“Copilot” was trained using billions of lines of open-source code hosted on sites like Github. The people who wrote the code are not happy.
Open-source coders are investigating a potential class-action lawsuit against Microsoft after the company used their publicly-available code to train its latest AI tool.
On a website launched to spearhead an investigation of the company, programmer and lawyer Matthew Butterick writes that he has assembled a team of class-action litigators to lead a suit opposing the tool, called GitHub Copilot. Read More
Tag Archives: DevOps
Microsoft’s GitHub Copilot AI is making rapid progress. Here’s how its human leader thinks about it
GitHub’s Copilot AI can write up to 40% of the code for programmers and is heading up to 80% within five years, says GitHub CEO Thomas Dohmke.
This rapid AI advance is letting coders get their work done in less than half the time it used to take and has implications across all industries where software development is now critical, Microsoft board member and venture capitalist Reid Hoffman recently told a gathering of tech executives.
Still, Dohmke says as artificial intelligence accelerates and is adopted more broadly across companies, innovation remains a skill only humans can dominate. Read More
A Model For Technical Debt In Machine Learning Systems
Machine Learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed. Machine learning algorithms use historical data as input to predict new output values.
Technical Debt describes what results when development teams take conscious actions to expedite the delivery of a piece of functionality or a project which later needs to be remediated via refactoring. In other words, prioritizing speedy delivery over perfect code is the result.
This article will present a simple yet powerful Model of Technical Debt for Machine Learning Systems. The model is simple to remember, easier to extend, and provides a reliable means for reliable and maintainable Machine Learning Systems. This, in a nutshell, is the value proposition of this post. Read More
Part 2
Amazon Launches CodeWhisperer, a GitHub Copilot-like AI pair programming tool
At its re:Mars conference, Amazon today announced the launch of CodeWhisperer, an AI pair programming tool similar to GitHub’s Copilot that can autocomplete entire functions based on only a comment or a few keystrokes. The company trained the system, which currently supports Java, JavaScript and Python, on billions of lines of publicly available open source code and its own codebase, as well as publicly available documentation and code on public forums.
It’s now available in preview as part of the AWS IDE Toolkit, which means developers can immediately use it right inside their preferred IDEs, including Visual Studio Code, IntelliJ IDEA, PyCharm, WebStorm and Amazon’s own AWS Cloud 9. Support for the AWS Lambda Console is also coming soon. Read More
Copilot, GitHub’s AI-powered programming assistant, is now generally available
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.
Copilot was previously only available in technical preview. But after signaling that the tool would reach generally availability this summer, GitHub today announced that Copilot is now available to all developers. As previously detailed, it’ll be free for students as well as “verified” open source contributors — starting with roughly 60,000 developers selected from the community and students in the GitHub Education program. Read More
Copilot, GitHub’s AI-powered coding tool, will be free for students
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
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
Rick's Cafe AI 