artificial intelligence

98 Articles

Render of life-size robot rat animatronic on blue plane

Robot Rodents: How AI Learned To Squeak And Play

December 9, 2024 by 9 Comments

In an astonishing blend of robotics and nature, SMEO—a robot rat designed by researchers in China and Germany — is fooling real rats into treating it like one of their own.

What sets SMEO apart is its rat-like adaptability. Equipped with a flexible spine, realistic forelimbs, and AI-driven behavior patterns, it doesn’t just mimic a rat — it learns and evolves through interaction. Researchers used video data to train SMEO to “think” like a rat, convincing its living counterparts to play, cower, or even engage in social nuzzling. This degree of mimicry could make SMEO a valuable tool for studying animal behavior ethically, minimizing stress on live animals by replacing some real-world interactions.

For builders and robotics enthusiasts, SMEO is a reminder that robotics can push boundaries while fostering a more compassionate future. Many have reservations about keeping intelligent creatures in confined cages or using them in experiments, so imagine applying this tech to non-invasive studies or even wildlife conservation. In a world where robotic dogs, bees, and even schools of fish have come to life, this animatronic rat sounds like an addition worth further exploring. SMEO’s development could, ironically, pave the way for reducing reliance on animal testing.

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Artificial Intelligence Runs On Arduino

October 24, 2024 by 23 Comments

Fundamentally, an artificial intelligence (AI) is nothing more than a system that takes a series of inputs, makes some prediction, and then outputs that information. Of course, the types of AI in the news right now can handle a huge number of inputs and need server farms’ worth of compute to generate outputs of various forms, but at a basic level, there’s no reason a purpose-built AI can’t run on much less powerful hardware. As a demonstration, and to win a bet with a friend, [mondal3011] got an artificial intelligence up and running on an Arduino.

This AI isn’t going to do anything as complex as generate images or write clunky preambles to every recipe on the Internet, but it is still a functional and useful piece of software. This one specifically handles the brightness of a single lamp, taking user input on acceptable brightness ranges in the room and outputting what it thinks the brightness of the lamp should be to match the user’s preferences. [mondal3011] also builds a set of training data for the AI to learn from, taking the lamp to various places around the house and letting it figure out where to set the brightness on its own. The training data is run through a linear regression model in Python which generates the function that the Arduino needs to automatically operate the lamp.

Although this isn’t the most complex model, it does go a long way to demonstrating the basic principles of using artificial intelligence to build a useful and working model, and then taking that model into the real world. Note also that the model is generated on a more powerful computer before being ported over to the microcontroller platform. But that’s all par for the course in AI and machine learning. If you’re looking to take a step up from here, we’d recommend this robot that uses neural networks to learn how to walk.

Building AI Models To Diagnose HVAC Issues

August 8, 2024 by 10 Comments

HVAC – heating, ventilation, and air conditioning – can account for a huge amount of energy usage of a building, whether it’s residential or industrial. Often it’s the majority energy consumer, especially in places with extreme climates or for things like data centers where cooling is a large design consideration. When problems arise with these complex systems, they can go undiagnosed for a time and additionally be difficult to fix, leading to even more energy losses until repairs are complete. With the growing availability of platforms that can run capable artificial intelligences, [kutluhan_aktar] is working towards a system that can automatically diagnose potential issues and help humans get a handle on repairs faster.

The prototype system is designed for hydronic (water-based) systems and uses two separate artificial intelligences, one to analyze thermal imagery of the system and look for problems like leaks, hot spots, or blockages, and the other to listen for anomalous sounds especially relating to the behavior of cooling fans. For the first, a CNC-like machine was built to move a thermal camera around a custom-built model HVAC system and report its images back to a central system where they can be analyzed for anomalies. The second system which analyses audio runs its artificial intelligence on a XIAO ESP32C6 and listens to the cooling fans running in the model.

One problem that had to be tackled before any of this could be completed was actually building an open-source dataset to train the AI on. That’s part of the reason for the HVAC model in this project; being able to create problems to train the computer to detect before rolling it out to a larger system. The project’s code and training models can be found on its GitHub page. It seems to be a fairly robust solution to this problem, though, and we’ll be looking forward to future versions running on larger systems. Not everyone has a hydronic HVAC system, though. As heat pumps become more and more popular and capable, you’ll need systems to control those as well.

Analyzing Feature Learning In Artificial Neural Networks And Neural Collapse

July 26, 2024 by 9 Comments

Artificial Neural Networks (ANNs) are commonly used for machine vision purposes, where they are tasked with object recognition. This is accomplished by taking a multi-layer network and using a training data set to configure the weights associated with each ‘neuron’. Due to the complexity of these ANNs for non-trivial data sets, it’s often hard to make head or tails of what the network is actually matching in a given (non-training data) input. In a March 2024 study (preprint) by [A. Radhakrishnan] and colleagues in Science an approach is provided to elucidate and diagnose this mystery somewhat, by using what they call the average gradient outer product (AGOP).

