algorithm


Customize your machine learning needs through our automated platform: PredictSense

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Machine learning algorithms have come a long way, and new computing technologies are supporting its evolution. The most challenging task while developing a machine learning based solution is data processing and choosing the customized algorithm. PredictSense, a machine learning automated platform, which is a part of such evolution, meets this challenge. It customizes the machine learning needs for businesses through its data transformation and model enhancement techniques. Built on an open API structure, PredictSense uses high-end algorithms to address real-time complications in a short span of time.


How is Artificial Intelligence (AI) used in Healthcare for better outcomes? Data Driven Investor

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Artificial Intelligence has made major strides in recent years. It has found application is different industrial verticals and businesses. It has revolutionized and revamped business processes and operations, which has resulted in increased efficiency and productivity. AI allows any particular digital device to easily observe as well as recognize a subject or object, deeply understand it and reply to different recognizable messages. Also, it can easily make decisions as well as learn to adapt its behaviour as well as the thinking process as it completely analyses the huge volume of data points.


Autonomous In Action: Self-Driving Cars Get All The Publicity, But Other Industries Are Already Getting Exceptional Value From Ai-based Systems

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Truly "autonomous" systems are starting to replace or augment many of the routine tasks and processes people perform every day, improving efficiency while freeing individuals for higher-level pursuits. But what's often overlooked is how much progress is happening in other areas and industries: healthcare, air travel, energy provision, retail, logistics, agriculture, and construction. Autonomous systems are even helping governments match refugees with the most suitable communities to live, as detailed in one of the four real-world vignettes we present below. Such optimism makes sense, given advances such as self-managing and self-patching databases in IT. But our survey's other findings might underestimate the pace of change: Just 24% say they expect to see significant use of autonomous tech in construction, for example, even though self-driving bulldozers already are in use on select projects.


Artificial Intelligence and Medicine: Is It Overhyped?

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AI is seen as a possible silver bullet for medicine and healthcare. But can AI truly transform these realms? The answer is no in the short term, but very likely in the long term. Right now billions of dollars are being invested in AI research for medicine, medically oriented human biology, and health care. It is not surprising why upon the waves of myriads of sensational headlines from media outlets, we are becoming more and more intrigued about what results it can bring.


#Industry40_2020-01-25_17-38-35.xlsx

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The graph represents a network of 4,720 Twitter users whose recent tweets contained "#Industry40", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Sunday, 26 January 2020 at 02:13 UTC. The tweets in the network were tweeted over the 8-day, 1-hour, 49-minute period from Friday, 17 January 2020 at 23:40 UTC to Sunday, 26 January 2020 at 01:29 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.


A Quick Overview on the Kaggle Competition for Avito

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I didn't have much time for this competition, so didn't invest much into feature engineering, creating ensembles or other things. As I participated in the Avazu competition as well, which included the use of tinrtgu's now-famous code, I decided to use the same approach here. The overall goal of the competition is to analyze user behavior in order to generate a model for recommending ads to be shown in front of users, with the success metric being whether or not the user clicks on the ad. There is already a lot of work on this topic, so there is no need to rebuild everything from scratch. If you haven't read the paper from Google on FTRL for ad prediction and their view from the trenches then I can really recommend that as a first step.


The Road to Artificial Intelligence: An Ethical Minefield

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The term "Artificial Intelligence" conjures, in many, an image of an anthropomorphized Terminator-esque killer robot apocalypse. Hollywood movies, in recent decades, have served to only further this notion. Physicists and moral philosophers like Max Tegmark and Sam Harris, however, claim we need not fear a runaway superintelligence to adequately worry about the deleterious effects endemic to the AI space, but rather that competence on behalf of machines is a sufficiently frightening springboard from which an irreversibly harmful future can be launched. That said, there are currently a number of far more nefarious, insidious, and relevant ethical dilemmas which warrant our attention. In a world increasingly controlled by automated processes, rapidly approaching is a time in which adaptive, self-improving algorithms guide or even dictate most of the decisions that define human experience.


Five Ways Companies Can Adopt Ethical AI

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Does your company have an AI ethics officer? In 2014, Stephen Hawking said that AI would be humankind's best or last invention. Six years later, as we welcome 2020, companies are looking at how to use Artificial Intelligence (AI) in their business to stay competitive. The question they are facing is how to evaluate whether the AI products they use will do more harm than good. Many public and private leaders worldwide are thinking about how to address these questions around the safety, privacy, accountability transparency and bias in algorithms.


Deep learning enables real-time imaging around corners: Detailed, fast imaging of hidden objects could help self-driving cars detect hazards

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"Compared to other approaches, our non-line-of-sight imaging system provides uniquely high resolutions and imaging speeds," said research team leader Christopher A. Metzler from Stanford University and Rice University. "These attributes enable applications that wouldn't otherwise be possible, such as reading the license plate of a hidden car as it is driving or reading a badge worn by someone walking on the other side of a corner." In Optica, The Optical Society's journal for high-impact research, Metzler and colleagues from Princeton University, Southern Methodist University, and Rice University report that the new system can distinguish submillimeter details of a hidden object from 1 meter away. The system is designed to image small objects at very high resolutions but can be combined with other imaging systems that produce low-resolution room-sized reconstructions. "Non-line-of-sight imaging has important applications in medical imaging, navigation, robotics and defense," said co-author Felix Heide from Princeton University.


Deep learning vs. machine learning: Understand the differences

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Machine learning and deep learning are both forms of artificial intelligence. You can also say, correctly, that deep learning is a specific kind of machine learning. Both machine learning and deep learning start with training and test data and a model and go through an optimization process to find the weights that make the model best fit the data. Both can handle numeric (regression) and non-numeric (classification) problems, although there are several application areas, such as object recognition and language translation, where deep learning models tend to produce better fits than machine learning models. Machine learning algorithms are often divided into supervised (the training data are tagged with the answers) and unsupervised (any labels that may exist are not shown to the training algorithm).