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☕️ Greetings, and welcome to my daily dive into the Generative AI landscape.
I want to ensure this newsletter delivers valuable insights that keep you updated on Generative AI. My goal is to streamline its content, making it a concise, under-five-minute read containing 1500 words or less. Today’s newsletter is around 1558 words long. For those interested in deeper dives, I always provide references for extended reading. Most of my content springs from my ongoing research and development projects at INVENEW.
In today’s issue:
Superintelligence is Coming
Emerging Trends in Generative AI Research: A Selection of Recent Papers
The "Normsky" architecture for AI coding agents
Is connecting Grok AI to X ‘spicy’ or risky?
And more
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♨️ HEADLINE
OpenAI thinks superhuman AI is coming — and wants to build tools to control it
Amidst internal turmoil at OpenAI, including Sam Altman's ouster and return, the Superalignment team, led by Ilya Sutskever, remains focused on controlling superintelligent AI systems. The team is exploring methods to align AI models smarter than humans, a challenge presented at NeurIPS in New Orleans. They aim to use less sophisticated AI models to guide more advanced ones, ensuring alignment with human values and safety. OpenAI has also announced a $10 million grant program, partially funded by Eric Schmidt, to encourage broader research in AI alignment.
Key Points:
Sam Altman's ouster and subsequent return to OpenAI created a backdrop for the ongoing work of the Superalignment team.
The team, led by Ilya Sutskever, focuses on aligning AI systems that surpass human intelligence.
At NeurIPS, team members Collin Burns, Pavel Izmailov, and Leopold Aschenbrenner presented their approach to AI alignment.
The team uses weaker AI models to direct stronger ones, aiming for safety and adherence to human values.
OpenAI has launched a $10 million grant program for research in superintelligent alignment, with contributions from Eric Schmidt.
The team commits to sharing their research publicly, aligning with OpenAI's mission to benefit humanity.
Reference/Source: TechCrunch.com OpenAI thinks superhuman AI is coming — and wants to build tools to control it
📈 TRENDS
Emerging Trends in Generative AI Research: A Selection of Recent Papers: The recent NLP papers curated by Cohere For AI cover a diverse range of topics, highlighting advancements and challenges in the field of natural language processing. These papers, brought to light by the C4AI research Discord community, explore themes like data provenance, efficient evaluation of language models, toxicity mitigation, privacy in document generation, model pruning, self-reflective generation, 1-bit Transformers, controlled decoding, AI transparency, and innovative sequence modeling. Read more at Cohere.
Key Insights:
Data Provenance Initiative: Auditing AI datasets for legal compliance and transparency.
Efficient Human LLM Evaluation: Reducing evaluation time for language models using prioritization metrics.
GOODTRIEVER: Adaptive toxicity mitigation in language models using retrieval-augmented techniques.
Privacy with DP-Prompt: Enhancing privacy in document generation using LLMs and paraphrasing.
Sheared LLaMA: Pruning large language models for smaller, efficient versions.
SELF-RAG: Improving quality and factuality in LLMs through self-reflection and retrieval.
BitNet: 1-bit Transformer architecture for scalable, energy-efficient LLMs.
Controlled Decoding: Aligning LMs with specific goals using reinforcement learning.
Representation Engineering: Focusing on high-level cognitive phenomena for AI transparency.
GateLoop: Advancing sequence modeling with data-controlled state transitions.
🗞️ IN THE NEWS
AI isn’t and won’t soon be evil or even smart, but it’s also irreversibly pervasive: Artificial intelligence – or rather, the variety based on large language models we’re currently enthralled with – is already in the autumn of its hype cycle, but unlike crypto, it won’t just disappear into the murky, undignified corners of the internet once its ‘trend’ status fades. Instead, it’s settling into a place where its use is already commonplace, even for purposes for which it’s frankly ill-suited. Read more at TechCrunch.
Is connecting Grok AI to X ‘spicy’ or risky?: xAI claims its fledgling chatbot will have an edge by accessing real-time information on X, but the platform has been criticized and investigated for spreading misinformation. Read more: SiliconRepublic.
Computational model captures the elusive transition states of chemical reactions: Using generative AI, MIT chemists created a model that can predict the structures formed when a chemical reaction reaches its point of no return. Read more at news.mit.edu.
The Hidden Influence of Data Contamination on Large Language Models: Data contamination in Large Language Models (LLMs) is a significant concern that can impact their performance on various tasks. It refers to the presence of test data from downstream tasks in the training data of LLMs. Read more at www.unite.ai.
