In recent months, we've marveled at the quality of computer-generated faces, cat pictures, videos, essays, and even art. Artificial intelligence (AI) and machine learning (ML) have also quietly slipped into software development, with tools like GitHub Copilot, Tabnine, Polycode, and others taking the logical next step of putting existing code autocomplete functionality on AI steroids. Unlike cat pics, though, the origin, quality, and security of application code can have wide-reaching implications -- and at least for security, research shows that the risk is real. Prior academic research has already shown that GitHub Copilot often generates code with security vulnerabilities. More recently, hands-on analysis from Invicti security engineer Kadir Arslan showed that insecure code suggestions are still the rule rather than the exception with Copilot.

We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowsheets as strings using the text-based SFILES 2.0 notation and learn the grammatical structure of the SFILES 2.0 language and common patterns in flowsheets using a transformer-based language model. We pre-train our model on synthetically generated flowsheets to learn the flowsheet language grammar. Then, we fine-tune our model in a transfer learning step on real flowsheet topologies. Finally, we use the trained model for causal language modeling to autocomplete flowsheets. Eventually, the proposed method can provide chemical engineers with recommendations during interactive flowsheet synthesis. The results demonstrate a high potential of this approach for future AI-assisted process synthesis.

We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation. We dynamically suggest relevant clinical concepts as a doctor drafts a note by leveraging features from both unstructured and structured medical data. By constraining our architecture to shallow neural networks, we are able to make these suggestions in real time. Furthermore, as our algorithm is used to write a note, we can automatically annotate the documentation with clean labels of clinical concepts drawn from medical vocabularies, making notes more structured and readable for physicians, patients, and future algorithms. To our knowledge, this system is the only machine learning-based documentation utility for clinical notes deployed in a live hospital setting, and it reduces keystroke burden of clinical concepts by 67% in real environments.

Update (August 19): We've released TabNine Local, which lets you run Deep TabNine on your own machine. TL;DR: TabNine is an autocompleter that helps you write code faster. We're adding a deep learning model which significantly improves suggestion quality. You can see videos below and you can sign up for it here. There has been a lot of hype about deep learning in the past few years.

Update (July 18): We've sent beta invites to all existing customers of TabNine who signed up for the beta. Follow us on Twitter for more updates. TL;DR: TabNine is an autocompleter that helps you write code faster. We're adding a deep learning model which significantly improves suggestion quality. You can see videos below and you can sign up for it here.

We aim to identify and control unintentional humor occurring in human-computer interaction, and recreate it intentionally. In this research we focus on text prediction systems, a type of interactive programs employed in mobile phones, search engines, and word processors. More specifically, we identified two design principles, inspired by humor and emotion theories, and implemented them in a proof-of-concept tool simulating a specific type of text prediction.