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Never Ignore these 5 Machine Learning Modeling Challenges

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Okay, You have decided to build your own machine learning model. You are using Sklearn that is popular machine learning libraries for modeling. But wait do you know the common machine learning modeling challenges faced by every data scientist. No, then you have come to the right place. Here You will know each modeling challenges you face while building the model. When you have a categorical target dataset.


AI early adopters in the public sector

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As one of the hottest technologies of recent years, artificial intelligence (AI) has started penetrating both the US public and the private sectors--though to differing degrees. While the private sector seems bullish on AI, the public sector's approach appears tempered with more caution--a Deloitte survey of select early adopters of AI shows high concern around the potential risks of AI among public sector organizations (see the sidebar "About the survey"). They give a peek into how public sector organizations are approaching AI; and how the approaches, in many cases, differ from those of their private sector counterparts. AI is not completely new to the public sector. The first AI contract was awarded in 1985 by the US Social Security Administration,1 but the technology still wasn't advanced enough to become common in the following decades.


SoftBank brings food service robot to labour-strapped Japan – IAM Network

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By Sam Nussey2 Min ReadTOKYO (Reuters) – SoftBank's robotics arm said on Monday it will bring a food service robot developed by California-based Bear Robotics to Japan as restaurants grapple with labour shortages and seek to ensure social distancing during the COVID-19 pandemic.Slideshow ( 3 images)The robot named Servi, which has layers of trays and is equipped with 3D cameras and Lidar sensors for navigation, will launch in January, SoftBank Group Corp said.Servi will cost 99,800 yen ($950) per month excluding tax on a three year plan.The launch leverages SoftBank's long experience in bringing overseas technology to Japan but reflects the shift away from CEO Masayoshi Son's earlier focus on humanoid robots.Servi has been tested by Japanese restaurant operators, including Seven & i Holdings at its Denny's chain, as the sector grapples with an aging workforce and deepening labour shortages.SoftBank's humanoid Pepper robot became the face of the company following its 2014 unveiling but failed to find a global customer base.The firm in 2018 announced cleaning robot Whiz, which employs technology from group portfolio company Brain Corp and has sold more than 10,000 units worldwide.SoftBank is touting the use of Whiz as a coronavirus countermeasure, …


Differential machine learning: the shape of things to come – Risk.net – IAM Network

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Brian Huge and Antoine Savine combine automatic adjoint differentiation with modern machine learning. Pricing approximation has proved tremendously useful with advanced Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content. To access these options, along with all other subscription benefits, please contact [email protected] or view our subscription options here: http://subscriptions.risk.net/subscribe You are currently unable to print this content. Please contact [email protected] to find out more.


AI Machine Learning Breakthrough Is a Twist on Brain Replay

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Recently, researchers affiliated with the Baylor College of Medicine, the University of Cambridge, the University of Massachusetts Amherst, and Rice University created a new way of adapting a neuroscience concept called "brain replay" to the digital realm of artificial neural networks to enable continuous learning. From a neuroscience perspective, the concept of brain replay is analogous to a streaming service that activates repeat showings from its vast archives of stored pre-recorded content. The brain can replay memories by reactivating the neural activity patterns that represent prior experiences, whether asleep or awake. This ability for memory replay starts in the hippocampus, then continues in the cortex. The research trio of Hava Siegelmann, Andreas Tolias, and Gido van de Ven published a study in Nature Communications on August 13, 2020, that shows state-of-the-art performance from neural networks by deploying a new twist on mimicking brain replay.


How Will AI Change the C-Suite?

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This may not be the best time to be thinking 15 years into the future, I know. For many associations, the rest of 2020 is stressful enough, and 2021 seems plenty forbidding too. But any association wise enough to have a strategic planning process knows that it has to look for potential headwinds. And a study released last week by the software company Citrix suggests that automation will have a substantial impact on leadership--calling to question what a leader might be good for, if AI can make decisions nearly as well as a human can. Citrix's report, Work 2035 [PDF], is based on the responses of 500 executives and 1,000 employees at large and mid-size companies in the United States and Europe, with a focus on artificial intelligence and productivity. In general, an always-on work mentality, combined with better analytics, have led people to wonder what role the C-suite ought to play.


AI can detect how lonely you are by analysing your speech

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Artificial intelligence (AI) can detect loneliness with 94 per cent accuracy from a person's speech, a new scientific paper reports. Researchers in the US used several AI tools, including IBM Watson, to analyse transcripts of older adults interviewed about feelings of loneliness. By analysing words, phrases, and gaps of silence during the interviews, the AI assessed loneliness symptoms nearly as accurately as loneliness questionnaires completed by the participants themselves, which can be biased. It revealed that lonely individuals tend to have longer responses to direct questions about loneliness, and express more sadness in their answers. 'Most studies use either a direct question of "how often do you feel lonely", which can lead to biased responses due to stigma associated with loneliness,' said senior author Ellen Lee at UC San Diego (UCSD) School of Medicine.


Machine Learning Takes on Synthetic Biology: Algorithms Can Bioengineer Cells for You

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Tijana Radivojevic (left) and Hector Garcia Martin working on mechanical and statistical modeling, data visualizations, and metabolic maps at the Agile BioFoundry last year. If you've eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine – both products that are "grown" in the lab – then you've benefited from synthetic biology. It's a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach. Now scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically.


Oracle Open-Sources Tribuo, A Machine Learning Library in Java

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Oracle open-sources Tribuo to fill the gap for enterprise applications focused on machine learning in Java. Committed to deploying machine learning models to large-scale production systems, Oracle has released Tribuo under an Apache 2.0 license. What does Tribuo provide under machine learning? Tools for building and deploying classificationTools for clustering and regression models Unified interface for many popular third-party machine learning librariesA full suite of evaluations for each of the supported prediction tasksData loading pipelines, text processing pipelines, and feature level transformations for operating on dataIn addition to its implementations of Machine Learning algorithms, Tribuo also provides a common interface to popular ML tools on the JVM. Apart from the features mentioned above, Tribuo Model knows when you've given it features it has never seen before, which is particularly useful when working with natural language processing.


4 things to remember when adapting AI/ML learning models during a pandemic – IAM Network

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Pedro Alves is the founder and CEO of Ople.AI, a software startup that provides an automated machine learning platform to empower business users with predictive analytics. The machine learning and AI-powered tools being deployed in response to COVID-19 arguably improve certain human activities and provide essential insights needed to make certain personal or professional decisions; however, they also highlight a few pervasive challenges faced by both machines and the humans that create them. Nevertheless, the progress seen in AI/machine learning leading up to and during the COVID-19 pandemic cannot be ignored. This global economic and public health crisis brings with it a unique opportunity for updates and innovation in modeling, so long as certain underlying principles are followed. Here are four industry truths (note: this is not an exhaustive list) my colleagues and I have found that matter in any design climate, but especially during a global pandemic climate.