Our metallurgical history is a little bit like a game of Rock, Paper, Scissors, only without the paper; we’re always looking for something hard enough to cut whatever the current hardest metal is. We started with copper, the first metal to be mined and refined. But then we needed something to cut copper, so we ended up with alloys like bronze, which demanded harder metals like iron, and eventually this arms race of cutting led us to steel, the king of metals.
But even a king needs someone to keep him in check, and while steel can be used to make tools hard enough to cut itself, there’s something even better for the job: tungsten, or more specifically tungsten carbide. We produced almost 120,000 tonnes of tungsten in 2022, much of which was directed to the manufacture of tungsten carbide tooling. Tungsten has the highest melting point known, 3,422 °C, and is an extremely dense, hard, and tough metal. Its properties make it an indispensible industrial metal, and it’s next up in our “Mining and Refining” series.
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. 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. (If you’d prefer a video discussion, one is embedded below the page break.)
OpenAI’s ChatGPT is a large language model (LLM) neural network, or more conventionally, an AI system capable of conversing in natural language. Thanks to a recently announced plugin system, ChatGPT can now interact with remote APIs and therefore use external resources.
ChatGPT’s natural language processing ability enables some pretty impressive interactions with Wolfram, enabling the kind of exchange you see here (click to enlarge.)
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. 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.
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. Both sides use their strengths in this arrangement. 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. In short, ChatGPT can now ask for help to get data or perform a computation, and it can show the receipts when it does.
Stephen Wolfram, inventor of the Wolfram computational language and the Mathematica software, announced that he may have found a path to the holy grail of physics: A fundamental theory of everything. Even with the subjunctive, this is certainly a powerful statement that should be met with some skepticism.
What is considered a fundamental theory of physics? In our current understanding, there are four fundamental forces in nature: the electromagnetic force, the weak force, the strong force, and gravity. Currently, the description of these forces is divided into two parts: General Relativity (GR), describing the nature of gravity that dominates physics on astronomical scales. Quantum Field Theory (QFT) describes the other three forces and explains all of particle physics. Continue reading “Wolfram Physics Project Seeks Theory Of Everything; Is It Revelation Or Overstatement?”→
You’ve probably used Wolfram Alpha and maybe even used the company’s desktop software for high-powered math such as Mathematica. One of the interesting things about all of Wolfram’s mathematics software is that it shares a common core engine — the Wolfram Engine. As of this month, the company is allowing free use of the engine in software projects. The catch? It is only for preproduction use. If you are going into production you need a license, although a free open source project can apply for a free license. Naturally, Wolfram gets to decide what is production, although the actual license is pretty clear that non-commercial projects for personal use and approved open source projects can continue to use the free license. In addition, work you do for a school or large company may already be covered by a site license.
Given how comprehensive the engine is, this is reasonably generous. The engine even has access to the Wolfram Knowledgebase (with a free Basic subscription). If you don’t want to be connected, though, you don’t have to be. You just won’t be able to get live data. If you want to play with the engine, you can use the Wolfram Cloud Sandbox in which you can try some samples.
There’s a time in every geek’s development when they learn of Conway’s Game of Life. This is usually followed by an afternoon spent on discovering that the standard rule set has been chosen because most of the others just don’t do interesting things, and that every idea you have has already been implemented. Often enough this episode is then remembered as ‘having learned about cellular automata’ (CA). While important, the Game of Life is not the only CA out there and it’s not even the first. The story starts decades before Life’s publication in 1970 in a place where a lot of science happened at that time: the year is 1943, the place is Los Alamos in New Mexico and the name is John von Neumann.
The ‘cellular’ part in the name comes from the fact that CAs represent a grid of cells that can be in a number of defined states. The grid can have any number of dimensions, but with three dimensions the visual representation starts to get into the way, and above that most human brains stop working, so two-dimensional grids are the most common — with the occasional one-dimensional surprise. The cells’ states are in most cases discrete but a subset of continuous CAs exists. During the operation of a CA the future state of every cell in the grid is determined from each cells state according to a set of rules which in most cases take into account the states of neighboring cells.
The second video, below, shows some older example projects including a simple home alarm with a PIR sensor. Not the kind of thing that Wolfram is known for, but fine as a “hello world” project. There is even a project that uses an Arduino for more I/O. Between the two videos, you can get a good idea of the sort of things you can accomplish using a Pi with the language.
For those of you unfamiliar with Mathematica, it’s a piece of software that allows you to compute anything. Combined with the educational pedigree of the Raspberry Pi, [Wolfram] and the Pi foundation believe the use of computer-based math will change the way students are taught math.
Besides bringing a free version of Mathematica to the Raspberry Pi, [Wolfram] also announced the Wolfram language. It’s a programming language that keeps most of its libraries – for everything from audio processing, high level math, strings, graphs, networks, and even linguistic data – on the Internet. It sounds absurdly cool, and you can check out a preliminary version of the language over on the official site.
While a free version of Mathematica is awesome, we’re really excited about the new Wolfram language. If it were only an interactive version of Wolfram Alpha, we’d be interested, but the ability to use this tool as a real programming language shows a lot of promise for some interesting applications.
