Although Large Language Models (LLMs) have established pre-dominance in automated code generation, they are not devoid of shortcomings. The pertinent issues primarily relate to the absence of execution guarantees for generated code, a lack of explainability, and suboptimal support for essential but niche programming languages. State-of-the-art LLMs such as GPT-4 and LLaMa2 fail to produce valid programs for Industrial Control Systems (ICS) operated by Programmable Logic Controllers (PLCs). We propose LLM4PLC, a user-guided iterative pipeline leveraging user feedback and external verification tools including grammar checkers, compilers and SMV verifiers to guide the LLM's generation. We further enhance the generation potential of LLM by employing Prompt Engineering and model fine-tuning through the creation and usage of LoRAs. We validate this system using a FischerTechnik Manufacturing TestBed (MFTB), illustrating how LLMs can evolve from generating structurally flawed code to producing verifiably correct programs for industrial applications. We run a complete test suite on GPT-3.5, GPT-4, Code Llama-7B, a fine-tuned Code Llama-7B model, Code Llama-34B, and a fine-tuned Code Llama-34B model. The proposed pipeline improved the generation success rate from 47% to 72%, and the Survey-of-Experts code quality from 2.25/10 to 7.75/10. To promote open research, we share the complete experimental setup, the LLM Fine-Tuning Weights, and the video demonstrations of the different programs on our dedicated webpage.

Google is taking aim at the likes of Grammarly with a feature it added to Search recently. The service now includes a grammar checker, which can analyze the construction of a phrase or sentence and offer suggestions if it something seems off. Punching in a phrase like "grammar check," "check grammar" or "grammar checker" will make sure the tool activates, as 9to5 Google points out. But Search might still offer grammar suggestions if you don't include one of those phrases with your query. If the tool notices an error, including a spelling mistake, Google will rewrite the sentence and indicate what's different.

Last week, Grammarly secured $90 million in funding for its artificial intelligence–based grammar and writing tools. But this specific case drew my attention because amidst all the hype and confusion surrounding artificial intelligence, I believe that Grammarly is solving a real problem. Understanding and processing natural language are among the most challenging areas of AI. Many companies have engaged in ambitious AI-based language projects. But a considerable number of them have failed miserably for not having considered the limits of current AI technologies. Meanwhile, Grammarly has found a niche suitable for the narrow capabilities of deep learning, the current bleeding edge of AI.

US Internet giant Google has said its artificial intelligence (AI)-based grammar checker, corporate web tools and services are available for all users of G Suite, to help them improve writing. Unlike standard spellcheck in Google Docs, Google applies Machine Learning technology to grammar checker as one of the AI tools that can let software understand complex grammar rules and identify any "tricky" grammatical errors by users in writing, Xinhua news agency reported. "Using machine translation, we are able to recognize errors and suggest corrections as work is getting done," G Suite product manager Vishnu Sivaji said in a statement on Tuesday. He said that G Suite customers will see inline, contextual grammar suggestions in their documents as they type, just like spellcheck. "If you've made a grammar mistake, a squiggly blue line will appear under the phrase as you write it," Sivaji added.

Google today announced that its new machine learning-powered grammar checker is now live in Google Docs for G Suite users. The company first introduced this new feature at Cloud Next 2018, but it lingered in early access ever since. Grammar checkers are nothing new, of course, and even Docs itself has long had one. What's new here is that Google uses machine translation techniques to find obvious mistakes (see headline) as well as more subtle issues. It's one thing, after all, to compare words in a dictionary to what you're writing and mark up mistakes. It's another to understand complex grammar rules, which can vary by region and style.

You probably don't want to make grammar errors in your emails (or blog posts), but every now and then, they do slip in. Your standard spell-checking tool won't catch them unless you use an extension like Grammarly. Well, Grammarly is getting some competition today in the form of a new machine learning-based grammar checker from Google that's soon going live in Google Docs. These new grammar suggestions in Docs, which are now available through Google's Early Adopter Program, are powered by what is essentially a machine translation algorithm that can recognize errors and suggest corrections as you type. Google says it can catch anything from wrongly used articles ("an" instead of "a") to more complicated issues like incorrectly used subordinate clauses.

I make a lot of dumb mistakes when I write, and I've always dreamed of having a smart computer that could point out the errors that escape my notice. Building such a grammar checker is hard. You can't just write down the rules of English grammar and check that they are followed like you can when building a compiler for a programming language. Natural languages such as English have some syntactic regularity, but they are squishy, and a grammar checker needs to have some understanding of the content to see that underlying regularity. This means that a computer must understand what you intended to write to know if you have written it correctly.