Company revives Alan Turing as an AI chatbot, hilarity, no, wait, outrage ensues.

The company is Genius Group, based in Singapore, which provides "AI-powered business education."

"Software engineer Grady Booch, a former Turing Talk speaker, wrote on Twitter/X: 'Absolute and complete trash. I hope that Turing's heirs sue you into oblivion.'"

"Another user told Genius Group's CEO: 'This is so incredibly unethical, disrespectful, and disgusting. You are pillaging the image of a deceased person (who frankly has suffered enough from exploitation) and the voice of an actor to suit your purposes. Vile.'"

Company revives Alan Turing as an AI chatbot, outrage ensues

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Want to learn deep learning on your phone? The Little Book of Deep Learning was designed to be read on a phone.

The Little Book of Deep Learning

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DenseWiki is a new site that aims to provide simple, plain-English explanations of popular concepts in machine learning. They've started by adding explanations of a few popular concepts in reinforcement learning, starting with "Actor-Critic Methods".

"As a human, when you get better at playing a game (say soccer or boxing), isn't the improvement also usually accompanied by getting better at evaluating games -- i.e. answering questions such as 'which side is doing better' at any given point in a game?"

"It also goes the other way around -- being good at evaluating your own performance during a game also enables you to coach yourself, thus being able to try new things and get better over time -- without necessarily needing external supervision."

"And that is the fundamental intuition behind 'actor critic' methods. In essence, being your own critic helps you grow as an actor, growing as an actor makes you a better critic, and the cycle continues."

Actor Critic Methods -- A simple explanation

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UK AI safety summit: 28 countries sign up to the Bletchley declaration

The UK has announced the world's first agreement to help manage the most highly advanced forms of Artificial Intelligence.It happened at a two-day AI safety ...#AIdevelopment #AIeducation #AlJazeera #AlJazeeraEnglish #ArtificialIntelligencesafetysummit #Bletchleydeclaration #Bletchleypark #UKAIsafetysummit2023 #US #UnitedKingdom #aljazeeralive #aljazeeravideo #aljazeeraEnglish #aljazeeralatest #aljazeeralive #aljazeeralivenews #china #elonmusk #rishisunak #scienceandtechnology #techcompanies
UK AI safety summit: 28 countries sign up to the Bletchley declaration

The AI tutor Khanmigo, demonstrated by Sal Khan. Rather than AI destroying education, AI will turbocharge it, by giving every student on the planet an artificially intelligent but amazing personal tutor. And give every teacher on the planet an amazing, artificially intelligent teaching assistant. According to Khan, 1-on-1 tutoring boosts educational results by 2 sigmas, but most students have not had access to a 1-on-1 tutor. That's about to change.

He demos a simple math equation solving problem and shows Khanmigo is not a cheating tool. When the student says, "Tell me the answer," it says, "I'm your tutor. What do you think is the next step for solving the problem?"

If the student makes a mistake, not only does it notice the mistake, it asks the student to explain their reasoning. It guesses what is probably the misconception in that student's mind (they didn't use the distributive property).

He demos a computer programming exercise on Khan Academy to show it understands the code and the full context of what the student is doing. (The code draws elipses but it understands that those ellipses combine to draw clouds.)

It can engage in Socratic dialogue, if the student asks, for example, "the age-old question, 'Why do I need to learn this?'". It can connect the lesson to knowledge outside the lesson. It can act as a school guidance counselor.

Rather than writing "for" you it can write "with" you and teach writing.

In "teacher" mode, when you say, "Tell me the answer", instead of refusing and going into tutoring mode, not only will it tell you the answer but it will give you explanations and advice on how best to teach it. As such it helps teachers create lesson plans and progress reports, and figure out how to grade the students.

How AI could save (not destroy) education | Sal Khan | TED

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Microsoft has launched a new course, "Artificial Intelligence for Beginners". A 12-week, 24-lesson curriculum where you learn: different approaches to Artificial Intelligence, including the 'good old' symbolic approach with knowledge representation and reasoning (GOFAI), neural networks and deep learning, using code in two of the most popular frameworks, TensorFlow and PyTorch, neural architectures for working with images and text, and less popular AI approaches, such as genetic algorithms and multi-agent systems.

Artificial Intelligence for Beginners - A curriculum

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AI-For-Beginners/README.md at main · microsoft/AI-For-Beginners

12 Weeks, 24 Lessons, AI for All! Contribute to microsoft/AI-For-Beginners development by creating an account on GitHub.

"DeepHyper is a distributed machine learning (AutoML) package for automating the development of deep neural networks for scientific applications. It can run on a single laptop as well as on 1,000 of nodes."

"It comprises different tools such as: optimizing hyper-parameters for a given black-box function, neural architecture search to discover high-performing deep neural network with variable operations and connections, and automated machine learning, to easily experiment many learning algorithms from Scikit-Learn."

Looks like a pretty powerful tool for neural architecture and hyper-parameter search to be publicly available.

DeepHyper: scalable neural architecture and hyperparameter search for deep neural networks

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Machine Learning for Art is a collection of tools for machine learning for art. Models for DeepDream, neural style transfer, salient object detection (detecting foreground images), image-to-image translation, StyleGAN2 (generates photorealistic images), super-resolution (also known as upsampling), cartoonization, semantic segmentation (pixel-by-pixel labeling of what's in an image), text-to-speech synthesis, reversible generative models and GAN inversion (going from images to parameters of a generative model rather than the other way around), processing faces (detecting faces in images, identifying people, track faces from image to image), photo sketching (going from photographs to sketches), lip-syncing videos, and optical flow (detecting and measuring motion in video).

Machine Learning for Art

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Machine learning for arts

ml4a is a collection of tools and educational resources for making art with machine learning.

The full text of Nicolas Rougier's book Scientific Visualization: Python + Matplotlib is available online for free.

Skimming through the book it looks like something I'm going to want to learn. I previously invested a lot of effort in learning ggplot, but the rest of the world decided Matplotlib was the way to go, so I think I should switch gears and learn all about Matplotlib.

Scientific Visualization: Python + Matplotlib

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The Physics-based Deep Learning Book, written by the Thuerey Group at the Technical University of Munich (TU Munich) (team of 20 people), is available online for free. The book "contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. As much as possible, all topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started. Beyond standard supervised learning from data, we'll look at physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, as well as reinforcement learning and uncertainty modeling. We live in exciting times: these methods have a huge potential to fundamentally change what computer simulations can achieve."

Topics include "How to train networks to infer a fluid flow around shapes like airfoils, and estimate the uncertainty of the prediction", "How to use model equations as residuals to train networks that represent solutions, and how to improve upon these residual constraints by using differentiable simulations", and "How to more tightly interact with a full simulator for inverse problems".

The Physics-based Deep Learning Book

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