STEM


Teaching Today's AI Students To Be Tomorrow's Ethical Leaders: An Interview With Yan Zhang - Future of Life Institute

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Some of the greatest scientists and inventors of the future are sitting in high school classrooms right now, breezing through calculus and eagerly awaiting freshman year at the world's top universities. They may have already won Math Olympiads or invented clever, new internet applications. We know these students are smart, but are they prepared to responsibly guide the future of technology? Developing safe and beneficial technology requires more than technical expertise -- it requires a well-rounded education and the ability to understand other perspectives. But since math and science students must spend so much time doing technical work, they often lack the skills and experience necessary to understand how their inventions will impact society.


Can Machine Learning Read Chest X-rays like Radiologists?

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Today, only about 10% of 7B population in the world have access to good healthcare service, and half of the world don't even access to essential health services. Even among the developed countries, healthcare system is under strain, with rising cost and long wait time. To train up enough physicians and care providers for the growing demands within a short period of time is impractical, if not impossible. The solution has to involve technological breakthroughs. And that's where Machine Learning (ML) and Artificial Intelligence (AI) can make a big impact.


Machine Learning: Lessons Learned from the Enterprise

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This article summarizes the lessons learned after two years of our team engaging with dozens of enterprise clients from different industries including manufacturing, financial services, retail, entertainment, and healthcare, among others. What are the most common ML problems faced by the enterprise? What is beyond training an ML model? How to address data preparation? How to scale to large datasets?


Bill Gates: If I were starting a company today, it would use AI to teach computers how to read

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If Bill Gates were to drop out of Harvard University and start a new company today, it would be one that focuses on artificial intelligence, he said in an interview on Monday. The perspective shows that the Microsoft co-founder hasn't lost interest in the technology industry where his company has operated for the past 44 years. "Given my background, I would start an AI company whose goal would be to teach computers how to read, so that they can absorb and understand all the written knowledge of the world. That's an area where AI has yet to make progress, and it will be quite profound when we achieve that goal," Gates told David Rubinstein at an Economic Club of Washington event in the nation's capital on Monday. Gates has invested in Luminous, a start-up developing silicon for AI.


7 Steps to Mastering Intermediate Machine Learning with Python -- 2019 Edition

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Are you interested in learning more about machine learning with Python? I recently wrote 7 Steps to Mastering Basic Machine Learning with Python -- 2019 Edition, a first step in an attempt to updated a pair of posts I wrote some time back (7 Steps to Mastering Machine Learning With Python and 7 More Steps to Mastering Machine Learning With Python), a pair of posts which are getting stale at this point, having been around for a few years. It's time to add on to the "basic" post with a set of steps for learning "intermediate" level machine learning with Python. We're talking "intermediate" in a relative sense, however, so do not expect to be a research-caliber machine learning engineer after getting through this post. The learning path is aimed at those with some understanding of programming, computer science concepts, and/or machine learning in an abstract sense, who are wanting to be able to use the implementations of machine learning algorithms of the prevalent Python libraries to build their own machine learning models.


7 Steps to Mastering Intermediate Machine Learning with Python -- 2019 Edition

#artificialintelligence

Are you interested in learning more about machine learning with Python? I recently wrote 7 Steps to Mastering Basic Machine Learning with Python -- 2019 Edition, a first step in an attempt to updated a pair of posts I wrote some time back (7 Steps to Mastering Machine Learning With Python and 7 More Steps to Mastering Machine Learning With Python), a pair of posts which are getting stale at this point, having been around for a few years. It's time to add on to the "basic" post with a set of steps for learning "intermediate" level machine learning with Python. We're talking "intermediate" in a relative sense, however, so do not expect to be a research-caliber machine learning engineer after getting through this post. The learning path is aimed at those with some understanding of programming, computer science concepts, and/or machine learning in an abstract sense, who are wanting to be able to use the implementations of machine learning algorithms of the prevalent Python libraries to build their own machine learning models.


How 15 women in engineering discovered their passion for technology

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It's not hard to find a good story in the tech industry. The problem is that due to the industry's staggering gender gap, most of these stories center on the struggles and accomplishments of men. In this article, we aim to provide a platform for female technologists to share the stories of how they got into engineering, the biggest challenges they've faced, and their advice to the next generation of women in tech. You'll meet a former geologist turned product manager, an academic who fell in love with data science, a senior tech leader who discovered her dream job after the first two companies she worked for folded, and more. CCC's technology solutions are designed to increase connectedness among companies in the automotive industry, including insurance carriers, manufacturers, parts suppliers and collision repair shops. Ranjini Vaidyanathan was in academia and earned a PhD before realizing she had a passion for data science. While changing focuses wasn't always easy, Vaidyanathan said the transition was made easier by some simple, yet powerful, advice from her mentors. "When the going gets tough, what'll help you pull through is your passion for the technical work." How did you get into engineering? I studied applied science and mathematics before finally switching to data science after my PhD. It took me some time to decide what, exactly, I wanted to pursue. I had been doing pen-and-paper theory work as a student, but after a certain point, I realized I found applied problems more interesting. What's the biggest challenge you've faced in your career, and how have you worked to overcome it? Switching fields from academia to data science was challenging. I had to brush up industry-relevant skills like programming, and also adjust to the paradigm shift in thinking, both in terms of technical and soft skills.


Why AI talent is so hard to come by and what can be done to fill the gap

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Nearly every industry is using artificial intelligence in one way or another to improve business outcomes. AI holds great promise as new and exciting applications are discovered, but there is a catch. There aren't enough trained AI engineers capable of carrying out the work. Karen Roby talks with Sameer Maskey, a professor of AI at Columbia University and founder of Fusemachines, about the shortage and what can be done. The following is an edited transcript of the interview.


10 Compelling Machine Learning Dissertations from Ph.D. Students

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This dissertation proposes efficient algorithms and provides theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine learning. Specifically, the focus is on two types of non-convex optimization problems: learning the parameters of latent variable models and learning in deep neural networks. Learning latent variable models is traditionally framed as a non-convex optimization problem through Maximum Likelihood Estimation (MLE). For some specific models such as multi-view model, it's possible to bypass the non-convexity by leveraging the special model structure and convert the problem into spectral decomposition through Methods of Moments (MM) estimator. In this research, a novel algorithm is proposed that can flexibly learn a multi-view model in a non-parametric fashion.


Open Data for Machine Learning

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The P2P Foundation includes the definition of Open Data as the philosophy and practice requiring that certain data be freely available to everyone, without restrictions from copyright. In recent years as an exercise of transparency governments and city councils create open data portals where the authorities push a lot of data to be freely accessible to citizens. Nowadays is easy to find a dataset of almost everything, in a few clicks you can find, as for instance datasets related to territory (parking spaces of a city), population (Level of education), governance (electoral results)... The benefits of Open Data from an ethical movement essentially focus on empowering the resident with data that somehow can be used for his own profit. Clearly stated in the article "5 benefits of open government data" [3] we found: As Stated Before Open Data can unlock the potential of Machine Learning.