Defined as the uncentered covariance matrix of the ANN’s input-output gradients averaged over the training dataset, this property can provide information on the data set’s features used for predictions. This turns out to be strongly correlated with repetitive information, such as the presence of eyes in recognizing whether lipstick is being worn and star patterns in a car and truck data set rather than anything to do with the (highly variable) vehicles. None of this was perhaps too surprising, but a number of the same researchers used the same AGOP for elucidating the mechanism behind neural collapse (NC) in ANNs.

NC occurs when an ANN gets overtrained (overparametrized). In the preprint paper by [D. Beaglehole] et al. the AGOP is used to provide evidence for the mechanism behind NC during feature learning. Perhaps the biggest take-away from these papers is that while ANNs can be useful, they’re also incredibly complex and poorly understood. The more we learn about their properties, the more appropriately we can use them.

By kallerna - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=122952945

ChatGPT And Other LLMs Produce Bull Excrement, Not Hallucinations

July 1, 2024 by 99 Comments

In the communications surrounding LLMs and popular interfaces like ChatGPT the term ‘hallucination’ is often used to reference false statements made in the output of these models. This infers that there is some coherency and an attempt by the LLM to be both cognizant of the truth, while also suffering moments of (mild) insanity. The LLM thus effectively is treated like a young child or a person suffering from disorders like Alzheimer’s, giving it agency in the process. That this is utter nonsense and patently incorrect is the subject of a treatise by [Michael Townsen Hicks] and colleagues, as published in Ethics and Information Technology.

Much of the distinction lies in the difference between a lie and bullshit, as so eloquently described in [Harry G. Frankfurt]’s 1986 essay and 2005 book On Bullshit. Whereas a lie is intended to deceive and cover up the truth, bullshitting is done with no regard for, or connection with, the truth. The bullshitting is only intended to serve the immediate situation, reminiscent of the worst of sound bite culture.

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Testing Large Language Models For Circuit Board Design Aid

June 24, 2024 by 27 Comments

Beyond bothering large language models (LLMs) with funny questions, there’s the general idea that they can act as supporting tools. Theoretically they should be able to assist with parsing and summarizing documents, while answering questions about e.g. electronic design. To test this assumption, [Duncan Haldane] employed three of the more highly praised LLMs to assist with circuit board design. These LLMs were GPT-4o (OpenAI), Claude 3 Opus (Anthropic) and Gemini 1.5 (Google).

The tasks ranged from ‘stupid questions’, like asking the delay per unit length of a trace on a PCB, to finding parts for a design, to designing an entire circuit. Of these tasks, only the ‘parsing datasheets’ task could be considered to be successful. This involved uploading the datasheet for a component (nRF5340) and asking the LLM to make a symbol and footprint, in this case for the text-centric JITX format but KiCad/Altium should be possible too. This did require a few passes, as there were glitches and omissions in the generated footprint.

When it came to picking components for a design, it’s clear that you’re out of luck here unless you’re trying to create a design that a million others have made before you in exactly the same way. This problem got worse when trying to design a circuit and ultimately spit out a netlist, with the best LLM (Claude 3 Opus) giving nonsensical suggestions for filter designs and mucking up even basic amplifier designs, including by sticking decoupling capacitors and random resistors just about everywhere.

Effectively, as a text searching tool it would seem that LLMs can have some use for engineers who are tired of digging through yet another few hundred pages of poorly formatted and non-indexed PDF datasheets, but you still need to be on your toes with every step of the way, as the output from the LLM will all too often be slightly to hilariously wrong.

Kaffa Roastery founder Svante Hampf shows a bag of their AI-conic coffee blend.

AI-Created Coffee Blend Isn’t Terrible

May 10, 2024 by 12 Comments

Weren’t we just talking about coffee-based sacrilege the other day? Here’s something to make the single-origin bean snobs chew their espresso cups: an artisan roastery in Helsinki is offering a coffee blend created by artificial intelligence called AI-conic. The idea, of course, is that technology will lighten the workload needed to produce coffee.

This is an interesting development because Finland consumes the most coffee in the world, according to the International Coffee Organization. Coffee roasting is a highly-valued traditional artisan profession there, so it stands to reason that they might turn to technology for help.

Just like with scotch whisky, there’s nothing wrong with coffee blends outright. Bean blends are good for consistency, when you want every cup to taste pretty much exactly the same. Single-origin beans, though, are traceable to one location, and as a result, they usually have a distinct flavor based on the climate they’re grown in.

If you’re new to coffee, blends are a nice, safe way to start out. And, interestingly, the AI chose to make the blend out of four different types of beans instead of the usual two or three, despite being tasked with creating a blend that would suit the palates of coffee enthusiasts. But the coffee experts agreed that the AI blend was “perfect” and needed no human intervention. We probably won’t be getting to Finland anytime soon, so if you try it, let us know how it tastes!

Do you like cold brew? How would you like to be able to brew some in just three minutes?