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Data Science News
VentureBeat
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AnyChat brings together ChatGPT, Google Gemini, and more for ultimate AI flexibility
- AnyChat unites multiple large language models (LLMs) to offer developers unprecedented flexibility and choice.
- Developed by machine learning growth lead Ahsen Khaliq, the tool makes it easier for developers to experiment with and deploy LLMs between multiple sources.
- At its core, AnyChat exposes no restrictions on traditional LLM platforms, offering its users total control over which models they can use.
- However, the AI industry has seen more companies commit to individual platforms, restricting their ability to integrate AIs easily into their operations.
- AnyChat addresses this by offering a unified interface for both proprietary and open-source models.
- AnyChat supports a range of models via Hugging Face, a popular platform for open-source AI methods.
- The added support for multimodal AI capabilities makes AnyChat an ideal platform for text and image analytics.
- The tool also supports real-time search and multimodal abilities, which makes it perfect for more complex applications.
- AnyChat leverages its open architecture to encourage more developers to experiment and contribute to the platform, further enhancing its capabilities.
- In conclusion, AnyChat is an exciting, new tool with unlimited potential to shape the AI landscape, providing developers and enterprises with unmatched control over AI experimentation, deployment, scalability, and choice.
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Brighter Side of News
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Groundbreaking new AI algorithm can decode human behavior
- Researchers from the University of Southern California (USC) have developed an AI algorithm called Dissociative Prioritized Analysis of Dynamics (DPAD), that isolates neural patterns tied to specific behaviours from overall brain activity to improve the accuracy of decoding movement for brain computer interfaces. The algorithm's training process prioritises learning behaviour-related patterns, preventing important data from being masked by analysing the remaining signals. DPAD can also track internal mental states and provide real-time feedback on symptom states in mental health conditions.
- BCIs aim to restore functionality in paralyzed patients by decoding intended movements directly from brain signals. Shanechi’s research addresses limitations of earlier models, providing neuroscientists with a tool to study the brain and enable personalised treatments.
- The algorithm dissociates brain patterns encoding a particular behaviour, like eye movements, from all other concurrent patterns improving the accuracy of BCI movement decoding and uncovering new brain patterns that were previously overlooked.
- Many models struggle to prioritize behaviourally relevant dynamics, focusing instead on overall neural variance. DPAD overcomes this limitation by giving precedence to the signals linked to behaviour during the learning phase.
- DPAD's flexible framework supports diverse behaviours including categorical choices or irregularly sampled data like mood scores, broadening its applicability. The simulation suggests that DPAD may be applicable with sparse sampling methods.
- DPAD has the potential to revolutionize mental health treatment by providing real-time feedback on a patient's symptom states, paving the way for behaviour computer interfaces that help manage not just movement disorders but also mental health conditions.
- The algorithm could one-day de-code internal mental states like pain or mood. This capability could revolutionize mental health treatment by providing real-time feedback on a patient’s symptom states.
- DPAD provides a powerful tool for studying the brain and developing BCIs, which could improve the lives of patients with paralysis and mental health conditions, offering more personalised and effective treatments.
- Shanechi’s research using DPAD marks a significant step forward in neurotechnology enabling researchers to better understand how the brain orchestrates behaviour. Tools like DPAD promise not only to decode the brain’s complex language but also to unlock new possibilities in treating both physical and mental ailments.
- The algorithm gives neuroscientists a more complete picture of how the brain functions, despite crippling limitations which have historically hindered the development of robust neural-behavioural dynamical models.
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Medium
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The Future of DeFi: How XBANKING is Shaping Next-Generation Financial Platforms
- XBANKING introduces a novel approach to liquidity provision, offering non-custodial staking, restaking, and liquid pools.
- The platform focuses on user-centric design, making DeFi accessible to a broader audience.
- XBANKING emphasizes transparency, community engagement, and sustainable profit distribution.
- XBANKING is leading the charge in shaping the future of DeFi and redefining financial platforms.
