LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics

This could be an interesting new paper by Yann LeCun and his team.

It is another paper in a series of JEPA papers by Yann.

From the abstract:

"Video carries the temporal structure of the physical world, yet learning representations from it has remained computationally expensive:

prevailing self-supervised methods either prevent representation collapse through architectural asymmetries, coupling an exponential-moving-average target encoder, a stop-gradient, and a capacity-limited predictor, or circumvent it by reconstructing masked content in pixel space.

We introduce LeVJEPA, the first video encoder trained under LeJEPA's collapse-free objective, which dispenses with both.

A single encoder is trained with an invariance loss over global and local views of a clip, regularized by SIGReg, which excludes collapse with a provable guarantee. The architecture reduces to an encoder and a projector, and the objective to a single hyperparameter.

This formulation admits two properties.

First, the cost of pretraining is governed by the number of tokens the encoder observes; uniform random token dropping renders this number small while simultaneously improving downstream accuracy. At matched epochs on identical data, LeVJEPA matches or surpasses V-JEPA 2 across ViT-S/B/L at 5.6 to 20.8x less pretraining compute, and at matched total FLOPs it exceeds the strongest video baseline by 7.6 points on ImageNet-1K while remaining competitive on motion-centric benchmarks.

Second, since no asymmetry between branches is required, the encoder can be trained with block-causal attention at no measurable accuracy cost: temporal ordering becomes a property of the encoder itself.

Against a compute-matched DINOv2 trained on frames of the same videos, LeVJEPA approaches the image-pretrained encoder on appearance-centric evaluation while nearly doubling its motion-centric accuracy.

These results indicate that, once its computational overhead is removed, video becomes a viable and in several respects preferable substrate for general-purpose visual pretraining."

[2608.27395] LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics

LpWM: A Case for Sparse Representations in World Models

This could be an interesting new paper by Yann LeCun and his team.

Notice that Yann has written a whole series of articles on JEPA.

From the abstract:

"Joint-embedding predictive architectures (JEPAs) learn latent dynamics for planning and avoid representation collapse by matching features to maximum-entropy distributions such as isotropic Gaussians, yielding dense representations. However, it is unclear whether dense representations are the most favorable geometry for modeling dynamics.

In this work, we ask whether a different geometry, sparse representations, can make action-conditioned latent dynamics easier to model, and what dynamical structure emerges from such representations.

We first show that nonlinear Lipschitz dynamics can be approximated arbitrarily well by action-conditioned linear dynamics in a sufficiently high-dimensional one-hot latent space, with rollout error vanishing as the dimension grows.

This motivates distributed sparse representations as a practical relaxation of one-hot sparsity.

We introduce LpWorldModel (LpWM), a JEPA model regularized with Rectified Distribution Matching Regularization (RDMReg) to match encoder features to a Rectified Generalized Gaussian distribution, yielding non-negative sparse codes. Empirically, sparsity lowers the predictor complexity required for successful planning: on PushT, sparse LpWM outperforms dense LeWM by up to 57% in planning success at intermediate predictor capacities.

This advantage also extends beyond Gaussian distribution matching, with LpWM outperforming dense VICReg representations across multiple predictor families. We further find that the learned sparse representations are mode-factored, with support encoding discrete dynamical regimes and feature magnitudes capturing continuous within-regime state.

Together, these results suggest that sparse representations can reduce the predictor complexity required for control while revealing interpretable structure."

[2608.22764] LpWM: A Case for Sparse Representations in World Models

Patch Policy: Efficient Embodied Control via Dense Visual Representations

This could be an interesting new paper by Yann LeCun and his team!

From the abstract:

"Pretrained dense visual features from Vision Transformers (ViTs) are powerful yet have been underutilized in robot learning. Modern robot policies either compress each observation into a single global token, or rely on visual backbones trained from scratch, sacrificing both fine-grained spatial detail and the benefits of large-scale visual pre-training.

While there exist policies that do operate on dense patch features like large vision-language-action models (VLAs), they tend to be heavy and slow, inheriting the full cost of a billion-parameter vision-language model (VLM) backbone.

We close this gap with Patch Policy, a minimal architectural extension that enables transformer-based policies to consume dense pre-trained patch tokens directly without the computational overhead of a full VLM.

