AI + LEGO = A Brickton Of Ideas

April 22, 2024 by Kristina Panos 37 Comments

What if there was some magic device that could somehow scan all your LEGO and tell you what you can make with it? It’s a childhood dream come true, right? Well, that device is in your pocket. Just dump out your LEGO stash on the carpet, spread it out so there’s only one layer, scan it with your phone, and after a short wait, you get a list of all the the fun things you can make. With building instructions. And oh yeah, it shows you where each brick is in the pile.

We are talking about the BrickIt app, which is available for Android and Apple. Check it out in the short demo after the break. Having personally tried the app, we can say it does what it says it does and is in fact quite cool.

As much as it may pain you to have to pick up all those bricks when you’re finished, it really does work better against a neutral background like light-colored carpet. In an attempt to keep the bricks corralled, we tried a wooden tray, and it didn’t seem to be working as well as it probably could have — it didn’t hold that many bricks, and they couldn’t be spread out that far.

And the only real downside is that results are limited because there’s a paid version. And the app is kind of constantly reminding you of what you’re missing out on. But it’s still really, really cool, so check it out.

We don’t have to tell you how versatile LEGO is. But have you seen this keyboard stand, or this PCB vise?

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Generative AI Now Encroaching On Music

March 22, 2024 by Bryan Cockfield 62 Comments

While it might not seem like it to a novice, music turns out to be a highly mathematical endeavor with precise ratios between chords and notes as well as overall structure of rhythm and timing. This is especially true of popular music which has even more recognizable repeating patterns and trends, making it unfortunately an easy target for modern generative AI which is capable of analyzing huge amounts of data and creating arguably unique creations. This one, called Suno, does just that for better or worse.

Unlike other generative AI offerings that are currently available for creating music, this one is not only capable of generating the musical underpinnings of the song itself but can additionally create a layer of intelligible vocals as well. A deeper investigation of the technology by Rolling Stone found that the tool uses its own models to come up with the music and then offloads the text generation for the vocals to ChatGPT, finally using the generated lyrics to generate fairly convincing vocals. Like image and text generation models that have come out in the last few years, this has the potential to be significantly disruptive.

While we’re not particularly excited about living in a world where humans toil while the machines create art and not the other way around, at best we could hope for a world where real musicians use these models as tools to enhance their creativity rather than being outright substitutes, much like ChatGPT itself currently is for programmers. That might be an overly optimistic view, though, and only time will tell.

Reggaeton-Be-Gone Disconnects Obnoxious Bluetooth Speakers

February 23, 2024 by Bryan Cockfield 29 Comments

If you’re currently living outside of a Spanish-speaking country, it’s possible you’ve only heard of the music genre Reggaeton in passing, if at all. In places with large Spanish populations, though, it would be more surprising if you hadn’t heard it. It’s so popular especially in the Carribean and Latin America that it’s gotten on the nerves of some, most notably [Roni] whose neighbor might not do anything else but listen to this style of music, which can be heard through the walls. To solve the problem [Roni] is now introducing the Reggaeton-Be-Gone. (Google Translate from Spanish)

Inspired by the TV-B-Gone devices which purported to be able to turn off annoying TVs in bars, restaurants, and other places, this device can listen to music being played in the surrounding area and identify whether or not it is hearing Reggaeton. It does this using machine learning, taking samples of the audio it hears and making decisions based on a trained model. When the software, running on a Raspberry Pi, makes a positive identification of one of these songs, it looks for Bluetooth devices in the area and attempts to communicate with them in a number of ways, hopefully rapidly enough to disrupt their intended connections.

In testing with [Roni]’s neighbor, the device seems to show promise although it doesn’t completely disconnect the speaker from its host, instead only interfering with it enough for the neighbor to change locations. Clearly it merits further testing, and possibly other models trained for people who use Bluetooth speakers when skiing, hiking, or working out. Eventually the code will be posted to this GitHub page, but until then it’s not the only way to interfere with your neighbor’s annoying stereo.

Thanks to [BaldPower] and [Alfredo] for the tips!

