Last June, Microsoft-owned GitHub and OpenAI launched Copilot, a service that provides suggestions for whole lines of code inside development environments like Microsoft Visual Studio. Available as a downloadable extension, Copilot is powered by an AI model called Codex that’s trained on billions of lines of public code to suggest additional lines of code and functions given the context of existing code. Copilot can also surface an approach or solution in response to a description of what a developer wants to accomplish (e.g. “Say hello world”), drawing on its knowledge base and current context.
While Copilot was previously available in technical preview, it’ll become generally available starting sometime this summer, Microsoft announced at Build 2022. Copilot will also be available free for students as well as “verified” open source contributors. On the latter point, GitHub said it’ll share more at a later date. Read More
Tag Archives: DevOps
Adept aims to build AI that can automate any software process
In 2016 at TechCrunch Disrupt New York, several of the original developers behind what became Siri unveiled Viv, an AI platform that promised to connect various third-party applications to perform just about any task. The pitch was tantalizing — but never fully realized. Samsung later acquired Viv, folding a pared-down version of the tech into its Bixby voice assistant.
Six years later, a new team claims to have cracked the code to a universal AI assistant — or at least to have gotten a little bit closer. At a product lab called Adept that emerged from stealth today with $65 million in funding, they are — in the founders’ words — “build[ing] general intelligence that enables humans and computers to work together creatively to solve problems.” Read More
‘No-Code’ Brings the Power of A.I. to the Masses
A growing number of new products allow anyone to apply artificial intelligence without having to write a line of computer code. Proponents believe the “no-code” movement will change the world.
This article is part of a new series on how artificial intelligence has the potential to solve everyday problems.
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Tools such as Teachable Machine from Google and Lobe from Microsoft, in addition to natural language low-code options, like those from OpenAI and DeepMind , are making applications development increasingly accessible.
Towards better data discovery and collection with flow-based programming
Despite huge successes reported by the field of machine learning, such as voice assistants or self-driving cars, businesses still observe very high failure rate when it comes to deployment of ML in production. We argue that part of the reason is infrastructure that was not designed for data-oriented activities. This paper explores the potential of flow-based programming (FBP) for simplifying data discovery and collection in software systems. We compare FBP with the currently prevalent service-oriented paradigm to assess characteristics of each paradigm in the context of ML deployment. We develop a data processing application, formulate a subsequent ML deployment task, and measure the impact of the task implementation within both programming paradigms. Our main conclusion is that FBP shows great potential for providing data-centric infrastructural benefits for deployment of ML. Additionally, we provide an insight into the current trend that prioritizes model development over data quality management. Read More
#devopsFILM: Frame Interpolation for Large Scene Motion
Tensorflow 2 implementation of our high quality frame interpolation neural network. We present a unified single-network approach that doesn’t use additional pre-trained networks, like optical flow or depth, and yet achieve state-of-the-art results. We use a multi-scale feature extractor that shares the same convolution weights across the scales. Our model is trainable from frame triplets alone. Read More
Too Lazy to Write Documentation? Let the AI Write It for You
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
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
Intel’s ControlFlag Debugging Tool Uses AI To Clean Up Code And It’s Now Open Source
In 2020 a study showed the IT industry spent an estimated $2 trillion in software development associated with debugging code. The study also showed that 50 percent of IT budgets were allocated to debugging code alone. Intel hopes to change those numbers by making its ControlFlag tool open-source.
ControlFlag is an AI-powered tool created by Intel to detect bugs within computer code using advanced self-supervised machine learning (ML). The software developed last year was able to locate hundreds of confirmed software defects in proprietary, production-quality software systems in just a few analyses of source code repositories. Its machine learning techniques enable it to find coding anomalies, reduce time spent debugging and improving the quality and security of systems autonomously. Read More
AI can write code like humans-mistakes and all
Some software developers Now let artificial intelligence Help write their code. They found that artificial intelligence is as flawed as humans.
Last June, GitHub, Subsidiary Microsoft Provide tools for hosting and collaborative code, Released A beta version of a program that uses AI to assist programmers.Start typing commands, database queries or requests to API, and then call the program Co-pilot, Will guess your intentions and write the rest.
Alex NakaA data scientist at a biotechnology company, he signed up for the Copilot test. He said that the program was very helpful and changed the way he works. “It allows me to spend less time jumping to the browser to find API documentation or examples on Stack Overflow,” he said. “It feels a bit like my job has changed from a code generator to a code discriminator.” Read More
Machine learning’s crumbling foundations
Technological debt is insidious, a kind of socio-infrastructural subprime crisis that’s unfolding around us in slow motion. Our digital infrastructure is built atop layers and layers and layers of code that’s insecure due to a combination of bad practices and bad frameworks.
Even people who write secure code import insecure libraries, or plug it into insecure authorization systems or databases. Like asbestos in the walls, this cruft has been fragmenting, drifting into our air a crumb at a time.
We ignored these, treating them as containable, little breaches and now the walls are rupturing and choking clouds of toxic waste are everywhere. Read More
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