AI Reading List 1: Understand Transformers, Reflection-70B Update, and LLMs Still Cannot Reason

Society's Backend Reading List 10-07-2024

I’ve had a lot of names for my weekly articles sharing my updates, resources, and the things I read/watch throughout the week with all of you. I’ve tried to be creative with the naming to make it more indicative of the information I’m sharing, but nothing’s really stuck. So instead, I’m just going to start calling them what they are: an AI Reading List.

If you want a comprehensive list of AI sources to read and watch that will help you learn and keep up with AI, these will be articles for you. I will only include the best sources that I’ve found interesting/helpful this week myself. As always, I’ll post the top ten for everyone and a full list (usually around 50 sources) to paid supporters of Society’s Backend.

I’m also planning to change up how I report on ML-related jobs. Previously I’ve included small blurbs within these weekly articles with interesting opportunities and info about finding a job in ML. I’ve realized weekly reports aren’t the proper cadence. I’m moving these to monthly and I’ll be sending them out to paid supporters first. I’m going to try and get someone on the recruiting side of things involved so these can be more helpful.

A huge thanks to all supporters! 😊

I’ll continue to include the paper podcasts and I’ll include any interesting information I want to share in a forward (like this!). If you want to support Society’s Backend for the full reading list or the job reports, you can do so for $1/mo.

What’s Happened this Past Week

If you want a good overview of the happenings in the world of AI this past week:

• Read

  Charlie Guo
  ‘s AI Round Up. He does an excellent job of going over pretty much everything that has happened in the world of AI and he provides links for digging deeper into it.

• Read (and subscribe to) The Batch’s newsletter. You can read the most recent edition here.

Papers Podcast

ML papers are difficult to keep up with. Here’s the week’s NotebookLM-generated podcast going over important papers you should know:

Reading List

Character-driven AI

Jurgen Gravestein

discusses the importance of crafting the character of AI models, arguing that character influences how AI aligns with human values and responds to various situations. He notes that while general-purpose AI models have similar capabilities, their unique characters can enhance user engagement and differentiate products in a competitive market. By focusing on character, AI can better align with human expectations and provide a more personalized experience.

The significance lies in highlighting the potential of character-driven AI to improve alignment, safety, and product differentiation in a rapidly maturing technological landscape.

Source

Cursor Team: Future of Programming with AI | Lex Fridman Podcast #447

In Lex Fridman's podcast episode #447, the Cursor Team discusses the transformative impact of artificial intelligence on the future of programming. They explore how AI tools can enhance productivity, automate coding tasks, and potentially revolutionize the software development landscape. The conversation delves into both the opportunities and challenges that come with integrating AI into programming.

Understanding the role of AI in programming is crucial as it can significantly alter how software is developed, leading to more efficient processes and innovative solutions.

Transformers, explained: Understand the model behind GPT, BERT, and T5

Transformers are a groundbreaking type of neural network architecture that revolutionized the field of natural language processing by efficiently handling large text data sets through innovations like positional encodings, attention, and self-attention. Unlike previous models like Recurrent Neural Networks (RNNs), transformers can process words in parallel, enabling the training of massive models like GPT-3 on vast amounts of data, which significantly improves language tasks such as translation, summarization, and code generation. Key models based on transformers, such as BERT, have become essential tools for various applications, including Google Search and language processing tasks.

The importance of transformers lies in their ability to efficiently learn and apply complex language patterns, drastically improving the performance and scalability of machine learning models in natural language processing.

How I taught myself to get cutting edge in AI

The article by

Devansh

emphasizes that learning Machine Learning and AI effectively requires a personalized and flexible approach rather than following a standardized path of courses and certifications. It suggests immersing oneself in cutting-edge work and focusing on understanding real-world problems, which fosters a mentality and skill set crucial for success in the rapidly evolving AI field. The approach is tailored for technical individuals willing to commit time to unstructured learning over several months.

Understanding and adapting to the dynamic nature of AI knowledge is crucial for effectively navigating and succeeding in this rapidly advancing field.

