joe dev

Live your dream, neon beam.

[joe dev @joe_developer]

@StopAntisemites @Variety Ziontologists in a nutshell: "Those that appropriate Judaism when politically convenient"

[joe dev @joe_developer]

@rohanpaul_ai Thanks for sharing, super timely for me.

Not on X much, but are all of these dead internet comments normal?

[joe dev @joe_developer]

@BrammerRan78030 @SawyerMerritt @farzyness Not just that, a few influencers did head to head or changed on the return leg. iirc waymo left a better impression overall

[joe dev @joe_developer]

@rauchg Good effort - This should lead to a broader evaluation suite that gives insight across all frameworks, not just those in the vercel ecosystems.

[Carlos E. Perez @IntuitMachine]

AI's Secret Pattern: The Surprising Role of Fractals in Neural Networks

In the realm of artificial intelligence (AI), a groundbreaking discovery has emerged, challenging our conventional understanding of neural network training and optimization. This revelation centers around the identification of fractal patterns at the boundary between trainable and untrainable neural network hyperparameters, presenting a series of profound implications and avenues for further research.

Fractals, known for their intricate, self-similar patterns that recur at every scale, have long fascinated mathematicians and scientists alike. Typically associated with simple, one-dimensional iterative functions, the appearance of fractals within the complex, multivariate domain of neural network training introduces a striking contrast. The organic and asymmetric nature of these fractals, as derived from the training processes, suggests a deeper, unexplored connection between the mathematical properties of fractals and the functional dynamics of neural networks.

The study’s focus on two-dimensional slices of hyperparameter space barely scratches the surface of the complexity inherent in neural networks, which are characterized by a vast array of hyperparameters. The existence of fractals in this context hints at an underlying high-dimensional structure, a concept that challenges our current capabilities and understanding. Extending fractal analysis to these higher dimensions represents a significant, yet exciting, challenge that could illuminate new aspects of neural network behavior and learning capabilities.

An unexpected finding from the research is the persistence of clean fractal patterns even in the presence of stochastic elements introduced during minibatch training. This resilience suggests a parallel to Lyapunov fractals, where the iterative process involves randomly changing functions. This phenomenon prompts a reevaluation of how stochastic and deterministic processes influence fractal formation within neural networks, potentially offering new insights into the fundamental mechanisms of learning and adaptation.

From a practical standpoint, the fractal nature of the boundary between trainable and untrainable hyperparameters has significant implications for the field of metalearning. The chaotic behavior of the meta-loss landscape, attributed to its extreme sensitivity, presents a formidable challenge for algorithms designed to optimize hyperparameters. Understanding the fractal characteristics of this landscape could provide valuable guidance for navigating its complexities, ultimately improving the efficiency and effectiveness of metalearning strategies.

Beyond the technical and theoretical implications, the discovery also reveals an unexpected aesthetic dimension to neural network fractals. The visual beauty and meditative qualities of these patterns offer a unique opportunity to engage with the material in a deeply personal and contemplative manner. This aspect suggests potential psychological and physiological benefits from exposure to the intricate designs of neural network fractals, opening up novel intersections between technology, art, and well-being.

In conclusion, the identification of fractal patterns within neural network hyperparameter spaces unveils a fascinating new frontier at the intersection of fractal geometry and deep learning. This discovery not only challenges existing paradigms but also opens up myriad possibilities for mathematical characterization, algorithmic development, and even subjective exploration. As researchers continue to delve into this rich vein of inquiry, the promise of uncovering new knowledge and advancing our understanding of neural networks and their training processes remains as compelling as ever.

[Jason Kint @jason_kint]

‘If they decide to kill me…’

[joe dev @joe_developer]

@tamarygil quick question, were you detained by authorities related to echoing the obvious fact that Netanyahu has little to no incentive to secure the release of the hostages?
Thanks!

[joe dev @joe_developer]

@SmartLess @SeanHayes @batemanjason @arnettwill @ApplePodcasts @WonderyMedia @amazonmusic Love your work, but:
1. False negative doesn't mean what you seem to think it means.
2. Listen to your episode with Brendan Fraser, because it felt like you weren't listening to him at all during the interview.
All the best <3

[joe dev @joe_developer]

@mattgaetz The McCarthy captcha joke was funny, this one is just silly.

[Sahil Bloom @SahilBloom]

Today is my 32nd birthday.

Over the last month, I asked several 90-year-olds what advice they would give to their 32-year-old selves.

Here's the life advice everyone needs to hear:

[joe dev @joe_developer]

@mattgaetz Ok, that was pretty funny

[joe dev @joe_developer]

Not big on Elon, but this seems like a wrong that sounds be righted, no?

[Farzad 🇺🇸 🇮🇷 @farzyness]

The car on the left has a gas engine and gets 20 MPG. It has 21 miles of electric range. It gets a $7,500 EV Tax Credit.

The car on the right is ELECTRIC ONLY and gets 113 MPEe. It has 330 miles of range.

IT DOES NOT GET A $7,500 EV TAX CREDIT.

[joe dev @joe_developer]

@elonmusk Err is any of the following false?
1. All payments were mandated by law.
2. There are no orders to block or remove any posts.

[joe dev @joe_developer]

@cyrusrezvanian @jack_neo9 @alex_avoigt @elonmusk @christine_rez That is exactly what people have been saying every year.

[Ben Kuhn @benkuhn]

A thing I often find myself suggesting to new managers is to "exert more backpressure."

Backpressure is a concept from fluid dynamics (and distributed systems) meaning the way in which a system resists overload—e.g. by slowing down, dropping requests, or completely failing.

[joe dev @joe_developer]

@SwayamSatapath2 @DrEliDavid @jihadwatchRS

[joe dev @joe_developer]

@realGeorgeHotz @csakon @block_saw Exactly the same issue, stop being dense.

[joe dev @joe_developer]

@realGeorgeHotz Jesus wtf, mask wearing is about impacting R0 - from both sides.

[joe dev @joe_developer]

@elonmusk The human condition and all that - probably alot of human scrolling as well, factor in linger time to reduce false positives regarding actual reads?

[joe dev @joe_developer]

@calebgamman Full video: https://t.co/04cABIaQoy the section under discussion is ~9:49 fairly interesting and a bit more nuanced than I expected.