울프램 버전 15 발표: 내장 AI 어시스턴트와 핵심 기능 강화

View All Release Announcements » Contents Top An Impressive Release for Modern Times An AI Assistant in Every Notebook Use Wolfram from Your AI Environment Time Series (and Event Series) Go Big Computation Comes to Categorical Data Introducing the ModelFit Superfunction Introducing Symbolic Music Bigger and Better Connectivity for Tabular More for Tabular Visualization Tuneups Multipanel Visualization Gigabyte-Sized Notebooks and Real-Time Find Notebooks Get Their First Sidebars Visual Themes Come to Notebooks When It’s Too Long, It’s Torn Off Going Dark in the Light What’s Happening in that Computation? The One-Argument Form of Monitor Subvalues Can Now Be Held! Introducing Ready-to-Use Incremental Data Structures Exceptions and Error Handling in Large Codebases Introducing the Structured Package Format Plotting over Graphs How Do You Put Ticks on a Map of the Earth? When Will Your City See a Solar Eclipse? Launching into Orbit(s) Grassmann, Clifford, Weyl & Friends Zetas, Polylogs and Harmonic Numbers Go Multivariate Partial Fractions Get Streamlined Lots of New Matrix Decompositions The Corners of DSolve Get a Little Help from AI Methods PDEs Go Curvilinear Derived Quantities in PDE Solutions How Do You Approximate a Systems Engineering Model? Reinforcement Learning for Control Systems Importing & Exporting the Latest Formats Real-Time Connection with Web Sockets Richer UX for Using Python & More in Notebooks Optimization & GPUification Continues CUDA Kernels as External Functions Wolfram Compute Services Gets GPUs Using the Wolfram Foundation Tool in LLM Functions And Yet More... Launching Version 15 of Wolfram Language & Mathematica: Built-in (Useful) AI & Lots of New Core Functionality Exploring Wolfram Language 15 with Stephen Wolfram » (June 16, 2026 @ 4:30 PM ET) Launching Version 15 of Wolfram Language & Mathematica: Built-in (Useful) AI & Lots of New Core Functionality June 16, 2026 An Impressive Release for Modern Times An AI Assistant in Every Notebook Use Wolfram from Your AI Environment Time Series (and Event Series) Go Big Computation Comes to Categorical Data Introducing the ModelFit Superfunction Introducing Symbolic Music Bigger and Better Connectivity for Tabular More for Tabular Visualization Tuneups Multipanel Visualization Gigabyte-Sized Notebooks and Real-Time Find Notebooks Get Their First Sidebars Visual Themes Come to Notebooks When It’s Too Long, It’s Torn Off Going Dark in the Light What’s Happening in that Computation? The One-Argument Form of Monitor Subvalues Can Now Be Held! Introducing Ready-to-Use Incremental Data Structures Exceptions and Error Handling in Large Codebases Introducing the Structured Package Format Plotting over Graphs How Do You Put Ticks on a Map of the Earth? When Will Your City See a Solar Eclipse? Launching into Orbit(s) Grassmann, Clifford, Weyl & Friends Zetas, Polylogs and Harmonic Numbers Go Multivariate Partial Fractions Get Streamlined Lots of New Matrix Decompositions The Corners of DSolve Get a Little Help from AI Methods PDEs Go Curvilinear Derived Quantities in PDE Solutions How Do You Approximate a Systems Engineering Model? Reinforcement Learning for Control Systems Importing & Exporting the Latest Formats Real-Time Connection with Web Sockets Richer UX for Using Python & More in Notebooks Optimization & GPUification Continues CUDA Kernels as External Functions Wolfram Compute Services Gets GPUs Using the Wolfram Foundation Tool in LLM Functions And Yet More… An Impressive Release for Modern Times June 23, 1988 is when we launched Version 1.0 of Mathematica . Today—almost 38 years later—we’re launching Version 15 of what—in recognition of how far it’s expanded beyond “math”—we now call Wolfram Language . It’s an impressive release, with a lot of new core functionality. It might perhaps seem surprising that after 38 years there’d still be more to add. But it’s like the typical arc of intellectual history: the more one’s figured out, the further one can see, and the more one becomes able to do. And for all of us working on it, it’s been a very satisfying process: year after year building an ever taller tower of ideas and technology, with which we can reach ever further—today to all the functionality of Version 15. For the past four decades we’ve had a consistent mission: to apply the computational paradigm as broadly and deeply as possible—and to do so by building our unique computational language to represent and compute about the world. Over these four decades the use of computation and the computational paradigm has spread greatly—not least, I think, as a result of tools and ideas we’ve introduced. But now there’s also a new driver: modern AI. And it’s been exciting to see so much unexpected progress happen in the world of AI. For us, one of the immediate consequences has been that our base of users has expanded from just humans, to humans and AIs. And it’s turned out that all the effort we put into the coherent design of the Wolfram Language—aimed at making it easy and efficient for humans to use—now also makes it easy and efficient for AIs. For years we’ve put great emphasis on interfaces for human users, starting from the concept of notebooks that we invented for Version 1.0 . Now we’re also putting emphasis on interfaces for AIs, to make it as easy as possible for AIs and AI systems (and the humans who use them) to have good access to our technology. Our technology is certainly a powerful tool for AIs. But it’s also a powerful tool for humans using AIs. Because it provides a unique way for humans to formalize things , and know exactly what’s being said, or done. I’ve always seen the development of Wolfram Language as doing for the computational paradigm an extended version of what mathematical notation did centuries ago for the mathematical paradigm: providing a streamlined and precise way to represent and communicate ideas. When you tell an AI in natural language what you want, it’s convenient, but—except in rather simple cases—quite imprecise. But if the AI generates Wolfram Language code, then that shows you in precise terms what the AI understood, and allows you to see whether it’s really what you want. The Wolfram Language has a unique role here. Traditional programming languages are intended as something humans write, and computers read. But the Wolfram Language is something beyond a programming language—it’s a full-scale computational language . That’s intended not just to be written by humans, but also to be read by them, as a way to help formalize and crispen up their thoughts. And now, in the time of AI, it’s a unique way to represent precisely what one’s talking about—leveraging the computational paradigm, and the computational way of representing the world. Yes, AIs don’t always get things right. But the point is to use Wolfram Language as a carrier of precision (and correctness)—and as a way to anchor what one’s doing, and generate solid output that one can confidently use in systematic ways. There’s been a big trend—particularly this year—to “use AI for coding”. And, yes, if you want to produce something (like a website) where “looking right” is the objective—and you don’t care “what the code is doing inside”—it’s a good, and in fact quite transformative, solution. But there are many situations, particularly in more technical areas, where “looking right” isn’t good enough: you need to actually know what is being computed. And that’s where the Wolfram Language is crucial. Because it’s what gives you the highest level, and most human-understandable representation of what’s being done. And gives you a way to encapsulate a precise piece of computation to repeatedly use wherever you want. The success of modern AI in coding is remarkable, and unexpected. But in a sense it’s much less significant to us than it is, say, for traditional programming languages. Because it’s been our mission for decades to automate as much