"Wolfram, random number" provides a 0 to 1 floating point value. Say "Wolfram, random integer." Wolfram returns a random value between. weyhe.tech liked Web security everywhere.By subscribing, you are agreeing to Engadget's Terms and Privacy Policy.Collindb20 liked A Cheap 24-bit Differential ADC for Raspberry Pi.Andrey Kalmatskiy has updated the log for Argentum programming language.Ildaron has updated the project titled The easiest way to neuroscience with PiEEG.Andrey Kalmatskiy has added a new log for Argentum programming language.Ildaron has updated details to The easiest way to neuroscience with PiEEG.Dubious on 3D Model Subscriptions Are Coming, But Who’s Buying?.shod on Simulated ET To Phone Home From Mars This Afternoon.None on Getting Into NMR Without The Superconducting Magnet.Dan on When The Professionals Trash Your Data Tape, Can It Still Be Recovered?.Michael Fitzmayer on Nokia N-Gage QD Becomes Universal Bluetooth Gamepad.Shane Curless on Nokia N-Gage QD Becomes Universal Bluetooth Gamepad.itsthatidiotagain on Hacking The IKEA OBEGRÄNSAD LED Wall Lamp.poiuyt on This Week In Security: Gitlab, KeyPassMini, And Horse.MinorHavoc on The Voltaic Pile: Building The First Battery.Supercon 2022: Nick Poole Makes A Jolly Wrencher Tube 5 Comments Posted in Artificial Intelligence Tagged ChatGPT, openai, plugins, Stephen Wolfram, Wolfram, wolfram alpha Post navigation At this writing, access to plugins for ChatGPT has a waiting list but if you’ve had a chance to check it out, let us know in the comments! Stephen also makes a great case for what an effective human-AI workflow based on Wolfram Language could look like. We’ve looked at Wolfram Alpha’s abilities before, especially the educational value of its ability to show every step of its work. In short, ChatGPT can now ask for help to get data or perform a computation, and it can show the receipts when it does. First, ChatGPT interprets the user’s question and formulates it as a query, which is then sent to Wolfram Alpha for computation, and ChatGPT structures its response based on what it got back. Both sides use their strengths in this arrangement. So how does the Wolfram plugin change that? When asked to produce data or perform computations, ChatGPT can now hand it off to Wolfram Alpha instead of attempting to generate the answer by itself. It’s not so much that ChatGPT is especially prone to confabulation, it’s more that the nature of an LLM neural network makes it difficult to ask “why exactly did you come up with your answer, and not something else?” In addition, asking ChatGPT to do things like perform nontrivial calculations is a bit of a square peg and round hole situation. This is meaningful because LLMs are very good at processing natural language and generating plausible-sounding output, but whether or not the output is factually correct can be another matter. ChatGPT’s natural language processing ability enables some pretty impressive interactions with Wolfram, enabling the kind of exchange you see here (click to enlarge.) Thanks to a recently announced plugin system, ChatGPT can now interact with remote APIs and therefore use external resources. OpenAI’s ChatGPT is a large language model (LLM) neural network, or more conventionally, an AI system capable of conversing in natural language. (If you’d prefer a video discussion, one is embedded below the page break.) That link goes to a long blog post from Stephen Wolfram that showcases exactly how and why the two make such a wonderful match, with loads of examples. Ever looked at Wolfram Alpha and the development of Wolfram Language and thought that perhaps Stephen Wolfram was a bit ahead of his time? Well, maybe the times have finally caught up because Wolfram plus ChatGPT looks like an amazing combo.
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