During a whirlwind romance, they decided to get married in Las Vegas, but Mitchell got cold feet. What if she was jumpin

Artificial Intelligence has had a crazy couple of year and 2020 has been like no other. With a pandemic, a global recession along with incredible gains in AI, there’s simply so much to keep an eye on.

A Short Introduction to Bayesian Neural Networks * David Stutz With the rising success of deep neural networks, their reliability in terms of robustness (for example, against various…davidstutz.de

A Bayesian Neural Network differs from ordinary Neural Networks as the weights are actually assigned probability distributions, rather than single-updating point estimates. Bayesian networks have been around for a while but with increased computational power, we’re much better placed to use the distributions of each weight to estimate uncertainty in the predictions of a neural network.

Agreeably, not much has changed, but the number of challenges that AI has smashed is startling. In what follows, I’ll briefly go over a few topics that have really set the tone for this year, but are also important topics for the coming years.

We asked designers and friends of the UX Collective: what was your biggest lesson from 2020, that we can use as we go into 2021? Share your lessons with us: [email protected]

To avoid being alone, Mitchell filled the Cage with visitors. One afternoon, she invited Kirsty Williams and her husband Neil for drinks. Neil, who was serving in the British Army, was the first to see the blood. Twenty or thirty droplets of deep red blood splattered the floor, as if dropped from a pipette, or a bleeding nose. Williams told me: “I’m a nurse. I work in cancer, in hematology. I definitely knew that was blood, but couldn’t explain it…it wasn’t there when we arrived.”

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nce against women] is even more intense for Black women, who struggle against stereotypes and are seen as angry or threatening when we try to stand up for ourselves and our sisters. There’s not much room for passionate advocacy if you are a Black woman.”

Mitchell was relieved when Kirtly’s boyfriend Jim moved into the Cage. “He didn’t believe in ghosts or the paranormal,” Mitchell wrote, but after several months in the Cage he “flatly refused to be left alone inside the house.” The three roommates made a pact: “Under no circumstances would we leave any one of us alone inside the old prison, for any reason at all. We began to plan our lives and daily schedules around that single, unbreakable rule.” But in October of 2006, Kirtly told Mitchell that she and Jim were moving out. They were expecting a baby. “I couldn’t blame them,” Mitchell recalled. The Cage was no place for children. By then, she was slowly accepting that it might not be a place for any living being.

Pre 2015, the theory was pretty sophisticated but the active day-to-day implementation of AI or Machine Learning was quite old. Highly sophisticated algorithms looked a bit more like Kalman Filters, Support Vector Machines, and maybe even a Random Forest.

Deep Learning essentially emulates the concepts reflected in your brain. Given some input data (and the black box of your brain), a neural network will break down the input and be able to make sense of it. With enough training data, it’ll be able to infer something from it.

Deep Learning has been around for what feels like a while but in reality, it’s only the past 5 years that it exploded with interest because of the gains that Deep Mind made. Pre-2015, yes it was interesting, but there wasn’t that element of explosive interest.

What is Deep Learning? — Machine Learning Mastery Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the…machinelearningmastery.com

Deep Learning is interesting because it doesn’t really have a pure statistical foundation, but rather, a layered functional form. A Deep Neural Network is built in a lattice like structure, where through an optimisation process, the weights (located in each adjoining lattice connection) will be optimised for the input to match the output. By adding more layers, you increase the expressivity here, but also more complicated connections (like LSTM or Gates) and also complicated net forms (e.g. Encoders) can produce amazing results in their specific domains.

I studied my MPhil in Machine Learning in 2015 and back then, AI was in relative infancy but it was actually the perfect time to see the foundation of the industry that was to form.

Making Your Neural Network Say “I Don’t Know” — Bayesian NNs using Pyro and PyTorch A tutorial code on writing a bayesian image classifier on MNIST dataset.towardsdatascience.com