🚀 Artefact’s New Report: Generative AI in Healthcare 🚀

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Artefact, a global leader in data & AI consulting and data-driven marketing services, has released an insightful report titled “Generative AI Report for Healthcare – Unlocking the potential of Generative AI for patients, practitioners, and pharmaceutical companies.”

This report explores exciting GenAI applications and use cases in healthcare, including:

1. 🌐 Synthetic patient data generation to accelerate clinical trials

2. 🏥 Personalized care recommendation support

3. 💼 Administrative assistant for healthcare professionals

4. 🏨 Medical coding assistant for hospitals and clinics

5. 🩺 Preventive and informational agent for patients

6. 🤝 Trust and control: Critical for realizing GenAI’s potential in healthcare, emphasizing it as a human transformation, not just a technical one.

Additionally, the report delves into the current limitations, challenges, and opportunities in Generative AI for healthcare. It’s a must-read for healthcare practitioners, developers, and IT business leaders!

Artefact eBook – Generative AI for Healthcare

🚀 ColPali: Advancing Document Retrieval with Vision Language Models 📄🤖

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ColPali is setting a new standard in document retrieval by leveraging Vision Language Models to handle visually rich documents. Traditional systems often fall short in utilizing visual cues, limiting their effectiveness in real-world applications like Retrieval Augmented Generation. ColPali addresses this by creating high-quality contextualized embeddings directly from document images, resulting in superior performance and speed. 🌟

🔍 Top 3 Use Cases for ColPali:

1. Legal Document Analysis: Efficiently retrieve and analyze visually complex legal documents, including contracts and case files, with enhanced accuracy and speed. ⚖️📚

2. Healthcare Records Management: Streamline retrieval of medical records, combining text and visual data (like charts and scans) to improve patient care and administrative efficiency. 🏥💉

3. Academic Research: Enhance academic research by enabling quick and precise retrieval of scholarly articles, textbooks, and research papers across various languages and domains. 🎓📖

ColPali: Efficient Document Retrieval with Vision Language Models

Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks

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Microsoft introduced Florence-2, a cutting-edge vision foundation model with a unified, prompt-based representation for diverse computer vision and vision-language tasks. Unlike existing models, Florence-2 handles various tasks using simple text instructions, covering captioning, object detection, grounding, and segmentation. It relies on FLD-5B, a dataset with 5.4 billion visual annotations on 126 million images, created through automated annotation and model refinement. Florence-2 employs a sequence-to-sequence structure for training, achieving remarkable zero-shot and fine-tuning capabilities. Extensive evaluations confirm its strong performance across numerous tasks.
Read the full research paper.

The Fortune500 Moving Onchain

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America’s top public companies are more active onchain than ever. Fortune 100 companies saw a 39% year-over-year increase in cryptocurrency, blockchain, or web3 initiatives, hitting a record high in Q1 2024, according to Coinbase and The Block. A survey of Fortune 500 executives reveals that 56% are working on onchain projects, including consumer payments. This surge underscores the need for clear crypto regulations to retain talent in the U.S., enhance access, and solidify U.S. leadership in the global crypto space. 
Read full report.

RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback

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Large Language Models (LLMs) have static knowledge, making updates costly and time-consuming. Retrieval-augmented generation (RAG) helps, but irrelevant info can degrade performance.
Solution: Retrieval Augmented Iterative Self-Feedback (RA-ISF) refines RAG by breaking tasks into subtasks. It uses: 1. Task Decomposition: Splits tasks into subtasks. 2. Knowledge Retrieval: Fetches relevant info for each subtask. 3. Response Generation: Integrates info to generate accurate answers. What’s Next: RA-ISF reduces hallucinations and boosts performance, enhancing LLMs for complex tasks. As it evolves, expect more powerful, knowledge-enhanced LLMs.
Read the full research paper.

MetRag – Enhanced Retrieval Augmented Generation Framework

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Significance: MetRag demonstrates improved performance in knowledge-intensive tasks by addressing the limitations of traditional RAG methods through a multi-layered thought approach.

