Alberto Falossi

🚨 Fascinating AI paper alert: Luciano Floridi publishes "Hypersuasion - On AI’s Persuasive Power and How to Deal With It," and it's a must-read for everyone in AI. Quotes: "The relentless nature of AI’s hypersuasion, the magnitude of its scope, its availability, affordability, and degree of efficiency based on machine-generated content accurately tailored to individual users or consumers who spend increasing amounts of their lives onlife (both online and offline) in the infosphere overshadow its precursors, not only in terms of the depth of personalised influence but also for the potential scale of distribution and impact (Burtell and Woodside 2023). AI can and will be used, evermore commonly and successfully, to manipulate people’s views, preferences, choices, inclinations, likes and dislikes, hopes, and fears (...)" - "For the moment, one may be tempted to conclude that AI and, indeed, any persuasive technology is neutral (perhaps with the only exception of the erosion of autonomy, more on this presently). However, as I argued elsewhere (Floridi 2023), this would be a mistake. It is much better to interpret it as double-charged, in tension between evil and good uses. The forces pulling in the wrong direction may be as strong as those pulling in the right. Arguably, if some autonomy is eroded (but see below), this may be to the advantage of the individuals persuaded, their societies, or the environment (...)" - To conclude, AI has introduced a new and potent form of persuasion (hypersuasion). Preventing, minimising, and withstanding the negative impact of hypersuasion requires a comprehensive strategy, at the individual and societal level, that includes the protection of privacy, the development of fair competition among actors, transparent allocation of accountability, and good education and engagement. These and other factors – not highlighted in this article because they are more generally relevant, from responsible design to good social practices – require, as usual, ethical, and legal frameworks, regulatory oversight, implementation, and enforcement(...)."  ➡ Link to the full paper below. ➡ To stay up to date with the latest developments on AI policy, regulation, and excellent papers: subscribe to my weekly newsletter. #AI #AIregulation #AIpolicy #AIresearch #persuasion #AIpapers

151

10 Comments

𝗛𝗼𝘄 𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝘀𝗺𝗮𝗿𝘁 𝗴𝗹𝗮𝘀𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗼𝗻𝗹𝘆 $𝟮𝟬??? Meta launched 🦙 LLaMA3 to show the world what’s possible with open source LLMs. Why this is a game changer? Since LLaMA3 was released one month ago, more than 700 fine tuned versions have been uploaded into Hugging Face, we've seen extending the context window into 1M and co-lead the chatbot arena for english language. This weekend they hosted a 24h LLaMA3 hackathon with Cerebral Valley and more than 500 AI engineers. The results were pretty amazing and these demonstrates the real power and value of these models. A summary of the finalists: 𝗝𝗼𝗲𝗖𝗥𝗠: AI-first CRM that receives signals about customers, performs research about them, and automatically drafts outreach campaigns 🥉 𝗔𝗰𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝗔𝗯𝗹𝗮𝘁𝗶𝗼𝗻 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Research team that identified how to nullify activation layers in Llama3 responsible for censorship 🥇 𝗢𝗽𝗲𝗻𝗚𝗹𝗮𝘀𝘀 😎 : $20 budget smart glasses that can answer questions about what you’re looking at 𝗙𝗲𝘆𝗰𝗵𝗲𝗿: Fast AI avatars that speaks responses with lip-syncing. Context loaded by recent events from the Brave news API 𝗛𝗼𝘂𝗻𝗱: AI to help law enforcement crack down on human trafficking 𝗠𝗼𝗻𝗴𝗼𝗼𝘀𝗲 𝗠𝗶𝗻𝗲𝗿: Improved Python codegen made by providing llama 3 with up to date documentation 🥈 𝗗𝗲𝗯𝟴: AI agent debate arena where different models compete in Lincoln-Douglas debate. A third agent rates the quality of the debaters and scores them 𝗖𝗶𝘁𝘆 𝗛𝘂𝗯: Llama3 powered chat agent that lets residents ask questions about city laws and regulations. RAG’s over city documents to inform the answers 🔗 More info about OpenGlass: https://lnkd.in/dH_q7c9C 🔗 Github: https://lnkd.in/dQta8Uuv #ai #genai #llama

