Well File:

How Important Is Data Quality In Machine Learning?

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Day after day, machine learning is becoming an important function in several business sectors. Machine learning programs run on data and the need for large amounts of data to train the machine like a well-oiled engine is more than ever. But more than large amounts of data, good data quality is crucial to get the desired end result. Data management deals with data quality, which is what makes the output given by analytical applications authentic. Analytical applications give businesses an insight into their standing in the industry.


Events

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Participate in DeepLearning.AI events for the opportunity to network with a diverse community of peers, learn best practices from industry leaders, get advice and hands-on practice from mentors, benefit from thought-provoking discussions, and more!


Artificial Intelligence Could Reduce Time To Diagnose Breast Cancer - AI Summary

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Google Health has teamed up with Northwestern Medicine to explore whether artificial intelligence (AI) could prioritise reviews of mammograms with a higher suspicion of breast cancer. Women whose mammograms show a higher likelihood of breast cancer might be able to be seen the same day for follow up, according to a statement from Northwestern Medicine. Dr Sarah Friedewald, associate professor of radiology at Northwestern University Feinberg School of Medicine, said: "With the use of artificial intelligence, we hope to expedite the process to diagnosis of breast cancer by identifying suspicious findings on patients' screening examinations earlier than the standard of care. The Goolge-funded study builds on research conducted by Northwestern Medicine, Google Health and the NHS in 2020, which found AI screening of mammograms was as accurate as human experts. Dr Mozziyar Etemadi, research assistant professor of anesthesiology at Northwestern Medicine, added: "This study is the next step by applying the AI models in a prospective study to better understand how AI can be the most helpful for clinicians and patients in the real world."


AI Council advises government to do artificial intelligence moonshots

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The AI Council has published a "roadmap" of advice for government in respect of developing a UK state strategy for artificial intelligence (AI). Eye-catchingly, it advocates what it calls "moonshots" that "could tackle fundamental challenges such as creating'explainable AI' and developing smart materials for energy storage". The council is a non-statutory body chaired by Tabitha Goldstaub, consisting of 20 people from academia and industry, including Wendy Hall, professor of computer science at the University of Southampton, Marc Warner, the CEO of AI consultancy firm Faculty, and Adrian Smith, chief executive of The Alan Turing Institute. The council was launched in 2018, on the back of the government's response to a House of Lords AI report that recommended the UK pick ethics as a realistic niche in the related fields of artificial intelligence and machine learning. It was bolstered in 2019 with recruits from online retailer Ocado and the Independent Commission on Freedom of Information.


What Powers Artificial Intelligence? A Guide for Business

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Artificial Intelligence (AI) is an increasing part of our everyday lives, powering our smartphones and the internet of things. But few people really understand what it is, how it works and more importantly, why it is so important to their business. The Oxford English Dictionary defines artificial intelligence (AI) as the theory and development of computer systems able to perform tasks normally requiring human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. For many people in Business, the language used in data science can be confusing. It is far simpler to explain by simply saying, 'powered by AI'.


We don't need to worry about Overfitting anymore

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Motivated by prior work connecting the geometry of the loss landscape and generalization, we introduce a novel, effective procedure for instead simulta- neously minimizing loss value and loss sharpness. In particular, our procedure, Sharpness-Aware Minimization (SAM), seeks parameters that lie in neighbor- hoods having uniformly low loss; this formulation results in a min-max optimiza- tion problem on which gradient descent can be performed efficiently. We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets[1] In Deep Learning we use optimization algorithms such as SGD/Adam to achieve convergence in our model, which leads to finding the global minima, i.e a point where the loss of the training dataset is low. But several kinds of research such as Zhang et al have shown, many networks can easily memorize the training data and have the capacity to readily overfit, To prevent this problem and add more generalization, Researchers at Google have published a new paper called Sharpness Awareness Minimization which provides State of the Art results on CIFAR10 and other datasets. In this article, we will look at why SAM can achieve better generalization and how we can implement SAM in Pytorch.


Japanese Companies Go High-Tech in the Battle Against Food Waste

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Japanese convenience store chain Lawson is using artificial intelligence to identify goods on shelves that go unsold or fall short of demand. Japanese firms are accelerating the use of advanced technology to combat food waste and reduce costs during the Covid-19 pandemic. Convenience store chain Lawson is using artificial intelligence (AI) from U.S. company DataRobot to identify goods on shelves that go unsold or fall short of demand. Meanwhile, multinational brewing and distilling company Suntory Beverage & Food is testing AI from Fujitsu to ascertain if inventory has been damaged in shipping. Suntory hopes the AI will reduce the return of goods by 30% to 50%, slash food waste costs, and yield a common standard for use by other food manufacturers and shipping firms.


'Machinehood' Upgrades Asimov's 3 Laws Of Robotics

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For anyone who has purchased a pair of shoes online, only to be immediately pursued across the Internet by enthusiastic algorithms exclaiming that we will love exactly the same pair of shoes (which is, technically speaking, true), the globe-spanning future of 2095 that Machinehood presents through the eyes of two women caught in its web feels disconcertingly logical. From the very first page, Machinehood, the debut science fiction novel from Nebula- and Hugo-award nominated machine intelligence specialist and biomedical engineer S.B. Divya, achieves what the very best science fiction aspires to -- it establishes our future by making it relatable, plausible, and infinitely strange at the same time. That Machinehood goes on to upend long-established laws of robotics, question longstanding political machinations, establish a credible voyeurism-based sub-economy, and take us on a thrilling who-done-it through the advent of the singularity are only a few of the novel's accomplishments. Machinehood also introduces us to the plight of humans caught within a future where everything is faster, better, and smarter -- everything except humans. That'Machinehood' goes on to upend long-established laws of robotics, question longstanding political machinations, establish a credible voyeurism-based sub-economy, and take us on a thrilling who-done-it through the advent of the singularity are only a few of the novel's accomplishments.


State of AI in 10 Charts

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This week, Stanford HAI released its 2021 AI Index. The index is an independent program developed by an interdisciplinary team at HAI in partnership with academia, industry, and government. It takes a comprehensive look at AI's impact and progress each year, analyzing and distilling patterns about AI's impact on everything from national economies to job growth, diversity, and research. Dig into the report to learn in-depth what happened to the industry this past year, or scroll through some of the highlights here.


How battery swapping could reduce EV charge time to just 10 minutes

Mashable

The fastest electric vehicle charging stations currently get an empty battery to 80 percent full in about 30 minutes. But a new company is working on swapping out empty battery packs for fully charged ones. That would get an electric vehicle to 100 percent full in about 10 minutes. Ample, which officially launched this week at two sites in San Francisco and another Oakland, builds and operates battery-swapping stations that use a robot to pluck out dead battery packs from under the car and replace them with packs fully charged and ready to go. The Ample stations can be set up anywhere close to a power source so that the robot machine can get under the belly of the car and also charge a waiting supply of replacement batteries. The stations are completely autonomous and you don't even have to get out of the car while the batteries are switched.