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Machine Learning


Study finds gender and skin-type bias in commercial artificial-intelligence systems

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"What's really important here is the method and how that method applies to other applications," says Joy Buolamwini, a researcher in the MIT Media Lab's Civic Media group and first author on the new paper. "The same data-centric techniques that can be used to try to determine somebody's gender are also used to identify a person when you're looking for a criminal suspect or to unlock your phone. I'm really hopeful that this will spur more work into looking at [other] disparities." Buolamwini is joined on the paper by Timnit Gebru, who was a graduate student at Stanford when the work was done and is now a postdoc at Microsoft Research. The three programs that Buolamwini and Gebru investigated were general-purpose facial-analysis systems, which could be used to match faces in different photos as well as to assess characteristics such as gender, age, and mood. All three systems treated gender classification as a binary decision -- male or female -- which made their performance on that task particularly easy to assess statistically.


Protecting against APTs with Machine learning

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Machine learning is a science that uses existing data on a subject to train a computer how to identify related data. Just like with humans, the more training a machine learning algorithm gets, the more likely it is to succeed at its task. We have an extensive amount of information on attacks that can be used to train machines. After all, new attacks come out every day and over a hundred million malware samples have been collected each year since 2014. This information, as well as the historical information, can be fed into machine learning algorithms to better understand the attacks that haven't happened yet.


Deep learning helps robots grasp and move objects with ease

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In the past year, lockdowns and other COVID-19 safety measures have made online shopping more popular than ever, but the skyrocketing demand is leaving many retailers struggling to fulfill orders while ensuring the safety of their warehouse employees. Researchers at the University of California, Berkeley, have created new artificial intelligence software that gives robots the speed and skill to grasp and smoothly move objects, making it feasible for them to soon assist humans in warehouse environments. The technology is described in a paper published online today (Wednesday, Nov. 18) in the journal Science Robotics. Automating warehouse tasks can be challenging because many actions that come naturally to humans -- like deciding where and how to pick up different types of objects and then coordinating the shoulder, arm and wrist movements needed to move each object from one location to another -- are actually quite difficult for robots. Robotic motion also tends to be jerky, which can increase the risk of damaging both the products and the robots.


AI has cracked a problem that stumped biologists for 50 years. It's a huge deal.

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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).


Microsoft Cloud Security Exec Talks New Tech, WFH, Gamification

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Organizations have experienced "two years' worth of digital transformation in two months," Microsoft CEO Satya Nadella said earlier this year. Much of this change has centered on cloud adoption, which has been a key part of supporting the shift to work-from-home environments. Gunter Ollmann, chief security officer for Microsoft's Cloud and AI Security division, saw the redesign of access to corporate assets and the cloudification of on-premises assets as more businesses decided to work remotely in the long term. While end-user security has improved throughout the acceleration, a few security gaps remain that still need to be addressed. "The digital transformation has added a whole other layer to the environment that has to be managed and secured," Ollmann said in an Interop Digital keynote interview with Dark Reading executive editor Kelly Jackson Higgins.


Best of arXiv.org for AI, Machine Learning, and Deep Learning

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Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. The articles listed below represent a small fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Links to GitHub repos are provided when available. Especially relevant articles are marked with a "thumbs up" icon.


Machine Learning experiments and engineering with DVC

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Online video course to teach basics for Machine Learning experiment management, pipelines automation and CI/CD to deliver ML solution into production. During these lessons you'll discover base features of Data Version Control (DVC), how it works and how it may benefit your Machine Learning and Data Science projects. During this course listeners learn engineering approaches in ML around a few practical examples. Screencast videos, repositories with examples and templates to put your hands dirty and make it easier apply best features in your own projects.


Unlocking the secrets of chemical bonding with machine learning

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A new machine learning approach offers important insights into catalysis, a fundamental process that makes it possible to reduce the emission of toxic exhaust gases or produce essential materials like fabric. In a report published in Nature Communications, Hongliang Xin, associate professor of chemical engineering at Virginia Tech, and his team of researchers developed a Bayesian learning model of chemisorption, or Bayeschem for short, aiming to use artificial intelligence to unlock the nature of chemical bonding at catalyst surfaces. "It all comes down to how catalysts bind with molecules," said Xin. "The interaction has to be strong enough to break some chemical bonds at reasonably low temperatures, but not too strong that catalysts would be poisoned by reaction intermediates. This rule is known as the Sabatier principle in catalysis." Understanding how catalysts interact with different intermediates and determining how to control their bond strengths so that they are within that'goldilocks zone' is the key to designing efficient catalytic processes, Xin said. The research provides a tool for that purpose.


Using CRISP-DM to Grow as Data Scientist

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First, let us clarify what CRISP-DM (short for cross-industry standard process for data mining) is. As its name suggests, CRISP-DM is a process model that can be employed to structure the analysis of data in different fields. The process consists of six major phases (see Figure 1 below). It is important to note that the process is highly non-linear and moving back and forth between different stages is the norm rather than an exception. On a high level, we can regard our own careers and goals as data scientists as (big) projects and use CRISP-DM to move forward as we would do with any other project.


Clustering in Python

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This article was published as a part of the Data Science Blogathon. Cluster analysis or clustering is an unsupervised machine learning algorithm that groups unlabeled datasets. It aims to form clusters or groups using the data points in a dataset in such a way that there is high intra-cluster similarity and low inter-cluster similarity. In, layman terms clustering aims at forming subsets or groups within a dataset consisting of data points which are really similar to each other and the groups or subsets or clusters formed can be significantly differentiated from each other. Let's assume we have a dataset and we don't know anything about it.