At its core is a block-causal attention mask that preserves the temporal causality of standard policies while letting the model attend over many patch tokens per observation, alongside other state information. Patch Policy is lightweight, fast, and highly effective.

Across four simulated and three real-world environment suites, our method achieves a 40% relative improvement over policies using state-of-the-art global-pooled representations.

Furthermore, it surpasses fine-tuned OpenVLA-OFT by 18% while using roughly 0.7% of the parameters.

We believe Patch Policy provides a pipeline for the robotics community to readily leverage continuing progress in visual representation learning, without sacrificing the training efficiency or inference speed required for high-frequency, reactive control. Videos can be viewed at this https URL"

[2607.18236] Patch Policy: Efficient Embodied Control via Dense Visual Representations

You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences

This could be an interesting new paper by Yann LeCun and his team!

From the abstract:

"Progress in AI has largely been driven by methods that assume less. As compute and data increase, approaches with weaker inductive biases generally outperform those with stronger assumptions.

This is particularly characteristic of the field of Visual Representation Learning, where approaches have gone from being dominated by Supervised Learning, to Weakly Supervised Learning, to the now widespread success of Self-Supervised Learning without human labels.

Yet, even modern Self-Supervised Learning approaches still depend on strong inductive biases such as augmentations, masking, or cropping. If this trend holds, even these remaining biases should become bottlenecks at scale -- and our experiments confirm this: the optimal strength of inductive biases decreases as data grows.

This motivates the search for approaches that rely on fewer assumptions. To this end, we introduce Temporal Difference in Vision (TDV), a new paradigm for self-supervised learning from video that avoids existing inductive biases, relying instead on a causal assumption that the past causes the future.

TDV functions by jointly training an image encoder and a motion encoder so that the current frame's representation plus the encoded motion equals the next frame's representation. Despite not leveraging any strong inductive biases, TDV matches state-of-the-art recipes on dense spatial tasks, laying the foundation for representation learning without strong assumptions."

[2606.15956] You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences (preprint, open access)

Anatomy of Massive Activations and Attention Sinks

This could be an interesting paper by Yann LeCun and his team!

From the abstract:

"We study two recurring phenomena in Transformer language models:

massive activations, in which a small number of tokens exhibit extreme outliers in a few channels, and 

attention sinks, in which certain tokens attract disproportionate attention mass regardless of semantic relevance.

Prior work observes that these phenomena frequently co-occur and often involve the same tokens, but their functional roles and causal relationships remain unclear. Through systematic experiments, we show that the 

co-occurrence is largely an architectural artifact of modern Transformer design, and that 

the two phenomena serve related but distinct functions. 

Massive activations operate globally: they induce near-constant hidden representations that persist across layers, effectively functioning as implicit parameters of the model. 

Attention sinks operate locally: they modulate attention outputs across heads and bias individual heads toward short-range dependencies.

We identify the pre-norm configuration as the key choice that enables the co-occurrence and show that ablating it causes the two phenomena to decouple."

Anatomy of Massive Activations and Attention Sinks | OpenReview (open access)

Who's behind AMI Labs, Yann LeCun's ‘world model’ startup

More commercialization of machine learning & AI!

When top ML & AI researchers become entrepreneurs!

"Yann LeCun’s new venture, AMI Labs, has drawn intense attention since the AI scientist left Meta to found it. This week, the startup finally confirmed what it’s building — and several key details have been hiding in plain sight.

On its newly launched website, the startup disclosed its plans to develop “world models” in order to “build intelligent systems that understand the real world.” The focus on world models was already hinted at by AMI’s name, which stands for Advanced Machine Intelligence, but it has now officially joined the ranks of the hottest AI research startups. ...

World Labs, a direct rival founded by AI pioneer Fei-Fei Li, became a unicorn shortly after coming out of stealth. After launching its first product, Marble, which generates physically sound 3D worlds, World Labs is now reportedly in talks to raise fresh funding at a valuation of $5 billion. ..."

Who's behind AMI Labs, Yann LeCun's ‘world model’ startup | TechCrunch

AMI Labs website not quite ready for business?

On VL-JEPA: Joint Embedding Predictive Architecture for Vision-language

This seems to be an interesting new paper by Yann LeCun and his team!