26

🚀 AiSalon Launches in #Turin, #Italy 🇮🇹 on Thursday, October 10th! We are thrilled to announce the launch of AI Salon's Turin chapter, a new hub of the global community bringing together AI founders and builders, investors, and partners to connect and collaborate! 🎉 As one of Italy’s leading cities in the emerging AI space—alongside #Milan and #Rome—Turin is poised to make a significant impact on the #AI landscape. This exciting launch is organized in partnership with Francesco Ronchi, Enrico Beraldo, and their team at Synesthesia, as well as #AISalonIitaly lead organizer Roberto Magnifico of Zest Group, who has played a crucial role in fostering collaboration among these top #ItalianAI cities. #AISalonTurin is also supported by Innovazione.AI and operated in partnership with #AISalonMilan to ensure a broader Italian AI network. Why Turin? Turin boasts a strong research ecosystem, thanks to prestigious institutions like the Politecnico di Torino, renowned for its contributions to #AIandML, and engineering. With a rich history of innovation in industries like #automotive, #robotics, and #advancedmanufacturing, #Turin is uniquely positioned to lead #GenerativeAI development efforts in Italy. The city is also supported by organizations like the Istituto Italiano di Tecnologia (IIT), contributing to its AI research efforts. With top-tier talent from local universities and strong government incentives, Turin’s industrial base, particularly in automotive (think Stellantis), provides a natural edge for AI applications in smart mobility and manufacturing. Event Details: 📅 Date: Thursday, October 10th 📍 Location: Talent Garden, Turin 🔗 Invitation Link: https://lnkd.in/gpfpcY-6 Agenda: 18:00 - Benvenuto e registrazione (Welcome and registration) 18:10 - Opening speech by Davide Mantovani 18:30 - AI Startup Pitches: ----Nefele AI - Your AI Partner for Success in English Language Certifications ----Nebuly - User Experience Platform for Large Language Models (LLMs) @Rafla - Robotics Inspiring the Future We can't wait to see the #AIcommunity in Turin grow and thrive! Don't miss this opportunity to network with some of the brightest minds in #AinItalyI and get inspired by innovative #AI startups. Join us in making #Turin a leading city for AI development in Italy and beyond! 🚀 #AiSalon #AISalonTurin #AICommunity #AIDevelopment #TechInnovation #AIStartups #TurinTech #AIItaly Ilya Kulyatin Fabio Blandino Andrea Danielli Giacomo Valentini Carlo Pagnoncelli Massimo Mauri Eric Larsen Riccardo Vanelli Emanuele Eco Stefano Eco Marco Gafforini Vittorio M. Aldo Comi Lorenzo Franchini Pietro De Nardis Maria Matloub Gianmarco Ferrante Elena Spaltini Filip Filiposki Giuseppe Mayer Gianluca Dettori Gianluca Vanni Michele Novelli Pasquale Scognamiglio Matteo Flora Enrico Sassoon Gianluca D'Agostino

35

3 Comments

Yes - You can't "add AI" to an enterprise application. You have to design an application for AI, from scratch. In the same way we couldn't just "add the internet" to client-server software. I’d also add, you can’t “add AI” to a business without job redesign and change management. You have to redesign how people work.

3

2 Comments

Visualising AI training cost (estimated) ... The AI Index collaborated with research firm Epoch AI to estimate AI model training costs based on cloud compute rental prices, which have been adjusted for inflation since 2017. Although not 100% accurate, these figures iterate the narrative that fine-tuning and RAG are the only cost-effective ways for the majority of companies who want to run and tune their own models based on open-source models. For more info, check out this article https://lnkd.in/eEzDGfyV

1

 #Llama 3🦙Meta just released new best in-class open models https://lnkd.in/eWsF9Vwb Those new models are real improvement on the previous Llama 2 versions in terms of performance across various benchmarks: ### Llama 3 8B vs. Llama 2 70B - GPQA (0-shot): Llama 3 8B achieves a score of 34.2, while Llama 2 70B scores 21.0 - HumanEval (0-shot): Llama 3 8B scores 62.2, a substantial increase compared to Llama 2 70B's score of 25.6 - GSM-8K (8-shot, CoT): The Llama 3 8B model outperforms with a score of 79.6, compared to Llama 2 70B's 57.5 - MATH (4-shot, CoT): Llama 3 8B scores 30.0, while Llama 2 70B scores 11.6 ### Llama 3 70B vs. Llama 2 70B - GPQA (0-shot): Llama 3 70B scores 39.5, compared to Llama 2 70B's 21.0 - HumanEval (0-shot): Llama 3 70B significantly outperforms with a score of 81.7, while Llama 2 70B scores 25.6 - GSM-8K (8-shot, CoT): Llama 3 70B achieves a score of 93.0, which is much higher than Llama 2 70B's 57.5 - MATH (4-shot, CoT): Llama 3 70B scores 50.4, more than quadrupling Llama 2 70B's score of 11.6 ### Additional Improvements in Llama 3 - Context Window Size: Llama 3 has increased the context window size from 4k to 8k tokens, which allows for better understanding and generation of longer text passages - Training Data: Llama 3 models have been trained on a dataset of 15 trillion tokens, compared to Llama 2's 2 trillion tokens, which contributes to their improved performance - Tokenizer: A new tokenizer in Llama 3 expands the vocabulary size to 128,256 tokens, up from 32K in Llama 2, allowing for more efficient text encoding and potentially stronger multilingual capabilities - Grouped-Query Attention (GQA): The 8B version of Llama 3 uses GQA, which is an efficient representation that should help with longer contexts - Training Techniques: Llama 3 Instruct has been optimized for dialogue applications and was trained on over 10 million human-annotated data samples with a combination of advanced training techniques.

