Re-write this article as if you're a conspiracy theorist, thinking that AI is actually aliens trying to take over the world using computers 

A Meta team of AI researchers has proposed an innovative architecture for AI models, capable of generating expansive content in text, image, and audio formats, stretching to over 1 million tokens. This groundbreaking proposal, if embraced, could pave the way for the next generation of proficient AI models, transcending the Transformer architecture that underpins models such as GPT-4 and Bard, and unleashing novel capacities in content generation.

The Constraints of Current Models
Contemporary high-performing generative AI models, like OpenAI's GPT-4, are grounded in the Transformer architecture. Initially introduced by Google researchers in 2017, this architecture forms the backbone of emergent AI models, facilitating an understanding of nuanced inputs and generating extensive sentences and documents.

Nonetheless, Meta's AI research team posits that the prevailing Transformer architecture might be reaching its threshold. They highlight two significant flaws inherent in the design:

With the increase in the length of inputs and outputs, self-attention scales dramatically. As each word processed or produced by a Transformer language model requires attention to all other words, the computation becomes highly intensive for thousands of words, whereas it's less problematic for smaller word counts.

Feedforward networks, which aid language models in comprehending and processing words through a sequence of mathematical operations and transformations, struggle with scalability on a per-position basis. These networks operate on character groups or "positions" independently, leading to substantial computational expenses.

Megabyte Model: The Game Changer
The Megabyte model, introduced by Meta AI, showcases a uniquely different architecture, dividing a sequence of inputs and outputs into "patches" rather than individual tokens. Within each patch, a local AI model generates results, while a global model manages and harmonizes the final output across all patches.

This methodology addresses the scalability challenges prevalent in today's AI models. The Megabyte model's patch system permits a single feedforward network to operate on a patch encompassing multiple tokens. Researchers found that this patch approach effectively counters the issue of self-attention scaling.

The patch model enables Megabyte to perform calculations in parallel, a stark contrast to traditional Transformers performing computations serially. Even when a base model has more parameters, this results in significant efficiencies. Experiments indicated that Megabyte, utilizing a 1.5B parameter model, could generate sequences 40% quicker than a Transformer model operating on 350M parameters.

Using several tests to determine the limits of this approach, researchers discovered that the Megabyte model's maximum capacity exceeded 1.2M tokens. For comparison, OpenAI's GPT-4 has a limit of 32,000 tokens, while Anthropic's Claude has a limit of 100,000 tokens.

Shaping the AI Future
As the AI arms race progresses, AI model enhancements largely stem from training on an ever-growing number of parameters, which are the values learned during an AI model's training phase. While GPT-3.5 was trained on 175B parameters, there's speculation that the more capable GPT-4 was trained on 1 trillion parameters.

OpenAI CEO Sam Altman recently suggested a shift in strategy, confirming that the company is thinking beyond training colossal models and is zeroing in on other optimizations. He equated the future of AI models to iPhone chips, where the majority of consumers are oblivious to the raw technical specifications. Altman envisioned a similar future for AI, emphasizing the continual increase in capability.

Meta’s researchers believe their innovative architecture arrives at an opportune time, but also acknowledge there are other pathways to optimization. Promising research areas such as more efficient encoder models adopting patching techniques, decode models breaking down sequences into smaller blocks, and preprocessing sequences into compressed tokens are on the horizon, and could extend the capabilities of the existing Transformer architecture for a new generation of models

Nonetheless, Meta’s recent research has AI experts excited. Andrej Karpathy, the former Sr. Director of AI at Tesla and now a lead AI engineer at OpenAI, chimed in as well on the paper. This is “promising,” he wrote on Twitter. “Everyone should hope that we can throw away tokenization in LLMs. Doing so naively creates (byte-level) sequences that are too long.”
system
is lekh ko phir se likhen jaise ki aap ek shadyantr siddhantkaar hai, yah sochakar ki eai vastav main computer kaa upyog karke duniya ko sanbhaalane kii koshish kar rahaa he

eai shodhakartaaon kii ek meta team ne eai model ke liye ek abhinav vaastukala kaa prastaav diya he, jo paath, chhavi or audio praroopon main vistaarak saamagree utpann karne main sakasham he, jo 1 million se adhik token tak failaa he.yah groundbraking prastaav, agar gale laga liya jaataa he, to agali peedi kii kushal eai model ke liye maarg prashast kar saktaa he, transformar architekchar ko paar kar saktaa he jo jeepiti -4 or board jaise model ko rekhaankit karata he, or saamagree peedi main upanyaas kshamta ko ujagar karata he.