"A joint image-text embedding model with a predictive training objective delivers strong multi-task performance across vision-language benchmarks while keeping model size and architectural changes minimal."

From the abstract:

"We introduce VL-JEPA, a vision-language model built on a Joint Embedding Predictive Architecture (JEPA). Instead of autoregressively generating tokens as in classical VLMs, VL-JEPA predicts continuous embeddings of the target texts. By learning in an abstract representation space, the model focuses on task-relevant semantics while abstracting away surface-level linguistic variability.
In a strictly controlled comparison against standard token-space VLM training with the same vision encoder and training data, VL-JEPA achieves stronger performance while having 50% fewer trainable parameters.
At inference time, a lightweight text decoder is invoked only when needed to translate VL-JEPA predicted embeddings into text. We show that VL-JEPA natively supports selective decoding that reduces the number of decoding operations by 2.85x while maintaining similar performance compared to non-adaptive uniform decoding.
Beyond generation, the VL-JEPA's embedding space naturally supports open-vocabulary classification, text-to-video retrieval, and discriminative VQA without any architecture modification.
On eight video classification and eight video retrieval datasets, the average performance VL-JEPA surpasses that of CLIP, SigLIP2, and Perception Encoder. At the same time, the model achieves comparable performance as classical VLMs (InstructBLIP, QwenVL) on four VQA datasets: GQA, TallyQA, POPE and POPEv2, despite only having 1.6B parameters."

Last Week in AI #330 - Groq->Nvidia , ChatGPT Apps, US AI Genesis Mission

VL-JEPA: Joint Embedding Predictive Architecture for Vision-language (open access)

On LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Could be an interesting paper by Yann LeCun!

Caveat: I have not read the paper.

From the abstract:

"Learning manipulable representations of the world and its dynamics is central to AI. Joint-Embedding Predictive Architectures (JEPAs) offer a promising blueprint, but lack of practical guidance and theory has led to ad-hoc R&D.

We present a comprehensive theory of JEPAs and instantiate it in {\bf LeJEPA}, a lean, scalable, and theoretically grounded training objective.

First, we identify the isotropic Gaussian as the optimal distribution that JEPAs' embeddings should follow to minimize downstream prediction risk.

Second, we introduce a novel objective--{\bf Sketched Isotropic Gaussian Regularization} (SIGReg)--to constrain embeddings to reach that ideal distribution.

Combining the JEPA predictive loss with SIGReg yields LeJEPA with numerous theoretical and practical benefits:

(i) single trade-off hyperparameter,

(ii) linear time and memory complexity,

(iii) stability across hyper-parameters, architectures (ResNets, ViTs, ConvNets) and domains,

(iv) heuristics-free, e.g., no stop-gradient, no teacher-student, no hyper-parameter schedulers, and 

(v) distributed training-friendly implementation requiring only \approx50 lines of code.

Our empirical validation covers 10+ datasets, 60+ architectures, all with varying scales and domains. As an example, using imagenet-1k for pretraining and linear evaluation with frozen backbone, LeJEPA reaches 79\% with a ViT-H/14. We hope that the simplicity and theory-friendly ecosystem offered by LeJEPA will reestablish self-supervised pre-training as a core pillar of AI research (\href{this https URL}{GitHub repo})."

[2511.08544] LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Yann Le Cun leaves Meta, striking out his own future for AI

Recommendable! I am curious what Yann LeCun will do next!

On V-JEPA: The next step toward advanced machine intelligence

Recommendable!

"Takeaways:

1. Today, we’re publicly releasing the Video Joint Embedding Predictive Architecture (V-JEPA) model, a crucial step in advancing machine intelligence with a more grounded understanding of the world.
2. This early example of a physical world model excels at detecting and understanding highly detailed interactions between objects.
3. In the spirit of responsible open science, we’re releasing this model under a Creative Commons NonCommercial license for researchers to further explore.

 ...

V-JEPA is a non-generative model that learns by predicting missing or masked parts of a video in an abstract representation space. This is similar to how our Image Joint Embedding Predictive Architecture (I-JEPA) compares abstract representations of images (rather than comparing the pixels themselves). Unlike generative approaches that try to fill in every missing pixel, V-JEPA has the flexibility to discard unpredictable information, which leads to improved training and sample efficiency by a factor between 1.5x and 6x.