6

2 Comments

Anthropic just released its new multimodal AI model: Claude 3.5 Sonnet. The benchmarks sure need a refresh as AI models keep improving at breakneck speed. But more importantly, each benchmark represents a real-world application, so it's crucial to steer clear of thin wrappers over any benchmarked capability. #claude #anthropic #chatgpt #gpt #openai

5

1 Comment

🔹#LiquidFoundationModels (#LFMs) are a new generation of #AIsystems developed by #LiquidAI. They are designed to be both highly efficient and powerful, using minimal system memory while delivering exceptional computing performance. 🔹Key Features of LFMs: Efficiency and Performance: LFMs are built to handle various types of sequential data, including text, audio, images, video, and signals, with minimal memory usage. 🔹Innovative Architecture: They are based on Liquid Neural Networks (LNNs), a newer architecture developed at MIT’s Computer Science and Artificial Intelligence Laboratory. LNNs use fewer neurons than traditional deep learning models by combining them with advanced mathematical formulations. 🔹Real-Time Adjustments: LFMs can perform real-time adjustments during inference without the heavy computational overheads typical of traditional large language models (LLMs). This allows them to handle up to 1 million tokens efficiently. Models in the #LFM Family: 🔹LFM-1B: A dense model with 1.3 billion parameters, designed for resource-constrained environments. 🔹LFM-3B: A model with 3.1 billion parameters, optimized for edge deployments such as mobile applications, robots, and drones. 🔹LFM-40B: A powerful “mixture of experts” model with 40.3 billion parameters, intended for complex tasks on cloud servers. 🔹Liquid AI’s LFMs are grounded in the principles of dynamical systems, numerical linear algebra, and signal processing, making them versatile for a wide range of applications. 🔹Liquid AI Inc., an MIT spinoff, has launched its first set of generative AI models called Liquid Foundation Models (LFMs). These models are built on a new architecture known as Liquid Neural Networks (LNNs), which differ significantly from traditional Generative Pre-trained Transformer (GPT) models like OpenAI’s GPT series and Google’s Gemini models. ➡️Key Points: 🔹Founders: The startup was founded by MIT researchers Ramin Hasani, Mathias Lechner, Alexander Amini, and Daniela Rus. 🔹Architecture: LFMs are based on LNNs, which use fewer neurons and advanced mathematical formulations to achieve high performance with greater efficiency. 🔹Performance: LFMs are designed to deliver performance on par with or superior to some of the best large language models available today. 🔹Mission: Liquid AI aims to create highly capable and efficient general-purpose models suitable for organizations of all sizes, from network edge deployments to enterprise-grade applications. 🔹Liquid AI’s LFMs are designed to be adaptable and efficient, capable of real-time adjustments during inference without significant computational overheads. This makes them suitable for a wide range of applications, including text, audio, images, video, and signals. 🔹 #liquid.ai #LiquidFoundationModels #Ai

23

11 Comments

I did Armand Ruiz's 15-day FREE course on #GenAI, and it's a must-do if you are interested in GenAI and you want to learn as much as you can! You can sign up here: https://lnkd.in/g3naYH7S

