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This AI newsletter is all you need #52

This AI newsletter is all you need #52

Last Updated on June 20, 2023 by Editorial Team

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This AI newsletter is all you need #52

What happened this week in AI by Louie

Are we on the right track to achieve Artificial General Intelligence (AGI)? This is a discussion that’s popping up all over the place now that ChatGPT and Large Language Models (LLMs) are taking over. First, what does AGI mean? It refers to the development of AI systems that possess the ability to understand, learn, and perform tasks across a wide range of domains with the same level of proficiency as a human being.

Meta’s AI Chief scientist, Yann Lecun, believes we have a long way to go! He pointed out that the current challenge lies in the fact that LLMs primarily focus on language and lack emotions, creativity, sentience, or consciousness. It makes him believe that these models are not even as smart as dogs. Furthermore, the Allen Institute for AI and three universities have collaborated on a project to investigate the constraints and limitations of the current NLP cutting-edge architecture, Transformers. Their research showed that these models learn to solve complex problems by pattern matching in a step-by-step manner. It means that they do not develop problem-solving skills.

While it’s true that achieving Artificial General Intelligence (AGI) may still be far off, it is essential to acknowledge the limitations of current technologies, especially during this time of heightened excitement. Identifying and addressing current limitations can pave the way for future advancements!

Hottest News

  1. GPT-4 Can Use Tools Now — That’s a Big Deal

OpenAI’s GPT-4 language model can now leverage external tools to accomplish tasks, marking a significant improvement over previous versions of GPT. This development holds two major implications: Firstly, it significantly enhances the power of GPT models. Secondly, it replaces some functionalities of open-source libraries that serve the same purpose.

2. AMD reveals new A.I. chip to challenge Nvidia’s dominance

AMD announced last Tuesday that its highly advanced GPU for artificial intelligence, the MI300X, is set to begin shipping to select customers later this year. This announcement presents the strongest challenge to Nvidia, which currently holds a dominant position in the AI chip market.

3. LLMs aren’t even as smart as dogs, says Meta’s AI chief scientist

According to Yann LeCun, Meta’s AI chief scientist, LLMs are not even as smart as dogs. He argues that LLMs lack true intelligence as they cannot understand, interact with, or comprehend reality; their output is solely based on language training. LeCun asserts that genuine intelligence goes beyond language and highlights that most human knowledge has little to do with language.

4. OpenAI CEO Sam Altman Asks China to Help in AI Regulation

OpenAI CEO Sam Altman has extended an invitation to China, seeking collaboration in the development of ‘guardrails’ for the AI sector in response to mounting concerns. China is anticipated to have a draft of its AI regulations ready for review this year, as the country takes steps to manage the proliferation of AI systems inspired by ChatGPT.

5. Signal’s Meredith Whittaker: ‘These are the people who could actually pause AI if they wanted to’.

Singal is one of the platforms opposing the bill introduced by the UK government that includes provisions to scan users’ messages for harmful content, among other things. The president of this not-for-profit messaging app expresses her belief that existential warnings about AI enable big tech companies to solidify their power. She also discusses the potential unworkability of the online safety bill.

Five 5-minute reads/videos to keep you learning

  1. The Secret Sauce Behind 100K Context Window in LLMs: all tricks in one place

The article explores various techniques aimed at accelerating the training and inference processes of large language models (LLMs) while utilizing a substantial context window of up to 100K input tokens. These techniques encompass ALiBi positional embedding, Sparse Attention, FlashAttention, Multi-Query attention, Conditional computation, and the utilization of 80GB A100 GPUs.

2. The New Language Model Stack

The adoption of language model APIs is giving rise to a new technology stack. This article examines data gathered from a survey conducted across 33 companies within the Sequoia network, aiming to gain insights into the applications being developed and the stacks being utilized. Nearly all of the surveyed companies utilize OpenAI’s GPT and consider a retrieval system to be a crucial component of their stack.

3. A machine learning engineer’s Guide to the AI Act

The AI Act marks a significant milestone in the regulatory framework for AI, indicating notable forthcoming changes for machine learning engineers. The enactment of the EU AI Act is expected in early 2024, with full enforcement set for 2026. This article provides essential information that AI/ML teams should be aware of regarding this newly regulated future.

