AI Is Only Coming For Fun Jobs

In the past few years, what marketers and venture capital firms term “artificial intelligence” but is more often an advanced predictive text model of some sort has started taking people’s jobs and threatening others. But not tedious jobs that society might like to have automated away in the first place. These AI tools have generally been taking rewarding or enjoyable jobs like artist, author, filmmaker, programmer, and composer. This project from a research team might soon be able to add astronaut to that list.

The team was working within the confines of the Kerbal Space Program Differential Game Challenge, an open-source plugin from MIT that allows developers to test various algorithms and artificial intelligences in simulated spacecraft situations. Generally, purpose-built models are used here with many rounds of refinement and testing, but since this process can be time consuming and costly the researchers on this team decided to hand over control to ChatGPT with only limited instructions. A translation layer built by the researchers allows generated text to be converted to spacecraft controls.

We’ll note that, at least as of right now, large language models haven’t taken the jobs of any actual astronauts yet. The game challenge is generally meant for non-manned spacecraft like orbital satellites which often need to make their own decisions to maintain orbits and avoid obstacles. This specific model was able to place second in a recent competition as well, although we’ll keep rooting for humans in certain situations like these.

Why GitHub Copilot Isn’t Your Coding Partner

These days ‘AI’ is everywhere, including in software development. Coming hot on the heels of approaches like eXtreme Programming and Pair Programming, there’s now a new kind of pair programming in town in the form of an LLM that’s been digesting millions of lines of code. Purportedly designed to help developers program faster and more efficiently, these ‘AI programming assistants’ have primarily led to heated debate and some interesting studies.

In the case of [Jj], their undiluted feelings towards programming assistants like GitHub Copilot burn as brightly as the fire of a thousand Suns, and not a happy kind of fire.

Whether it’s Copilot or ChatGPT or some other chatbot that may or may not be integrated into your IDE, the frustration with what often feels like StackOverflow-powered-autocomplete is something that many of us can likely sympathize with. Although [Jj] lists a few positives of using an LLM trained on codebases and documentation, their overall view is that using Copilot degrades a programmer, mostly because of how it takes critical thinking skills out of the loop.

Regardless of whether you agree with [Jj] or not, the research so far on using LLMs with software development and other tasks strongly suggests that they’re not a net positive for one’s mental faculties. It’s also important to note that at the end of the day it’s still you, the fleshy bag of mostly salty water, who has to justify the code during code review and when something catches on fire in production. Your ‘copilot’ meanwhile gets off easy.

AI Might Kill Us All (With Carbon Emissions)

So-called artificial intelligence (AI) is all the rage right now between your grandma asking ChatGPT how to code in Python or influencers making videos without having to hire extras, but one growing concern is where the power is going to come from for the data centers. The MIT Technology Review team did a deep dive on what the current situation is and whether AI is going to kill us all (with carbon emissions).

Probably of most interest to you, dear hacker, is how they came up with their numbers. With no agreed upon methods and different companies doing different types of processing there were a number of assumptions baked into their estimates. Given the lack of information for closed-source models, Open Source models were used as the benchmark for energy usage and extrapolated for the industry as a whole. Unsurprisingly, larger models have a larger energy usage footprint.

While data center power usage remained roughly the same from 2005 to 2017 as increases in efficiency offset the increase in online services, data centers doubled their energy consumption by 2023 from those earlier numbers. The power running into those data centers is 48% more carbon intensive than the US average already, and expected to rise as new data centers push for increased fossil fuel usage, like Meta in Louisiana or the X data center found to be using methane generators in violation of the Clean Air Act.

Technology Review did find “researchers estimate that if data centers cut their electricity use by roughly half for just a few hours during the year, it will allow utilities to handle some additional 76 gigawatts of new demand.” This would mean either reallocating requests to servers in other geographic regions or just slowing down responses for the 80-90 hours a year when the grid is at its highest loads.

If you’re interested in just where a lot of the US-based data centers are, check out this map from NREL. Still not sure how these LLMs even work? Here’s an explainer for you.

ELIZA Reanimated

The last time we checked in with the ELIZA archeology project, they had unearthed the earliest known copy of the code for the infamous computer psychiatrist written in MAD-SLIP. After a lot of work, that version is now running again, and there were a number of interesting surprises.

While chatbots are all the modern rage, [Joseph Weizenbaum] created what could be the first one, ELIZA, in the mid-1960s. Of course, it wasn’t as capable as what we have today, but it is a good example of how simple it is to ape human behavior.

The original host was an IBM 7094, and MAD-SLIP fell out of favor. Most versions known previously were in Lisp or even Basic. But once the original code was found, it wasn’t enough to simply understand it. They wanted to run it.

