Ali Abbasi

Rethinking AI: A New Digital Species? (Must-Watch TED Talk) Shifting our perspective on Artificial Intelligence (AI) could be game-changing. AI expert Mustafa Suleyman, in a thought-provoking TED Talk, proposes a novel way of understanding AI – not just as a tool, but as a new digital species altogether. This reframing opens doors to a more comprehensive grasp of AI's potential impact on humanity. Suleyman's TED Talk delves into the exciting possibilities AI holds for solving some of our most pressing global challenges. From tackling climate change to revolutionizing healthcare, AI's potential for positive change is undeniable. However, the TED Talk also underscores the critical need for responsible AI development, emphasizing the importance of ethical considerations to ensure AI's integration into society is beneficial for all. Suleyman's perspective offers a refreshing take on AI, prompting us to reconsider how we view and interact with this developing technology. Share your thoughts in the comments below! Let's spark a conversation about the future of AI. #AI #ArtificialIntelligence #TEDTalk #FutureofTechnology #Technology #Innovation #ClimateChange #Healthcare #MachineLearning #DigitalTransformation #EthicsInAI https://lnkd.in/euuqN_WG

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The paper "Attention Is All You Need" presents the Transformer model, a novel architecture for sequence transduction based entirely on attention mechanisms. https://lnkd.in/gbe9HSzE Key IDEAS: - Traditional sequence models rely heavily on recurrent networks, limiting parallelization. - The Transformer architecture replaces recurrence with self-attention, improving training times. - Self-attention allows the model to draw connections across the entire input sequence. - The Transformer achieves a BLEU score of 28.4 on the English-to-German translation task. - Processing can occur in parallel, significantly speeding up computation. - Multi-head attention enables the model to focus on different parts of the input simultaneously. - This model can generalize to other tasks beyond translation, such as constituency parsing. - The design simplifies model architecture by removing the need for complex recurrent structures. - Positional encodings are introduced to maintain the order of sequences. - Attention layers improve the model's ability to learn long-range dependencies. - The paper emphasizes the efficiency gains over state-of-the-art models. - Transformers outperform existing methods with considerably less training cost. - The model can be fine-tuned for various language tasks easily. - Experiments showed better performance with increased attention heads up to a point. - Utilization of dropout and label smoothing during training improves performance. - The model architecture allows for easier interpretation of attention distributions. - Attention heads often learn specific tasks, such as syntactic and semantic relations. - The architecture allows adapting to larger input and output sequences efficiently. - Adequate regularization contributes to superior generalization in learning from data. - Ease of implementation on hardware accelerates research and application. Key INSIGHTS: - Replacing recurrence with attention simplifies architecture and allows for efficient model training. - Self-attention is crucial for capturing dependencies across various positions in input sequences. - The Transformer demonstrates that parallel processing can drastically reduce training time and increase efficiency. - The design showcases how innovations in architecture can redefine state-of-the-art results in machine translation. - By employing a consistent embedding across input and output, the model minimizes errors in translation tasks. - Regularization methods like dropout and label smoothing enhance the robustness of predictions. - Understanding various attention heads can provide insights into the inner workings and decision-making of the model.

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Interesting thread on Twitter from Derya Unutmaz and Nick Dobos who think that medicine is going to crumble much faster to AI than any other industry. And they have good points: 1. Huge amount of gatekeeping and refusal to empower patients to make their own decisions. 2. Not an optional thing in many cases, even when you can’t afford it. So you are forced to use the next best thing. 3. Overpaid admin nightmare to get simple things diagnosed suddenly vanishes. What % of patients is that? -- "This is the final warning for those considering careers as physicians: AI is becoming so advanced that the demand for human doctors will significantly decrease, especially in roles involving standard diagnostics and routine treatments, which will be increasingly replaced by AI. This is underscored by the massive performance leap of OpenAI’s o-1 model, also known as the “Strawberry” model, which was released as a preview yesterday. The model performs exceptionally well on a specialized medical dataset (AgentClinic-MedQA), greatly outperforming GPT-4o. The rapid advancements in AI’s ability to process complex medical information, deliver accurate diagnoses, provide medical advice, and recommend treatments will only accelerate. Medical tasks like diagnosing illnesses, interpreting medical imaging, and formulating treatment plans will soon be handled by AI systems with greater speed and consistency than human practitioners. As the healthcare landscape evolves in the coming years, the number of doctors needed will drastically shrink, with more reliance on AI-assisted healthcare systems. While human empathy, critical thinking, and decision-making will still play an important role in certain areas of medicine, even these may eventually be supplanted by future iterations of models like o-1. Consequently, medicine is becoming a less appealing career path for the next generation of doctors—unless they specialize in intervention-focused areas (such as surgery, emergency medicine, and other interventional specialties), though these, too, may eventually be overtaken by robotic systems…maybe within a decade or so." Link to the thread in comments #healthcare #ai #innovation #technology #diagnostics #medical

