Artificial Intelligence/Machine Learning Roundup #48

1. Simplifying Rough Sketches using Deep Learning – Ashish Sinha –

The Model Architecture The best part of this model is that, it works with Raster images, and converts multiple rough sketch lines into a single clean line. The architecture is a rather simple one, the first part acts as an encoder and spatially compresses the image, the second part, processes and extracts the essential lines from the image, and the third and last part acts as a decoder which converts the small more simple representation to an grayscale image of the same resolution as the input. The Loss Function The model loss is calculated using the weighted mean square criterion, where Y is the model output, Y* is the target output, M is the loss map, and element-wise matrix multiplication is performed on them, to calculate the loss. Read More

2. The future of fraud detection – Simudyne –

By way of example, Deutsche Bank’s $630 million fine for for failing to prevent $10bn of Russian money laundering shows just how crippling the punishments can be. These agents can adapt to changes in the system — taking on new behaviours as the environment they live in alters. This could involving creating different types of agents pursuing a range of fraudulent strategies observed in the real world. Read More

3. Neural Networks: What are they and how can they be applied to solve problems(Part 1)

Neural Networks: What are they and how can they be applied to solve problems(Part 1) I first wanna talk about why I’m here, I’m here to teach you A. Machine Learning has been for decades and its only recently that we have found out what works and what doesn’t. A machine learning model uses data for finding patterns in it and then uses those patterns to solve a problem Now, lets get into a working example to know how this works. Read More

4. Cambridge Artificial Intelligence Summit 2018 –

Cambridge Artificial Intelligence Summit 2018 We hosted our Cambridge Artificial Intelligence Summit, sponsored by Cambridge Judge Business School Executive Education, on 15–16 June, welcoming Analysts, Data Scientists and Researchers to network, develop new skills and gain insight into the evolving field of Data Science. provides immediate feedback to students and employees on the quality of their code. Steven McDermottQualitative Analysis and Social Media Lead, HMRC Session: AI as Moderator/Mediator in the Recognition of Citizen’s Voice with Social Media Dr. Read More

5. The AI bubble won’t burst anytime soon, but change is on the horizon

During an AI podcast a couple months ago, someone asked me if AI would be the next big tech bubble to burst. This question led me to think about what AI is today and where it’s headed. For starters, today’s computer power has increased astronomically with GPUs (graphics processing unit, as opposed to CPU, central processing unit), and companies have created open source frameworks and made them public to developers over the past few years. Read More

6. Google Brain researchers demo method to hijack neural networks

Computer vision algorithms aren’t perfect. Just this month, researchers demonstrated that a popular object detection API could be fooled into seeing cats as “crazy quilts” and “cellophane. org titled “Adversarial Reprogramming of Neural Networks,” researchers at Google Brain, Google’s AI research division, describe an adversarial method that in effect reprograms machine learning systems. Read More

7. The Skills Needed to Survive the Robot Invasion of the Workplace

The Skills Needed to Survive the Robot Invasion of the Workplace Automation is coming to the workplace. Millions of jobs will be destroyed, but many jobs will also be simultaneously created in the process as well. It can be daunting to think about automation’s role in the future – but if you’re a bookkeeper, legal secretary, insurance underwriter, credit analyst, or any other person in a job with high automation potential, it would be prudent to be thinking long and hard about what you can offer beyond your existing set of skills and competencies. Read More

8. Automation has the potential to improve gender equality at work

If predictions are right, automation will transform work as we know it. Right now, there are considerable differences in pay, employment levels, and the types of activities that men and women perform in the workplace. As women are often expected to take more responsibility for care at home, there are fewer job opportunities. Read More

9. The Top GitHub Repositories & Reddit Threads Every Data Scientist should know (June 2018)

Introduction Half the year has flown by and that brings us to the June edition of our popular series – the top GitHub repositories and Reddit threads from last month. During the course of writing these articles, I have learned so much about machine learning from either open source codes or invaluable discussions among the top data science brains in the world. You can check out the top GitHub repositories and top Reddit discussions (from April onwards) for the last five months below: GitHub Repositories Facebook’s DensePose Human pose estimation has garnered a lot of attention in the deep learning community this year. Read More

10. Robots could solve the social care crisis – but at what price? | John Harris

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11. [1806.11463] Bayesian Deep Learning on a Quantum Computer

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12. How to use Machine Learning and Quilt to Identify Buildings in Satellite Images

Recently there has been interest in using satellite images as investing tools. is an area a building or not a building) from satellite images. This project was a proof of concept for the Insight Data Fellows Program. Read More

13. Here Are Free AI Learning Resources For Beginners

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14. The New Energy Storage Tech Gates, Bezos, Ma, and Branson Are Investing In

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15. Ray Kurzweil – The Age of Spiritual Machines – The Future of The 21st Century

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