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Media Political scale
Pew research has ranked the media from liberal to conservative. Below is a handy graph I like to use as reference.
Exa-what?
We’re talking about a machine that’s capable of a billion billion calculations per second, or one exaflop. Every person on earth would have to do a calculation every second of every day for over four years to match that. Researchers could use the power to run massively complex simulations that can help advance fields like climate science, genomics, renewable energy, and artificial intelligence.
What if our brains could function at exaflop speeds? They will and they can. Waiting for Sapien 2.0
Message for Humanity
Those worlds in space are as countless as all the grains of sand on all the beaches of the Earth. Each of those worlds is as real as ours. In every one of them, there’s a succession of incidence, events, occurrences which influence its future. Countless worlds, numberless moments, an immensity of space and time. And our small planet, at this moment, here we face a critical branch-point in the history. What we do with our world, right now, will propagate down through the centuries and powerfully affect the destiny of our descendants. It is well within our power to destroy our civilization, and perhaps our species as well. If we capitulate to superstition, or greed, or stupidity we can plunge our world into a darkness deeper than time between the collapse of classical civilization and the Italian Renaissance. But, we are also capable of using our compassion and our intelligence, our technology and our wealth, to make an abundant and meaningful life for every inhabitant of this planet. To enhance enormously our understanding of the Universe, and to carry us to the stars.
— Carl Sagan
Learning AI
Here are some great resources to get you started:
- Practical Deep Learning for coders ( you must have at least 1 year of coding to follow the examples )
- Practical Deep Learning Part II
- MIT’s deep learning course
- The best data science blog from one of my favorite people Rachel Thomas
- The Montreal Institute for Learning Algorithms – MILA
I will add to this post as I discover new resources.
AI(i)
For those of you thinking about tackling AI or wanting to learn “how to AI” here are some myths about getting into AI:
- You need a deep learning PhD
- You need “big data”
- Deep learning replaces domain experts
- You need lots of expensive computing power
- Deep Learning only works for very limited problems
- AI experts are scarce and hard to find
That’s all bull. Dive into AI and make something happen!
