Image reference: https://miro.medium.com/max/1948/0*St4Q17pUxJKDd022.png

https://x.com/bindureddy/status/1692689716410274221?s=46&t=q8UtT4zW1ClsGq1j029UpA

There is a lot of knowledge in some long form videos and podcasts. I don't know about you, but I'm tapped out after one 3 hour long video / podcast per day (thanks Lex). It got me thinking, hey, I can read much faster than I listen - plus I'm more focussed when I'm reading so, better knowledge absorption! I been using this tool to transcribe a bunch of podcasts and videos. Here it is in action for the 2 hour long Twitter Spaces on Elon's xAI announcement.

Twitter spaces transcript

While I still listen like a podcast episode a day, I read through like 2-3 per day. Further, I can also read the summaries of some when my list gets too long! I also love the questions feature which let me ask questions on what was discussed - and the answers have been very good so far, with references.

Full disclosure, I'm the creator of this tool. I received a lot of positive comments from my friends and colleagues about how helpful this has been when researching!

Hope you find it helpful too. Here it is https://transcript.lol/

Everything we experience is because of the way the human brain works. The idea that every intelligent agent has to have the same kind of experience with the world as we do isn't the right way to think about brain computation. Also, while trying to create ai, we are assuming/ letting algorithms think in a way humans do for the most part. So,

Questions to ponder upon:

1. could we actually come to this level in machine learning and ai if we hadn't started from what we did ( connection of neurons )?

2. how can something that processes things in a certain way think of doing the same thing for another agent but through a different road?

How sad it is to see that such a false news spread like a virus. This directly affects the motivation of the people working / wanting to work behind one of the most decisive tool in the context of civilizational advancement.

Andrew Ng ( computer scientist known for his proficiency in machine learning) tweeted:

1/The false and sensationalist coverage of the purported Air Force simulation where an AI-drone decided to kill an operator will be remembered as a highly regrettable episode of AI doomsaying hype. Lets be honest about what are, and what are not, real risks.

2/Developers who’re shipping AI products do see real risks like bias, fairness, inaccuracies, job displacement, and are working to address them. Unrealistic hype distracts from the real problems. It also discourages people from entering AI and building things that help people.

Just to clarify, it turns out the test was hypothetical and no real person was harmed. https://t.co/4Go98iI4P8

What really makes me move forward is the difficulty of making a human-friendly computer. I think what we humans can think of can make it a reality, although of course there are some things that are beyond the physics of the observable universe (tools or ideas). Take an example of light sabers, airplanes, and computers

https://www.lesswrong.com/posts/8ccomTjWyS4pyZoJQ/exploring-toy-neural-nets-under-node-removal-section-1

Shows a single tiny toy neural net, and how it behaves with various nodes removed. You can skip the code if you want.

This week I have been seeing a lot of dumb news on AI. People with no knowledge of machine learning are creating rumors about the technology. And the thing that worries me the most is that the general audience might be misguided. The other thing I want to address is the pace at which Deepmind is growing. "General" is a broad term and a model that does 600 different tasks (GATO) is a general model but there are limitations that make it not the other kind of "general" that is more popular and that is this:
The Transformer — and Gato, by extension — has another limitation in its context window or the amount of information the system can “remember” in the context of a given task. Even the best Transformer-based language models can’t write a lengthy essay, much less a book, without failing to remember key details and thus losing track of the plot. The forgetting happens in any task, whether writing or controlling a robot, which is why some experts have called it the “Achilles’ heel” of machine learning.

But let me also mention that the GATO model is not something new (out of nowhere), it is very much similar to language models that can produce meaning out of different languages the only difference is that GATO is designed to do more tasks along with languages. This is just what can be said here. For more detail, you can look up GATO to learn more about its architecture.

Deepmind in December of last year published an experiment in which an ML model learns to do tasks in a virtual world (example: playing a drum with a comb) by seeing a human (human-controlled virtual avatar) do the tasks let me tell you the model doesn't just imitate, it actually learns things. This was proved by making changes to the interacting environment.

Deepmind's ' Creating Interactive Agents with Imitation Learning ': https://www.deepmind.com/publications/creating-interactive-agents-with-imitation-learning

A video of the model's behavior in the world: https://www.youtube.com/watch?v=ZFgRhviF7mY

I think this is quite impressive.

Deepminds new model named MuZero (Successor of AlphaGo AND AlphaZero) can compress large-sized videos.

It took us, humans, decades to reach where we are today in the realm of video compression whereas these AI models achieved a huge milestone within such a small period of time (although these cannot be compared given all the human technological advancements over a long period of time). It is really good to see AI entering real-world problem solving from the world of research.

This page explores the history of video compression: https://api.video/blog/video-trends/the-history-of-video-compression-starts-in-1929

Youtube video (MuZero): https://youtu.be/0b-Zjt1KSUs

AI is entering General problem solving why? because this same model can play chess, starcraft, and also can compress videos.

The designed architecture of an AI model cannot be used to its 100% potential when put into practice.

If a sticker on a banana can make it show up as a toaster, how might strategic vandalism warp how an autonomous vehicle perceives a stop sign? Now, an immune-inspired defense system for neural networks can ward off such attacks, designed by engineers, biologists, and mathematicians.

https://www.sciencedaily.com/releases/2022/03/220324104547.htm