Futurist Ray Kurzweil is still making waves years after his initial singularity claims as artificial intelligence continues to progress. With singularity milestones coming, Kurzweil believes immortality is achievable by 2030. Kurzweil’s predictions are met with a healthy dose of skepticism. A new video from the YouTube channel Adagio revisits futurist Ray Kurzweil’s ideas about how for humans, both singularity and immortality are shockingly imminent—as in, potentially just seven years away.
A post-apocalyptic exhibit features an AI that expresses remorse for being the reason for the near-extinction of humanity.
The beating heart of tech-revolution, a museum in San Francisco, has visualized a memorial to the extinction of the human race, considering the fast and significant advances coming in artificial intelligence.
The pieces in the temporary exhibition combine the frightening with the humorous.
Bulgac/iStock.
“Sorry for killing most of humanity person with smile cap and mustache,” says a monitor welcoming a visitor to the “Misalignment Museum”, a new exhibit on the controversial technology.
It could be a solid step against inaccurate, racist, and sexist responses from the likes of OpenAI’s ChatGPT and Google’s Bard.
Meta hopes to assist AI researchers in making their tools and procedures more universally inclusive, with the launch of Casual Conversations v2, according to a statement from the firm on March 9.
The vast new dataset, which includes face-to-face video clips from a broad spectrum of human participants across varied geographic, cultural, racial, and physical demographics, serves as an upgrade to its 2021 AI audio-visual training dataset.
It wouldn’t be completely out of character for Joe Rogan, the comedian turned podcaster, to endorse a “libido-boosting” coffee brand for men.
But when a video circulating on TikTok recently showed Mr. Rogan and his guest, Andrew Huberman, hawking the coffee, some eagle-eyed viewers were shocked — including Dr. Huberman.
“Yep that’s fake,” Dr. Huberman wrote on Twitter after seeing the ad, in which he appears to praise the coffee’s testosterone-boosting potential, even though he never did.
Empowered by artificial intelligence technologies, computers today can engage in convincing conversations with people, compose songs, paint paintings, play chess and go, and diagnose diseases, to name just a few examples of their technological prowess.
These successes could be taken to indicate that computation has no limits. To see if that’s the case, it’s important to understand what makes a computer powerful.
There are two aspects to a computer’s power: the number of operations its hardware can execute per second and the efficiency of the algorithms it runs. The hardware speed is limited by the laws of physics. Algorithms—basically sets of instructions —are written by humans and translated into a sequence of operations that computer hardware can execute. Even if a computer’s speed could reach the physical limit, computational hurdles remain due to the limits of algorithms.
Have you ever wondered what life would be like if artificial intelligence became too powerful?
A new exhibition titled the ‘Misalignment Museum’ has opened to the public in San Francisco — the beating heart of the tech revolution — looks to explore just that, and features AI artworks meant to help visitors think about the potential dangers of artificial intelligence.
The exhibits in this temporary show mix the disturbing with the comic, and this first display has AI give pithy observations to visitors that cross into its line of vision.
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Kaggle notebook with all the code: https://www.kaggle.com/wwsalmon/simple-mnist-nn-from-scratch-numpy-no-tf-keras.
Blog article with more/clearer math explanation: https://www.samsonzhang.com/2020/11/24/understanding-the-mat…numpy.html
An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.
INFO:
Website: https://deeplearning.mit.edu.
GitHub: https://github.com/lexfridman/mit-deep-learning.
Slides: http://bit.ly/deep-learning-basics-slides.
Playlist: http://bit.ly/deep-learning-playlist.
Blog post: https://link.medium.com/TkE476jw2T
OUTLINE:
0:00 — Introduction.
0:53 — Deep learning in one slide.
4:55 — History of ideas and tools.
9:43 — Simple example in TensorFlow.
11:36 — TensorFlow in one slide.
13:32 — Deep learning is representation learning.
16:02 — Why deep learning (and why not)
22:00 — Challenges for supervised learning.
38:27 — Key low-level concepts.
46:15 — Higher-level methods.
1:06:00 — Toward artificial general intelligence.
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What are the neurons, why are there layers, and what is the math underlying it?
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Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it’s supposed to in fact be a k. Thanks for the sharp eyes that caught that!
For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy.
There are two neat things about this book. First, it’s available for free, so consider joining me in making a donation Nielsen’s way if you get something out of it. And second, it’s centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning!
https://github.com/mnielsen/neural-networks-and-deep-learning.
I also highly recommend Chris Olah’s blog: http://colah.github.io/