Source

7 Machine Learning Algorithms Every Data Scientist Should Know

The article outlines seven essential machine learning algorithms that data scientists should know: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, and K-Means Clustering. Each algorithm's basic workings, key considerations, and potential project applications using the scikit-learn library are discussed. The guide emphasizes understanding these algorithms as foundational tools in machine learning for solving various data-driven problems.

Understanding these core algorithms is crucial for data scientists to effectively apply machine learning methods to diverse real-world challenges, enhancing their analytical capabilities.

Source

Update on Reflection-70B

Sahil Chaudhary discusses the miscommunication surrounding the benchmark scores of the Reflection-70B model, clarifying how to reproduce these scores and addressing the initial errors and discrepancies. He provides access to the model's weights, training data, scripts, and evaluation code, aiming to ensure transparency and reproducibility. Chaudhary acknowledges mistakes made during the model's rushed release, including inadequate testing and communication of its capabilities and limitations.

The significance of this article lies in its commitment to transparency and accountability within the open-source AI community, offering tools and insights to ensure more reliable benchmarking practices.

Source

These 14 Health AI Companies Have Been Lying About What Their AI Can Do (Part 1 of 2)

The article investigates 14 health AI companies accused of misrepresenting their AI models' accuracy or stealing AI technology, highlighting concerns over misleading claims in the industry. The author aims to assist regulatory bodies by exposing these companies, and advises skepticism towards AI accuracy claims, providing key questions for evaluating such assertions. The critique extends to major tech players like OpenAI, IBM, and Epic for their role in setting low standards for AI in healthcare, contributing to a tarnished reputation for AI in medicine.

This investigation is significant as it addresses the ethical concerns and potential risks of unreliable AI in healthcare, emphasizing the need for accountability and rigorous validation of AI technologies.

Source

Introducing the Realtime API

OpenAI has launched a public beta of the Realtime API, allowing paid developers to incorporate low-latency, multimodal speech-to-speech capabilities into their applications, similar to the Advanced Voice Mode in ChatGPT. This API simplifies the development of natural conversational experiences by streaming audio inputs and outputs directly, eliminating the need to combine multiple models and reducing latency. The Realtime API is designed with safety measures and privacy commitments, and pricing is structured per audio and text tokens.

The introduction of the Realtime API is significant because it enables developers to create faster and more seamless voice interactions, enhancing user experiences across various applications, including language learning, customer support, and accessibility.

Source

No, LLMs Still Cannot Reason - Part II

The article by Alejandro Piad Morffis argues that Large Language Models (LLMs) are fundamentally incapable of performing deductive reasoning due to their probabilistic nature and lack of a robust validation mechanism. It critiques common misconceptions such as comparing LLMs' reasoning abilities to humans and the role of randomness in AI, emphasizing the inherent limitations of LLMs in solving reasoning tasks reliably. The article stresses the importance of recognizing these limitations, as reliance on LLMs for critical decision-making could have severe consequences, highlighting the need for significant advancements to make LLMs trustworthy for reasoning tasks. 

Understanding the limitations of LLMs is crucial as their widespread use in decision-making processes grows, potentially leading to significant risks if their reasoning capabilities are overestimated.

Source

From Liquid Neural Networks to Liquid Foundation Models

Liquid AI has developed liquid neural networks, a type of adaptable, brain-inspired system capable of efficiently learning new skills, modeling long-term dependencies, and providing causal and interpretable insights. They have also advanced continuous-time models and introduced state-of-the-art architectures for time series, video, and generative design in DNA applications. Their work extends to scaling language models with deep signal processing, enhancing graph neural networks, and improving interpretability and dataset distillation in neural networks.

These innovations are significant as they push the boundaries of machine learning, making models more adaptable, efficient, and applicable to a wide range of complex tasks, from language processing to biological design.

Source

What Comes After SB 1047?

California Governor Gavin Newsom vetoed SB 1047, a bill that aimed to create a comprehensive regulatory framework for AI, citing its overly ambitious scope. Despite this veto, Newsom showed commitment to AI regulation by signing 17 other AI-related bills and signaled that California might take a leading role in AI oversight if federal action lags. The article discusses potential future regulatory approaches and the importance of balancing regulation with technological advancement.

The significance of the article lies in highlighting the ongoing debate and strategic considerations around AI regulation in California, which could influence AI policy across the United States.

Source