10

1 Comment

Introducing MemoRAG: A New Approach to Retrieval-Augmented Generation ... What is MemoRAG? MemoRAG stands out by employing a "dual-system architecture" that combines a lightweight long-range LLM for global memory and a more expressive LLM for generating final answers. 👉 How MemoRAG Works This innovative approach allows MemoRAG to generate draft answers that serve as clues for retrieval, significantly improving the handling of complex queries.  It does this by leveraging a memory module to construct a global memory across the entire database, enabling the generation of context-dependent clues that effectively link the knowledge to the precise information required for accurate answers.  👉 Key Insights 1. "Dual-System Architecture"      - MemoRAG employs a lightweight LLM for maintaining global memory and a heavier LLM for generating final answers.   - This architecture allows MemoRAG to handle complex queries that require contextual awareness. 2. "Memory Module"   - The memory module progressively compresses raw input tokens into a smaller set of memory tokens while preserving essential semantic information.   - It generates staging answers that serve as clues for retrieval, bridging the gap between the query and the relevant knowledge in the database. 3. "Retrieval Process"      - Based on the memory-generated clues, MemoRAG retrieves the most relevant information from the database to answer the query.   - The retrieved information is then used by the generation model to produce the final answer. 👉 In simple terms Imagine you have a large library with books on various topics. When you want to find information on a specific subject, you can either search through the books directly or use a librarian to help you find the relevant sections. In MemoRAG, the memory module acts like a knowledgeable librarian. It has a comprehensive understanding of the contents of all the books in the library (the database). When you ask a question, the memory module generates clues that help guide you to the specific sections of the books that contain the most relevant information to answer your query. This allows you to quickly find the information you need, rather than having to search through the entire library yourself. MemoRAG works by: 1. "Compressing" the information in the database into a compact memory representation 2. "Using" this memory to generate clues that point to the relevant information needed to answer a query 3. "Retrieving" the information based on the clues 4. "Generating" the final answer using the retrieved information The key idea is that by generating clues, MemoRAG can effectively link the query to the relevant knowledge in the database, even if the query is complex or the knowledge is unstructured. This allows it to handle a wide range of queries and tasks that traditional systems struggle with.

58

3 Comments

Highlighting: "The first Genie model generated its (2D) world at roughly one frame per second, a rate that was orders of magnitude slower than would be tolerably playable in real time. The full version of Genie 2 operates at something well below the real-time interactions implied by those flashy GIFs." ---- Google's DeepMind Genie 2 shows how badly mismatched Gen AI is for games and yet how desperate Gen AI is to get into games. Genie 2 illustrates the flaws of Gen AI rendering game worlds. Generative AI is shit for rendering games on multiple levels: (1) the math of rendering games (2) the math of streaming games (3) the memory and state change of games (4) the components of game rendering Rendering describes all the engine processes that are resolved each frame and across frames. GenAI isn't simulating any of them, nor can it. Gen AI is not an engine for: games, virtual worlds, simulations, nor digital twins. (1) Realtime games need to be rendered locally at between 8 and 33 milliseconds. This isn't compatible with cloud gpu datacenters. It requires cheap consumer hardware on the local device. A $200 GPU not an array of $20,000 cloud GPUs. How long till a cloud GPU array of hundreds or thousands can condense into one consumer GPU to run locally? Try half a century. Or longer. (2) Game streaming over cloud for 30 fps requires that the game is on a server that is under 300 miles away. I'm skipping the math because I don't have the space to elaborate. Now shift from a game streaming cloud server to instead a cloud GPU array of thousands of GPUs. Will these massive datacenters be within 60-300 miles from each user? Are we now going to have Cloud GPU datacenters everywhere around the Earth? Render time, network latency, and the speed of light, collectively kill cloud gpu game streaming for gen AI. (3) Gen AI models can generally hold a "world render" for seconds to a few minutes. Then they go of the rails. Genie 2 can handle under a minute and usually 5-20 seconds. Great game length. Try 40 hours of play instead. And 120GB in the prompt. (4) A game engine renders graphics, physics, audio, player character, player input & controls, characters & creatures (AI), user interface, world systems, resource economy, cash economy, inventory, items, props, & objects, network, and more. Let's see gen AI keep track of all that every frame. Now do multiplayer at under 60 millisecond ping for everyone. Gen AI is a mismatch for rendering games because of real physics, network latency, the cost of cloud GPU datacenters, the lack of memory in gen AI, and the slowing state of transformer models hitting a wall, also progress would need 3-5 decades. ---- ArsTechnica: Google’s Genie 2 “world model” reveal leaves more questions than answers. Long-term persistence, real-time interactions remain huge hurdles for AI worlds." (Kyle Orland) (Dec 6, 2024) (good article, please read) ArsTechnica: https://lnkd.in/gtK8F8WR #gameindustry #gamedev #ai #fakeai #genai

