BingGPT Brings AI Chat To The Desktop

Interested in AI, but sick of using everything in a browser? Miss clicking on a good old desktop icon to open a local bit of software? In that case, BingGPT could be just the thing for you.

It’s nothing too crazy—just a desktop application that gives you access to Bing’s AI-powered chatbot. It’s available on a range of platforms, from Windows, to Apple, and Linux, and binaries are available for Intel, Apple Silicon, and ARM processors.

Using BingGPT is simple. Sign in with your Microsoft account, and away you go. There’s no need to use Microsoft Edge or any ugly browser plugins, and you can export your conversations to Markdown, PNG, and PDF for sharing beyond the program. It’s also complete with a range of keyboard shortcuts to speed your interaction with the large language model when it gets off track. There’s also the Compose button which can actually go ahead and write stuff for you.

Fundamentally, all the cool stuff is still coming in via the web, but it’s nice to be able to use Bing’s chatbot without having to succumb to the horrors of a Microsoft browser. It’s interesting to see how large language models are becoming an all-pervasive tool of late. If you’re building your own nifty projects in this area, don’t hesitate to let us know!

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Hackaday Links: September 10, 2023

Most of us probably have a vision of how “The Robots” will eventually rise up and deal humanity out of the game. We’ve all seen that movie, of course, and know exactly what will happen when SkyNet becomes self-aware. But for those of you thinking we’ll get off relatively easy with a quick nuclear armageddon, we’re sorry to bear the news that AI seems to have other plans for us, at least if this report of dodgy AI-generated mushroom foraging manuals is any indication. It seems that Amazon is filled with publications these days that do a pretty good job of looking like they’re written by human subject matter experts, but are actually written by ChatGPT or similar tools. That may not be such a big deal when the subject matter concerns stamp collecting or needlepoint, but when it concerns differentiating edible fungi from toxic ones, that’s a different matter. The classic example is the Death Cap mushroom (Amanita phalloides) which varies quite a bit in identifying characteristics like color and size, enough so that it’s often tough for expert mycologists to tell it apart from its edible cousins. Trouble is, when half a Death Cap contains enough toxin to kill an adult human, the margin for error is much narrower than what AI is likely to include in a foraging manual. So maybe that’s AI’s grand plan for humanity — just give us all really bad advice and let Darwin take care of the rest.

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Programming A Poker Game With GPT Help

Although ChatGPT generated a huge amount of hype around replacing white collar workers completely when it was first released to the public, the general consensus now is that it won’t outright replace anyone yet, but rather people who know how to use it as a tool will replace those who don’t. Getting started with it is not too hard, either, but you’ll of course need a project to work on to familiarize yourself with the tool. [Volos Projects] gave himself the challenge of writing a poker game using ChatGPT not as the opposing player, but as a co-designer in order to learn more about it as an assistant.

The poker game is being built on an ESP32 board with a built-in AMOLED screen. Five buttons are wired to the microcontroller to allow the player to select which cards to discard and which to keep. The bet for each hand can be raised or lowered much like the tabletop poker games often seen in bars and restaurants. To program it, though, ChatGPT was used to help design the code at each step of the way, first describing the overall goal and then building each function one-by-one like shuffling the deck, dealing the hand, and then replacing and dealing new cards.

For anyone who hasn’t yet explored using ChatGPT to help design their programming projects, this effort goes a long way to showing just how useful a tool it can be. For more complex tasks, though, it does take a little bit of knowledge on the part of the user because ChatGPT can often turn out nonsense or factually inaccurate information, but at least in a programming environment you’ll generally find out quickly when that happens. It’s not just a useful tool for writing programs, either. It can accomplish a lot of ancillary tasks related to programming as well, even if it’s not writing the code directly.

Thanks to [Peter] for the tip!

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AI Assistant Translates Your Every Request For The Command Line

If you don’t live on the command line, it can be easy to forget the exact syntax of commands. It often leaves you running to the “/?” or “–help” switches, or else a quick Google search to find the proper incantations. Shell-AI is a machine-learning assistant that could change all that by helping you find the proper command for the job, right on the command line!

Shell-AI accepts natural-language inputs — simply type in “shai” followed by what you’re trying to do. It will then take in your request, run it through an OpenAI language model like GPT-3.5-Turbo, and then present you with three (or more) potential commands. You can then select which command to use and get on with your day.