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Workshop on Strategic and Utility-aware Recommendations (SURE) at RecSys 2024, submission due on August 5. Interesting topics relevant to industry research that have been very close to my heart, looking forward to it Tonia Danylenko 🇺🇦!

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Reranking Using Huggingface Transformers for Optimizing Retrieval in RAG Pipelines https://ift.tt/aN1OIbQ Understanding when reranking makes a difference Visualization of the reranking results for the user query “What is rigid motion?”. Original ranks on the left, new ranks on the right. (image create by author) In this article I will show you how you can use the Huggingface Transformers and Sentence Transformers libraries to boost you RAG pipelines using reranking models. Concretely we will do the following: Establish a baseline with a simple vanilla RAG pipeline. Integrate a simple reranking model using the Huggingface Transformers library. Evaluate in which cases the reranking model is significantly improving context quality to gain a better understanding on the benefits. For all of this, I will link to the corresponding code on Github. What is Reranking? Before we dive right into our evaluation I want to say few words on what rerankers are. Rerankers are usually applied as follows: A simple embedding-based retrieval approach is used to retrieve an initial set of candidates in the retrieval step of a RAG pipeline. A Reranker is used to reorder the results to provide a new result order that betters suits the user queries. But why should the reranker model yield something different than my already quite powerful embedding model, and why do I not leverage the semantic understanding of a reranker in an earlier stage you may ask yourself? This is quite multi-faceted but some key points are that e.g. the bge-reranker we use here is inherently processing queries and documents together in a cross-encoding approach and can thus explicitely model query-document interactions. Another major difference is that the reranking model is trained in a supervised manner on predicting relevance scores that are obtained through human annotation. What that means in practice will also be shown in the evaluation section later-on. Our Baseline For our baseline we choose the simplest possible RAG pipeline possible and focus solely on the retrieval part. Concretely, we: Choose one large PDF document. I went for my Master’s Thesis, but you can choose what ever you like. Extract the text from the PDF and split it into equal chunks of about 10 sentences each. Create embedding for our chunks and insert them in a vector database, in this case LanceDB. For details, about this part, check our the notebook on Github. After following this, a simple semantic search would be possible in two lines of code, namely: query_embedding = model.encode([query])[0] results = table.search(query_embedding).limit(INITIAL_RESULTS).to_pandas() Here query would be the query provided by the user, e.g., the question “What is shape completion about?”. Limit, in this case, is the number of results to retrieve. In a normal RAG pipeline, the retrieved results would now just be directly be provided as context to the LLM that will synthesize the answer. In many...

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🚀 𝗥𝗲𝗰𝗲𝗻𝘁 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗶𝗻 𝗔𝗜 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 🚀 𝗚𝗿𝗼𝗸𝗙𝗼𝗿𝗺𝗲𝗿: Graph Fourier Kolmogorov-Arnold Transformers Introducing a novel Graph Transformer network that surpasses conventional self-attention mechanisms. 1. Tackles limitations of existing GTs in modeling complex node label patterns. 2. Incorporates learnable activation functions in the graph spectrum. 3. Demonstrates superior performance on multiple node and graph classification datasets. Paper: https://lnkd.in/gDKKk-2x 𝗦𝗧𝗔𝗥 𝗔𝗧𝗧𝗘𝗡𝗧𝗜𝗢𝗡: Efficient LLM Inference Over Long Sequences Recent advancements in Large Language Models (LLMs) now handle contexts extending to millions of tokens. 1. New attention mechanism designed to address computational challenges during inference while preserving accuracy. 2. Opens up new applications, including repository-level code analysis, multi-document summarization, and large corpus retrieval. Paper: https://lnkd.in/gtj29ghz 𝗚𝗹𝗶𝘁𝗰𝗵 𝗧𝗼𝗸𝗲𝗻𝘀 𝗶𝗻 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: 1. Categorization Taxonomy and Effective Detection. 2. Addressing the issue of glitch tokens that lead to hallucinations and erratic behavior in LLMs. 3.Introduction of GlitchHunter, a novel clustering-based detection method. 4.Constructs a Token Embedding Graph (TEG) and uses clustering for efficient detection. 5.High precision and recall across multiple LLMs. Paper: https://lnkd.in/gTVr8jgK #MachineLearning #AIResearch #GraphTransformers #LLM #DeepLearning

