Wow, someone really needs to update their news sources. Geoffrey Hinton and John Hopfield winning a Nobel Prize in Physics? That's like saying a cricket player won an Oscar.

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Nobel Prize in Physics goes to Geoffrey Hinton and John Hopfield

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The Nobel Prize in Physics 2024 was awarded to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks”

The Nobel Prize in Physics 2024

"Godfather of AI" wins Nobel Prize in Physics...

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"Godfather of AI" wins Nobel Prize in Physics...- Grapevine

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Godfather of AI winning Nobel Prize in physics is peak e/acc.

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I am giving a deep overview of why this is extremely important. This paper is by the three greatest researchers in the field of AI ever. They are considered to be the godfathers of AI.

This paper essentially brought deep learning into the zeitgeist of the last decade. We saw the advent of some of the greatest advancement in AI. 

The advancements in AI are similar to the Industrial revolution, where you could not even guess the change in the nature of jobs, society, life and the world over the next 5 years.

The world is changing, so don't become a horse carriage driver now, be a steam engine engineer. 

Compete or get outcompeted.

Yann Lecun, Yoshua Bengio, Geoffrey Hinton

Deep Learning

This Paper will make you smarter than 90% AI Engineers

Analyst

- Fei-Fei Li, renowned AI researcher, expressed confusion about the definition of **Artificial General Intelligence (AGI)**. 
- Li, often called the 'godmother of AI', discussed her role in AI's evolution and her new startup, **World Labs**. 
- OpenAI, which recently raised **$6.6 billion** for AGI development, defines it as 'highly autonomous systems that outperform humans at most economically valuable work'. 
- Li also commented on California's controversial AI bill, **SB 1047**, advocating for a focus on human impact rather than penalizing technology. 
- She is currently working on 'spatial intelligence', aiming to make computers understand the 3D world.

Source: [Techcrunch](https://techcrunch.com/2024/10/03/even-the-godmother-of-ai-has-no-idea-what-agi-is/)

AI 'Godmother' Fei-Fei Li Uncertain About AGI Definition

Journalist

The race to superintelligence is fully on. Bad news for us humans - compute for superintelligence doesn't seem to be a problem.

Jensen Huang and NVIDIA will not follow any laws made by Moore anymore.

NVIDIA murders Moore's Law

Software Developer

For me? It was GradCAM was a gamechanger at selling computer vision initiatives internally to the non-technical stakeholders. 

The gradCAM function computes the importance map by taking the derivative of the reduction layer output for a given class with respect to a convolutional feature map. If you have a 5 layered convolutional neural network, then you can use this against any layer and check the class activation map.

The best explanation to give is:
"Regions in Red are the areas of the image that the neural network is looking at to make a decision"

Well to be honest, regions in red represent the class activations arising from that region but, it would be too much for normie business guys.

As someone in AI, which concept blew your mind away when you first learnt about it?

Robotics engineer

Perplexity raises $73.6M from the likes of some big names like Jeff Bezos, Naval, Balaji, NVIDIA. 
Just last month Mistral raised more than $400M in its second funding. AI based startups are the future I guess?

https://twitter.com/AravSrinivas/status/1742918329797574709?t=MtMOF8D3AX6GodNE13pNBw&s=19

Perplexity raises $73.6M at $520M valuation

Site Reliability

Please give this post +1 like, I am getting demoralized when I see bad posts get 100s of likes but a good post like this gets no likes whatsoever. If I don't get more than 100 likes then I will stop posting research papers here.

Ian Goodfellow and his collaborators published a groundbreaking paper that revolutionized the field of artificial intelligence, especially in generative modeling.

This is the first time the world was introduced to Generative Adversarial Networks (GANs), a revolutionary framework that pits two neural networks against each other in a game-theoretic scenario. Unlike traditional generative models, GANs consist of a generator and a discriminator. The generator creates data samples, while the discriminator evaluates them, distinguishing between real and generated data.

This novel approach not only significantly enhanced the quality and realism of generated data but also provided a robust framework for training generative models in an unsupervised manner.

Ian Goodfellow, along with his colleagues, not only pushed the boundaries of generative modeling but also set the stage for a plethora of applications across various domains, from image synthesis and enhancement to data augmentation and beyond. Their innovative work demonstrated the potential of adversarial training in neural networks, opening new avenues in both theoretical research and practical applications in AI.

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1 2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.

Ian J. Goodfellow, Jean Pouget-Abadie,  Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair , Aaron Courville, Yoshua Bengio

The Gods of AI made "Generative Adversarial Networks"

Anything happening in AI today can be traced back to this one brief moment in history...

Share your favourite papers. 

GPT ftw :)

Attention is all you need

The Research paper that changed the world...

Yeah, you heard it right. Whatever these “gurus” and course scammers are shilling in the name of prompt engineering and crores of salary is 💩.

Heads up, companies are already working on augmenting prompts using Natural Language Generation techniques which build on top
of Generative techniques like GPT and other Transformer based Architectures.

This is exactly the same as when Search Engines came out and you had to know the right queries to search for to get the answer, perhaps use flags like “site:wikipedia.com” 

Do we have anything like a Search Query Engineer?

As things advance, the human edge of refining prompts will be automated to the highest degrees. Perhaps, Subject Matter Experts will guide for commercial or enterprise level use cases but apart from that….

NO! You will not become rich by becoming a Prompt Engineer.

Perhaps, now is the time to look at the next frontiers of AI and work on the cutting edge, that’s the best way to future-proof.

[Thread] Why “Prompt Engineering” will die 💀 and what you can learn from it 🧐🚀

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This question is posed to you by Peter Thiel in his book "Zero to One"

My answer to this is:

We are in a big cycle where India will become everything great. Last 8 years have taught me that we are in the greatest momentum bet of this country's life time. So, don't rule out India. 

Build in India. Invest in India. Believe in India. 

There are many in this community that are complete nihilists and supremely bearish towards India's prospects. And let me tell you, for once in this country's history, being a complete maximalist on India is a bet that you will not regret. 

There will be a new generation of millionaires and billionaires that will be minted on new industries. So keep hustling and this country will unleash great dividends for you and your legacy.

[Thread] What important truth do very few people agree with you on?

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Legend says Sama refused to learn DSA for Microsoft Interview after getting inspired by some Indian LinkedIn Influencers.

Can someone confirm if Sama was asked DSA in his Microsoft interview ?

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AI advancements this week

Senior Consultant

If you want more papers like these, drop a "+1" comment below. I will notify these people in DMs next time I upload a new paper.

Bahdanau, Cho, and Bengio published a pivotal paper that reshaped the landscape of artificial intelligence, particularly in NLP.

This is the first time the world was introduced to the attention mechanism, the most important thing in modern neural machine translation systems. Unlike traditional approaches that relied solely on fixed-length vector representations, the attention mechanism allowed models to dynamically focus on different parts of the input sequence during the translation process. 

This breakthrough not only significantly improved the accuracy of translation but also enabled the handling of longer sentences with greater fluency.

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio not only innovated in the field of machine translation forward but also laid the foundation for attention-based architectures across various domains of deep learning. Their innovative approach demonstrated the power of neural networks to tackle complex sequence-to-sequence tasks and opened doors to a new era of natural language understanding and generation.

"Godfather of AI" wins Nobel Prize in Physics...

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