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Medium
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Martony outside
- Web 3, also known as the Decentralized Web, utilizes blockchain technology, AI, and crypto-currencies to create a secure and decentralized network.
- Features of Web 3 include decentralization, blockchain, crypto-currencies, and AI-powered apps and services.
- Advantages of Web 3 include enhanced security, ownership of data, resistance to censorship, and the emergence of innovative apps and services.
- Examples of Web 3 technologies include blockchain-based social media, DeFi platforms, NFTs, and cryptocurrency wallets.
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Medium
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Explainable AI (XAI): Making AI Models Transparent and Trustworthy
- Explainable AI is a process through which the functioning of AI system and the decisions it takes can be easily explained by the AI to the intended users.
- Explainable models provide end-users a clear and understandable reasoning process used by the AI model in arriving at certain results or making decisions.
- Explainability helps increase trust, allows verification, and makes AI systems accountable.
- Explainability is important in critical areas because it builds trust, creates accountability, and transparency by providing access to decision-making processes.
- People are reluctant to trust AI in critical areas like healthcare, finance, and law enforcement if they cannot explain how an AI arrives at a decision.
- Through explainability, the problem of bias patterns is easier to notice, and addressing issues of fairness is made easier.
- The next advances in AI development can be expected to pay more attention to the development of powerful but at the same time explainable AI.
- Explainable AI is crucial in ensuring integrity, credibility and instilling confidence in different industries.
- Companies adopting Explainable AI will bridge the middle ground between artificial intelligence and society.
- It is important to make AI systems' decision-making transparent and easy to comprehend because they are increasingly being integrated in decision-making processes that shape human's life.
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VentureBeat
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Mistral unleashes Pixtral Large and upgrades Le Chat into full-on ChatGPT competitor
- French startup Mistral has launched Pixtral Large, a new monumental model with 124 billion parameters suitable for multilingual OCR, chart understanding and reasoning.
- Pixtral Large includes a 1-billion-parameter vision encoder and a 123-billion-parameter decoder, ensuring it can process both text and visual data.
- Additionally, the new upgrades will enhance the company's free web-based chatbot, Le Chat, with image generation, web search and an interactive canvas added to its functionalities.
- Le Chat will now have the ability to process PDFs, extract data from tables, graphs, and equations, and produce high-quality visuals.
- Mistral hopes the features will enable users to use Le Chat as a versatile AI assistant to save time and effort performing tasks that would require multiple tools otherwise, as well as creating a better AI experience to co-design models and product interfaces.
- Pixtral Large's weights and model are publicly available but distributed under a Mistral AI Research Licence limited to non-commercial, research-focused applications.
- Le Chat can be accessed by users provided they have a Mistral, Google, or Microsoft account.
- Mistral AI competes with giants of the industry such as Google and OpenAI, with the latter releasing its own interactive sidebar feature Canvas for ChatGPT, while Mistral has launched Le Chat's Canvas.
- A recent survey suggested that usage of Mistral’s models and API by large enterprises remained far behind those of US-based companies such as OpenAI and Microsoft.
- However, in the post-presidential election world, there are indications that European options such as Mistral may become more attractive compared to their US counterparts.
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Medium
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Image Credit: Medium
Beyond Proof of Concept: Building RAG Systems That Scale
- The "LLM Twin: Building Your Production-Ready AI Replica" free course teaches how to design, train, and deploy an LLM twin AI character that writes like you by incorporating your style, personality, and voice into an LLM. This article discusses lesson 9 out of the 12 lessons of this course, which focuses on building an LLM system that scales beyond the proof of concept stage. The end goal is to build and deploy the LLM Twin, and this lesson discusses how to hook together the key components of the AI inference pipeline in a scalable and modular system architecture.
- The article discusses two options to design the inference pipeline: monolithic LLM & business service and different LLM & business microservices. Decoupling the components empowers scaling individually as required, providing a cost-effective solution to meet the system's needs.
