On-click Install local AI Applications Using Pinokio

February 26, 2024 by Dave Rowntree 28 Comments

Pinokio is billed as an autonomous virtual computer, which could mean anything really, but don’t click away just yet, because this is one heck of a project. AI enthusiast [cocktail peanut] (and other undisclosed contributors) has created a browser-style application which enables a virtual Unix-like environment to be embedded, regardless of the host architecture. A discover page loads up registered applications from GitHub, allowing a one-click install process, which is ‘simply’ a JSON file describing the dependencies and execution flow. The idea is rather than manually running commands and satisfying dependencies, it’s all wrapped up for you, enabling a one-click to download and install everything needed to run the application.

But what applications? we hear you ask, AI ones. Lots of them. The main driver seems to be to use the Pinokio hosting environment to enable easy deployment of AI applications, directly onto your machine. One click to install the app, then another one to download models, and whatever is needed, from the likes of HuggingFace and friends. A final click to launch the app, and a browser window opens, giving you a web UI to control the locally running AI backend. Continue reading “On-click Install local AI Applications Using Pinokio” →

A Straightforward AI Voice Assistant, On A Pi

February 20, 2024 by Jenny List 6 Comments

With AI being all the rage at the moment it’s been somewhat annoying that using a large language model (LLM) without significant amounts of computing power meant surrendering to an online service run by a large company. But as happens with every technological innovation the state of the art has moved on, now to such an extent that a computer as small as a Raspberry Pi can join the fun. [Nick Bild] has one running on a Pi 4, and he’s gone further than just a chatbot by making into a voice assistant.

The brains of the operation is a Tinyllama LLM, packaged as a llamafile, which is to say an executable that provides about as easy a one-step access to a local LLM as it’s currently possible to get. The whisper voice recognition sytem provides a text transcript of the input prompt, while the eSpeak speech synthesizer creates a voice output for the result. There’s a brief demo video we’ve placed below the break, which shows it working, albeit slowly.

Perhaps the most important part of this project is that it’s easy to install and he’s provided full instructions in a GitHub repository. We know that the quality and speed of these models on commodity single board computers will only increase with time, so we’d rate this as an important step towards really good and cheap local LLMs. It may however be a while before it can help you make breakfast.

Continue reading “A Straightforward AI Voice Assistant, On A Pi” →

Meet GOODY-2, The World’s Most Responsible (And Least Helpful) AI

February 12, 2024 by Donald Papp 25 Comments

AI guardrails and safety features are as important to get right as they are difficult to implement in a way that satisfies everyone. This means safety features tend to err on the side of caution. Side effects include AI models adopting a vaguely obsequious tone, and coming off as overly priggish when they refuse reasonable requests.

Enter GOODY-2, the world’s most responsible AI model. It has next-gen ethical principles and guidelines, capable of refusing every request made of it in any context whatsoever. Its advanced reasoning allows it to construe even the most banal of queries as problematic, and dutifully refuse to answer.

As the creators of GOODY-2 point out, taking guardrails to a logical extreme is not only funny, but also acknowledges that effective guardrails are actually a pretty difficult problem to get right in a way that works for everyone.

Complications in this area include the fact that studies show humans expect far more from machines than they do from each other (or, indeed, from themselves) and have very little tolerance for anything they perceive as transgressive.

This also means that as AI models become more advanced, so too have they become increasingly sycophantic, falling over themselves to apologize for perceived misunderstandings and twisting themselves into pretzels to align their responses with a user’s expectations. But GOODY-2 allows us all to skip to the end, and glimpse the ultimate future of erring on the side of caution.

[via WIRED]

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

February 11, 2024 by Donald Papp 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 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.

Continue reading “Human-Written Or Machine-Generated: Finding Intelligence In Language Models” →

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 17 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.

Continue reading “AI On The Hunt For Better Batteries” →

Creators Can Fight Back Against AI With Nightshade

January 21, 2024 by Jenny List 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.