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Revolutionizing IoT with Machine Learning at the Edge

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In episode 88 of the IoT For All Podcast, Perceive Founder and CEO Steve Teig joins us to talk about how Perceive is bringing the next wave of intelligence to IoT through machine learning at the edge. Steve shares how Perceive developed Ergo, their chip announced back in March, and how these new machine learning capabilities will transform consumer IoT. Steve Teig is an award-winning technologist, entrepreneur, and inventor on 388 US patents. He's been the CTO of three EDA software companies, two biotech companies, and a semiconductor company – of these, two went public during his tenure, two were acquired, and one is a Fortune 500 company. As the CEO and Founder of Perceive, Steve is leading a team building solutions and transformative machine learning technology for consumer edge devices.


Making AI, Machine Learning Work for You!

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Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.


Making AI, Machine Learning Work for You!

#artificialintelligence

Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.


Ten Research Challenge Areas in Data Science · Harvard Data Science Review

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To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue. Since data science is broad, with methods drawing from computer science, statistics, and other disciplines, and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning science, technology, and society. We preface our enumeration with meta-questions about whether data science is a discipline. We then describe each of the 10 challenge areas. The goal of this article is to start a discussion on what could constitute a basis for a research agenda in data science, while recognizing that the field of data science is still evolving. Although data science builds on knowledge from computer science, engineering, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: fundamental scientific questions and pressing problems of societal importance.


UMD Center for Machine Learning Announces 2020 Class of Rising Stars

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The University of Maryland Center for Machine Learning will host four female researchers this fall as part of a program that encourages and supports underrepresented doctoral candidates whose scientific work is focused on machine learning. Diana Cai, Irene Chen, Mahsa Ghasemi and Nan Rosemary Ke (pictured clockwise from top left) were recently selected as this year's Rising Stars in Machine Learning based on their novel research, academic accomplishments and exceptional work experience. The Rising Stars program, launched by the center last year, is focused on supporting upper-level graduate students from disadvantaged or underrepresented groups as they pursue new scientific discoveries and academic opportunities in machine learning. This year's cohort--who hail from Princeton University, the Massachusetts Institute of Technology, the University of Texas at Austin and the University of Montreal--were chosen from a competitive pool of 17 applicants. "After extensive review, we chose these four candidates based on their record of excellence in research and scholarship," said Soheil Feizi, assistant professor of computer science and a core faculty member in the Center for Machine Learning.


AI-Powered Education for a Better Tomorrow

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There was a time when Artificial Intelligence (AI) was often portrayed as robots. Machines that exhibited human-like characteristics (learning and decision making) with an artificial brain. Today, AI encompasses anything and everything. Be it vehicles, entertainment, corporations, smart homes, google search algorithms, education, law, or medical services, AI has transformed all the sectors for the better. Until today, artificial intelligence has outperformed humans in specific tasks. The worldwide AI market is expected to grow by $120 billion by the end of 2025.


Smarter AI & Deep Learning

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MIT CSAIL project shows that neural nets contain "subnetworks" 10x smaller that can just learn just as well - and often faster These days, nearly all AI-based products in our lives rely on "deep neural networks" that automatically learn to process labeled data. For most organizations and individuals, though, deep learning is tough to break into. To learn well, neural networks normally have to be quite large and need massive datasets. This training process usually requires multiple days of training and expensive graphics processing units (GPUs) - and sometimes even custom-designed hardware. But what if they don't actually have to be all that big after all?


What Is the Future of AI? - IT Peer Network

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When it comes to artificial intelligence (AI), few people can match Andrew Ng's breadth and depth of experience, combining research in a world-leading academic environment, accelerating AI education (one of his courses really helped me when I was breaking into AI myself) with founding AI teams at some of the most successful tech companies. Ng lead the Stanford AI Lab, was a founding leader at Google Brain where he worked with legendary engineer Jeff Dean, lead AI at Baidu, and currently serves as an adjunct professor in Computer Science at Stanford University. Among Andrew's other pursuits: being the founder of deeplearning.ai, In a recent episode of the Intel on AI podcast, Ng and host Intel's Abigail Hing Wen discuss why most of the important work yet to be done with AI is in industries outside of Silicon Valley, such as manufacturing, agriculture, and healthcare. Ng sees AI as driving huge growth for successful adopters, to the point where he sees a need for society to be prepared to offer additional support to workers in disrupted industries.


The Future of AI Part 1

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It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".


Top 15 Data Science Experts of the World in 2020

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To learn the best, you must learn from the finest. Geoffrey Hilton is called the Godfather of Deep Learning in the field of data science. Mr. Hinton is best known for his work on neural networks and artificial intelligence. A Ph.D. in artificial intelligence, he is accredited for his exemplary work on neural nets. The co-founder of the term, "Data Science", Jeff Hammerbacher developed methods and techniques for capturing, storing and analysing a large amount of data.