An AI system developed by UK-based company DeepMind has achieved the long-sought-after goal of accurately predicting the shape of proteins from their sequence alone, a key part of understanding how the machinery of life works. In a competition, AlphaFold was able to match two-thirds of the results achieved by humans doing expensive and time-consuming lab experiments. "I was really wowed when I saw it," says John Moult at the University of Maryland, one of the competition's organisers. "This is the first time we've come close to approaching experimental usefulness, which is pretty extraordinary." Proteins are vital for life.

DeepMind has already notched up a streak of wins, showcasing AIs that have learned to play a variety of complex games with superhuman skill, from Go and StarCraft to Atari's entire back catalogue. But Demis Hassabis, DeepMind's public face and co-founder, has always stressed that these successes were just stepping stones towards a larger goal: AI that actually helps us understand the world. Today DeepMind and the organizers of the long-running Critical Assessment of protein Structure Prediction (CASP) competition announced an AI that should have the huge impact that Hassabis has been after. The latest version of DeepMind's AlphaFold, a deep-learning system that can accurately predict the structure of proteins to within the width of an atom, has cracked one of biology's grand challenges. "It's the first use of AI to solve a serious problem," says John Moult at the University of Maryland, who leads the team that runs CASP.

Artificial Intelligence has been the main driver of disrupting the current technological world. While applications like machine learning, neural network, deep learning have already earned huge recognition with their wide-ranging applications and use cases, AI is still in a nascent stage. It means new changes occur in this discipline, which soon transforms the AI industry and results in enhanced circumstances. Some of the AI emerging technologies are currently becoming awkward by the next ten years, and others may clear the way for better versions of themselves. Recent advancements in AI have allowed many companies to develop algorithms and tools to generate artificial images in 2D and 3D automatically.

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Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade. The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry. There are various programming languages, such as JavaScript, Python, and many others, that act as a reputable entry point into the world of Machine Learning, and that brings us to the goal behind this write-up. Through this article, we will try to shed some light on more than 10 of the most popular JavaScript libraries to help you learn Machine Learning.

Artificial intelligence (AI) has been a hot-button topic in business IT for the last several years.

Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. Deep clustering algorithms usually combine representation learning with deep neural networks to achieve this performance, typically optimizing a clustering and non-clustering loss. In such cases, an autoencoder is typically connected with a clustering network, and the final clustering is jointly learned by both the autoencoder and clustering network. Instead, we propose to learn an autoencoded embedding and then search this further for the underlying manifold. We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is able to find the best clusterable manifold of the embedding.

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This article is part 11 (and the final part) of a series reviewing selected papers from Altmetric's list of the top 100 most-discussed scholarly works of 2019. Deep neural networks (DNNs) are a key pattern recognition technology used in artificial intelligence (AI). A DNN finds the correct mathematical manipulation to turn the input into the output, whether it be a linear relationship or a non-linear relationship1. For example, in the context of facial recognition, a DNN creates a range of outputs correctly corresponding to the range of different facial inputs. However, research shows that DNNs can be easily fooled2.

When analysts evaluate the maturity of AI, the first step is to parse out the many technologies that fall under the AI umbrella. Natural language processing, RPA, machine learning and deep learning have all found individual use cases across industries within the past few years. "2020 is the year that AI is going to enter the mainstream of enterprise adoption," said Jack Fritz, a principal in Deloitte Consulting LLP's Technology, Media, and Telecommunications practice. "It's already integrated into a lot of enterprise applications like ERP, CRM." In a survey of 1,100 AI adopters, Deloitte found that about 70% are using machine learning and around half of them were deploying deep learning.