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Space-Based Datacenters Take The Cloud Into Orbit

Where’s the best place for a datacenter? It’s an increasing problem as the AI buildup continues seemingly without pause. It’s not just a problem of NIMBYism; earthly power grids are having trouble coping, to say nothing of the demand for cooling water. Regulators and environmental groups alike are raising alarms about the impact that powering and cooling these massive AI datacenters will have on our planet.

While Sam Altman fantasizes about fusion power, one obvious response to those who say “think about the planet!” is to ask, “Well, what if we don’t put them on the planet?” Just as Gerard O’Neill asked over 50 years ago when our technology was merely industrial, the question remains:

“Is the surface of a planet really the right place for expanding technological civilization?”

O’Neill’s answer was a resounding “No.” The answer has not changed, even though our technology has. Generative AI is the latest and greatest technology on offer, but it turns out it may be the first one to make the productive jump to Earth Orbit. Indeed, it already has, but more on that later, because you’re probably scoffing at such a pie-in-the-sky idea.

There are three things needed for a datacenter: power, cooling, and connectivity. The people at companies like Starcloud, Inc, formally Lumen Orbit, make a good, solid case that all of these can be more easily met in orbit– one that includes hard numbers.

Sure, there’s also more radiation on orbit than here on earth, but our electronics turn out to be a lot more resilient than was once thought, as all the cell-phone cubesats have proven. Starcloud budgets only 1 kg of sheilding per kW of compute power in their whitepaper, as an example. If we can provide power, cooling, and connectivity, the radiation environment won’t be a showstopper.

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Flopped Humane “AI Pin” Gets An Experimental SDK

The Humane AI Pin was ambitious, expensive, and failed to captivate people between its launch and shutdown shortly after. While the units do contain some interesting elements like the embedded projector, it’s all locked down tight, and the cloud services that tie it all together no longer exist. The devices technically still work, they just can’t do much of anything.

The Humane AI Pin had some bold ideas, like an embedded projector. (Image credit: Humane)

Since then, developers like [Adam Gastineau] have been hard at work turning the device into an experimental development platform: PenumbraOS, which provides a means to allow “untrusted” applications to perform privileged operations.

As announced earlier this month on social media, the experimental SDK lets developers treat the pin as a mostly normal Android device, with the addition of a modular, user-facing assistant app called MABL. [Adam] stresses that this is all highly experimental and has a way to go before it is useful in a user-facing sort of way, but there is absolutely a workable architecture.

When the Humane AI Pin launched, it aimed to compete with smartphones but failed to impress much of anyone. As a result, things folded in record time. Humane’s founders took jobs at HP and buyers were left with expensive paperweights due to the highly restrictive design.

Thankfully, a load of reverse engineering has laid the path to getting some new life out of these ambitious devices. The project could sure use help from anyone willing to pitch in, so if that’s up your alley be sure to join the project; you’ll be in good company.

Robot Dinosaur YOLOs Colors And Shapes For Kids

YOLO can mean many things, but in the context of [be_riddickulous]’s AI Talking Robot Dinosaur it refers to the “You Only Look Once” YOLOv11 object-detection algorithm by Ultralytics, the method by which this adorable dino recognizes colors and shapes to teach them to children.

If you’re new to using YOLO or object recognition more generally, [be_riddiculous]’s tutorial is not a bad place to get started. She goes through how many images you’ll need and what types to get the shape-and-color recognition needed for this project, as well as how to annotate them and train the model, either locally or in the cloud.

The project itself is an adorable paper-mache dinosaur with a servo-actuated mouth hiding some LEDs and a Raspberry Pi camera module to provide images. In operation, the dinosaur “talks” to children using pre-recorded voice lines, inviting them to play a game and put a specific shape, or shape of a specific color (or both) in its mouth. Then the aforementioned object detection (running on a laptop) goes “YOLO” and identifies the shape so the toy can provide feedback on the child’s choice via a speaker in the belly of the beast.

The link to the game code is currently not valid, but it looks like they used PyGame for the audio output code. A servo motor controls the mouth, but without that code it’s not entirely clear to us what it’s doing. We expect by the time you read this there’s good odds [be_riddickulous] will have fixed that link and you can see for yourself.

The only thing that holds this back from being a great toy to put in every Kindergarten class is the need to have a laptop close by to plug the webcam into. A Raspberry Pi 5 ought to have the horsepower to run YOLOv11, so with a little extra effort the whole thing could be standalone — there might even be room in there for batteries.We’ve had other hacks aimed at little ones, like a kid-friendly computer to relive the glory days of the school computer lab or one of the many iterations of the RFID jukebox idea. If you want to wow the kiddos with AI, perhaps take a look at this talking Santa plush.

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