artificial intelligence


How high-tech does an electric toothbrush really need to be?

USATODAY - Tech Top Stories

Know Your Stuff is a new column that unlocks the hidden secrets about the everyday products you own. Dental care has come a long way since we were first using bone and hog hair brushes in sixth-century China, but based on some of the raised eyebrows I've seen at the recent CES electronics show, some might argue that the pendulum has swung too far in the other direction. Oral-B and Colgate, two household names in oral hygiene, each released state-of-the-art toothbrushes that promise to get your teeth cleaner than a standard brush. They join the ranks of dozens of other "smart brushes" that sport a list of features rivaling some laptops, which of course begs the question, "Why?" Aren't we fine with toothbrushes as they already are? Vision of the future:Is your eye the next frontier for small screen tech?


From anime to reality: Mobile 25-ton Gundam robot to be built in Yokohama

The Japan Times

What was once thought limited to the realm of animation is set to become reality in Yokohama this fall when an 18-meter mobile Gundam robot steps into action. Fans of the iconic anime series will be able to get an up-close look at the 25-ton machine at Gundam Factory Yokohama, a 9,000 sq.-meter facility set to open at Yamashita Pier on Oct. 1 for a year. Tickets for the facility will go on sale in July, though the price has not been disclosed. Other details remain a mystery, such as the exact movements the robot will be able to perform using its 24 fully functional joints. Gundam Factory Yokohama will consist of two areas: a 25-meter-tall Gundam-Dock that will serve as its maintenance site, and a two-story building with shops and event space.


Undersampling Algorithms for Imbalanced Classification

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Taken from Improving Identification of Difficult Small Classes by Balancing Class Distribution. This technique can be implemented using the NeighbourhoodCleaningRule imbalanced-learn class.


How to build a machine learning project in Elixir.

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Machine learning is an ever-growing area of interest for developers, businesses, tech enthusiasts and the general public alike. From agile start-ups to trendsetting industry leaders, businesses know that successful implementation of the right machine learning product could give them a substantial competitive advantage. We have already seen businesses reap significant benefits of machine learning in production through automated chat bots and customised shopping experiences. Given we recently demonstrated how to complete web scraping in Elixir, we thought we'd take it one step further and show you to apply this in a machine learning project. The traditional approach has always been algorithm centric.


How LinkedIn Is Using AI To Detect & Handle Abuse

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With organisations transitioning from a traditional business model into adopting emerging technologies, popular professional social networking platform, LinkedIn has also adopted machine learning technologies to help professionals in a more sophisticated way. According to a source, it has 303 million active users per month, and on an average, two people create an account on this platform every second. With these amounts of data generation, the developers of this platform have been striving hard to make its state-of-the-art machine learning model a robust one, so that it provides more accurate decisions or results for its users. The ML researcher team at LinkedIn built a domain-specific language (DSL) and a Jupyter notebook to integrate the selected features and for tuning the parameters. In one of our articles, we discussed how LinkedIn's recommendation system is generating the perfect job match for its users.


Monitoring the environment with artificial intelligence - Communiqués de presse - UNIGE

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Microorganisms perform key functions in ecosystems and their diversity reflects the health of their environment. However, they are still largely under-exploited in current biomonitoring programs because they are difficult to identify. Researchers from the University of Geneva (UNIGE), Switzerland, have recently developed an approach combining two cutting edge technologies to fill this gap. They use genomic tools to sequence the DNA of microorganisms in samples, and then exploit this considerable amount of data with artificial intelligence. They build predictive models capable of establishing a diagnosis of the health of ecosystems on a large scale and identify species that perform important functions.


The costs of random acts of AI: The right culture is the new strategy imperative

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In my experience leading transformation initiatives, I have seen many similarities between broader business transformations and AI transformations, but there is one key difference – in AI, the world is still highly experimental. In fact, 3 out of 4 of AI projects fail to show positive returns on investment. Because of this, there hasn't been a map of what AI success at scale really looks like. It remains uncharted territory for enterprises. In October 2019, we commissioned a study with Inc.digital, where we talked to 550 executives across a variety of industries to understand why enterprises don't more frequently see tangible, measurable results from AI. Through this research, we were able to determine the DNA of the few that are successful with their AI strategy – the one common thread is keeping the AI strategy, data, technology, people and processes close to the core and controlled.


Artificial Intelligence Software Market to Reach $126.0 Billion in Annual Worldwide Revenue by 2025

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Artificial intelligence (AI) within the consumer, enterprise, government, and defense sectors is migrating from a conceptual "nice to have" to an essential technology driving improvements in quality, efficiency, and speed. According to a new report from Tractica, the top industry sectors where AI is likely to bring major transformation remain those in which there is a clear business case for incorporating AI, rather than pie-in-the-sky use cases that may not generate return on investment for many years. "The global AI market is entering a new phase in 2020 where the narrative is shifting from asking whether AI is viable to declaring that AI is now a requirement for most enterprises that are trying to compete on a global level," says principal analyst Keith Kirkpatrick. According to the market intelligence company, AI is likely to thrive in consumer (Internet services), automotive, financial services, telecommunications, and retail industries. Not surprisingly, the consumer sector has demonstrated its ability to capture AI, thanks to the combination of three key factors – large data sets, high-performance hardware and state of the art algorithms.


Manufacturing, supply chain see greatest cost savings from AI: McKinsey

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Manufacturing and supply chain management are complicated operations, which often leads to waste, according to Freightflows CEO Matt Morgan. "Any time that there's complexity, machine learning and AI can add significant benefit," Morgan said in an interview with Supply Chain Dive. The MAPI Foundation, a group that advocates for the manufacturing sector, says AI can optimize various stages of the supply chain from warehouse management to supplier relationships, according to a recent report. One example is Procter & Gamble's use of AI and Internet of Things (IoT) technology to automate warehouses and distribution centers. P&G was able to automate delivery of about 7,000 SKUs and cut supply chain costs by about $1 billion annually, the MAPI Foundation noted in its report.


Manufacturing, supply chain see greatest cost savings from AI: McKinsey

#artificialintelligence

Manufacturing and supply chain management are complicated operations, which often leads to waste, according to Freightflows CEO Matt Morgan. "Any time that there's complexity, machine learning and AI can add significant benefit," Morgan said in an interview with Supply Chain Dive. The MAPI Foundation, a group that advocates for the manufacturing sector, says AI can optimize various stages of the supply chain from warehouse management to supplier relationships, according to a recent report. One example is Procter & Gamble's use of AI and Internet of Things (IoT) technology to automate warehouses and distribution centers. P&G was able to automate delivery of about 7,000 SKUs and cut supply chain costs by about $1 billion annually, the MAPI Foundation noted in its report.