2020 was definitely a step backwards. If you're wondering how great civilizations can end up collapsing: they just have many 2020s in a row over several decades, with exponentially compounding cascade effects at each new development.
Factors of decline are multiplicative. E.g. cultural & educational deterioration leads to an incompetent government. An incompetent government makes a pandemic much worse. A bad pandemic accelerates institutional decline
For the record, I don't think civilization will collapse in the near future (within the next 400 years). Not even as a consequence of catastrophic climate change over the next two centuries. But we will go through some pretty rough patches
Our particular civilization, as a system, features significant structural risk factors that could enable collapse, but it also has important collapse-preventing characteristics. I think the latter factors will win out
Hardships and setbacks can be catalysts of progress -- progress doesn't happen without challenges. Decline happens when we lose the ability to respond to challenges. Collapse happens when decline accelerates past a point of no return
I think the coming hardships are more likely to become catalysts of progress than triggers of collapse

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More from @fchollet

6 Jan
DALL-E is the kind of application that you'd expect deep learning to be able to pull off in theory (people have been building various early prototype of text-guided image generation since 2015) that becomes really magical when done at a crazy scale.
As usual with deep learning, scaling up is paying off.
In the future, we'll have applications that generate photorealistic movies from a script, or new video games from a description. It's only a matter of years at this point.
Read 5 tweets
5 Jan
Here's an overview of key adoption metrics for deep learning frameworks over 2020: downloads, developer surveys, job posts, scientific publications, Colab usage, Kaggle notebooks usage, GitHub data.

TensorFlow/Keras = #1 deep learning solution.
Note that we benchmark adoption vs Facebook's PyTorch because it is the only TF alternative that registers on the scale. Another option would have been sklearn, which has massive adoption, but it isn't really a TF alternative. In the future, I hope we can add JAX.
TensorFlow has seen 115M downloads in 2020, which nearly doubles its lifetime downloads. Note that this does *not* include downloads for all TF-adjacent packages, like tf-nightly, the old tensorflow-gpu, etc.
Read 13 tweets
4 Jan
Here's a word-level text generation example with LSTM, starting from raw text files, in less than 50 lines of Keras & TensorFlow. colab.research.google.com/drive/1B9yLXcJ…
Of course, I should point out it's not 50 lines because Keras has some kind of built-in solution for text generation (it doesn't). It's 50 lines because Keras makes it easy to implement anything. It only uses generic features.
It uses a utility to read text files, a text vectorization layer (useful for any NLP), the LSTM layer and the functional API, the callbacks infrastructure, and the default training loop.
Read 4 tweets
1 Jan
The thing with pointing out "AI can't do X!" is that, if you keep refining X into something narrow and precise enough, you'll eventually cross a threshold where a realistic amount of engineering and training data make X possible.
AI can always do *specific* things -- as long as they're sufficiently specific and you're investing sufficient effort / data.

The problem with AI isn't that it can't do a specific X, it's that it has basically no intelligence at all at this time. No general cognitive abilities.
Intelligence simply means moving to a different part of the specificity / effort spectrum, one where you can master broad tasks with little effort.

You can always make up for a lack of intelligence by reducing task uncertainty (making X more specific) or investing more effort.
Read 5 tweets
31 Dec 20
The Turing test was *never* a relevant goal for AI. We should remember that Turing never intended it as a literal test to be passed by a machine designed for that purpose, but as a philosophical device in an argument about the nature of thinking.

fastcompany.com/90590042/turin…
The major flaw of the Turing test is that it entirely abdicates the responsibility of defining intelligence and how to evaluate it (the value of a test). Instead, it delegates the task to human judges, who themselves don't have a proper definition or a proper evaluation process.
As a result, the Turing test does not at all provide incentives to develop greater intelligence, it solely encourages developers to figure out how to trick humans into believing a chatbot is intelligent.
Read 5 tweets
26 Dec 20
I keep coming back to the importance of self-image in one's life trajectory. You become who you believe you are. You do what you believe you can do.
Belief is a greater determinant than ability or environment.
"Man often becomes what he believes himself to be. If I keep on saying to myself that I cannot do a certain thing, it is possible that I may end by really becoming incapable of doing it...."
Read 5 tweets

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