There was a gang of us who preferred working the Jazz Standard to working upstairs in the cheery, well-lit barbecue rest
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.
When I first arrived at Blue Smoke to get a job as a server, I was 22, had just finished college, and had just moved to New York a week ago. Determined to be — you know where this is going — an actor, and paying an exorbitantly high rent on the Upper East Side of all places (I’ve since worked my way down), I walked through the doors of Blue Smoke, a high-volume, service-forward restaurant, desperately needing a job. Somehow, even though I was green and had no city experience and almost crashed several trays of glassware, I got the job, and I knew I had cleared my first New York hurdle (learning how to carry trays was one of the hardest things I’ve ever had to do; when I finally passed my server trails and was officially hired, my then-roommate made me a congratulatory sign that said “tray bien!”). But every Blue Smoke server was assigned a shift downstairs in the basement jazz club, the Jazz Standard, and like anyone else, I had to work mine.
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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d other POCs might implode if their proximity to Whiteness disappeared though. And that’s the thing! When I was writing the book, I hoped people would recognize that we all uphold this. White men alone can’t uphold the system as much as they like to think they do. It’s vital that we see all of the ways in which we’ve been upholding this and have tied ourselves to it because that will get in the way of our liberation.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
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).
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.
No windows, no ventilation, a constantly leaking ceiling. A magnificent Steinway piano on the red-velvet plush stage and mirrors lining the walls. A warm amber glowing bar, skinny rows of black tables pushed together against red vinyl booths. Chairs that constantly required fixing. Candles that guests consistently ruined by dipping their fingers in the wax. The rattling of cocktails against a quiet bass solo. The roar of a brass band as you tried to take someone’s order. Our little server hutch with a velvet curtain we could slip behind when we needed a break, to stand in stacks of fresh linens and chef’s coats to gossip and laugh and sip 9PM coffees. The music, and the people who listened to the music. It was dark, even when it was 3PM. On rainy days, you could smell fresh rot. I wanted to be a part of it.
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.
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.
When I heard the Jazz Standard was closing, I didn’t cry. I closed my eyes. I inhaled. And six years’ worth of memories came tumbling to me. I promised them I would write them down, that I wouldn’t forget this tiny club in the basement of Danny Meyer’s Blue Smoke on East 27th Street. Then again, how could I forget the place that taught me who I was? For six years, the Jazz Standard was my home. It was my life. It gave me a life. And now it’s gone. Like so many other New York heartbreaks, this one is especially tough to bear.
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.
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.
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.
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.