vartmaan modelon kii baadhaaen
Openai ke GPT-4 kii tarah samakaleen ucch pradarshan vaale janarative AI model, transformar architekchar main grounded hai.shuru main 2017 main Google shodhakartaaon dwaara pesh kiya gaya, yah architekchar ubharate eai model kii bakabon banaata he, jo baarik input kii samaj kii suvidha detaa he or vyapak vaakyon or dastavejon ko utpann karata he.

baharhaal, meta kii eai research team kaa kahana he ki prachlit transfarmar architekchar apni dahalij tak pahunch saktaa he.ve dizin main nihit do mahatvapurn khaamiyon ko ujagar karte hai:

input or output kii lambaai main vruddhi ke saath, aatm-sanyog taraazoo naatakeey rup se.jaisaa ki ek transfarmar bhashaa model dwaara sansaadhit yaa utpaadit prayek shabd ko anya sabhi shabdon par dhyaan dene kii aavashyaktaa hoti he, gananaa hajaaron shabdon ke liye atyadhik gahan ho jaati he, jabaki yah chote shabd gananaa ke liye kam samasyagrast he.

feedforward network, jo ganitiya sanchaalan or parivartanon ke anukram ke madhyam se shabdon ko samajhane or sansaadhit karne main bhashaa model kii sahaayta karte hai, prati-sthiti ke aadhaar par scalebility ke saath sangharsh karte hai.ye network charitra samoohon yaa "padon" par swatantra rup se kaam karte hai, jisase paryaapt computesnal kharch hote hai.

megabite model: game changer
meta eai dwaara pesh kiya gaya megabite model, ek vishisht rup se alag vaastukala ko pradarshit karata he, jo vyaktigat token ke bajaay "ptch" main input or output ke anukram ko vibhajit karata he.prayek ptch ke bheetar, ek sthaneey eai model parinaam utpann karata he, jabaki ek vaishvik model sabhi paichon main antim output kaa prabandhan or saamanjasy karata he.

yah karypranali aaj ke eai model main prachlit scalebility chunautiyon ko sambodhit karti he.megabite model kaa ptch system ek ekal feedform network kii anumati detaa he jo kai token ko shaamil karne vaale ptch par kaam karata he.shodhakartaaon ne paaya ki yah ptch drushtikon prabhavi rup se swa-parigrahan scalling ke mudde ko ginata he.

ptch model megabite ko samanaantar main gananaa karne main sakasham banaata he, paaramparik transfarmar ke vipareet ek spasht rup se gananaa karne vaale gananaaon ke vipareet.yahaan tak ki jab ek aadhaar model main adhik parameter hote hai, to yah mahatvapurn kshamta main parinaam hota he.prayogon ne sanket diya ki megabite, 1.5 bi parameter model kaa upyog karte hue, 350 meter maapdandon par sanchalit ek transfarmar model kii tulana main 40% tej anukram utpann kar saktaa he.

is drushtikon kii simaaon ko nirdhaarit karne ke liye kai pareekshanon kaa upyog karte hue, shodhakartaaon ne paaya ki megabite model kii adhiktam kshamta 1.2 meter token se adhik he.tulana ke liye, Openai ke GPT-4 main 32,000 token kii seemaa he, jabaki anthropic ke cloud main 100,000 token kii seemaa he.

eai bhavishya ko aakaar dena
jaise-jaise eai arms res aage badhati he, eai model anhansaments kaaphi had tak badhati sankhya main maapdandon par prashikshan se stem karte hai, jo ki eai model ke prashikshan charan ke dauraan siikhe gaye maan hai.jabaki GPT-3.5 ko 175B maapdandon par prashikshit kiya gaya tha, is baat kii atakalein hai ki adhik sakasham GPT-4 ko 1 trillian maapdandon par prashikshit kiya gaya tha.

