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Prepare for Artificial Intelligence to Produce Less Wizardry

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Early last year, a large European supermarket chain deployed artificial intelligence to predict what customers would buy each day at different stores, to help keep shelves stocked while reducing costly spoilage of goods. The company already used purchasing data and a simple statistical method to predict sales. With deep learning, a technique that has helped produce spectacular AI advances in recent years--as well as additional data, including local weather, traffic conditions, and competitors' actions--the company cut the number of errors by three-quarters. It was precisely the kind of high-impact, cost-saving effect that people expect from AI. But there was a huge catch: The new algorithm required so much computation that the company chose not to use it.


Seamlessly Scaling AI for Distributed Big Data

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Originally published at LinkedIn Pulse. Early last month, I presented a half-day tutorial on at this year's virtual CVPR 2020. This is a very unique experience, and I would like to share some of the highlights of the tutorial. The tutorial focused on a critical problem that arises as AI moves from experimentation to production; that is, how to seamlessly scale AI to distributed Big Data. Today, AI researchers and data scientists need to go through a mountain of pains to apply AI models to production dataset that is stored in distributed Big Data cluster.


Prepare for Artificial Intelligence to Produce Less Wizardry

WIRED

Early last year, a large European supermarket chain deployed artificial intelligence to predict what customers would buy each day at different stores, to help keep shelves stocked while reducing costly spoilage of goods. The company already used purchasing data and a simple statistical method to predict sales. With deep learning, a technique that has helped produce spectacular AI advances in recent years--as well as additional data including local weather, traffic conditions, and competitors' actions--the company cut the number of errors by three-quarters. It was precisely the kind of high-impact, cost-saving effect that people expect from AI. But there was a huge catch: The new algorithm required so much computation that the company chose not to use it.


Must try Artificial Intelligence Platforms - NewsDeskIndia.com

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With the mankind being largely dependent on artificial intelligence, here is a list of AI platforms that are pulling the strings in the industry. For those unaware, Artificial Intelligence alludes the re-enactment of human insight into machine so as to enable them to think like members of the human race. Thus, attributes like problem solving, learning and critical thinking are carried on by machines. Artificial intelligence brings along a colossal potential to the table which is ultimately sculpturing the fate of technology in future. Thus, its no surprise that business industry is investing more and more in this platform that holds the promise of changing the world as we know it.


Natural Language Processing (NLP) with Python: 2020

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BESTSELLER Created by Ankit Mistry, Vijay Gadhave, Data Science & Machine Learning Academy English English [Auto] PREVIEW THIS COURSE - GET COUPON CODE Description Recent reviews: "Very practical and interesting, Loved the course material, organization and presentation. Thank you so much" "This is the best course to learn NLP from the basic. According to statista dot com which field of AI is predicted to reach $43 billion by 2025? If answer is'Natural Language Processing', You are at right place. How Android speech recognition recognize your voice with such high accuracy.


Computer vision(CV): Leading public companies named

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CV is a nascent market but it contains a plethora of both big technology companies and disruptors. Technology players with large sets of visual data are leading the pack in CV, with Chinese and US tech giants dominating each segment of the value chain. Google has been at the forefront of CV applications since 2012. Over the years the company has hired several ML experts. In 2014 it acquired the deep learning start-up DeepMind. Google's biggest asset is its wealth of customer data provided by their search business and YouTube.


How Machine Learning Impact Product Personalization

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Machine learning-based personalization has gained traction over the years due to volume in the amount of data across sources and the velocity at which consumers and organizations generate new data. Traditional ways of personalization focused on deriving business rules using techniques like segmentation, which often did not address a customer uniquely. Recent progress in specialized hardware (read GPUs and cloud computing) and a burgeoning ML and DL toolkits enable us to develop 1:1 customer personalization which scales. Recommender systems are beneficial to both service providers and users. They reduce transaction costs of finding and selecting items in an online shopping environment and improves customer experience.


Kaggle: Where data scientists learn and compete

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Data science is typically more of an art than a science, despite the name. You start with dirty data and an old statistical predictive model and try to do better with machine learning. Nobody checks your work or tries to improve it: If your new model fits better than the old one, you adopt it and move on to the next problem. When the data starts drifting and the model stops working, you update the model from the new dataset. Doing data science in Kaggle is quite different.


How Edge AI is a Roadmap to Future AI and IoT Trends?

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Change has always been integral to development. With fast-evolving technologies, companies, too, need themselves to embrace these for maximized benefits. Artificial Intelligence (AI) moving to edge IoT devices and networks, just like we witnessed computing switch from mainframes to the cloud. And as data continues to grow, we need to opt for data storage and data computation to be located on the device. Companies like Qualcomm, NVIDIA, and Intel are helping us achieve this reality.


Top 5 Artificial Intelligence Platforms that Transform Modern Software Development

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Unlocking the huge potential AI has to offer will shape the future of software development. The strategic business interest in this disruptive technology is increasing, companies across the world have gained smartly investing in AI. With more and more mature enterprises defining AI strategy it is predicted that AI tools alone will create trillions of dollars in business value in the years to come. AI algorithms and advanced analytics have an immense potential into software development, offering seamless real-time decisions at scale. AI applications can perform complex and intelligent functions associated with human thinking.