Cooking Up A Storm: AI Will Drive Process Industry Innovation: The author shares experiences of cooking international seasonal dishes, underscoring the challenges in achieving consistency with natural ingredients. This reflects difficulties in manufacturing scale-up and process simulation. The article highlights the importance of IT/OT integration and process simulation in manufacturing, particularly in preparing for the advent of generative AI. Read more at www.forrester.com.
🗣️ ChatGPT
OpenAI faces stiff competition, leading to rumors of an imminent GPT-4.5 release, fueled by a user's claim and a supposed Google document. However, Sam Altman's succinct 'nah' in response to a leak query casts doubt on these speculations. Despite this, the timing seems opportune for an OpenAI launch. The landscape is competitive, with Google's Gemini Ultra and other rivals challenging OpenAI's GPT-4. OpenAI's advancements, like its use of new AMD GPUs and focus on Super Alignment, indicate ongoing progress. Meta's upcoming Llama 3 and Altman's hints about GPT-5 and project Q* add to the dynamic environment. Amidst this, releasing GPT-4.5 could be a strategic move for OpenAI. Read more at analyticsindiamag.com.
🔦 INDUSTRY SPOTLIGHT
Why retailers are betting that natural language search and generative AI are the future of shopping: Microsoft's Keith Mercier envisions a future where natural language enhances online shopping, offering personalized experiences like finding specific items or budget-friendly gifts. This concept bridges the gap between data-rich e-commerce and service-oriented physical stores. Walmart's CTO Sravana Karnati sees generative AI as a shopping assistant, simplifying customer experiences. However, older consumers and regulators might be hesitant, while Hanshow's VP Klaus Smets believes younger generations will adapt quickly, recognizing AI's potential in retail. Read more at fortune.com.
🧰 AI TOOLS
🧰 Schemawriter.ai: Automated webpage schema and entity optimization. Published on 14 Dec 2023. Read more at: futurepedia.io.
🧰 BeeDone: A Gamified Task Management App. Published on 14 Dec 2023. Read more at: futurepedia.io.
🧰 Lutra AI: User-Friendly AI Integration into your Workflows. Published on 14 Dec 2023. Read more at: futurepedia.io.
🧰 Mistral AI: Unlock the Power of Open-Source AI with Mistral AI. Published on 14 Dec 2023. Read more at: futurepedia.io.
👩💻👨💻 AI CAREERS
The AI job market is rapidly growing due to various factors: rising consumer expectations for personalized services, industry-wide AI adoption, technological advancements, data proliferation, diverse job roles, automation goals, government initiatives, educational programs addressing skill gaps, ethical considerations, and global competitiveness. These elements collectively fuel an increasing demand for AI professionals across multiple domains. Read more at DeadLineNews.
📚LEARNING
Build or buy cloud-based generative AI:
Generative AI is pushing some critical decisions and doing so very fast. Every organization faces the crucial decision of whether to build a custom generative AI platform internally or buy a prepackaged solution from an AI vendor, generally delivered as a cloud service. The numbers and the opportunities are working in favor of DIY. That’s very strange, but the reasons may surprise you. They may even lead you to rethink your enterprise GenAI strategy. Building a generative AI platform from scratch gives an enterprise total control over its features and functions. The AI tech can precisely adapt to the organization’s requirements. This ensures compliance with the company’s unique workflows and provides a bespoke user experience. Remember that DIY generative AI can be done on public, private, or traditional platforms. Read more at www.infoworld.com.
🎙️PODCAST
The "Normsky" architecture for AI coding agents
Listen at Latent.Space
The podcast episode from Latent Space focused on the evolution and importance of the Retrieval-Augmented Generation (RAG) in AI engineering.
Key Points:
RAG is crucial in AI coding, enhancing workflows with efficient code search and retrieval.
Beyang Liu and Steve Yegge from SourceGraph have extensive experience in code indexing and retrieval.
Cody, SourceGraph's AI coding assistant, utilizes a dense-sparse vector retrieval system, offering high-quality context for code completions.
SourceGraph's success with Cody involves integrating various models and focusing on context enhancement.
SCIP, an efficient code indexing format developed by SourceGraph, surpasses LSIF indexers in performance.
Discussions highlighted the balancing act between Chomsky's formal systems approach and Norvig's data-driven models in AI development.
SourceGraph aims to address broader challenges in software development, moving beyond just code generation to managing complex codebases and aiding engineering managers.
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