Because it takes a self-supervised learning approach, V-JEPA is pre-trained entirely with unlabeled data. Labels are only used to adapt the model to a particular task after pre-training. This type of architecture proves more efficient than previous models, both in terms of the number of labeled examples needed and the total amount of effort put into learning even the unlabeled data. With V-JEPA, we’ve seen efficiency boosts on both of these fronts.

With V-JEPA, we mask out a large portion of a video so the model is only shown a little bit of the context. We then ask the predictor to fill in the blanks of what’s missing—not in terms of the actual pixels, but rather as a more abstract description in this representation space. ...

Masking Methodology

The team also carefully considered the masking strategy—if you don’t block out large regions of the video and instead randomly sample patches here and there, it makes the task too easy and your model doesn’t learn anything particularly complicated about the world.

It’s also important to note that, in most videos, things evolve somewhat slowly over time. If you mask a portion of the video but only for a specific instant in time and the model can see what came immediately before and/or immediately after, it also makes things too easy and the model almost certainly won’t learn anything interesting. ..."

V-JEPA: The next step toward advanced machine intelligence

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (open access)

How existential risk became the biggest meme in AI thanks to two Turing Award winners

Very recommendable! The alarmism and hysteria recently raised by two leading AI researchers, i.e. Geoffrey Hinton and Yoshua Bengio, was irresponsible!

"Yann LeCun, a Turing Award winner, ... why he thinks the idea that a superintelligent AI system will take over the world is “preposterously ridiculous.” 

People are worried about AI systems that “are going to be able to recruit all the resources in the world to transform the universe into paper clips,” LeCun said. “That’s just insane.” (He was referring to the “paper clip maximizer problem,” a thought experiment in which an AI asked to make as many paper clips as possible does so in ways that ultimately harms humans, while still fulfilling its main objective.) 

He is in stark opposition to Geoffrey Hinton and Yoshua Bengio, two pioneering AI researchers (and the two other “godfathers of AI”), who shared the Turing prize with LeCun. Both have recently become outspoken about  existential AI risk.

Joelle Pineau, Meta’s vice president of AI research, agrees with LeCun. She calls the conversation ”unhinged.” The extreme focus on future risks does not leave much bandwidth to talk about current AI harms, she says. ...  [The existential-risk crowd] have essentially put an infinite cost on that outcome,” says Pineau. 

“When you put an infinite cost, you can’t have any rational discussions about any other outcomes. And that takes the oxygen out of the room for any other discussion, which I think is too bad.”

While talking about existential risk is a signal that tech people are aware of AI risks, tech doomers have a bigger ulterior motive, LeCun and Pineau say: influencing the laws that govern tech. ..."

How existential risk became the biggest meme in AI

On Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods

Very recommendable! I think, it is an impressive work.

This is a theoretical machine learning paper hot off the press, co-written by Yann LeCun (Chief AI Scientist at Facebook/Meta)!

This is a serious attempt at unifying major self supervised learning approaches. It is loaded with math.

[2205.11508] Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods

On The Effects of Regularization and Data Augmentation are Class Dependent

Just read this paper from Facebook AI written by among others senior author Yann LeCun (Chief AI Scientist of Facebook).

This research paper describes a serious critique of two very common practices in machine/deep learning, i.e. data augmentation and weight decay.

However, I suspect a similar critique also applies to the imbalance of classes represented in the usual datasets.

[2204.03632] The Effects of Regularization and Data Augmentation are Class Dependent

Notes on "Learning in High Dimension Always Amounts to Extrapolation"

Recommendable! This latest paper by Facebook AI research and co-authored by Yann LeCun seems to raise some serious issues about machine learning and its prediction capabilities.

From the abstract: 

"The notion of interpolation and extrapolation is fundamental in various fields from deep learning to function approximation. ... One fundamental (mis)conception is that state-of-the-art algorithms work so well because of their ability to correctly interpolate training data. A second (mis)conception is that interpolation happens throughout tasks and datasets, in fact, many intuitions and theories rely on that assumption. We ... demonstrate that on any high-dimensional (>100) dataset, interpolation almost surely never happens. Those results challenge the validity of our current interpolation/extrapolation definition as an indicator of generalization performances."

