TheSequence - 👾 ML that Improves Code Writing
Was this email forwarded to you? Sign up here 📝 EditorialThe idea of machine learning (ML) models that can create production-ready code has been one of the iconic use cases in the history of AI. A few months ago, the AI world was astonished by the release of models like OpenAI’s Codex and DeepMind’s AlphaCode that can generate sophisticated text from natural language. These types of models are an important step for code generation, and they have opened the door to all sorts of research about using ML models to improve the process of writing software. The lifecycle of code in software applications goes beyond authoring. Typically, developers in peripheral tasks such as testing or bug fixing. This is an area that ML can help too. Just this week, there were several research contributions to the ML-first code space. Microsoft Research unveiled Jigsaw, a model that can fix bugs in the code generated by large language-to-code models. Similarly, Stanford University published details about a model used to evaluate coding tests. Salesforce is also jumping into the ML code generation space with CodeGen, a mode that can generate code from language instructions. From code generation to testing to bug fixing, ML is rapidly penetrating all areas of software development. 🔺🔻 TheSequence Scope is our Sunday free digest. To receive high-quality educational content about the most relevant concepts, research papers, and developments in the ML world every Tuesday and Thursday, please subscribe to TheSequence Edge 🔺🔻 🗓 Next week in TheSequence Edge: Edge#179: we do a recap of GANs series; Edge#180: a deep dive into SuperAnnotate, an annotation platform for real-world scenarios. Now, let’s review the most important developments in the AI industry this week 🔎 ML ResearchGenerating Code from Natural Language Salesforce Research published a paper introducing CodeGen, an autoregressive language model that turns language instructions into code →read more on Salesforce blog Grading Coding Assignments Stanford University published a paper proposing a reinforcement learning model that can grade coding assignments particularly related to coding games →read more on Stanford AI Lab blog Fixing Bugs in AI-Generated Code Microsoft Research published a paper discussing Jigsaw, a tool that can fix bugs in programs generated by large language models →read more on Microsoft Research blog Improving Wikipedia Content Meta (Facebook) AI Research (FAIR) published a paper proposing a method to provide more inclusive content in Wikipedia →read more on FAIR blog 🤖 Cool AI Tech ReleasesDataRobot Cloud 8.0 DataRobot announced the release of AI Cloud 8.0, a new release with a strong focus on predictive models and continuous ML →read more in DataRobot’s press release Arize AI vNext Arize AI released a new version of its ML observability platform →read more in Arize AI’s press release 🗯 Interesting Tweet🛠 Real World MLGNNs at Amazon Amazon Research published a blog post detailing their use of gran neural networks across different scenarios →read more on Amazon Research blog TensorFlow.js at LinkedIn LinkedIn published a detailed blog post about their use of TensorFlow.js to customize the performance of web and mobile apps →read more on LinkedIn Engineering blog 💸 Money in AIData/AI/ML
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