TheSequence - 🌠🎇 Generative AI for DevOps
Was this email forwarded to you? Sign up here 📝 EditorialGenerative AI is the trend of the moment in the deep learning space. While we typically associate generative AI with media formats such as images, audio and video, some of the most exciting work is taking place in the code generation and automation space. Models such as OpenAI Codex, which is powering the ultra-popular GitHub Copilot, clearly demonstrated that the dreams of using AI for coding are within reach. While generating entire software applications just using natural language might still be years away, plenty of more targeted use cases can be implemented with today’s generative AI technology. One of those domains is the world of DevOps. A few days ago, IBM and RedHat announced their collaboration in Project Wisdom, a framework that enables the creation of cloud automation scripts using natural language. Scripts are the cornerstone of DevOps platforms and an ideal candidate for generative AI models given its structure nature. Project Wisdom uses simple natural language instructions for generating YAML files for Ansible playbook, RedHat’s marquee cloud automation framework. While you still can’t provision an entire data center using Project Wisdom, users can certainly instruct simpler tasks such as installing dependencies, provisioning servers and scaling a specific application. Because it is coming from RedHat, we can expect Project Wisdom to be fully open-sourced upon its release. Project Wisdom is taking a pragmatic approach to bringing generative AI to the enterprise software space. IT automation is about to get a lot simpler with generative AI. Saying all the above, we think generative AI will be everywhere soon. As an experiment, we starting to work with generative AI to improve the content experience. Here is a Halloween illustration created with Midjourney: 🔺🔻TheSequence Scope – our Sunday edition with the industry’s development overview – is free. To receive high-quality content about the most relevant developments in the ML world every Tuesday and Thursday, please subscribe to TheSequence Edge 🔺🔻 🗓 Next week in TheSequence Edge: Edge#239: we dive deeper into Stable Diffusion; discuss retrieval augmented diffusion models that brings memory to text-to-image synthesis; explore Stable Diffusion interfaces Edge#240: we explore the AI powering Imagen Video: Google’s new text-to-video super model 📌Now, let’s review the most important developments in the AI industry this week 🔎 ML ResearchNatural Language Assessment Google Brain published a blog post introducing natural language assessment (NLA), a technique used to evaluate the effectiveness of answers in NLU applications →read more Audio Hypercompression Meta AI published a paper proposing an AI-based method for high-performance compression of audio files →read more FSNet Salesforce Research published a paper detailing FSNet, deep time-series forecasting models on the fly in nonstationary environments →read more 🤖 Cool AI Tech ReleasesPyTorch 1.13 PyTorch released version 1.13 which includes an improved BetterTransformer library and support for Apple’s M1 chips among other features →read more Project Wisdom IBM Research unveiled some details around Project Wisdom, a AI-for-code model that can generate cloud automation scripts using natural language →read more Open Images v7 Google Brain released a new version of its Open Images datasets for advanced computer vision tasks →read more 🛠 Real World MLPresto at Uber Uber discusses the architecture used to accelerate their Presto infrastructure for real-time analytics →read more 💸 Money in AIML&AI
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🚩 Edge#238: The New ImageNet: DeepMind’s New Perception Benchmark for Deep Learning Models
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📝 Guest post: SuperData is the new oil – How to win the AI race in the 21st century*
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