“A team of researchers from the University of Kansas has developed a tool to weed out #AI-generated academic writing from the stuff penned by people, with over 99 percent accuracy”
It is amazing to see how the LLMs models become more accessible and easier to train. The llama2.c is an open-source project made by Andrej Karpathy that enables training Llama 2 model in PyTorch locally and then compiling the weights to a binary C file that inferences the model.
Out of the nearly 4000 people where I work, I'm the only one who's voiced any opposition to the introduction of AI or ML systems to our workflows. Everyone has become super caught up in the hype. We've got internal workgroups exploring tools like Copilot, ChatGPT, and a bunch of proprietary options that promise to magically solve all kinds of complex issues. Enough "AI no-code" platforms to make my head spin. There's monthly/weekly/daily "AI news" digest emails.
Management just met with a vendor that promises a tool to replace the entire role of DBA while also magically optimizing our data access to impossible speeds (sub 1ms round-trip for ALL queries, updates, and inserts on dozens of TB). Were they skeptical after such oulandish claims? No, they scheduled more demos and asked for a quote.
Some of this stuff is genuinely useful, like Copilot, but most doesn't even pass the sniff test. Legal issues? "our dataset is anonymized, so no one can prove that their content was used." Practical challenges? Who cares, "we can figure that out later". Don't even get me started on the ethical nightmare of mixing AI and PII, and all from a company that makes a big public deal about being socially conscious.
I feel like a wet blanket, constantly raining on everyone's parade with problems, risks, and ethical concerns. But if I don't, then (apparently) no one else will. Some coworkers have privately confirmed that they agree with me, but aren't willing to publicly oppose the hype. Its so tiring and frustrating and I think I might just stop, and let them ride this train until it wrecks.
If you had the feeling that the online discussion about COVID-19 vaccines was biased depending on the actors, you are right.
Using #MachineLearning and #NetworkScience we have shown that being a human or a bot, verified or unverified (according to previous Twitter rules) and political leaning were relevant factors for choosing the words in posts and, accordingly, the corresponding emotions to trigger.
A genuine computational social science study, led by Anna Bertani for her Msc thesis, now published also in collaboration with Riccardo Gallotti and Pierluigi Sacco
Anybody out there looking for an ML or software engineer with >30 years total experience and ~20 years in the industry?
I have extensive experience with #Python and #ML frameworks, particularly #TensorFlow, and I've worked on #NLP and #ImageProcessing both in the workplace and in personal open source projects. My resume is available here:
The MLX is Apple's framework for machine learning applications on Apple silicon. The MLX examples repository provides a set of examples for using the MLX framework. This includes examples of:
✅ Text models such as transformer, Llama, Mistral, and Phi-2 models
✅ Image models such as Stable Diffusion
✅ Audio and speech recognition with OpenAI's Whisper
✅ Support for some Hugging Face models
Version 1.7.1 of the NeuralForecast #Python library was released last month by Nixtla. The NeuralForecast library, as the name implies, provides a neural network framework for time series forecasting. 🧵👇🏼
Meta released today Llama 3, the next generation of the Llama model. LLama 3 is a state-of-the-art open-source large language model. Here are some of the key features of the model: 🧵👇🏼
Fabric is a new open-source project that provides a framework to support AI applications. The goal of Fabric is to unify the communication with AI agents (e.g., LLMs, etc.) by creating a library of Patterns (e.g., prompts) for day-to-day use cases.
Our new publication is out in Earth's Future, which compares changes in summer temperatures across the USA between observations and climate model large ensembles using explainable machine learning...
(1/3)Modeling Short Time Series with Prior Knowledge in PyMC 🚀
Yesterday, I shared an article by Tim Radtke about forecasting insufficient time series data with a Bayesian approach using R. Here is the Python version 🧵👇🏼
No wonder #Amazon was trying to get me to give them more reviews.
An Amazon #chatbot that's supposed to surface useful information from customer reviews will also recommend a variety of racist books, lie about working conditions at Amazon, more. They just put a full, easy to abuse chatbot on their site