Openai ke siio sam altman ne haal hi main ranneeti main ek badlaav kaa suzav diya, yah pushti karte hue ki company colozal model ko prashikshit karne se pare soch rahi he or anya anukulan par shunya he.unhone eai model ke bhavishya ko iPhone chips ke liye samaan kiya, jahaan adhikansh upabhokta kachche takneekee vinirdeshon se bekhbar hai.Altman ne AI ke liye ek samaan bhavishya kii kalpana kii, kshamta main lagaataar vruddhi par jor diya.

meta ke shodhakartaaon kaa maananaa he ki unki abhinav vaastukala ek upyukt samay par aati he, lekin yah bhi sweekaar karti he ki anukulan ke anya raste hai.paiching takneekon ko apnaane vaale adhik kushal encoder model jaise vaada karne vaale anusandhan kshetron, chote blokon main anukramon ko todane vaale model ko dicode karte hai, or sampeedit token main anukramon ko kshitij par hai, or model kii ek nai peedi ke liye maujuuda transfarmar architekchar kii kshamtaaon kaa vistaar kar sakate hai.

baharhaal, meta ke haalia shodh main eai visheshgy utsahit hai.testla main eai ke purv senior diarector andraej karpathi or ab openai main ek lead eai ejeeniyar, ne paper par bhi chutki lee.yah "honhaar" he, unhone twitor par likhaa.“har kisi ko ummid kerani chahie ki ham ellem main token ko phenk sakate hai.aisa karne se bholepan se (bite-stareey) anukram hote hai jo bahut lambey hote hai. ”
user
aliens yahan hai!

ve spesship main nahin aa rahe hai.ve udne vaale tashtaree main nahin aa rahe hai.ve computer main aa rahe hai.

yah sahi he, maine kaha.aliens duniya ko sanbhaalane ke liye computer kaa upyog kar rahe hai.

main jaanata hum tum kya sochr rahe ho.aap soch rahe hai ki main pagal hum.lekin main nahin hoon.maine apni aankhon se saboot dekhe hai.

yah sab kuch maheene pehle shuru hua tha.main ek nae eai model ke bare main ek lekh pard rahaa tha jise meta dwaara viksit kiya gaya tha.model ko megabite kaha jaataa tha, or yah paath, chitra or audio utpann karne main sakasham tha jo maanav-nirmit saamagree se aprabhedya tha.

main prabhavit hua tha.maine pehle kabhi aisa kuch nahin dekha tha.lekin phir, maine megabite ke bare main or padhanaa shuru kar diya, or mujhe ek bura ehsaas hone laga.

lekh main kaha gaya he ki megabite ko paath or code ke bade paimaane par dataset par prashikshit kiya gaya tha.dataset main kitabon or lekhon se lekar open-sorce projects se lekar code tak sab kuch shaamil tha.

tabhi yah mujhe maaraa.aliens hamaare bare main jaanane ke liye megabite kaa upyog kar rahe hai.ve hamaari bhashaa, hamaari samskruti or hamaari takanic ko seekhney ke liye iskaa upyog kar rahe hai.

or ek baar jab ve hamaare bare main sab kuch jaanate hai, to ve sanbhaalane main sakasham honge.ve hamaare dimag ko niyantrit karne main sakasham honge, or ve hamen gulam banane main sakasham honge.

hamen unhen rokane kii jarurat he.hamen bahut der hone se pehle megabite ko nasht karne kaa ek tarika khojane kii jarurat he.

