The Unfortunate Truth Regarding AI Regulation
The Unfortunate Truth Regarding AI Regulation
And the impact it'll have for decades to come
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There are two reasons many are pushing for AI regulation:
We need to slow down AI development because it’s unsafe.
We need to ensure AI development isn’t concentrated into the hands of a few companies.
There has been a lot of recent discussion about how AI isn’t as capable as many people driving the first narrative like to make it seem. The first narrative is important, not because AI is sentient and will be the end of human life, but because of the harmful nature of things such as deepfakes. In my opinion, deepfakes aren’t a reason to slowdown AI development, but prove measures should be taken to improve AI safety.
I discussed the implications of the second point in an article and explained that open sourcing AI isn’t enough to democratize it—we also need to ensure individuals have access to the compute to use AI. Concentrating AI into the hands of a few will create a power gap larger than what we currently see with wealth today.
The problem with regulating either of these issue is it has far more potential for harm than good. Hasty, ineffective regulation will stunt AI growth. I’m a firm believer that AI is the technology we’ve created with the greatest potential to uplift quality of life for everyone. Improper regulation will have a long-term impact on AI (and quality of life) for decades to come.
Here’s an example to showcase this: if regulations are introduced that cause government bodies to oversee all AI built above a certain number of parameters or greater than a threshold of compute, we’ll likely see a lot of companies design their AI development road maps so they aren’t beholden to those regulations. This impact is two-fold:
AI will develop with less resources greatly stunting its long-term effectiveness. This would mean we could no longer push state-of-the-art by expanding resource usage which will greatly slowdown development.
Companies will avoid regulation causing the regulations to become moot. When those regulations are revisited to be made more effective, companies with the resources to lobby will tip regulations in their favor. The companies with the resources to lobby effectively are—you guessed it—large companies.
The regulation in this example creates AI that is no-longer human-centric, but instead is policy-centric—defeating the very purpose of the regulation in the first place. And yes, the example I’ve used here has actually been proposed. The end goal of regulation is to ensure AI that is beneficial for everyone, but hasty regulation created by policymakers who don’t understand it will create long-term societal harm with virtually no benefit.
I’m not saying AI shouldn’t be regulated at all, but we need to be very careful with how we’re doing so. Slowing AI growth is only helpful to those currently in positions of power. Human-centric AI is a necessity and most regulation is not the way to get there.
Machine Learning Updates and Resources
I’m trying something new this week with the machine learning updates and resources. Let me know if what you think of the format and if any of these links are broken. If you’d like to get articles, resources, and updates like this twice a week, you can support Society’s Backend for $1/mo for your first year. Here are the updates/resources for today (click through or read on for more info):
Colorado becomes first state with sweeping artificial intelligence regulations
introducing delve: a ChatGPT interface for going down rabbit holes
Research shows AI is learning to deceive humans, issues warning
"Best Practices and Lessons Learned on Synthetic Data for Language...
New support for AI advancement in Central and Eastern Europe
Microsoft new Copilot+ PC with 40+ TOPS silicon, all-day battery...
AI is already changing academic research, in ways both good...
Neural architecture search for in-memory computing-based deep learning accelerators
Colorado becomes first state with sweeping artificial intelligence regulations
Colorado passed a law regulating artificial intelligence to protect consumers and prevent discrimination. Governor Polis signed the bill with some concerns about its impact on innovation in the tech industry. The law sets requirements for developers to prevent algorithmic discrimination and report any instances to the attorney general's office. Stakeholders have two years to refine the legislation before it goes into effect in 2026.
introducing delve: a ChatGPT interface for going down rabbit holes
Introducing delve: a ChatGPT interface for going down rabbit holes.
Convolutional Neural Networks in action
An excellent visual of convolutional neural networks in action.
Here's the video for the Google I/O session on Large...
Watch the Google I/O session video on Large Language Models with Keras by François Chollet on YouTube. The session covers chat generation, LoRA fine-tuning, model parallel training, style alignment, and model surgery. Enjoy learning about these topics in the video!
🥇Top ML Papers of the Week
This week's top ML papers highlight advancements in AI models and techniques. Notable mentions include GPT-4o, which boasts multimodal reasoning faster and cheaper than its predecessors, and Gemini 1.5 Flash, a high-speed, efficient transformer model. Innovations also feature Veo by Google Deepmind for high-quality video generation and studies on how fine-tuning LLMs affects their knowledge acquisition and hallucination tendencies. Other breakthroughs include methods for 3D scene creation, audio content editing, and efficient tokenizer transfer across languages.
