amazing people, and I am grateful for meeting every one of them 🙏 BTW - I am still in Boston today (Friday the 19th), so if anyone wants to meet and talk, show me cool things in Boston or MIT, or geek about AI, or just chat, drop me a DM) Thank you John Werner for putting this amazing event together!

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AI-powered knowledge management is revolutionizing productivity, as highlighted in Sirjad Parakkat's insightful article. It's essential reading for understanding AI's potential in transforming traditional systems. Our "Tailored Intelligence" consultancy and tech services go further by ensuring that private data is not used to train public AI tools and keeping human experts in the loop. We combine AI's power with human insight to protect your proprietary knowledge and enhance decision-making. Discover how we take AI-driven knowledge management to the next level. #tailoredintelligence #ai #artificialintelligence #digitaltransformation #aiinsociety #impactai #innovation #visionaires #business #wikiexpert #solutions

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A take on how an LLM’s default performance can be improved by using Agentic Workflows – an iterative design to elicit better responses from an LLM. See Andrew’s talk on this topic at Sequoia Capital: https://lnkd.in/ePFdGeJ3 It was interesting that by using Agentic Workflow, GPT 3.5 produced far better responses than GPT 4.0 and potentially GPT 5.0 - see the above video from 3:00 to check out. Thoughts?

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The Economist's article “Large language models are getting bigger and better” on April 17, 2024 on the challenges of scaling large language models (LLMs) rightly identifies the limitations of current AI architectures. While the relentless pursuit of larger LLMs suggests a future of limitless progress, a deeper dive reveals significant flaws. The "scaling hypothesis"—that more data and power equals human-like intelligence—misses crucial aspects. LLMs face serious hurdles: the dwindling supply of high-quality data, the inefficiency of scaling, and the simple fact that size doesn't equate to cognitive complexity. Here, neurosymbolic AI presents a promising alternative, blending the data-handling prowess of neural networks with the logical precision of symbolic AI. This combination not only promises enhanced efficiency and structured reasoning but also aligns closer with human cognitive processes. Neurosymbolic AI excels in areas where traditional models falter, offering a path to more sustainable, intelligent systems capable of real-world applications like airplane design and financial risk assessment. It’s time for the AI community to pivot towards technologies that not only scale but think smarter. https://loom.ly/96rDCD0

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Excellent article! And just at the right time, when our news product organization has been developing more and more AI-powered products such as personalization or GenAI features. Understanding the non-deterministic nature in particular of GenAI is crucial. Risks must be carefully managed. Often you cannot solve everything and for sure you cannot test every behavior, not even most. You rather need to balance multiple risks, find ways to deal with quality issues and build tools and processes to tweak your AI solution in production and handle occurring problems quickly.

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Discover how AI agents, proprietary data, and innovative technology are transforming software development, business management, and human creativity in this insightful conversation with Yasir Drabu. #ai #software #aiagents #business https://lnkd.in/gsqauCYf

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LLMs: The Power Behind Multi-Agent Collaboration Large Language Models (LLMs) are transforming the landscape of AI, ushering in a new era of intelligent collaboration and problem-solving. By integrating LLMs into multi-agent architectures, we unlock unparalleled potential. Agents, utilizing LLMs, are like smart applications that dynamically generate inputs for various functions, leveraging natural language for enhanced user interaction and task completion. In a Multi-Agent Architecture, independent entities collaborate towards a common objective, tackling intricate tasks collectively. LLMs serve as the "brains" of these agents, facilitating effective communication, reasoning, and understanding of natural language. From customer service to finance and healthcare, multi-agent systems powered by LLMs find applications in handling inquiries, analyzing trends, optimizing investments, and aiding in healthcare services. The future holds promise for more sophisticated multi-agent systems as LLMs evolve, revolutionizing work processes for increased efficiency and innovation. Excited about the potential of multi-agent systems with LLMs? Let's connect to explore how this technology can elevate your business and pave the way for intelligent collaboration. #AI #LLMs #MultiAgentSystems #Collaboration #Innovation

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2024 Backward Pass: The Definitive Guide to AI in 2024 Authored by: Kelvin Mu , Principal, Translink Capital #ArtificialIntelligence (#AI) is set to play a crucial role in shaping the #business landscape in 2024. As AI #technologies continue to mature, they are driving significant #transformation across industries, presenting both unprecedented opportunities and notable #risks. This report examines the key trends and challenges that businesses will face in integrating AI into their operations, with a particular emphasis on #sectors such as #finance, #healthcare, and #consumerservices. AI’s most notable advancement in 2024 is the increased use of #generativeAI and machine learning models. These technologies are enabling companies to enhance #automation, improve #customerengagement, and make data-driven decisions with greater precision. In the #financialsector, AI’s ability to process large #datasets is revolutionizing #investmentstrategies, #creditriskassessment, and #fraud detection, creating opportunities for more efficient operations and tailored #financialproducts. Despite these advancements, AI adoption brings challenges related to data privacy, security, and ethical concerns. The complexity of AI models demands transparency and accountability, especially in high-stakes environments like #finance, where algorithmic trading and credit scoring depend on AI-driven decisions. Moreover, issues such as algorithmic bias need to be addressed to prevent detrimental outcomes for both businesses and consumers. With increasing regulatory attention, companies must establish robust governance frameworks to ensure AI systems are used responsibly and in compliance with evolving legal standards. In conclusion, 2024 will be a defining year for AI’s integration into business practices. While AI presents immense potential for driving innovation, its challenges cannot be overlooked. To succeed, organizations must strike a balance between leveraging AI’s capabilities and addressing its risks. For C-level executives, the key takeaway is that AI’s transformative power requires careful management, ensuring it is deployed ethically, transparently, and responsibly. Organizations that approach AI with this mindset will be well-positioned to achieve #sustainablegrowth and a competitive edge in the rapidly evolving #digitaleconomy.

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As we close out #2024, it’s incredible to look back at some of the greatest #GenerativeAI posts shared here by the top GenAi Experts. 🚀 Here are the top 9 posts from some of the brightest minds in the field: Sebastian Raschka, PhD Chip Huyen Tom Yeh Pascal Biese Denis Rothman Peter Gostev Pau Chang Liang Andrej Karpathy These experts have not only expanded our understanding of what’s possible with Generative AI but have also inspired countless innovations in the space. 🔗 Dive into their posts for some great year-end reading, and share your thoughts on which ones inspired you the most! #GenerativeAI #AIThoughtLeaders #2024Recap #ArtificialIntelligence #AIInnovation

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Great video case study of Neuralift AI using Weights & Biases Weave to deploy a real world GenAI application https://lnkd.in/gEU5hqPh

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