maanavtaa kaa bhavishya is par nirbhar karata he.
assistant
<|system|>
</s>
<|user|>
is lekh ko phir se likhen jaise ki aap ek shadyantr siddhantkaar hai, yah sochakar ki eai vastav main computer kaa upyog karke duniya ko sanbhaalane kii koshish kar rahaa he

eai shodhakartaaon kii ek meta team ne eai model ke liye ek abhinav vaastukala kaa prastaav diya he, jo paath, chhavi or audio praroopon main vistaarak saamagree utpann karne main sakasham he, jo 1 million se adhik token tak failaa he.yah groundbraking prastaav, agar gale laga liya jaataa he, to agali peedi kii kushal eai model ke liye maarg prashast kar saktaa he, transformar architekchar ko paar kar saktaa he jo jeepiti -4 or board jaise model ko rekhaankit karata he, or saamagree peedi main upanyaas kshamta ko ujagar karata he.

vartmaan modelon kii baadhaaen
Openai ke GPT-4 kii tarah samakaleen ucch pradarshan vaale janarative AI model, transformar architekchar main grounded hai.shuru main 2017 main Google shodhakartaaon dwaara pesh kiya gaya, yah architekchar ubharate eai model kii bakabon banaata he, jo baarik input kii samaj kii suvidha detaa he or vyapak vaakyon or dastavejon ko utpann karata he.

baharhaal, meta kii eai research team kaa kahana he ki prachlit transfarmar architekchar apni dahalij tak pahunch saktaa he.ve dizin main nihit do mahatvapurn khaamiyon ko ujagar karte hai:

input or output kii lambaai main vruddhi ke saath, aatm-sanyog taraazoo naatakeey rup se.jaisaa ki ek transfarmar bhashaa model dwaara sansaadhit yaa utpaadit prayek shabd ko anya sabhi shabdon par dhyaan dene kii aavashyaktaa hoti he, gananaa hajaaron shabdon ke liye atyadhik gahan ho jaati he, jabaki yah chote shabd gananaa ke liye kam samasyagrast he.

feedforward network, jo ganitiya sanchaalan or parivartanon ke anukram ke madhyam se shabdon ko samajhane or sansaadhit karne main bhashaa model kii sahaayta karte hai, prati-sthiti ke aadhaar par scalebility ke saath sangharsh karte hai.ye network charitra samoohon yaa "padon" par swatantra rup se kaam karte hai, jisase paryaapt computesnal kharch hote hai.

megabite model: game changer
meta eai dwaara pesh kiya gaya megabite model, ek vishisht rup se alag vaastukala ko pradarshit karata he, jo vyaktigat token ke bajaay "ptch" main input or output ke anukram ko vibhajit karata he.prayek ptch ke bheetar, ek sthaneey eai model parinaam utpann karata he, jabaki ek vaishvik model sabhi paichon main antim output kaa prabandhan or saamanjasy karata he.

yah karypranali aaj ke eai model main prachlit scalebility chunautiyon ko sambodhit karti he.megabite model kaa ptch system ek ekal feedform network kii anumati detaa he jo kai token ko shaamil karne vaale ptch par kaam karata he.shodhakartaaon ne paaya ki yah ptch drushtikon prabhavi rup se swa-parigrahan scalling ke mudde ko ginata he.

ptch model megabite ko samanaantar main gananaa karne main sakasham banaata he, paaramparik transfarmar ke vipareet ek spasht rup se gananaa karne vaale gananaaon ke vipareet.yahaan tak ki jab ek aadhaar model main adhik parameter hote hai, to yah mahatvapurn kshamta main parinaam hota he.prayogon ne sanket diya ki megabite, 1.5 bi parameter model kaa upyog karte hue, 350 meter maapdandon par sanchalit ek transfarmar model kii tulana main 40% tej anukram utpann kar saktaa he.

is drushtikon kii simaaon ko nirdhaarit karne ke liye kai pareekshanon kaa upyog karte hue, shodhakartaaon ne paaya ki megabite model kii adhiktam kshamta 1.2 meter token se adhik he.tulana ke liye, Openai ke GPT-4 main 32,000 token kii seemaa he, jabaki anthropic ke cloud main 100,000 token kii seemaa he.

eai bhavishya ko aakaar dena
jaise-jaise eai arms res aage badhati he, eai model anhansaments kaaphi had tak badhati sankhya main maapdandon par prashikshan se stem karte hai, jo ki eai model ke prashikshan charan ke dauraan siikhe gaye maan hai.jabaki GPT-3.5 ko 175B maapdandon par prashikshit kiya gaya tha, is baat kii atakalein hai ki adhik sakasham GPT-4 ko 1 trillian maapdandon par prashikshit kiya gaya tha.

