Yesterday, I attempted to clean my perpetually growing inbox. A terrifying task indeed. Normally, answering my emails takes forever because I’d have to think of how to compose them juuuust right. Working from my Gmail browser tab though, I didn’t have to. The perfect answers were simply offered to me.
California is burning. Indonesia is sinking. Greenland is melting.
Climate change is here. We’ve only just begun to feel its impact, and it’s sure to get worse from here if we don’t act now.
We’ve passed the time for denial. It’s time for solutions.
Like many of you, I always saw renewable energy generation as the most promising solution to climate change. I hadn’t considered the importance of energy storage until recently, but it turns out there’s a lot riding on the innovations in this industry.
In this article, I’ll cover four topics:
Maybe you’ve heard of the crazy winter storm that happened in Texas a couple weeks ago. If you have, then you also know how it left millions to face below-freezing temperatures without electricity. Some of the “lucky” few who did have electricity currently face utility bills in the thousands. Four of ERCOT’s board members resigned last week over intense criticism of their poor management of the crisis.
You know the feeling.
Doing “nothing” is never as restful as you think. Your brain goes crazy when you’re not focused, but sometimes we’re too tired to get focused.
P.S. I’m not an expert, but these strategies all have some scientific backing. I’ll link sources if you’re unconvinced. Better yet, though, just test them out yourself.
Block out some time for yourself. I’d suggest at least an hour to get into a full state of relaxation, but five minutes work too. Just don’t talk to other people. No matter how comfortable you are with someone, you will inevitably get distracted…
This is part two of a two-part series about the Fashion MNIST dataset. I’m writing about a convolutional neural network here. In part one, I built a basic neural network, which you can read about here.
The Fashion MNIST dataset is a vast collection of monochrome photos of 28 by 28 pixels of articles of clothing. The challenge is to train a computer to recognize different items of clothing using these photos.
We’ve already built a dense neural network that reached an average accuracy of about 85%. …
This is part one of a two-part series about the Fashion MNIST dataset. I’ll talk about how we can use a basic neural network here, and talk about convolutional neural networks in the next one.
Remember the opening scene of Clueless? It’s an insider’s view into the way normal life of a teenage girl: Cher gets up, brushes her teeth, and picks out her school clothes. Well, technically, her closet picks them out.
Unfortunately for us fashionably-challenged, this magical device hasn’t quite hit the market yet. That doesn’t mean it’s not possible, though. With AI’s exponential growth trajectory, someone…
Learning how tomorrow's technologies will transform today's future. Especially interested in artificial intelligence and climate solutions.