[2110.09485] Learning in High Dimension Always Amounts to Extrapolation

Self-supervised learning: The dark matter of intelligence

Recommendable! Co-written by Yann LeCun (VP and Chief AI Scientist of Facebook). Describes some of the latest advances in computer vision as an easy to digest overview.

"... Self-supervised learning enables AI systems to learn from orders of magnitude more data, which is important to recognize and understand patterns of more subtle, less common representations of the world. ...
Self-supervised learning obtains supervisory signals from the data itself, often leveraging the underlying structure in the data. The general technique of self-supervised learning is to predict any unobserved or hidden part (or property) of the input from any observed or unhidden part of the input ..."

Self-supervised learning: The dark matter of intelligence

When NIPS Became NeurIPS

Posted: 12/4/2018  Updated: 11/24/2020, 12/18/2019

Update Of 11/24/2020

To my horror, I had to discover today for the first time that an older paper from 2001 now has two official URLs because of this ridiculous name change:

1. https://papers.nips.cc/paper/2089-convolution-kernels-for-natural-language
2. http://papers.neurips.cc/paper/2089-convolution-kernels-for-natural-language

Will this nonsense now be applied retroactively to all older papers?

Update Of 12/18/2019

As someone who has never attended any of the NIPS conferences, I was stunned a few days ago when I read in MIT Technology Review. The Download. Your daily dose of what’s up in emerging technology (12/13/2019) following:

“Only two years ago, so I’m told, one of the hottest AI research conferences of the year [NIPS] was more giant party than academic exchange. In a fight for the best talent, companies handed out endless free swag and threw massive, blowout events, including one featuring Flo Rida, hosted by Intel. The attendees—mostly men in their early 20s and 30s—flushed with huge salaries and the giddiness of being highly coveted, drank free booze and bumped the night away. ... Internally, reports of sexual harrassment, anti-Semitism, racism, and ageism, were also driving conference-goers to question whether they should continue to attend.

So when I arrived in 2018, a diversity and inclusion committee had been appointed, and the long-standing abbreviation NIPS was swapped for another one”

Why did the responsible people in charge not simply prohibit or constrain these free booze parties etc. to start with?

NIPS Not Nipples

NIPS (as it was formerly known; Neural Information Systems Processing) has been for decades one of the global, premier conferences on artificial intelligence & machine learning was recently renamed to NeurIPS. The 32nd, first renamed, annual Conference of 2018 (12/2 through 12/8) is ongoing as I write this blog post.

Here are the official reasons for the name change (emphasis added):

1. “The current acronym NIPS has unintended connotations that some members of the community find offensive” (Source 1)
2. “In April, over 120 academics from John Hopkins University in Baltimore, Maryland, signed a letter calling on NIPS to be rebranded following reports of inappropriate behaviour. The letter stated that the "acronym of the conference is prone to unwelcome puns."” (Source 2)

Confusing, the official news release (Source 1) contains a table with polling results (“Do you think we should change the name of the NIPS conference?”), which indicates that a large majority of participants actually disagreed with changing the name (1,218 con answers, 674 pro answers). An astonishing 378 answered they were neutral about the name change (people without a spine?).  

I believe, this renaming is highly immature, childish, and infantile. Does not reflect well on the people responsible for it. Mature people would have disciplined the offenders!

Political Correctness And Its Enablers

Some of the gurus of Artificial Intelligence & Machine Learning, e.g. Yann LeCun & Yoshua Bengio (on the Advisory Board of NIPS in 2018), are hard leftists and feminists (blogged about them here & here). My suspicion is that these and other influential, likeminded gentlemen do not have the spine to admonish anyone in person who makes inappropriate remarks. Thus, these gentlemen prefer to rename the show.

Hope Springs Eternal

One can only hope that some more enlightened people at NIPS will reverse the dim witted decision to rename this conference!

Sources:

1. NIPS Name Change
2. 'NIPS' AI Conference Changes Name, Kind Of

Yann LeCun: One Of Mark Zuckerberg's Extremely Left Leaning Employees?

