Artificial Intelligence

183 Articles

AI’s Existence Is All It Takes To Be Accused Of Being One

February 11, 2024 by 31 Comments

New technologies bring with them the threat of change. AI tools are one of the latest such developments. But as is often the case, when technological threats show up, they end up looking awfully human.

Recently, [E. M. Wolkovich] submitted a scientific paper for review that — to her surprise — was declared “obviously” the work of ChatGPT. No part of that was true. Like most people, [E. M. Wolkovich] finds writing a somewhat difficult process. Her paper represents a lot of time and effort. But despite zero evidence, this casual accusation of fraud in a scientific context was just sort of… accepted.

There are several reasons this is concerning. One is that, in principle, the scientific community wouldn’t dream of leveling an accusation of fraud like data manipulation without evidence. But a reviewer had no qualms about casually claiming [Wolkovich]’s writing wasn’t hers, effectively calling her a liar. Worse, at the editorial level, this baseless accusation was accepted and passed along with vague agreement instead of any sort of pushback.

Showing Your Work Isn’t Enough

Interestingly, [Wolkovich] writes everything in plain text using the LaTeX typesetting system, hosted on GitHub, complete with change commits. That means she could easily show her entire change history, from outline to finished manuscript, which should be enough to convince just about anyone that she isn’t a chatbot.

But pondering this raises a very good question: is [Wolkovich] having to prove she isn’t a chatbot a desirable outcome of this situation? We don’t think it is, nor is this an idle question. We’ve seen how even when an artist can present their full workflow to prove an AI didn’t make their art, enough doubt is sown by the accusation to poison the proceedings (not to mention greatly demoralizing the creator in the process.)

Better Standards Would Help

[Wolkovich] uses this opportunity to reflect on and share what this situation indicates about useful change. Now that AI tools exist, guidelines that acknowledge them should be created. Explicit standards about when and how AI tools can be used in the writing process, how those tools should be acknowledged if used, and a process to handle accusations of misuse would all be positive changes.

Because as it stands, it’s hard to see [Wolkovich]’s experience as anything other than an illustration of how a scientific community’s submission and review process was corrupted not by undeclared or thoughtless use of AI but by the simple fact that such tools exist. This seems like both a problem that will only get worse with time (right now, it is fairly easy to detect chatbots) and one that will not solve itself.

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

January 24, 2024 by 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 18 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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Creators Can Fight Back Against AI With Nightshade

January 21, 2024 by 78 Comments

If an artist were to make use of a piece of intellectual property owned by a large tech company, they risk facing legal action. Yet many creators are unhappy that those same tech companies are using their IP on a grand scale in the form of training material for generative AI. Can they fight back?

Perhaps now they can, with Nightshade, from a team at the University of Chicago. It’s a piece of software for Windows and MacOS that poisons an image with imperceptible shading, to make an AI classify it in an entirely different way than it appears.

The idea is that creators use it on their artwork, and leave it for unsuspecting AIs to assimilate. Their example is that a picture of a cow might be poisoned such that the AI sees it as a handbag, and if enough creators use the software the AI is forever poisoned to return a picture of a handbag when asked for one of a cow. If enough of these poisoned images are put online then the risks of an AI using an online image become too high, and the hope is that then AI companies would be forced to take the IP of their source material seriously.

For this to work it depends on enough creators taking up and using the software, but we are guessing that an inevitable result will be an arms race between AIs and image poisoners. One thing is certain though, as the AI hype has fueled such a growth in generative AI systems, creators, whether they be major publishers, your favourite human-generated tech news website, or someone drawing a cartoon strip in their bedroom, deserve not to have their work stolen in this way.

AI Binoculars Know More About Birds Than You

January 15, 2024 by 20 Comments

2024 is the year of adding Artificial Intelligence to everything. Now, even a pleasant walk in the woods is getting a dose of AI: optics manufacturer Swarovski has announced the AX Visio, a binocular set with an AI bird identification feature. Not sure if that is a lesser or greater scaup on your pond? These binoculars will tell you, for the low, low price of  $4799.

While digital cameras built into binoculars have been around for a while, adding AI is new. That’s a cool thing, but a bit of digging into the specs reveals that there is a much cheaper way to do it.

  1. Buy a cheap digital camera, like the Kodak Pixpro AZ255, which has a higher resolution and longer zoom than these binoculars.
  2. Transfer the image to your cell phone with an $11 memory card reader.
  3. Run the free Cornell Merlin ID app to identify the bird.
  4. Send the $4500 you just saved to us, or your favorite charity.

These ludicrously overpriced binoculars use the same Cornell Merlin ID system that you can use for free from their app, which also has the advantage of being able to ID birds from their songs. This is helpful because birds are tricky creatures who will try and hide from the hideously overpriced gadget you just bought.

[Via DigitalCameraWorld]

Bringing The Voice Assistant Home

January 14, 2024 by 22 Comments

For many, the voice assistants are helpful listeners. Just shout to the void, and a timer will be set, or Led Zepplin will start playing. For some, the lack of flexibility and reliance on cloud services is a severe drawback. [John Karabudak] is one of those people, and he runs his own voice assistant with an LLM (large language model) brain.

In the mid-2010’s, it seemed like voice assistants would take over the world, and all interfaces were going to NLP (natural language processing). Cracks started to show as these assistants ran into the limits of what NLP could reasonably handle. However, LLMs have breathed some new life into the idea as they can easily handle much more complex ideas and commands. However, running one locally is easier said than done.

A firewall with some muscle (Protectli Vault VP2420) runs a VLAN and NIPS to expose the service to the wider internet. For actually running the LLM, two RTX 4060 Ti cards provide the large VRAM needed to load a decent-sized model at a cheap price point. The AI engine (vLLM) supports dozens of models, but [John] chose a quantized version of Mixtral to fit in the 32GB of VRAM he had available.

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Adding AI To NPCs Is Easy, Doing It Well Is Hard

January 10, 2024 by 18 Comments

Adding natural language interfaces to software is easier than ever, and that led [creikey] to prototype a game that hinges on communicating with NPCs. The prototype went through multiple iterations during which he mainly discovered things that did not work well. Ultimately, it led to [creikey] settling on a western-themed game called Dante’s Cowboy which he hopes to release as an experiment. He begins talking about the game around the 4:43 mark in the video, which directly precedes a recording of a presentation he gives at as an indie developer.

Games typically revolve around the player manipulating entities in an environment in order to make things happen. This interaction drives engagement and interesting decisions. But while adding natural language AI to NPCs makes them easy to talk with, talking by itself is a shallow interaction. Convincing NPCs to do things? That’s complex and far more difficult to implement. [creikey] realized the limitations large language models (LLMs) had and worked to overcome them to make a unique game experience.

The challenges boil down to figuring out how to drive meaningful interaction, aligning AI behavior with the gameplay context, and managing API costs. In his words, “it’s been a learning experience to figure out where [natural language AI] even belongs in a game, if it belongs at all.”

We’ve previously seen ChatGPT used to grant NPCs the ability to communicate naturally which is a fascinating tech demo, but gameplay-wise can boil down to being a complicated alternative to pressing a button. As [creikey] discovered, adding this technology into games in a way that feels meaningful takes a new kind of work.

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