The ELIZA Archaeology Project: Uncovering The Original ELIZA

February 22, 2024 by Maya Posch 30 Comments

Since ELIZA was created by [Joseph Weizenbaum] in the 1960s, its success had led to many variations and ports being written over the intervening decades. The goal of the ELIZA Archaeology Project by Stanford, USC, Oxford and other university teams is to explore and uncover as much of this history as possible, starting with the original 1960s code. As noted in a recent blog post by [Anthony Hay], most of the intervening ‘ELIZA’ versions seem to have been more inspired by the original rather than accurate replicas or extensions of the original. This raises the question of what the original program really looked like, a question which wasn’t answered until 2020 when the original source code was rediscovered. Continue reading “The ELIZA Archaeology Project: Uncovering The Original ELIZA” →

Human-Written Or Machine-Generated: Finding Intelligence In Language Models

January 24, 2024 by Maya Posch 45 Comments

What is the essential element which separates a text written by a human being from a text which has been generated by an algorithm, when said algorithm uses a massive database of human-written texts as its input? This would seem to be the fundamental struggle which society currently deals with, as the prospect of a future looms in which students can have essays auto-generated from large language models (LLMs) and authors can churn out books by the dozen without doing more than asking said algorithm to write it for them, using nothing more than a query containing the desired contents as the human inputs.

Due to the immense amount of human-generated text in such an LLM, in its output there’s a definite overlap between machine-generated text and the average prose by a human author. Statistical methods of detecting the former are also increasingly hamstrung by the human developers and other human workers behind these text-generating algorithms, creating just enough human-like randomness in the algorithm’s predictive vocabulary to convince the casual reader that it was written by a fellow human.

Perhaps the best way to detect machine-generated text may just be found in that one quality that these algorithms are often advertised with, yet which they in reality are completely devoid of: intelligence.

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Two researchers, a white woman and dark-skinned man look at a large monitor with a crystal structure displayed in red and white blocks.

AI On The Hunt For Better Batteries

January 23, 2024 by Navarre Bartz 16 Comments

While certain dystopian visions of the future have humans power the grid for AIs, Microsoft and Pacific Northwest National Laboratory (PNNL) set a machine learning system on the path of better solid state batteries instead.

Solid state batteries are the current darlings of battery research, promising a step-change in packaging size and safety among other advantages. While they have been working in the lab for some time now, we’re still yet to see any large-scale commercialization that could shake up the consumer electronics and electric vehicle spaces.

With a starting set of 32 million potential inorganic materials, the machine learning algorithm was able to select the 150 most promising candidates for further development in the lab. This smaller subset was then fed through a high-performance computing (HPC) algorithm to winnow the list down to 23. Eliminating previously explored compounds, the scientists were able to develop a promising Li/Na-ion solid state battery electrolyte that could reduce the needed Li in a battery by up to 70%.

For those of us who remember when energy materials research often consisted of digging through dusty old journal papers to find inorganic compounds of interest, this is a particularly exciting advancement. A couple more places technology can help in the sciences are robots doing the work in the lab or on the surgery table.

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FedEx Robot Solves Complex Packing Problems

September 29, 2023 by Bryan Cockfield 44 Comments

Despite the fact that it constantly seems like we’re in the midst of a robotics- and artificial intelligence-driven revolution, there are a number of tasks that continue to elude even the best machine learning algorithms and robots. The clothing industry is an excellent example, where the flimsy materials can easily trip up robotic manipulators. But one task like this that seems like it might soon be solve is packing cargo into trucks, as FedEx is trying to do with one of their new robots.

Part of the reason this task is so difficult is that packing problems, similar to “traveling salesman” problems, are surprisingly complex. The packages are not presented to the robot in any particular order, and need to be efficiently placed according to weight and size. This robot, called DexR, uses artificial intelligence paired with an array of sensors to get an idea of each package’s dimensions, which allows it to then plan stacking and ordering configurations and ensure secure fits between all of the other packages. The robot must also be capable of quickly adapting if any packages shift during stacking and re-order or re-stack them.

As robotics platforms and artificial intelligence continue to improve, it’s likely we’ll see a flurry of complex problems like these solved by machine instead of by human. Tackling real-world tasks are often more complex than they seem, as anyone with a printer an a PC LOAD LETTER error can attest to, even handling single sheets of paper can be a difficult task for a robot. Interfacing with these types of robots can be a walk in the park, though, provided you read the documentation first.