The idea of artificial general intelligence as we know it today starts with a dot-com blowout on Broadway. Twenty years ago--before Shane Legg clicked with neuroscience postgrad Demis Hassabis over a shared fascination with intelligence; before the pair hooked up with Hassabis's childhood friend Mustafa Suleyman, a progressive activist, to spin that fascination into a company called DeepMind; before Google bought that company for more than half a billion dollars four years later--Legg worked at a startup in New York called Webmind, set up by AI researcher Ben Goertzel. Today the two men represent two very different branches of the future of artificial intelligence, but their roots reach back to common ground. Even for the heady days of the dot-com bubble, Webmind's goals were ambitious. Goertzel wanted to create a digital baby brain and release it onto the internet, where he believed it would grow up to become fully self-aware and far smarter than humans.

I made a quick video on the basics of DeepMind's AlphaFold 2, one of the biggest accomplishments in the history of AI and life sciences. Hope the video is useful.

Machine Learning has seen tremendous growth recently, which has led to a larger adoption of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. Trustworthiness of ML and NLP systems is a crucial aspect and requires guarantee that the decisions they make are fair and robust. Aligned with this, we propose a framework GYC, to generate a set of counterfactual text samples, which are crucial for testing these ML systems. Our main contributions include a) We introduce GYC, a framework to generate counterfactual samples such that the generation is plausible, diverse, goal-oriented, and effective, b) We generate counterfactual samples, that can direct the generation towards a corresponding condition such as named-entity tag, semantic role label, or sentiment. Our experimental results on various domains show that GYC generates counterfactual text samples exhibiting the above four properties. %The generated counterfactuals can then be fed complementary to the existing data augmentation for improving the debiasing algorithms performance as compared to existing counterfactuals generated by token substitution. GYC generates counterfactuals that can act as test cases to evaluate a model and any text debiasing algorithm.

Artificial intelligence has been the buzzword in 2020 and with the benefits of this technology evident around us; AI has had its own share of controversies. From algorithms¹ unfairly discriminating women in hiring and students complaining about unrealistic grades, there is no doubt that AI has evolved in 2020 and as 2021 beckons, it is time to take stock of what the year has been. With GPT3, deepfakes, and facial recognition making headlines in 2020, there are many arguments surrounding privacy and regulations. Clearview AI provides organizations, predominantly law enforcement agencies, with a database that is able to match images of faces with over three billion other facial pictures scraped from social media sites. The company has recently been hit with a series of reprisals from social media platforms, who have taken a hostile stance in response to Clearview AI's operations.

Thanks to breakthroughs in natural language processing (NLP), machines can generate increasingly sophisticated representations of words. Every year, research groups release more and more powerful language models -- like the recently announced GPT-3, M2M 100, and MT-5 -- that are able to write complex essays or translate text into multiple languages with better accuracy than previous iterations. However, since machine learning algorithms are what they eat (in other words, they function based on the training data they ingest), they inevitably end up picking up on human biases that exist in language data itself. This summer, GPT-3 researchers discovered inherent biases within the model's results related to gender, race, and religion. Gender biases included the relationship between gender and occupation, as well as gendered descriptive words.

You may have heard about "DeepMind" in the past, and if you haven't, now you will. To this day, DeepMind has acquired a number of achievements since it was founded, but it is most notable for AlphaGo, an AI program that beat some of the best professional Go players in history including Ke Jie. DeepMind's AlphaFold 2 can now identify a protein's three-dimensional structures from its amino-acid sequence to the width of an atom. To give some context, AlphaFold2 competed with over 100 research groups worldwide in a competition known as the Critical Assessment of Protein Structure Prediction, or CASP. The goal was exactly what AlphaFold 2 achieved, to be able to predict a protein's structure from its amino-acid sequence.

A protein's function is determined by its 3D shape.Credit: DeepMind An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology's grandest challenges -- determining a protein's 3D shape from its amino-acid sequence. DeepMind's program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference -- held virtually this year -- that takes stock of the exercise. "This is a big deal," says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. "In some sense the problem is solved."

DeepMind solves a 50-year old problem in Protein Folding Prediction. AlphaFold 2 improves over DeepMind's 2018 AlphaFold system with a new architecture and massively outperforms all competition. In this Video, we take a look at how AlphaFold 1 works and what we can gather about AlphaFold 2 from the little information that's out there. CASP14 Result Bar Chart: https://www.predictioncenter.org/casp14/zscores_final.cgi Paper Title: High Accuracy Protein Structure Prediction Using Deep Learning Abstract: Proteins are essential to life, supporting practically all its functions. They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure.

DeepMind, an AI research lab that was bought by Google and is now an independent part of Google's parent company Alphabet, announced a major breakthrough this week that one evolutionary biologist called "a game changer." "This will change medicine," the biologist, Andrei Lupas, told Nature. The breakthrough: DeepMind says its AI system, AlphaFold, has solved the "protein folding problem" -- a grand challenge of biology that has vexed scientists for 50 years. Proteins are the basic machines that get work done in your cells. They start out as strings of amino acids (imagine the beads on a necklace) but they soon fold up into a unique three-dimensional shape (imagine scrunching up the beaded necklace in your hand).

My Name Is GPT-3 and I Approved This Article. The latest natural-language system generates tweets, pens poetry, summarizes emails, answers trivia questions, translates languages and….