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Machine Learning


Global Big Data Conference

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In the COVID era, computational biology is having a heyday – and machine learning is playing a massive role. With billions upon billions of compounds to search through for any given therapeutic application, strictly brute-force simulations are wildly unfeasible, necessitating more artificially intelligent methods of whittling down the options. Now, researchers from IRB Barcelona's Structural Bioinformatics and Network Biology lab have developed a deep learning method that predicts the biological activity of any given molecule – even in the absence of experimental data. The researchers, led by Patrick Aloy, are applying deep machine learning to a massive dataset: the Chemical Checker, which provides processed, harmonized, and integrated bioactivity data on 800,000 small molecules and is also produced by the Structural Bioinformatics and Network Biology lab. In total, any given molecule has 25 bioactivity "spaces," but for most molecules, data on only a few are known – if that.


Equipping AI with emotional intelligence can improve outcomes

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All the sessions from Transform 2021 are available on-demand now. There is a significant gap between an organization's ambitions for using artificial intelligence (AI) and the reality of how those projects turn out, Intel chief data scientist Dr. Melvin Greer said in a conversation with VentureBeat founder and CEO Matt Marshall at last week's Transf0rm 2021 virtual conference. One of the key areas is emotional intelligence and mindfulness. The pandemic highlighted this gap: The way people had to juggle home and work responsibilities meant their ability to stay focused and mindful could be compromised, Greer said. This could be a problem when AI is used in a cyberattack, like when someone is trying to use a chatbot or some other adversarial machine learning technique against us. "Our ability to get to the heart of what we're trying to achieve can be compromised when we are not in an emotional state and mindful and present," Greer said.


DeepMind releases AlphaFold database of nearly all human protein structures

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British artificial intelligence giant DeepMind has released a database of nearly all human protein structures that it amassed as part of its AlphaFold program. Last year, the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP) recognised AlphaFold as a solution to the grand challenge of figuring out what shapes proteins fold into. "We have been stuck on this one problem – how do proteins fold up – for nearly 50 years. To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we'd ever get there, is a very special moment." AlphaFold is a major scientific advance that will play a crucial role in helping scientists to solve important problems such as the protein misfolding associated with diseases such as Alzheimer's, Parkinson's, cystic fibrosis and Huntington's disease.


Vision Transformers or Convolutional Neural Networks? Both!

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Through the use of filters, these networks are able to generate simplified versions of the input image by creating feature maps that highlight the most relevant parts. These features are then used by a multi-layer perceptron to perform the desired classification. But recently this field has been incredibly revolutionized by the architecture of Vision Transformers (ViT), which through the mechanism of self-attention has proven to obtain excellent results on many tasks. In this article some basic aspects of Vision Transformers will be taken for granted, if you want to go deeper into the subject I suggest you read my previous overview of the architecture. Although Transformers have proven to be excellent replacements for CNNs, there is an important constraint that makes their application rather challenging, the need for large datasets.


Support Vector Machines -- the basics

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The important job that SVM's perform is to find a decision boundary to classify our data. This decision boundary is also called the hyperplane. Lets start with an example to explain it. Visually, if you look at figure 1, you will see that it makes sense for purple line to be a better hyperplane than the black line. The black line will also do the job, but skates a little to close to one of the red points to make it a good decision line.


Getting Started in Medical AI

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The course takes a top-down approach starting with a few lines of computer code, rather than establishing detailed mathematical notation. The first lesson will guide you through the construction and testing of your own deep learning model.


TensorRT 8 Is Out. Here is What you need to know.

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Whether you are building a machine learning model for research or for a business function, the whole of point of creating a model is to perform inference. Currently, TensorRT provides the most performant way to achieve just that. And TensorRT 8 takes it to the next level. In this article you will discover the latest capabilities of TensorRT8.


How To Do Keyword Recognition Using Simple Convolutional Network

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With the rapid development of mobile devices, speech-related technology is booming like never before. Many service providers like Google offer the ability to search through the voice on the android platform. For android mobile phones, 'Ok Google' uses this functionality to search a particular keyword to initiate the voice-based commands. Keyword recognition refers to speech technology that recognizes the existence of a word or short phrase within a given stream of audio. It is synonymously referred to as keyword spotting.


All The ML Papers Submitted By Apple At ICML 2021

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International Conference On Machine Learning (ICML) 2021 is back again with its 38th edition and is held virtually from July 18 – 24, 2021. The conference sees presentations of papers on all topics related to machine learning. The 38th ICML Conference is sponsored by Apple. Here, we have rounded up all the machine learning research papers submitted by Apple at ICML 2021. The recent work from Vitaly Feldman, Audra McMillan and Kunal Talwar show how random shuffling strengthens differential privacy guarantees of locally randomised data. Such amplification implies significantly higher privacy guarantees for systems where data is contributed anonymously, which has sparked interest in the shuffle model of privacy.


Neural Style Transfer

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Leon Gatys et al. introduced the Neural Style Transfer technique in 2015 in "A Neural Algorithm of Artistic Style". As stated earlier, Neural Style Transfer is a technique of composing images in the style of another image. Neural Style Transfer (NST) refers to a class of software algorithms that manipulate digital images or videos to adapt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation. If you want to go deep into the original technique, you can refer to the paper from this link.