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.
From ripples to waves: The transformational power of tokenising assets
Tokenised financial assets are moving from pilot to at-scale deployment. Adoption is not yet widespread, but financial institutions with blockchain capabilities in place will have a strategic advantage.
Read the full report.
MetRag – Enhanced Retrieval Augmented Generation Framework
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 🚨
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.
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. 🌍💡
Thoughts on Goldman Sachs’ “Gen AI: Too Much Spend, Too Little Benefit?”
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?”
Microsoft silently dropped vision model Florence-2: A Breakthrough in Vision Foundation Models
Microsoft introduce Florence-2, a groundbreaking vision foundation model that uses a unified, prompt-based approach for a variety of computer vision and vision-language tasks. Unlike existing models, Florence-2 excels at handling diverse tasks with simple text instructions, thanks to its innovative design and extensive training on FLD-5B, a dataset with 5.4 billion annotations across 126 million images. This model sets new standards in zero-shot and fine-tuning capabilities, showcasing its prowess in tasks such as captioning, object detection, and segmentation. Discover more about Florence-2 and its revolutionary impact.
🚀 Navigating the AI Plateau: The Next S-Curve in AI Innovation 🚀
Imagine asking an AI for pizza advice, only to have it suggest using glue for cheese! 🤦♂️ Such quirks highlight current AI limitations. As the article “The AI Plateau Is Real — How We Jump To The Next Breakthrough” explains, technological progress often follows an S-Curve, with rapid innovation eventually hitting a plateau.
🔍 The AI Plateau: We’ve seen phenomenal growth since the launch of ChatGPT in 2022, but recent improvements have been incremental. To leap to the next S-Curve, we need access to proprietary business data. Unlike the harvested public data, business data is richer and more valuable.
🏢 Proprietary Business Data: Zoom’s 55 billion hours of meeting minutes, Ironclad’s billion documents, and Slack’s billion weekly messages are goldmines for the next AI breakthroughs. This data, produced in work contexts, holds the key to higher-quality AI training.
🔑 Opportunities for Startups:
1. Engage Experts: Source high-quality training data from field experts.
2. Leverage Latent Data: Help businesses prepare and connect internal data.
3. Capture in Context: Seamlessly capture new data without disrupting workflows.
4. Secure the Secret Sauce: Enable enterprises to create and deploy custom models to protect proprietary IP.
The path forward is clear: to truly harness AI’s potential, businesses must own their models, protecting their competitive edge and advancing with human-centric attributes. 🌟
Sequoia argues that the tech industry needs $600B in AI revenue to justify the massive investments in GPUs and data centres.
🚀 This is an interesting article from Sequoia, which argues that the tech industry needs $600B in AI revenue to justify the massive investments in GPUs and data centers. 🖥️💸
OpenAI, currently the biggest AI pure play, is at a $3.4B annual run rate. While impressive, this figure underscores the challenge: without products worth buying, this feels like a bubble waiting to burst. 🎈
There is no doubt that AI will generate significant revenue, but will it be enough to support a $3 trillion valuation for Nvidia? 🤔 This brings us to a crucial point: replacing or significantly improving productivity is essential. Anything less simply isn’t big enough to justify these valuations. 🏢🔄🤖
World Bank research highlights AI’s potential to boost productivity.
🚀 The AI revolution is transforming education, offering game-changing opportunities to personalize learning, support teachers, and optimize management. Recent World Bank research highlights AI’s potential to boost productivity, with GPT-4 enhancing consultants’ task efficiency and output quality. Here are nine key AI-driven innovations making waves in Latin America and the Caribbean:
1. AI-powered lesson plans: Create engaging, effective lessons aligned with curriculum standards.
2. Automated routines: Reduce administrative burden, freeing up teachers for teaching and mentoring.
3. AI-powered tutors: Tailor learning to individual student needs.
4. AI for assignments: Assist students while fostering responsible use and academic integrity.
5. AI-powered assistants: Automate tasks, provide personalized support, and generate insights.
6. Early warning systems: Identify students at risk of dropping out.
7. Centralized administration: Optimize decision-making for resources.
8. AI-powered mentors: Offer personalized career guidance and support.
9. AI-powered feedback: Improve teacher quality with personalized feedback.
These innovations not only revolutionize education but also extend to corporate training and mentoring, enhancing workforce development. 🌟