The world celebrated Women's History Month in March, and it is a timely moment for us to look at the forces that will shape gender parity in the future. Even as the pandemic accelerates digitization and the future of work, artificial intelligence (AI) stands out as a potentially helpful--or hurtful--tool in the equity agenda. McKinsey recorded a podcast in collaboration with Citi that dives into how gender bias is reflected in AI, why we must consciously debias our machine-human interfaces, and how AI can be a positive force for gender parity. Ioana Niculcea: Before we start the conversation, I think it's important for us to spend a moment assessing the amount of change that has taken place with regard to AI, and how the pace of that change has accelerated over the past few years. And many people argue that in light of the current COVID-19 circumstance, we'll feel further acceleration as people move toward digitization. I spent the past eight years in financial services, and it all started with data. Datafication of the industry was sort of the point of origin. And we hear often that over 90 percent of the data that we have today was created over the past two years. You hear things like every minute, there's over one million Facebook logins and 4.5 million YouTube videos being streamed, or 17,000 different Uber rides. There's a lot of data, and only 1 percent of that is being analyzed, as said today.
Register for a free or VIP pass today. The past several years have made it clear that AI and machine learning are not a panacea when it comes to fair outcomes. Applying algorithmic solutions to social problems can magnify biases against marginalized peoples; undersampling populations always results in worse predictive accuracy. But bias in AI doesn't arise from the datasets alone. Problem formulation, or the way researchers fit tasks to AI techniques, can contribute.
I recently completed the Artificial Intelligence Product Manager Nanodegree Program on Udacity and I'd like to share a summary of everything I learned with you. This also includes bits from my experience as a technical product manager. This all a huge dump from my mind, written from the first stroke to last on my keyboard so kindly excuse any details I may miss or depths I didn't hit. It would be great to start with "why" and what motivated me to complete this program. In the past year, I've been working as a full-time product manager, sitting at the intersection of engineering and business and it's been fun. However, I'd recently been thinking deeply about the future of technology and what turns it could take.
Machine learning (ML) is about making predictions about new data based on old data. The quality of any machine-learning algorithm is ultimately determined by the quality of those predictions. However, there is no one universal way to measure that quality across all ML applications, and that has broad implications for the value and usefulness of machine learning. "Every industry, every domain, every application has different care-abouts," said Nick Ni, director of product marketing, AI and software at Xilinx. "And you have to measure that care-about." Classification is the most familiar application, and "accuracy" is the measure used for it. But even so, there remain disagreements about exactly how accuracy should be measured or what it should mean. With other applications, it's much less clear how to measure the quality of results.
Between 1989 and 2019, 258 607 adults [mean age 63 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased.
One autumn afternoon in the bowels of UC Berkeley's Li Ka Shing Center, I was looking at my brain. I had just spent 10 minutes inside the 3 Tesla MRI scanner, the technical name for a very expensive, very high maintenance, very magnetic brain camera. Lying on my back inside the narrow tube, I had swallowed my claustrophobia and let myself be enveloped in darkness and a cacophony of foghorn-like bleats. At the time I was a research intern at UC Berkeley's Neuroeconomics Lab. That was the first time I saw my own brain from an MRI scan. It was a grayscale, 3-D reconstruction floating on the black background of a computer screen. As an undergraduate who studied neuroscience, I was enraptured. There is nothing quite like a young scientist's first encounter with an imaging technology that renders the hitherto invisible visible--magnetic resonance imaging took my breath away. I felt that I was looking not just inside my body, but into the biological recesses of my mind. It was a strange self-image, if indeed it was one.
Here we're going to look at an application of the k-nearest neighbours (kNN) algorithm to predict whether or not a telescope signal is gamma or hadron radiation using a Kaggle dataset. This is one of the older ones. I've just looked it up and the internet assures me that this was developed in the 1950s. It still works well today. I'll be using the scikit-learn kNN classification model for the example.
Regularization is a strategy implemented in a deep neural network that will reduce the generalization error but not the training error to perform well on not just the training data but also on new unseen inputs. An effective regularizer reduces the variance significantly while not overly increasing the bias, thus preventing overfitting. We use regularization techniques like L1 and L2 to reduce overfitting, penalizing the loss function, or regularization techniques like Dropouts and Spatial Dropouts, which discourage model complexity. The principle behind regularization methods in a neural network is to inject noise into neural networks to avoid overfitting the training data. L2 regularization is commonly known as weight decay or ridge regression, or Tikhonov regularization.
It was nothing more than what Brad Stevens termed "a curveball," as it turned out. After an initial false positive COVID test, Evan Fournier turned in a string of negative tests, leading to his first-time availability for the Celtics Monday night against New Orleans. "He will play significant minutes, as he will all the rest of the year," Stevens said of how he planned to begin with the talented wing player, acquired from Orlando at the trade deadline for the since-waived Jeff Teague and two second-round draft picks. "We had an obvious need for another wing that can do what he does, and we're fortunate he's with us, and he's on our team," said the Celtics coach. "So I got a chance to go over to the gym (Sunday) while he was shooting around when we got back and then this morning we went through some stuff prior to our shootaround, we shot around as a team for 30 minutes, so he's gotten the crash course in a very short amount of time. He's been there, done that. He's played against us, you know, tons of times, probably knows our plays as well as anybody, and certainly we just want him to play to his strengths and not worry about anything else."
A project-based course to build an AIoT system from theory to prototype. Artificial Intelligence and Automation with Zang Cloud Sample codes are provided for every project in this course. You will receive a certificate of completion when finishing this course. There is also Udemy 30 Day Money Back Guarantee, if you are not satisfied with this course. This course teaches you how to build an AIoT system from theory to prototype particularly using Naive Bayes algorithm.