“Stop focusing on the flaws or things you dislike and tunnel vision yourself into looking only at what is going well. Th

This form of problem requires the optimisation process to balance exploitation (of rewards) and exploration (of different routes) to achieve the goal based on its objectives. But at times, the value function is not clear. DeepMind revolutionised this space by introducing Neural Networks and making it possible to apply Reinforcement learning to these incredibly complicated games, like Chess and Go.

Exponential models generally exhibit the doubling effect, where at each time step, the problem doubles. However, as the rate grows or slows, it may triple every time step (or half every time step). Either way, depending on the current state of a particular system, the rate of what you’re measuring can growth rapidly or decline rapidly.

A Western social scientist might say that Easterners put social responsibility first, and individual responsibility second, while Westerners do the opposite. I think that’s true, but obtusely put. It’s better to simply say that people in the East care about each other more than they do themselves.

COVID-19 has taken away from us more than most people like to talk about. It’s been a torrid year but one thing that was central to tackling the problem was the ability to model the growth rate of the epidemic. Analysing things like the growth rate and the half-life of the virus allowed us to form policy around the problem and importantly, give us the ability to control it (whether we did or not is a different question).

The rates of development for Machine Learning isn’t slowing down. Its ability, breadth and reach is startling so it’s imperative to keep an eye on the space and stay up to date with the latest trends, models and use-cases. The number of applications change every year and it’s amazing to see solutions to existing problems be improved on so quickly.

It’s easier with an example. Imagine you’re on a chessboard and you’re trying to go from the bottom left to the top right, where each step costs -1 and reaching the goal gets you 10,000. If you’re objective is to maximise your reward, you’ll realise that by playing this game (with random actions) 10,000 times, the ones with the highest score go as quickly as they can to the top right.

To the Western mind, that’s a bizarre thing to say. The Western mind — maybe even you — will deny that, object to it, try to find objections. You don’t want it to be true. Your mind can’t quite handle it, because it turns everything you’ve been taught on your head. And yet there the East is — having beaten Covid, precisely because people put others first, not just themselves.

What is reinforcement learning? The complete guide - deepsense.ai With an estimated market size of 7.35 billion US dollars, artificial intelligence is growing by leaps and bounds…deepsense.ai

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lly? You take up the attention. You’re getting the things. I’ve been shocked over the years of what has brought [them] to my door ’cause it has never been the things that I thought. I tweet about Cracker Barrel, and suddenly I’ve got thousands of hate letters. I can write an article titled “White People Always Let You Down”… crickets, no one cares. I realized you can literally be in any space and be defined as a threat. And you’ll be defined not by the substance of your work but just because you’re taking up space. I have yet to see a rhyme or reason other than “we don’t want you to exist here.”

Doing the (Coronavirus) Math: Exponentials, Bell Curves and Flattening At first, the spread of coronavirus COVID-19 trickled in slowly. But that trickle turned into an exponentially…www.designnews.com

Reinforcement Learning was popularised by the UK startup DeepMind who improved on traditional methods in control theory (e.g. Policy Optimisation) by incorporating the use of neural networks.

In this quote, Bernstein says that we can tap into “an unlimited source of creative energy to . . . receive abundance.” That’s huge. I want that. So I’ve been rereading her books for the past six months and practicing what she says every single day.

Never have I seen maths in the news as much as I had this year. Let’s be honest, it was both good and bad. It was bad because much of what was reported involved a terrible use of statistics, but, it was good to see the awareness and respect of statistics increase throughout.

Broadly speaking, reinforcement learning algorithms will ‘explore’ a problem to try find an optimal path or solution through a complicated network. It’s different to supervised learning in that it doesn’t need labels and it doesn’t need sub-optimal actions to be explicit. However, it focuses on balancing between exploration and exploitation.

Introduction to Deep Gaussian Processes at Gaussian Process Summer School, University of Sheffield, UK on Sep 10, 2019 [ jupyter][ google colab][ reveal]…inverseprobability.com