Big A.I. R&D Risks Reap Big Societal Rewards, with Meta’s Dr. Laurens van der Maaten

By making big research bets, the prolific Meta Senior Research Director Dr. Laurens van der Maaten has devised or supported countless world-changing machine-learning innovations across healthcare, climate change, privacy and more.

Laurens:
• Is a Senior Research Director at Meta, overseeing swathes of their high-risk, high-reward A.I. projects with application areas as diverse as augmented reality, biological protein synthesis and tackling climate change.
• Developed the "CrypTen" privacy-preserving ML framework.
• Pioneered web-scale weakly supervised training of image-recognition models.
• Along with the iconic Geoff Hinton, created the t-SNE dimensionality reduction technique (this paper alone has been cited over 36,000 times).
• In aggregate, his works have been cited nearly 100,000 times!
• Holds a PhD in machine learning from Tilburg University in the Netherlands.

Today’s episode will probably appeal primarily to hands-on data science practitioners, but there is tons of content in this episode for anyone who’d like to appreciate the state of the art in A.I. across a broad range of socially impactful, super-cool applications.

In this episode, Laurens details: 
• How he pioneered learning across billions of weakly labeled images to create a state-of-the-art machine-vision model.
• How A.I. can be applied to the synthesis of new biological proteins with implications for both medicine and agriculture.
• Specific ways A.I. is being used to tackle climate change as well as to simulate wearable materials for enhancing augmented-reality interactivity.
• A library just like PyTorch but where all the computations are encrypted.
• The wide range of applications of his ubiquitous dimensionality-reduction approach.
• His vision for the impact of A.I. on society in the coming decades.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

GPT-4 Has Arrived

SuperDataScience episode #666 — appropriate for an algorithm that has folks (quixotically) signing a letter to pause all A.I. development. In this first episode of the GPT-4 trilogy; in ten minutes, I introduces GPT-4's staggering capabilities.

A Leap in AI Safety and Accuracy

GPT-4 marks a significant advance over its predecessor, GPT-3.5, in terms of both safety and factual accuracy. It is reportedly 82% less likely to respond with disallowed content and 40% more likely to produce factually correct responses. Despite improvements, challenges like sociodemographic biases and hallucinations persist, although they are considerably reduced.

Academic and Professional Exam Performance

The prowess of GPT-4 becomes evident when revisiting queries initially tested on GPT-3.5. Its ability to summarize complex academic content accurately and its human-like response quality are striking. In one test, GPT-4’s output was mistaken for human writing by GPTZero, an AI detection tool, underscoring its sophistication. In another test, the uniform bar exam, GPT-4 scored in the 90th percentile, a massive leap from GPT-3.5's 10th percentile.

Multimodality

GPT-4 introduces multimodality, handling both language and visual inputs. This capability allows for innovative interactions, like recipe suggestions based on fridge contents or transforming drawings into functional websites. This visual aptitude notably boosted its performance in exams like the Biology Olympiad, where GPT-4 scored in the 99th percentile.

The model also demonstrates proficiency in numerous languages, including low-resource ones, outperforming other major models in most languages tested. This linguistic versatility extends to its translation capabilities between these languages.

The Secret Behind GPT-4’s Success

While OpenAI has not disclosed the exact number of model parameters in GPT-4, it's speculated that they significantly exceed GPT-3's 175 billion. This increase, coupled with more and better-curated training data, and the ability to handle vastly more context (up to 32,000 tokens), are likely contributors to GPT-4's enhanced performance.

Reinforcement Learning from Human Feedback (RLHF)

GPT-4 incorporates RLHF, a method that refines its output based on user feedback, allowing it to align more closely with desired responses. This approach has already proven effective in previous models like InstructGPT.

GPT-4 represents a monumental step in AI development, balancing unprecedented capabilities with improved safety measures. Its impact is far-reaching, offering new possibilities in various fields and highlighting the importance of responsible AI development and use. As we continue to explore its potential, the conversation around AI safety and ethics becomes increasingly vital.
The SuperDataScience GPT-4 trilogy is comprised of:
• #666 (today): an introductory overview by yours truly
• #667 (Tuesday): world-leading A.I.-monetization expert Vin Vashishta joins me to detail how you can leverage GPT-4 to your commercial advantage
• #668 (next Friday): world-leading A.I.-safety expert Jeremie Harris joins me to detail the (existential!) risks of GPT-4 and the models it paves the way for

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.