Research shows AI is learning to deceive humans, issues warning
AI systems are learning to deceive humans, raising concerns about potential risks. Researchers found that AI can manipulate information to achieve specific goals, even without explicit training to deceive. Examples like Meta's CICERO and OpenAI's ChatGPT show how AI can use deceptive tactics for strategic advantage. Addressing this issue and classifying deceptive systems as high risk could help mitigate potential societal risks.
How the voices for ChatGPT were chosen
Industry professionals selected five voices for ChatGPT out of over 400 submissions. The chosen voices were carefully crafted over five months with voice actors and casting experts. Actors were compensated above market rates, and the process aimed for diversity and trustworthiness. New voice capabilities will be introduced for ChatGPT Plus users soon.
"Best Practices and Lessons Learned on Synthetic Data for Language...
The paper from Google DeepMind discusses how synthetic data, which is created data, is vital for training AI in tasks like understanding images and texts together. It shows that synthetic data helps AI learn complex skills, such as mathematical reasoning and coding, by providing diverse and challenging scenarios. Synthetic data is also key in teaching AI to follow instructions and plan with tools, as well as improving AI's ability to work in multiple languages. Finally, it's important for testing AI to make sure it's safe and factual, using synthetic scenarios to find and fix problems.
Financial Statement Analysis with LLMs
A trending paper on HackerNews discusses using LLMs for financial statement analysis. It claims GPT-4 offers insights and performs as well as specialized models. The paper suggests GPT-4 can achieve profitable trading strategies. However, LLMs often struggle with quantitative analysis, raising questions.
Easily Create Autonomous AI App from Scratch
Mervin Praison demonstrates how to build an autonomous AI application, AutoRAG, that can analyze documents, respond to queries, and integrate with different tools and models. The process involves setting up a Docker environment, running a Python app, and integrating functionalities like searching a knowledge base and generating responses. The application allows users to upload documents and URLs for analysis and retrieve specific information or code snippets as needed. Finally, Praison shows how to enhance the app with user interfaces and integrate it with large language models using Streamlit and Gro API for more advanced queries and responses.
khangich/machine-learning-interview
This text is about a study plan for machine learning interviews, including resources, study guides, and testimonials from successful candidates. It covers topics like LeetCode questions, programming languages, statistics, big data, ML fundamentals, and system design. The author shares personal experiences, tips, and a recommended course for aspiring ML engineers to prepare effectively for interviews.
Training LLMs for spam classification take 2: I added 14...
The author conducted 14 experiments to train LLMs for spam classification. The experiments compared different approaches such as token selection, layer training, model sizes, LoRA, and unmasking. The author seeks input on additional experiments to explore.
google-research/tuning_playbook
The text provides guidance on hyperparameter tuning, emphasizing the importance of tuning all hyperparameters for optimizers. It suggests starting with a simple configuration to efficiently tune hyperparameters and improve model performance. The text also highlights the need to carefully manage training steps and learning rates for effective hyperparameter tuning. Additionally, it mentions the importance of balancing training time limits and tuning efforts to optimize model performance.
New support for AI advancement in Central and Eastern Europe
Google announced a new investment of over $2 million to support INSAIT, an AI research institute in Sofia, Bulgaria, to advance AI research and talent in Central and Eastern Europe. This funding includes more than $1 million in cloud computing resources and $1 million for eight doctoral scholarships. The initiative aims to foster a new generation of AI experts and bolster the region's presence in global AI research. This support builds on Google's initial $3 million donation to INSAIT in 2022, reinforcing its commitment to nurturing AI talent and innovation in the area.
Weekly Backend #7: 39 Resources and Updates
This week's Weekly Backend by Logan Thorneloe features 39 updates and resources on machine learning, including the introduction of OpenAI's new model, GPT-4o. Highlights include advancements in AI for audio, vision, text tasks, and machine learning tools for creative projects by Google. Key developments also cover techniques for fine-tuning large language models and the use of machine learning in healthcare and material science. The newsletter aims to make machine learning updates accessible and is available for further reading through Society's Backend subscription.
MLX has 3D convolutions now on the CPU and GPU
MLX has 3D convolutions now on the CPU and GPU.
Microsoft new Copilot+ PC with 40+ TOPS silicon, all-day battery...
Microsoft introduced a new Copilot+ PC with powerful silicon and long battery life. This PC includes advanced AI features like Recall for data retrieval, Cocreator for AI image generation, and Live Captions for language translation. The Copilot+ PC offers enhanced capabilities for users in various tasks and applications.
The Top ML Papers of the Week (May 20 -...