33

19 Comments

There seems to be a new trends in the area of UI based LLM agents. Recently we have seen the 'computer use' from Anthropic. Probably you have heard about Ferret UI from Apple. Now Microsoft released OmniParser for the same purpose While operating with the screen the LLM agent needs to have a very clear information about the placement of icons/information on the screen to execute certain action to achieve a goal. The idea behind these is to extract that information and provide this to the LLM agents for the better tool use. 𝗦𝗼 𝘄𝗵𝗮𝘁 𝗶𝘀 𝗢𝗺𝗻𝗶𝗣𝗮𝗿𝘀𝗲𝗿? OmniParser is a compact screen parsing module that converts UI screenshots into structured elements. This means it helps create agents that can interact with user interfaces using only visual input—no need for extra information like HTML or Android view hierarchies. 𝐈𝐭 𝐭𝐚𝐜𝐤𝐥𝐞𝐬 𝐭𝐰𝐨 𝐛𝐢𝐠 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐚𝐛𝐥𝐞 𝐈𝐜𝐨𝐧𝐬: It reliably detects clickable and actionable regions within a user interface. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐄𝐥𝐞𝐦𝐞𝐧𝐭 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜𝐬: It understands the function of various elements on the screen and accurately associates intended actions with the corresponding regions. ______________________________________ ♻ Repost if you like the post and find it useful 🔔 Follow me Naqqash Abbassi for regular updates and insights on Generative AI and my journey as a CTO in AI product development. I am no 'expert' just learning from the amazing people here each day

5

2 Comments

The budgeting… the cost of AI. Big news soon from BrainTrust Partners on how this all will change in 2025.

14

2 Comments

Yesterday, at "Nuclia RAG Academy", we hosted another session focusing on “AI Agents in RAG.” We explored how AI Agents shape and enhance the RAG landscape, and introduced our own ingestion AI Agents at Nuclia • The RAG-as-a-Service company. Exciting to see how these developments are pushing the boundaries of retrieval-augmented generation and improving how organizations leverage their data!

12

1 Comment

Despite its 671B parameters, DeepSeek-V3 achieves energy efficiency via its Mixture-of-Experts (MoE) architecture, activating only 37B parameters per token. Training required 2.788M H800 GPU hours, costing $5.57M, setting a new standard for cost-effective, scalable AI. Benchmarks on MMLU, MATH, and Chinese SimpleQA show DeepSeek-V3 outperforming GPT-4 and Claude 3.5 in coding and math tasks, with a 90.2% accuracy on MATH-500. DeepSeek-V3 leverages FP8 mixed precision with fine-grained quantization, reducing memory usage by 50% while maintaining accuracy through increased accumulation precision. Multi-token prediction accelerates inference by generating multiple tokens sequentially, supported by speculative decoding that handles error propagation through parallel verification. https://lnkd.in/dWR32WUp

1

In an astonishing leap towards AI democratisation, Ai2 is unveiling OLMo 2 - a cutting-edge open-source language model that is set to bridge the void between open and proprietary technologies. The novel model leds, consisting of both 7B and 13B parameter alternatives, exhibit a breakthrough in AI advancement, showing us a glimpse of how AI's future might look like. - What makes these models interesting is that they were trained on up to a whopping 5 trillion tokens. Their performance levels match or potentially surpass comparable end-to-end open solutions, signifying a massive stride in technical evolution and ability iteration. - Remarkably, these moves towards open source could unlock a new era of advancements and explorations to empower investigation. It also provides developers with the tools they need without resorting to technology hoarding. Intriguing times lie ahead. With these models, predictions and computations aren't just in reachable reach, but their improved performance is opening doors to possibilities we hadn't even fathomed until now. In my prediction: - Away from the constraints of proprietary environments, expect to see an unprecedented wave of innovation and new technology growth with increased collaboration and knowledge sharing. - This might also be the creation of a more levelled field in AI, with more room for participation and fewer barriers to entry. Ultimately leading to the acceleration of solutions to keep pace with the exponentially rising AI demand. The global excitement is palpable as we look forward to the insights and inventions these models will foster. Through the eyes of an AI enthusiast, the release of Ai2's OLMo 2 symbolizes a watershed moment in the history of Artificial Intelligence. Prepare to see innovation explode with these open-source torches lighting the path for more audacious explorations into the AI landscape! Let's embrace this era of shared innovation and growth with optimism. Ai2's OLMo 2 is not just an announcement – it's a milestone reminding us of the buoyant potential that AI offers for humanity's future. #Ai2 #OLMo2 #AI #OpenSource

2

🆕 Benchmarks 201 with Clémentine Fourrier of the Hugging Face Open LLM Leaderboard! Why Chat Arenas are **OVERRATED**: - they bias sycophancy/assertiveness over factuality - annotators are not representative of humanity - models are argmaxxing public arena data - not reproducible - can't give fine grained test of model capabilities Why you should STOP using LLMs as Judges: - Position bias (prefer first answers) - Self bias (prefer their own answers) - Length bias (prefer long answers) - Discrete bias (can't do continuous ranges) - Reproducibility (GPT4 closed source - prefer Seungone Kim's Prometheus or Beijing Academy of Artificial Intelligence(BAAI) JudgeLM) One of our best ones 🔥

30

1 Comment