- The article then describes how the microservice pattern is applied to the concrete LLM twin inference pipeline. The components include LLM microservice deployed on AWS SageMaker as an inference endpoint, a prompt monitoring microservice based on Opik (an open-source LLM evaluation and monitoring tool powered by Comet ML), and a business microservice implemented as a Python module that glues all the domain steps together and delegates the computation to other services.
- The article further illustrates the differences between training and inference pipelines, and the core differences you have to understand, which are accessed from an offline data store and an online database optimized for low latency, respectively.
- The article explains how to deploy the LLM microservice and how to test the inference pipeline by running a Gradio chat GUI. The article concludes by summarizing the major points discussed in the lesson on scaling the LLM architecture.
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Medium
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The Engineer’s Framework for LLM & RAG Evaluation
- The LLM Twin free course teaches how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices, and build and deploy your LLM Twin.
- The most efficient way when building AI apps, before optimizing anything, is to create an end-to-end flow of your feature, training, and inference pipelines and spend some serious time on your evaluation pipeline.
- Usually, heuristic metrics don’t work well when assessing GenAI systems as they measure exact matches between the generated output and GT. Therefore, LLM systems are primarily evaluated with similarity scores and LLM judges.
- The Opik framework is used to train, evaluate and compare multiple LLM experiments by quantizing the results of your experiments, including metadata such as the version of artifacts used to compute the dataset, the embedding model, and more.
- With RAG, you have an extra dimension that we have to check, which is the retrieved context. Thus, there are 4 dimensions to evaluate, and NDCG measures are used during the retrieval step, and similar strategies are leveraged during the generation step.
- Opik is used to compute metrics relevant to RAG, which track the embedding model used at the retrieval step in our experiment metadata and uses the ContextRecall and ContextPrecision metrics that use LLM judges to score the quality of the generated answers.
- By leveraging the Opik platform, you can quantify and optimize your LLM and RAG experiments by measuring various strategies and choosing the best one.
- Optimization of LLM & RAG evaluation pipelines can be done by computing predictions in batch instead of leveraging the AWS SageMaker inference endpoint, which can handle one request at a time.
- Ultimately, the course teaches how to evaluate LLM and RAG systems, enabling the creation of optimal AI applications.
- The LLM Engineer's Handbook is available to buy on Amazon or Packt.
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Medium
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How a Free Crypto App Helped Me Earn 0.15 BTC and Achieve My Biggest Dream
- A free crypto app called https://swapx.one/ helped the author earn 0.15 BTC and achieve their dream of creating a gaming platform where players can earn crypto rewards.
- The author signed up at https://swapx.one/ and activated the promo code MYBTC24, which instantly gave them 0.15 BTC.
- With the funding, the author invested in development tools and integrated features from the best free crypto apps into their gaming platform.
- The platform is now thriving, and players worldwide are earning rewards while playing games.
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Dev
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Questions Recognition System using NLP-BERT from Un-labeled Data
- The article showcases a NLP-BERT based questions recognition system that categorizes un-labeled question data into specific groups or clusters without the need for labeled data.
- The system involves loading a dataset containing questions, cleaning the text using regular expressions, and preprocessing it with the BERT natural language processing model to create embeddings.
- The embeddings are then clustered using the K-means algorithm, following which they are manually assigned a category for easy interpretation.
- This is followed by plotting the reduced features of the questions using PCA to visualize clusters.
- The final category results are exported to CSV, and metrics are used to evaluate clustering quality.
- The article also provides insight on how this system can help evaluate product/customer success through feedback and work on improving existing issues.
- Libraries like 're', 'pandas', and 'sklearn' are used for cleaning, data manipulation, and clustering.
- The project also leverages BERT natural language processing library along with GPUs for fast processing.
- A mapping of cluster labels to descriptive categories is used and sample verification is done for more accurate clustering.
- The goal is to extract the semantics of the text and simplify the mapping process for downstream applications.
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Hackernoon
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Peeling the Onion on AI Safety
- Discussions about Generative AI safety are more urgent than ever.