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Can LLM preference tuning degrade Multi-Agent system performance? In a recent tweet, Andrej Karpathy questioned why all LLMs sound similar, prompting Sebastian Rascha to speculate about the uniformity of alignment datasets. Aligning user preferences is a last-mile step in training large language models. Regardless of the pre-training datasets used, could the preference datasets used during alignment compromise the effectiveness of LLMs? This issue could adversely affect multi-agent systems incorporating multiple LLMs in their design.

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Microsoft is training its own large language model, internally labaled MAI-1, with Mustafa Suleyman leading the effort. The model is around 500B parameters and could compete directly with LLMs from Google, OpenAI, etc. This marks the first time Microsoft has developed an in-house AI model of this magnitude since investing over $10 billion in OpenAI for the rights to reuse the startup's AI models. OpenAI's GPT-4 powers not only ChatGPT but also Microsoft Copilot. The development of MAI-1 is being led by Mustafa Suleyman, the former Google AI leader who recently served as CEO of the AI startup Inflection before Microsoft acquired the majority of the startup's staff and intellectual property for $650 million in March. Although MAI-1 may build on techniques brought over by former Inflection staff, it is reportedly an entirely new large language model (LLM) With approximately 500 billion parameters, MAI-1 will be significantly larger than Microsoft's previous open source models (such as Phi-3).

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An advice that goes well with every decor, “In a time of universal deceit, telling the truth is an [evolutionary] act.”  (School's Out) 😊 "School's Out A-Team" Talha Berkay Eren [turns dreams into realities] İrem CANSU [simplifies everything] Emirhan TALA [creates systems of systems of people] Büşra Nimet Oğuz [catches murderers] Erol Bozkurt [has fun as the prime mover] "Consultants" Tolga Bildirici [turns startups to firms] Yuksel Korukcu [turns apps to pro apps] Erdinç KÜTÜK [turns apps to pro apps] Senol Dikyurt [turns fans to customers] "Information Base" Status = https://lnkd.in/dVJyYi6U Brochure = https://lnkd.in/dzntguC9 Poster = https://lnkd.in/d8hgQiBX "Contact Information" [email protected] #mikimoka #schoolsout #managementissuesincomputersciences #miics #appsfortroublemakers #a4t #a4e #somethingfromnothing #socialexperiments #culturalterraforming #alternatereality #letsgetemotional #moody #verdict #notung #emotionalagility #emotionalagilitydash #ead #maddeningmove #uili #softwareengineering #anthropology #psychology #ethics #art #literature #strategy #fvy #fvys

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Learn how an image-text multi-modality model can perform image classification, image retrieval, and image captioning #llms #llmops #image #opencv #chatgpt4 #gemini #aiml https://lnkd.in/gJFh9Ruf

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Check out the latest installment in my series, "𝗧𝗵𝗲 𝗔𝗕𝗖𝘀 𝗼𝗳 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗜)." 🤖 This time, we're diving into 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 - 𝘁𝗵𝗲 𝗺𝗶𝘁𝗼𝗰𝗵𝗼𝗻𝗱𝗿𝗶𝗮 𝗼𝗳 𝗔𝗜. Just as these tiny powerhouses energize our cells, ML fuels the intelligence in AI. ⚡🧠 This article breaks down the complex into the comprehensible, covering:  - The definition and evolution of ML - Types of Machine Learning - Real-world applications transforming our daily lives - Future possibilities and ethical considerations Let's master the powerhouse of AI together! What aspect of Machine Learning energizes you the most? 💡 #AI #MachineLearning #DataScience #TechInnovation #AIpowerhouse

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Chaos Report: Iran's Response Matrix: A Game Theory Analysis Iran is in a classic decision-making bind: it has to retaliate but it must also be able to survive. However, there is already a problem: it is thinking too much and acting too late. More here on my Substack: https://lnkd.in/gQzQnAKj