Applications: This framework can be applied to various domains requiring up-to-date and accurate knowledge, enhancing the reliability and efficiency of LLMs in real-world tasks.
Read the full paper.

🚨 Microsoft Bing’s Censorship in China: Implications for GenAI 🚨

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A recent article from Rest of World reveals that Microsoft Bing’s translate and search functionalities in China are heavily censored. Queries are altered or blocked to comply with local regulations, impacting the transparency and reliability of Bing’s services.

💡 Key Implications for Microsoft’s GenAI Products:

1. Trust and Reliability: The heavy censorship in Bing raises concerns about the trustworthiness of Microsoft’s GenAI products like Copilot. Users might question whether similar censorship or content moderation policies apply globally, potentially affecting user confidence.

2. Model Integrity: If Microsoft has access to OpenAI’s Assistant model for Copilot, it raises questions about whether these censorship practices could extend to the training data or response generation phases, impacting the model’s integrity and fairness.

3. Content Moderation during RLHF: The degree to which Copilot’s answers are moderated during the Reinforcement Learning from Human Feedback (RLHF) phase is crucial. If excessive moderation is applied, it could lead to biased or restricted outputs, limiting the model’s utility and scope.

4. Global Trust in GenAI for Content Creation: The perception of excessive censorship in one region can have global repercussions. For users worldwide, it may raise concerns about the impartiality and authenticity of content generated by GenAI tools like Copilot. Ensuring transparency and consistency in content moderation practices is vital for maintaining global trust in these technologies.

These implications suggest a need for greater transparency from Microsoft regarding their content moderation policies and practices across different regions and products to maintain trust and reliability in their GenAI offerings.


Rest of World: Microsoft Bing’s censorship in China is even “more extreme” than Chinese companies’

AIME AI Doctor is set to revolutionise healthcare for millions globally. 

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AIME, the AI doctor, is poised to improve the quality of life for millions globally significantly. This innovative project has shown remarkable potential in various aspects of medical care. The development team conducted extensive tests, evaluating AIME across 32 categories including diagnosis, empathy, quality of treatment plans, and decision-making efficiency. Impressively, AIME outperformed human doctors in 28 of these categories and matched them in the remaining four.

The training approach for AIME was particularly groundbreaking. Utilizing a self-play method, three independent agents (a patient, a doctor, and a critic) conducted over 7 million simulated consultations. In comparison, a human doctor typically performs only a few tens of thousands of consultations over their entire career. This vast experience enables AIME to deliver high-quality medical services to 99% of the global population who cannot afford personal doctors. In a few years, AIME is expected to surpass most general practitioners, radiologists, and pediatricians in performance. It offers tireless service, is conditionally free, and has instant access to vast medical literature, having been trained on millions of patient interactions.

However, the priority in medicine is “do no harm.” Since publishing their report in January, the team has focused on improving the product, enhancing safety, and preparing for necessary FDA and other regulatory approvals. While widespread adoption won’t happen overnight, the technical feasibility of AIME is already a reality. 🌍💡

About the AIME project.
Full research paper.

 Thoughts on Goldman Sachs’ “Gen AI: Too Much Spend, Too Little Benefit?”

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The $1tn+ investment in AI by tech giants and others is indeed staggering, and the debate on its payoff is crucial. 🤔

MIT’s Daron Acemoglu and GS’ Jim Covello raise valid points about the limited immediate economic impact and the mismatch between AI’s design and complex problem-solving needs. 📉💸

However, the optimism from GS’ Joseph Briggs, Kash Rangan, and Eric Sheridan about AI’s long-term economic potential is encouraging. The notion of being in a “picks and shovels” phase resonates deeply—AI’s transformative applications might still be on the horizon. 🚀💼

Challenges like the chip shortage and potential power constraints are real hurdles, but they also present opportunities for innovation and growth in adjacent sectors. 🔋💻

Ultimately, whether AI delivers on its promise or rides the wave of prolonged hype, the journey of this technological evolution is as critical as the destination. 🌐✨

Goldman Sachs’ “Gen AI: Too Much Spend, Too Little Benefit?”