The below course by Dhaval Patel is a beginner-level course for Deep Learning in Python with Tensorflow 2.0 and Kares. The course covers the foundations of neural network and deep learning, which includes the following topics: 🧵👇🏼
Wait, are people getting money just for pairing random #scifi concepts with #AI? Well, let me tell you my amazing idea for using an Oscillation Overthruster to bring #MachineLearning to the eighth dimension!
the ask envision on the @letsenvision app is cool, I loaded a 50 page pdf and it just do a RAG on it and answers my questions comprehensively, people should use it more, I hope in future I can load entire folders of document on desktop to do rag #machineLearning#AI#blind#disability
AI already uses as much energy as a small country. It’s only the beginning.
AI will make bitcoin's environmental devastation look like a picnic.
"If ChatGPT were integrated into the 9 billion searches done each day, the IEA says, the electricity demand would increase by 10 terawatt-hours a year — the amount consumed by about 1.5 million European Union residents."
There's a reason why some, in software behemoth companies, are interested in biological neural circuit architectures: the latter are extremely energy efficient.
At the same time, there's a subset of machine learning practitioners that dismiss neuroscience and neuroscientists as being entirely off track when it comes to understanding how neural networks compute.
This contraposition is quite the interesting dynamic to observe. Says more about the individual people than about the field, as is often the case.
(1/2)Statistical Inference via Data Science - New Edition 📚👇🏼
The Statistical Inference via Data Science by Chester Ismay and Prof. Albert Y. Kim recently released a new edition. This book focuses on the data analysis workflow and how to answer questions with data. This includes the following topics:
✅ Data wrangling
✅ Data visualization
✅ Simple and multivariate regression analysis
✅ Sampling and bootstrap
✅ Hypothesis testing
Well, you'd think #AI and #generativeAI like #DALL·E wouldn't be a problem for #fanfiction and #fanart, where you can't own the IP or sell it, but you'd be wrong. People posing as fan artists and LoRa's and artist style stealing are all in the article. The fandom is #MLP. There are links to other related issues.
Every couple of days I get approached on Discord by AI scam artists. And I am sure it will get worse.
It's sad. I've bought fan art from real artists that are so good, I've suggested they branch out and find a commercial outlet. I've commissioned them for cover art.
If this trend continues and AI kills all human artists off, creativity will stagnate. No further innovative styles, just ad nauseum repetition of what we've seen posed differently or put on different backgrounds.
It is a very human behavior to go with what requires the least amount of work to produce results. It's how rap took over the world and exists in every language. Every. Language. Even Bushman. It requires no training or instrument. Yes, I like some rap songs, I'm not ragging on the form, but everyone can do it and the musically inclined can produce passible songs. Maybe it is democratizing versus rock & roll or jazz, etm., but it is also often monochromatic and repetitive.
When #AI and #generativeAI stop producing monsters and hallucinations, I face it, it will become widely used. /The reason that companies like #Apple are interested in #machinelearning and #ai, is it will push the computing envelope—and sales./ Having to pay for renders, and, worse, having to wait for them, competing with hundreds or thousands for a shared computing resource, is not sustainable. Super-GPUs and ML chips are the future of home computing so you can use your AI tools at home.
Here we go again: The boom in artificial intelligence (AI) and quantum computing will drive a spike in energy use, the National Grid has predicted. Data centre power use 'to surge six-fold in 10 years' https://www.bbc.com/news/technology-68664182 all this power usage for shity LLM/AI to write poems, pictures, videos etc from stolen data to train AI. #artificialintelligence#machinelearning
Calling all data enthusiasts: ever heard of Orange (https://orangedatamining.com/)? Recently stumbled upon this tool for data mining and machine learning. It's Python-based and completely open-source. Sounds pretty good to me? Any users here?
I came across these well-documented lecture notes about Optimization for Machine Learning by Prof. Elad Hazan from Princeton University. This is a new level of lecture notes 🚀 👇🏼
Societal Impacts of Artificial Intelligence and Machine Learning by Carlo Lipizzi
This book goes beyond the current hype of expectations generated by the news on artificial intelligence and machine learning by analyzing realistic expectations for society, its limitations, and possible future scenarios for the use of this technology in our current society.
(1/2) Statistics for Applications - MIT Course 🚀👇🏼
If you are looking for a good intro to statistics, I recommend checking MIT's Statistics for Applications course by Prof. Philippe Rigollet. The course is a hard-core intro to applied statistics ❤️ and provides an in-depth of the math and theoretical foundation of statistical methods.
As impressive as Musk's solitary NeuraLink demo is, beneath the hype lies a misperception with a disturbing parallel to large language models. Both operate on statistical inference rather than scientific models of the brain, and garner attention for cherrypicked successes 1/3