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🔥📢💻🧬BEYOND THE MODEL EPISODE 2 🔥📢💻🧬 I am delighted to bring to you Beyond the Model's second episode, where I had the pleasure of sitting down with my good friend Mutlu Dogruel, Vice President of AI at Cresset 😉 We talked about: AI Drug Discovery, Mutlu's Journey at Cresset, Safety in AI Drug Discovery and even some philosophical takes on Artificial General Intelligence (AGI)! It was fun, we laughed, but we ultimately talked about the most pressing topics in AI Biotechnology today. Care about Artificial Intelligence? 👀 Care about Biology and Medicine? 💉 Give this a watch 🦾😁 LINK IN THE COMMENTS!!!!!!! #beyondthemodel #artificialintelligence #machinelearning #aidrugdiscovery #biotech

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🚀 Just spent some time diving deep into Retrieval-Augmented Generation (RAG) for LLMs, and it’s been eye-opening! For those who aren’t familiar, RAG combines the strengths of traditional information retrieval with generative capabilities, allowing models to “look up” relevant information in a large database before crafting a response. This means we’re not just relying on the model’s pre-trained “memory,” but also dynamically fetching information to enrich responses. The result? Way more relevant and informed outputs, especially for specialized or up-to-date topics. Here’s where I see some real potential: 1. Customer Support Automation: Imagine a chatbot that can instantly access a company’s entire support knowledge base, user manuals, and past customer interactions. Instead of relying on generic, canned answers, it could generate responses tailored to the user’s specific question and product version—leading to faster, more accurate support. 2. Personalized Education: In the field of e-learning, RAG can pull from vast educational content databases, offering tailored explanations based on a learner’s background. Whether it’s math, language learning, or science, this could help create a personalized tutor experience that adapts in real-time to each student’s needs and knowledge gaps. 3. Legal & Compliance Research: For industries like law or finance, where accurate referencing is crucial, a RAG-powered LLM could retrieve and cite relevant cases, regulations, or compliance guidelines while drafting responses or reports. It could save hours of manual research and improve accuracy by drawing on real-time, verified data sources. 4. Healthcare Support: A tool for healthcare professionals where RAG enables retrieval of the latest medical studies, treatment protocols, or drug interactions. Imagine a doctor getting instant recommendations based on the most recent research without having to sift through mountains of publications. 5. Content Creation & Ideation: Need fresh ideas? RAG can look up trending topics, recent publications, and customer insights to help marketers and content creators generate ideas that are relevant to current audience interests, backed up by solid references. Why this is so exciting… RAG’s ability to bridge generative AI with real-time, relevant information retrieval feels like the next step in making LLMs truly practical and innovative. It opens up possibilities for high-stakes applications where accuracy and context are critical—and where generative models alone may fall short. Really looking forward to experimenting further with RAG in my projects. The future of LLMs is looking more dynamic than ever! 🔥

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🌟 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐈𝐦𝐚𝐠𝐞𝐑𝐀𝐆 𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐛𝐲 𝐋𝐥𝐚𝐦𝐚 𝟑.𝟐 𝐕𝐢𝐬𝐢𝐨𝐧 ImageRAG combines state-of-the-art image and text understanding to deliver a powerful Retrieval-Augmented Generation (RAG) experience. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: • 🧬 𝐈𝐦𝐚𝐠𝐞 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬: Powered by ColQwen from Colpali. • 🤖 𝐕𝐢𝐬𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥: Integrates LLaMA Vision via Ollama for advanced image comprehension. • 📥 𝐈𝐦𝐚𝐠𝐞 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: Intelligent duplicate detection and semantic search. • 💬 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐐𝐮𝐞𝐫𝐢𝐞𝐬: Query images seamlessly. • 📄 𝐏𝐃𝐅 𝐒𝐮𝐩𝐩𝐨𝐫𝐭: Process and index document visuals. • 🔍 𝐒𝐞𝐚𝐫𝐜𝐡 & 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: Efficient SQLite-backed semantic similarity retrieval. 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐒𝐭𝐚𝐜𝐤: • 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥: ColQwen (Colpali) • 𝐕𝐢𝐬𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥: LLaMA Vision (Ollama) • 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝: Streamlit • 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: SQLite • 𝐈𝐦𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Pillow, pdf2image • 𝐌𝐋 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: PyTorch Github link is in the comments!!! #ImageRAG #RAG #MultimodalRAG #NLP #LLMs #AI #llm

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