As demonstrated, it’s more than capable of following commands like “download a random image” or “show only image files ls.” And, hilariously, it responds to the request “do something crazy” with just one suggestion: “rm -rf”. That seems rather fitting.

We wouldn’t blindly follow any commands coming out of a large language model, of course. But, if you know what you’re doing, it could prove a useful little tool to ease your regular duties on the command line.

Next-Gen Autopilot Puts A Robot At The Controls

While the concept of automotive “autopilots” are still in their infancy, pretty much any aircraft larger than an ultralight will have some mechanism to at least hold a fixed course and altitude. Typically the autopilot system is built into the airplane’s controls, but this new system replaces the pilot themselves in a manner reminiscent of the movie Airplane.

The robot pilot, known as PIBOT, uses both AI and robotics technology to fly the airplane without altering the aircraft. Unlike a normal autopilot system, this one can be fed the aircraft’s manuals in natural language, understand them, and use that information to fly the airplane. That includes operating any of the aircraft’s cockpit controls, not just the control column and pedal assembly. Supposedly, the autopilot can handle everything from takeoff to landing, and operate capably during heavy turbulence.

The Korea Advanced Institute of Science and Technology (KAIST) research team that built the machine hopes that it will pave the way for more advanced autopilot systems, and although this one has only been tested in simulators so far it shows enormous promise, and even has certain capabilities that go far beyond human pilots’ abilities including the ability to remember a much wider variety of charts. The team also hopes to eventually migrate the technology to the land, especially military vehicles, although we’ve seen how challenging that can be already.

2023 Cyberdeck Contest: A Toddler’s Cyberdeck

[Josh] has a child and what do children like more than stuffing random things into their mouths? Pushing buttons, twiddling knobs, and yanking things of course! So [Josh] did what any self-respecting hacker would do and built his little man a custom cyberdeck.

The build follows the usual route of some electronics wedged into a pelican-style waterproof case — which is a good choice for this particular owner — a repurposed all-in-one LCD video player in the lid and a bunch of switches in the base. The player is apparently a V100-base SBC the likes of which are used in shops for those annoying looping promotional videos, but it doesn’t really matter if all it’s doing is being a focus point.

There is no connection from the base to the ‘display’ but that doesn’t matter here. The base is the fun part, with lots of old-school toggle switches and rotary knobs to play with and a load of LEDs to flash in mysterious ways. The guts of this are controlled via an Arduino Mega 2560, with copious amounts of hot glue on display in true hacker style. On the coding side of things, [Josh] used ChatGPT to produce the code from his prompting and Wokwi  to simulate it before deployment to the hardware.

The Right Benchmark For GPT

Dan Maloney wanted to design a part for 3D printing. OpenSCAD is a coding language for generating 3D objects. ChatGPT can write code. What could possibly go wrong? You should go read his article because it’s enlightening and hilarious, but the punchline is that it ran afoul of syntax errors, but also gave him enough of a foothold that he could teach himself enough OpenSCAD to get the project done anyway. As with many people who have asked the AI to create some code, Dan finds that it’s not as good as asking someone who knows what they’re doing, but that it’s also better than nothing.

And this is where I start grumbling. When you type your desires into the word-follower machine, your alternative isn’t nothing. Your alternative is to fire up a search engine instead and type “openscad tutorial”. That, for nearly any human endeavor, will get you a few good guides, written by humans who are probably expert in the subject in question, and which are aimed at teaching you the thing that you want to learn. It doesn’t get better than that. You’ll be up and running with your design in no time.

Indeed, if you think about the relevant source material that the LLM was trained on, it’s exactly these tutorials. It can’t possibly do better than the best of them, although the resulting average tutorial might be better than the worst you’ll find. (Some have speculated on what happens when the entire Internet is filled with these generated texts – what will future AIs learn from?)

In Dan’s case, though, he didn’t necessarily want to learn OpenSCAD – he just wanted the latch designed. But in the end, he had to learn enough OpenSCAD to get the AI code compiling without error. He spent an hour learning OpenSCAD and now he’s good to go on his next project too.

So the next time you hear someone say that they got an answer back from a large language model that wasn’t perfect, but it was “better than nothing”, think critically if “nothing” is really the right benchmark.

Do you really want to learn nothing? Do you really have no resources to get started with? I would claim that we have the most amazing set of tutorial resources the world has ever known at our fingertips. Compared to the ability to teach millions of humans to achieve their own goals, that makes the LLM party tricks look kinda weak, in my opinion.