Welcoem to posts!!
in the future - u will be able to do some more stuff here,,,!! like pat catgirl- i mean um yeah... for now u can only see others's posts :c
ChatGPT is reasonably powerful, but I don't think it's going to be taking our jobs any time soon.
4 - 1
In case you missed it, I recently shared my thoughts on the (potentially) overblown thread due to self aware artificial super intelligence.
https://youtu.be/9onJjdVBE9U
5 - 0
Curiosity driven reinforcement learning is a hot new topic in deep reinforcement learning research. In case you've missed my recent video, let's see how an agent can learn in the absence of extrinsic rewards.
https://youtu.be/O5Z-3q-J18I
4 - 0
Despite years of research, an AGI is still far off. Let's find out some of the problems plaguing AI research, according to Melanie Mitchell's paper.
https://youtu.be/hYURJp9xvak
4 - 0
Choosing a grad school can be tough, but it doesn't have to be stressful. There are a few factors I would recommend looking at:
Prestige of the school
Prestige of the Adviser
Opportunities to demonstrate competence
We also got quite a few good comments from others' experiences.
Check it out here:
https://youtu.be/KvICwugGP80
1 - 0
I can tell I touched a bit of a nerve with this one.
I stand by what I said. For most of you, writing your own code is going to be the fastest way to learn.
Now, if you're prototyping something for a startup... don't bother learning to code entire websites.
But you really SHOULD be coding the important bits related to core functionality, on your own.
Once something is running, then you can check stack overflow, or use the CoPilot to see if there's a better way.
Let me know your thoughts.
https://youtu.be/-yOnhSYm-3w
6 - 2
This is the first (that I've seen) application of RL to a robust bipedal robot. The results are pretty incredible, all things considered. The authors use PPO + domain randomization to train a robust agent that is capable of dealing with some adversity in the real world. Check out the paper here:
https://youtu.be/X6R8S499dXg
3 - 3
It never ceases to amaze me how such a simple algorithm can work out such complex relationships between words. The skip-gram algorithm is able to figure out capital cities, simply by looking for correlations in a body of text. Check it out in the latest video.
https://youtu.be/gy0Xm9eK430
7 - 0
I've been beavering away on my new course: Natural Language Processing from First Principles. It's live now on Udemy, so check it out if you're in the market for a beginner level course in NLP.
Tomorrow I'll have a new video where we do an overview of the paper we implement in the course. Stay tuned!
www.udemy.com/course/natural-language-processing-fâŚ
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Congrats to the GTC21 giveaway winners!
Syed Hazim
#top_moments
Jarvis and Nvidia A100s
Piero Casusol
Hi Phil!
My favorite parts of GTC Keynote were mainly three. The first was the intro, very emotional and shocking. The second was Issac Sim and the various tools for robotics and simulation. I will definitely see more on this in this GTC. And the third was the introduction of many different NVIDIA AI tools like Jarvis, Maxine, and Merlin. Definitely useful and interesting!
Thank you for the opportunity! I'll stay tuned to the results and your content for sure.
shankar hariharan k
My fav moments were
1. BMW Digital twin solution using omniverse
2. Jarvis platform
3. EGX ecosystem and the reach
Josh Mann
I think Morpheus was the most interesting thing other than the nifty graphics to make the kitchen disappear. The potential capability to evaluate all traffic east west in a datacenter is game changing for detecting advanced persistent threats.
Simone de bonis
GRACE is just amazing, the future is closer than we think
You can check out my thoughts here:
https://youtu.be/ryG2FGPgjCw
4 - 1
Howdy! At Neuralnet.ai we cover artificial intelligence and deep learning tutorials in a variety of topics, ranging from reinforcement learning to natural language processing. The bulk of my content is in deep reinforcement learning, where I present lectures on actor critic and policy gradient methods, as well as deep q learning and all its varieties. You'll get to learn advanced algorithms like A3C, DDPG, TD3, SAC, PPO, DQN, DDQN, and D3QN. Tutorials are presented in both the PyTorch and Tensorflow frameworks.
I'm on a mission to educate 1,000,000 artificial intelligence engineers, so that we can stay one step ahead of our robot overlords.
I typically post written tutorials at www.neuralnet.ai
You can follow me on twitter at www.twitter.com/MLwithPhil