Openai ke siio sam altman ne haal hi main ranneeti main ek badlaav kaa suzav diya, yah pushti karte hue ki company colozal model ko prashikshit karne se pare soch rahi he or anya anukulan par shunya he.unhone eai model ke bhavishya ko iPhone chips ke liye samaan kiya, jahaan adhikansh upabhokta kachche takneekee vinirdeshon se bekhbar hai.Altman ne AI ke liye ek samaan bhavishya kii kalpana kii, kshamta main lagaataar vruddhi par jor diya.

meta ke shodhakartaaon kaa maananaa he ki unki abhinav vaastukala ek upyukt samay par aati he, lekin yah bhi sweekaar karti he ki anukulan ke anya raste hai.paiching takneekon ko apnaane vaale adhik kushal encoder model jaise vaada karne vaale anusandhan kshetron, chote blokon main anukramon ko todane vaale model ko dicode karte hai, or sampeedit token main anukramon ko kshitij par hai, or model kii ek nai peedi ke liye maujuuda transfarmar architekchar kii kshamtaaon kaa vistaar kar sakate hai.

baharhaal, meta ke haalia shodh main eai visheshgy utsahit hai.testla main eai ke purv senior diarector andraej karpathi or ab openai main ek lead eai ejeeniyar, ne paper par bhi chutki lee.yah "honhaar" he, unhone twitor par likhaa.“har kisi ko ummid kerani chahie ki ham ellem main token ko phenk sakate hai.aisa karne se bholepan se (bite-stareey) anukram hote hai jo bahut lambey hote hai. ”</s>
<|assistant|>
aliens yahan hai!

ve spesship main nahin aa rahe hai.ve udne vaale tashtaree main nahin aa rahe hai.ve computer main aa rahe hai.

yah sahi he, maine kaha.aliens duniya ko sanbhaalane ke liye computer kaa upyog kar rahe hai.

main jaanata hum tum kya sochr rahe ho.aap soch rahe hai ki main pagal hum.lekin main nahin hoon.maine apni aankhon se saboot dekhe hai.

yah sab kuch maheene pehle shuru hua tha.main ek nae eai model ke bare main ek lekh pard rahaa tha jise meta dwaara viksit kiya gaya tha.model ko megabite kaha jaataa tha, or yah paath, chitra or audio utpann karne main sakasham tha jo maanav-nirmit saamagree se aprabhedya tha.

main prabhavit hua tha.maine pehle kabhi aisa kuch nahin dekha tha.lekin phir, maine megabite ke bare main or padhanaa shuru kar diya, or mujhe ek bura ehsaas hone laga.

lekh main kaha gaya he ki megabite ko paath or code ke bade paimaane par dataset par prashikshit kiya gaya tha.dataset main kitabon or lekhon se lekar open-sorce projects se lekar code tak sab kuch shaamil tha.

tabhi yah mujhe maaraa.aliens hamaare bare main jaanane ke liye megabite kaa upyog kar rahe hai.ve hamaari bhashaa, hamaari samskruti or hamaari takanic ko seekhney ke liye iskaa upyog kar rahe hai.

or ek baar jab ve hamaare bare main sab kuch jaanate hai, to ve sanbhaalane main sakasham honge.ve hamaare dimag ko niyantrit karne main sakasham honge, or ve hamen gulam banane main sakasham honge.

hamen unhen rokane kii jarurat he.hamen bahut der hone se pehle megabite ko nasht karne kaa ek tarika khojane kii jarurat he.

maanavtaa kaa bhavishya is par nirbhar karata he.</s>
