Future Timeline

Generative Adversarial Networks (GANs) are a class of deep learning models that learn to produce new (or pseudo-real) data. Their advent in 2014 and refinement thereafter have led to them dominating the image generation domain for the past few years and laying the foundations of a new paradigm – deep fakes. Their ability to mimic training data and produce new samples similar to it has gone more or less unmatched. As such, they hold the state-of-the-art (SOTA) in most image generation tasks today.

Despite these advantages, GANs are notoriously hard to train and are prone to issues like mode collapse and unintelligible training procedures. Moreover, researchers have realized that GANs focus more on fidelity rather than capturing a diverse set of the training data's distribution. As such, researchers have been looking into improving GANs in this domain or eyeing other architectures that would perform better in the same domain.

Two researchers, Prafulla Dhariwal and Alex Nichol from OpenAI, one of the leading AI-research labs, took up the question and looked towards other architectures. In their latest work "Diffusion Models Beat GANs on Image Synthesis", published in the preprint repository arXiv this week, they show that a different deep learning architecture, called diffusion models, addresses the aforementioned shortcomings of GANs. They show that not only are diffusion models better at capturing a greater breadth of the training data's variance compared to GANs, but they also beat the SOTA GANs in image generation tasks.

OpenAI today launched the OpenAI Startup Fund, a $100 million fund to, in the words of OpenAI, “help AI companies have a profound, positive impact on the world.” The fund is managed by OpenAI, with investment from Microsoft and other partners, and OpenAI says that companies selected for it will get early access to future OpenAI systems, support from OpenAI’s team, and credits on Microsoft Azure.

According to Sam Altman, CEO of OpenAI and the former president of Y Combinator, the OpenAI Startup Fund will make “big, early bets” on a relatively small number of companies, likely no more than 10. It’ll look to partner with early-stage startups in fields where AI can have a “transformative” effect — like health care, climate change, and education — and where AI tools can empower people by helping them be more productive, like personal assistance and semantic search.

We’ve found we can improve language model behavior with respect to specific behavioral values by fine-tuning on a curated dataset of <100 examples of those values. We also found that this process becomes more effective as models get larger. While the technique is still nascent, we’re looking for OpenAI API users who would like to try it out and are excited to find ways to use these and other techniques in production use cases.

Language models can output almost any kind of text, in any kind of tone or personality, depending on the user’s input. Our approach aims to give language model operators the tools to narrow this universal set of behaviors to a constrained set of values. While OpenAI provides guardrails and monitoring to ensure that model use-cases are compatible with our Charter, we view selecting the exact set of Charter-compatible values for the model as a choice that our users must face for their specific applications.

What can a $1 billion investment buy?
On Tuesday this week, OpenAI and GitHub answered this question boldly with the preview of a new AI tool — GitHub Copilot. It can write user-compatible code and is much better at the task than its predecessor — GPT-3.
Copilot autocompletes code snippets, suggests new lines of code, and can even write whole functions based on the description provided. According to the GitHub blog, the tool is not just a language-generating algorithm based on user input — it is a virtual pair programmer.
It learns and adapts to the user’s coding habits, analyzes the available codebase, and generates suggestions backed by billions of lines of public code it has been trained on.

Yuli Ban wrote: ↑Fri Aug 13, 2021 9:35 pm
See also the animation. The complexity of the code that Codex generated from just 1 sentence is mind-blowing!

To safely deploy powerful, general-purpose artificial intelligence in the future, we need to ensure that machine learning models act in accordance with human intentions. This challenge has become known as the alignment problem.

A scalable solution to the alignment problem needs to work on tasks where model outputs are difficult or time-consuming for humans to evaluate. To test scalable alignment techniques, we trained a model to summarize entire books, as shown in the following samples.[1] Our model works by first summarizing small sections of a book, then summarizing those summaries into a higher-level summary, and so on.

Our best model is fine-tuned from GPT-3 and generates sensible summaries of entire books, sometimes even matching the average quality of human-written summaries: it achieves a 6/7 rating (similar to the average human-written summary) from humans who have read the book 5% of the time and a 5/7 rating 15% of the time. Our model also achieves state-of-the-art results on the BookSum dataset for book-length summarization. A zero-shot question-answering model can use our model’s summaries to obtain state-of-the-art on the NarrativeQA dataset for book-length question answering.

OpenAI has developed an AI model that can summarize books of arbitrary length. A fine-tuned version of the research lab’s GPT-3, the model works by first summarizing small sections of a book and then summarizing those summaries into higher-level summaries, following a paradigm OpenAI calls “recursive task decomposition.”

Summarizing book-length documents could be valuable in the enterprise, particularly for documentation-heavy industries like software development. A survey by SearchYourCloud found that workers take up to eight searches to find the right document, and McKinsey reports that employees spend 1.8 hours every day — 9.3 hours per week, on average — searching and gathering job-related information.

“OpenAI believes that this is an effective ‘recipe’ that can be used to help humans supervise many other tasks,” a spokesperson told VentureBeat via email. “A scalable solution to the alignment problem needs to work on tasks that are difficult or time-consuming for humans to evaluate.”