I’ve never met a developer that enjoys writing documentation. At the very least they understand the value of it and will begrudgingly write it, but will never enjoy the process of writing it.
Some people go by the philosophy that good code should document itself, but if this were true then why is that one person who is familiar with the entire codebase so valuable to a team? There is a lot of knowledge, reasoning, and context that cannot simply be deduced from raw code. Good documentation that’s well-maintained only adds value and context to a codebase.
… AI Doc Writer for Javascript, Typescript, Python, and PHP is a VS Code extension that generates documentation for you using AI. The way it works is that you select the code you want to document and you press the ‘Generate docs’ button or hit the keyboard shortcut Cmd/Ctrl + . Read More
Daily Archives: February 3, 2022
Meet the NSA spies shaping the future
For someone with a deeply scientific job, Gil Herrera has a nearly mystical mandate: Look into the future and then shape it, at the level of strange quantum physics and inextricable math theorems, to the advantage of the United States.
Herrera is the newly minted leader of the National Security Agency’s Research Directorate. The directorate, like the rest of the NSA, has a dual mission: secure American systems and spy on the rest of the world. The budget is classified, a secret among secrets, but the NSA is one of the world’s largest spy agencies by any measure and Herrera’s directorate is the entire US intelligence community’s biggest in-house research and development arm. The directorate must come up with solutions to problems that are not yet real, in a world that doesn’t yet exist.
In his first interview since getting the job, Herrera lays out the tech—and threats—his group will now be focusing on. His priorities show how much the NSA’s targets are changing, balancing its work surveilling terror groups with an appreciation of how rapidly the geopolitical landscape has shifted in recent years. And he explains why the rise of new technologies, in terms of both threat and opportunity, are at the heart of what his group must contend with. Read More
Fake It Till You Make It
We demonstrate that it is possible to perform face-related computer vision in the wild using synthetic data alone.
The community has long enjoyed the benefits of synthesizing training data with graphics, but the domain gap between real and synthetic data has remained a problem, especially for human faces. Researchers have tried to bridge this gap with data mixing, domain adaptation, and domain-adversarial training, but we show that it is possible to synthesize data with minimal domain gap, so that models trained on synthetic data generalize to real in-the-wild datasets.
We describe how to combine a procedurally-generated parametric 3D face model with a comprehensive library of hand-crafted assets to render training images with unprecedented realism and diversity. We train machine learning systems for face-related tasks such as landmark localization and face parsing, showing that synthetic data can both match real data in accuracy as well as open up new approaches where manual labelling would be impossible. Read More
Dataset
Competitive programming with AlphaCode
Creating solutions to unforeseen problems is second nature in human intelligence – a result of critical thinking informed by experience. The machine learning community has made tremendous progress in generating and understanding textual data, but advances in problem solving remain limited to relatively simple maths and programming problems, or else retrieving and copying existing solutions. As part of DeepMind’s mission to solve intelligence, we created a system called AlphaCode that writes computer programs at a competitive level. AlphaCode achieved an estimated rank within the top 54% of participants in programming competitions by solving new problems that require a combination of critical thinking, logic, algorithms, coding, and natural language understanding.
In our preprint, we detail AlphaCode, which uses transformer-based language models to generate code at an unprecedented scale, and then smartly filters to a small set of promising programs.
We validated our performance using competitions hosted on Codeforces, a popular platform which hosts regular competitions that attract tens of thousands of participants from around the world who come to test their coding skills. We selected for evaluation 10 recent contests, each newer than our training data. AlphaCode placed at about the level of the median competitor, marking the first time an AI code generation system has reached a competitive level of performance in programming competitions.
To help others build on our results, we’re releasing our dataset of competitive programming problems and solutions on GitHub, including extensive tests to ensure the programs that pass these tests are correct — a critical feature current datasets lack. We hope this benchmark will lead to further innovations in problem solving and code generation. Read More
Rick's Cafe AI 