4. Active learning clearly explained

Active learning enables the optimization of dataset annotation and facilitates the training of the best possible model with minimal training data. This tutorial offers an introduction to active learning, delving into its practical application through an innovative tool developed by Encord.

5. Using ChatGPT for Translation Effectively

ChatGPT has showcased its remarkable accuracy in handling translation tasks. In this post, you will learn how to utilize ChatGPT prompts to explore its translation capabilities. Specifically, you will discover how to translate a poem from English to Swedish, convert Julia code to Python, and enhance translation results.

Papers & Repositories

  1. Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks

This paper presents a case study on the prevalence of LLM usage among crowd workers. By employing a combination of keystroke detection and synthetic text classification, the study estimates that 33–46% of crowd workers utilized LLMs while completing the assigned tasks.

2. Demystifying GPT Self-Repair for Code Generation

This paper examines the self-repair capabilities of GPT-3.5 and GPT-4 on the challenging APPS dataset, comprising diverse coding challenges. The study finds that only GPT-4 demonstrates effective self-repair. It identifies the feedback stage as a bottleneck, and leveraging GPT-4 for providing feedback on programs generated by GPT-3.5, as well as expert human programmers providing feedback on programs generated by GPT-4, leads to substantial performance improvements.

3. Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models

The paper assesses the performance of large language models in meeting the graduation requirements for Mathematics and EECS majors at MIT. The curated dataset comprises 4,550 questions and solutions from problem sets, midterms, and finals across all relevant courses. GPT-3.5 accomplishes one-third of the MIT curriculum, while prompt-engineered GPT-4 achieves a perfect solve rate on a test set excluding image-based questions.

4. AntonOsika/gpt-engineer

GPT Engineer is another instance of the “AutoGPT” line of work. This model is designed for programming applications. Users can specify the desired output, the AI then seeks clarification if needed, and subsequently generates an entire codebase based on the given prompt.

5. Segment Any Point Cloud Sequences by Distilling Vision Foundation Models

This work introduces Seal, a novel framework that utilizes VFMs (Viewpoint Feature Matrices) for segmenting diverse automotive point cloud sequences. Seal exhibits three appealing properties: scalability, consistency, and generalizability. Moreover, Seal demonstrates substantial performance improvements over existing methods across 20 different few-shot fine-tuning tasks.

Enjoy these papers and news summaries? Get a daily recap in your inbox!

The Learn AI Together Community section!

Weekly AI Podcast

In this week’s episode of the “What’s AI” podcast, Louis Bouchard interviews Luis Serrano, an AI scientist, AI educator, and author known for his popular YouTube channel, Serrano.Academy, and the bestselling book “Grokking Machine Learning.” They delve into the fascinating world of artificial intelligence and its diverse applications. In this episode, Luis Serrano takes us on a captivating journey through the realm of LLMs, discussing their mind-blowing capabilities, the significance of AI education, and much more. If you are curious about the groundbreaking advancements in large language models (LLMs) and want to explore the world of AI education, tune in to the episode on YouTubeSpotify, or Apple Podcasts.

Meme of the week!

Meme shared by Rucha#8062

Featured Community post from the Discord

Craenius has just shared their latest project, NeuralDistance, which focuses on utilizing monocular vision to estimate distances and detect objects with remarkable accuracy. This project utilizes the YOLOv3 (You Only Look Once) object detection algorithm to detect objects in images or videos and estimate their distances from the camera. Additionally, the project includes a neural network model for distance estimation based on specific object annotations. Take a look at it on GitHub and support a fellow community member. Share your feedback and questions in the thread here.

AI poll of the week!

Join the discussion on Discord.

TAI Curated section

Article of the week

How AI is Used to Combat Social Engineering Attacks — Part 2 by John Adeojo

Social engineering attacks have emerged as the preferred form of cyber attack for criminals seeking to gain access to finances and data. This series of articles explores various AI approaches for detecting social engineering attacks. In this particular installment, we delve deeper into more advanced AI strategies, considering the potential of deep learning and generative AI approaches in combating phishing and other forms of social engineering attacks.

Our must-read articles

All About Time Series Pitfalls by Shrashti Singhal

Bagging vs. Boosting: The Power of Ensemble Methods in Machine Learning by Thomas A Dorfer

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This AI newsletter is all you need #52 was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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