AJAY

Apple Vision Pro users can now enable spatial Personas in SharePlay-enabled apps, allowing for collaboration, gaming, and media consumption with other users in a virtual space. More details 👇🏻

@rohanpaul_ai that would be the best non instrusive approach , no noise . no jittery seeing this post lol

Andrej Karpathy: "the industry just has to reconfigure in so many ways, like the customer is not the human anymore, it's agents who are acting on behalf of humans. And this refactoring will be probably substantial in the space."

The government in Iran is setting a puppet technique for our minds to be stationary through illusionism in media. A puppet moves because someone else controls it. it with strings. Now Iran is using these same tactics in perpetuity, like making historical figures speak and more manipulation. With the face swap technology, it became very hard to identify what is real and what is not. Wave2Lip and FaceSwap are all GANs; they all have a generator and discriminator to identify what's an illusion. Ultimately it creates ultra-realistic fake data. Currently I have noticed even this SOTA model shows high accuracy for light-skinned males and much lower accuracy for dark-skinned females; some kind of gender bias happening is detrimental even with this LenYun model, as well as the machine bias. Most of this agentic AI will run on feedback loops, like a system that reacts to its own results. + Feedback loops are reinforcing, but negative feedback loops are stabilizing. I am just assuming confusion matrices here, like is there a chance where this embodied AI shows a true negative (ignore a person from helping during an emergency), a false positive (attack an innocent person by mistake), and a false negative (misses a real patient from helping)? A true negative would be the cheapest AI-embodied robot you might see in a local place. But FP would not be tolerable. FN is dangerous here. I recommend using a fairness mitigation technique at this point. That's only the solution. part 1.

I recommend using a Gabor filter; you can detect edges and lines in images. It's like based on combining a sine wave and a Gaussian (localized region). Some slight bends in a line look straight to your brain; that is perpetual straightening. just examined the time to reach velocity, TTRV, of it; it's really instantaneous speed (velocity). Some in an exam paper, teachers have to choose the best answer in English literature, and teachers learn a new intuition from it. Group relative policy optimization is powerful due to that. If there is only one bright person in a class, policy optimization is efficacious because we only needed that answer. Most of the headmaster will give a recommendation to the teacher , Don't be too sure; keep exploring, like entropy regularization.

Auxiliary loss is added to main loss for faster learning. My misconception was that loss is detrimental and it's just noise, but rather it's just a feedback signal, not punishment in an absolute sense. Like a extra teacher hints in exam preparation.

poeple have a wrong discernment about appearance and reality ,which is kinda undue strawman stuffs. if you see lots of verbose ,just leave from that conversation.

This might be quasi linear ..

sublinear > linear > quasi linear ....

Money , thought is just vyavaharika , but paramarthika is the consciousness , god. Whatever we see around is just upadhi , a illusion or condition that makes us perceive differences.

.idxmax() → Give the hotel with the maximum rank. .max() → Give the value of that rank. print() → Speak it aloud.

groupby('hotel_reference') → Group all rows by hotel, like collecting all followers for each temple. ['user_id'] → Look at the jar with users who booked each hotel. .nunique() → Count unique users. Now, hotel_rank = how popular each hotel is. Dot mnemonic: Dot means “perform this magic on the group.”

part 2 of solving.

merge() → Like combining two scrolls based on a common column (hotel_reference). on="hotel_reference" → Tell the helper which column matches the two tables. how="left" → Keep all entries from the first table and match what we can from the second. Filtering for London: merged_df['city'] → Take the column city. == 'London' → Only keep rows where city equals London. & → “and” in Python, both conditions must be true (city = London and country = UK). Brackets [ ] → They select rows where the condition is true.

how would you solve this q ? part 1

important to memorize ..

For each: context = [x1, x2, x3] target = x4 👉 Model learns function: f([x1, x2, x3]) = x4 This is sequence prediction

DSRM part 5 “Given a sequence of numbers (or tokens), teach the model to predict the next item in the sequence using the previous items as context.”