- AI systems need to ensure safety and ethical alignment with human values.
- AI safety can be understood as an onion with multiple layers.
- Each layer addresses a critical facet of the AI lifecycle: training data, algorithm, inference, publication, and societal impact.
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Analyticsindiamag
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Synthetic data – The Missing Link to AGI
- Training AI models with synthetic data that simulate human-like reasoning can fill the 'thought process' gap in achieving AGI advancements.
- Current Artificial Intelligence models lack structured and conscious reasoning, which is rooted in their training models that mostly consist of diverse and unstructured data from the internet.
- Synthetic data has emerged as a promising solution, as it creates iterative and recursive improvements to simulate structured thought.
- Other leaders in AI industry such as Anthropic and Hugging Face are also exploring the potential of synthetic data.
- Anthropic is generating 'infinite' training data to bypass the limitations of real-world data while scaling AI models effectively.
- Microsoft AI CEO Mustafa Suleyman predicts recursive improvements driven by synthetic data could accelerate the AGI timeline to three to five years.
- Recursive improvements are grounded on training models on synthetic data to mimic human-like thought processes, which lets AI systems exhibit intelligence that can rival human cognition.
- Predictive models would evolve iteratively, contributing to the improvement of the next model.
- An AGI that generates outputs so profound that it surpasses human capabilities could emerge.
- Synthetic data aims to cultivate novelty and creativity, and the datasets might bridge the gap between current AI limitations and desired AGI capabilities.
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Global Fintech Series
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Blend Taps Digital Transformation Veteran Alex Sion as Financial Services Vertical Leader
- Blend has appointed Alex Sion as the Financial Services Vertical Leader.
- Alex brings extensive experience in digital transformation across financial services giants.
- He will spearhead Blend's mission to deliver AI-driven solutions for the financial services industry.
- Blend aims to reshape financial institutions through AI in client-facing and employee-facing processes.
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Analyticsindiamag
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Image Credit: Analyticsindiamag
AWS Launches Multi-Agent Orchestrator for Managing AI Agents
- Amazon Web Services (AWS) has introduced Multi-Agent Orchestrator, a framework for managing multiple AI agents and complex conversations.
- The orchestrator routes queries to suitable agents, maintains conversational context, and integrates with AWS Lambda, local setups, and other cloud platforms.
- It supports Python and TypeScript, provides pre-built options for rapid deployment, and offers features like intent classification, context management, and scalable integration of new agents.
- AWS published a demo showcasing the orchestrator's capabilities with specialized agents, and it supports voice-based interactions and integrates with tools like Amazon Connect and Lex.
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Analyticsindiamag
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2024 Marks the End of Moore’s Law
- Moore’s law, the guiding concept in computing, is an observation made by Intel co-founder Gordon Moore.
- NVIDIA unveiled the next-gen Blackwell GPU at NVIDIA GTC 2024, bidding adieu to the era of Moore’s Law.
- The International Technology Roadmap for Semiconductors shifted its focus in 2016 to a ‘More than Moore’ strategy.
- AMD CTO Mark Papermaster believes that Moore’s Law will remain relevant for another 6-8 years.
- Intel has acknowledged the challenges posed by physical limitations as transistors approach atomic scales and reflected a broader industry shift away from strict adherence to Moore’s Law.
- Lightmatter is developing photonic computing technologies that aim to address the limitations of traditional silicon-based chips.
- The semiconductor industry is exploring alternative computing paradigms, such as quantum computing and photonics.
- Cerebras is making significant strides in challenging Moore’s Law through its innovative approach to design chips.
- Its wafer-scale engine (WSE), a chip architecture that integrates a massive number of cores—up to 9,00,000—on a single silicon wafer, outperforms traditional GPUs like Nvidia H100 by a factor of ten in certain applications.
- The latest version, WSE-3, features 4 trillion transistors and is capable of handling AI models with up to 24 trillion parameters.
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