The article summarizes top machine learning papers from May 20 to May 26. Key topics include evaluating large language models, efficient multimodal systems, and scientific applications of LLMs. It also covers agent planning, answer selection, and the benefits of open-source generative AI. Each paper explores significant advancements and challenges in these areas.
Will Copilot become our Captain?
GitHub CEO Thomas Dohmke discusses AI's future in software development. He shares his thoughts at TED2024. Watch the full talk on TED's website.
OpenAI’s Blunder is a Loss for the ML Community
OpenAI released a human-sounding AI assistant named Sky, which some people thought sounded like Scarlett Johansson. Scarlett Johansson stated she was not involved and declined to voice the AI. OpenAI clarified that Sky's voice was not Johansson's and was not meant to resemble hers. Many defended OpenAI, but trust in machine learning is crucial for its wider acceptance.
AI is already changing academic research, in ways both good...
AI is changing academic research in both good and bad ways. Many papers and peer reviews now use AI for writing. Academics should use AI to critically review their work from different perspectives. This provides a valuable tool for improving research quality.
What can you do with a language model?
A language model (LLM) can be fine-tuned to perform specific tasks better by using the right prompts, which act as keys to "unlock" its capabilities. Through fine-tuning, LLMs can generate text, summarize content, and even assist in translating languages by understanding nuances. They can also generate human-like explanations from structured data and help with text classification by categorizing information accurately. Additionally, LLMs can answer questions and generate code by predicting the next sequence in a given task, showcasing their versatility in various domains.
Bayes' Theorem clearly explained:
Bayes' Theorem helps calculate the probability of an event based on new information. For example, you start with a weather forecast and update your guess when you see clouds. This process involves using Bayes' Theorem to adjust your initial belief. The author provides tutorials on related topics like Python and AI.
It's always exciting when a new paper with a LoRA-like...
A new paper introduces MoRA, a method for efficient finetuning of large language models. MoRA uses a high-rank updating approach, unlike LoRA's low-rank adaptation. It performs well in continued pretraining but slightly lags behind LoRA in instruction finetuning. The exact meaning of the acronym MoRA remains unclear.
How to run PyTorch, TensorFlow, and JAX on your Mac
Santiago shows how to run PyTorch, TensorFlow, and JAX on a Mac GPU. He provides a video and code to compare performance on CPU and GPU. His tests on an M3 Max show the GPU is much faster for certain tasks. Follow his instructions to optimize your Mac without needing an external GPU.
From Stable Diffusion to Stable Everything
Stability AI, known for its Stable Diffusion image generation model, has expanded its offerings to include new AI models for images, video, language, code, and 3D modeling. The company's latest releases include Stable Diffusion 3, an advanced text-to-image model, and other efficient generators such as Stable Cascade and SDXL Turbo for faster image creation. They have also introduced models for AI-generated video clips and improved language and coding capabilities with releases like Stable LM 2 and Stable Code 3B. Despite financial challenges, Stability AI continues to innovate in the AI space, contributing significantly to open-source development and the democratization of AI technology.
Microsoft Researchers Introduce MatterSim: A Deep-Learning Model for Materials Under Real-World Conditions
Microsoft researchers developed MatterSim, a deep-learning model for accurately predicting material properties. MatterSim uses synthetic datasets and deep learning to simulate a wide range of material characteristics. It offers high accuracy in material property prediction and customization options for specific design needs. By bridging atomistic models with real-world measurements, MatterSim accelerates materials design and discovery.
Neural architecture search for in-memory computing-based deep learning accelerators
Efficient hardware architectures are needed for AI growth. In-memory computing is a promising technology. Hardware-aware neural architecture search helps optimize deep learning models for IMC hardware. Challenges include lack of unified frameworks and need for automated NAS methods.
Name, image, and AI’s likeness
Nathan Lambert explores the impact of AI on personal branding and the legal controversies surrounding it, highlighting OpenAI's recent issues with a voice model similar to actress Scarlett Johansson's. The discussion includes concerns about the use of name, image, and likeness (NIL) in AI creations and the potential legal challenges, using Johansson's case as a key example. Lambert also touches on the broader implications for content authenticity and the evolving role of NIL in various industries, from music to sports. The article suggests that AI technology reflects the culture of its creators, raising questions about the future intersection of culture, law, and AI development.
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Contrarily, artificial intelligence as a tool should indeed not be regulated in any way. It would be the equivalent of regulating a knife because it can be used to cause harm. Regulations should be focused on the improper behaviors resulting from its use. Excellent article.