From the heartbreaking loneliness epidemic during the pandemic, to the rapid growth of everyday social media influencers

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any piece of the web, from the smallest thing to the biggest thing, and basically say, ‘What does the role of advertising play in shaping this?’ It’s sort of a gravitational force,” says Tim Hwang, author of Subprime Attention Crisis: Advertising and the Time Bomb at the Heart of the Internet. “It acts on everything you see online.”

I felt as if I had acquired a new pair of eyes. Street corners and front yards I’ve walked past hundreds of times suddenly had new meaning. Instead of feeling trapped in my neighborhood because of the pandemic, I saw how expansive it was. Not only did I get the best workout in months from the hills of San Francisco, getting to know my street trees has given me a sense of purpose. Instead of wandering around my neighborhood and letting my worries consume me, I learned to stop and observe. Tall and unwavering, the trees are a reminder to pause and appreciate what’s right in front of me.

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.

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]

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.

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]

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.

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

In addition to their beauty, urban trees are also highly functional. They provide habitats for birds, bees, and butterflies, reduce the heat-island effect in the city, help filter stormwater, and improve air quality by storing carbon dioxide. In other words, we couldn’t live without them.

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.

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.

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.