Posted: 7/14/2018  Updated: 4/14/2019, 7/15/2018

Update Of 4/14/2019

As of today, Yann LeCun decided to cut me off from commenting on his public posts on his Facebook page. Very fitting for an intolerant leftist full of hubris. He is also one of those hard and naive leftists who can not handle the truth or criticism!

It is his loss not mine! :-)

A Vice President & Chief Scientist At Facebook

Yann LeCun is the VP & Chief AI Scientist at Facebook according to his LinkedIn profile. Yann LeCun is actually one of the superstars of AI & machine learning.

I have recently liked his Facebook page, therefore about two posts by Yann LeCun appeared on my Facebook page in the past few days. Both of them were openly and kind of directly against President Trump. Not only that, he essentially and unreflectively (I hope) regurgitated typical leftist/progressive and absurd talking points like Trump has a mental issue (e.g. wordplay on paranoid and pronoid) or Peter Strzok’s “fiery” response during the Congressional Hearing about him (LeCun later also commented favorably about Strzok’s possibly fake outrage that Trump insulted a Veteran).

Yann LeCun Responded On His Facebook Posting

I had challenged Yann LeCun on both of his recent Facebook postings listed above. Today (7/15/2018) he responded to one of his two Facebook postings:

“You don't need to be "extreme left leaning" to see that Trump is a lying, incompetent racist who has no sense of basic human decency and no respect for democratic institutions. Anyone, conservative or progressive, who is in favor of democracy and human decency can see that. Are you? Can you?” (emphasis added)

My response to his response in return: “Mon dieu, you entertain a narrow, highly distorted and biased view of the current president. I suspect, it has to do with your upbringing in France (myself I am from Germany). I further suspect, you have a very poor and incomplete understanding of U.S. history and the Declaration of Independence as well as the U.S. Constitution/State constitutions. Recherchez vous si'l vous plait. Merci!”

Zuckerberg About An Extremely Left Leaning Place

About April 10, 2018, Mark Zuckerberg, CEO of Facebook, gave a testimony in the U.S. Congress where among other things, he had the courage to admit “I understand where that concern is coming from because Facebook and the tech industry are located in Silicon Valley, which is an extremely left-leaning place” (source; emphasis added). I guess, he knew what he was talking about.

I personally do not like these awful show trials of business leaders in the U.S. Congress at all. It is often not much more than grandstanding and chest beating of our elected politicians to drag a business leader before them and lash out at them. In the case of Mark Zuckerberg this was certainly the case!

The Bubble Of Silicon Valley

They think they are so smart, open minded, and sophisticated, working on cutting/bleeding edge technology to the benefit of all mankind. They are so proud of the wisdom of crowd sourcing etc.

Turns out, that they rather live in a serious bubble and they are mightily trapped/stuck in really narrow minded groupthink!

No Harm Intended

I don’t mean any harm to or to judge Yann LeCun’s political views. Everyone is entitled to their opinions! His professional work in AI is excellent/outstanding and greatly admired by many, including myself.

Living At Artificial Intelligence Speed

Posted: 7/1/2018  Updated: 8/6/2018

Update Of 8/6/2018

If you want to get an idea how fast AI is moving here is a blog post for you published on the BLAIR (Berkeley Artificial Intelligence Research) website: When Recurrent Models Don't Need to be Recurrent. Essentially, this post describes that celebrated AI approaches (e.g. recurrent neural networks) that were considered top of the line until just a few years if not a few a month ago are seriously questioned and about to be substituted with new AI approaches (Google’s Wavenet, Google’s Transformer).

Absolutely breathtaking!

Original Post

The Immediate Past Is Prologue

Some of the readers may remember such popular expressions as living at Internet speed.

However, since about 2005/2006 we are living at AI speeds. The speed at which AI is proliferating and evolving is absolutely incredible.

Just follow a talk like this one by Yann LeCun (here, Chief AI Scientist at Facebook) and you get an idea how fast and far AI has come within a span of a few years.

A Revolution For Everyone

Most amazing is the the general attitude to make AI open source and open publish and learnable by everyone who cares on the Internet. There probably never was a technological revolution before in history, where the leaders of the revolution want to make sure that everyone interested in this revolution can instantly participate, grow with it, and learn and practice all about it at any time.

No Hype

You better believe the hype! If you don’t it is at your own peril. AI will be so transformative like few other technologies ever before in human history.