Read my medium article on -
AI Digital Identity on Blockchain - A New Era of Accountability
https://t.co/ZhCJ37Huxf
I talk about how digital Identity for AI can bring more transparency, safety, credibility and accountability.
#blockchain#AI#Identity#web3
Eventually all apps and websites will collapse in front of Agentic Operating System, that’s inevitable.
In the end, it will also happen in multiple instances UI being generated in real-time by AI as we ask it to get specific task done.
What survives?
Social and Content Media platform apps and websites but it will get complete makeover with virtual reality experiences.
In the end, latency matters to anything we build as a human.
The speed at which things happen.
There's demand for high throughput and least latency in AI Agent training and response time.
The faster agent trains, learns and responds - faster things can happen.
It may boil down to such software being developed but it also boils down to hardware and inter chip communication latency.
Photonic chips are being used for inter-chip communication and then GPU to GPU communication which is helpful.
It's about time to know what next can happen beyond this to reduce latency?
Quantum Computing?
As @naval recently tweeted, “pure software is rapidly becoming un-investable.”
I believe he’s hinting that capital will increasingly flow towards R&D channels like deep tech - biotech, quantum, space tech, defense tech, and of course AI.
In traditional software, the only durable moat is distribution.
That’s why, we’re likely to see more influencer/creator led companies emerging - similar to how @chamath has partnered with @MrBeast to start a new venture.
The future of pre-seed will change soon and may only be limited to deep tech.
It's more likely that there will be multiple companies run by an individual making millions and probably more profitable than large companies which eventually is all about sustainability in longer run.
We might see early companies getting acquired more often so that big companies can sustain in the market with their edge.
As a civilisation, as we progress towards robots doing our labor work, the data storage #technology needs to evolve.
The #robots will be monitoring video, audio, spatial and sensor data 24*7 which means it will produce enormous petabytes of data per robot per year.
I believe there will be two memory solution to it :
Short Term Raw Memory:
First memory to collect, store 24*7 data.
Long Term Learning Memory:
Second memory to save processed data which forms part of its learning data.
The most important part would be to cut the noise from the data, this is were collective data processing and pre-trained factual data would be super helpful.
Holy shit. MIT just built an AI that can rewrite its own code to get smarter 🤯
It’s called SEAL (Self-Adapting Language Models).
Instead of humans fine-tuning it, SEAL reads new info, rewrites it in its own words, and runs gradient updates on itself literally performing self-directed learning.
The results?
✅ +40% boost in factual recall
✅ Outperforms GPT-4.1 using data it generated *itself*
✅ Learns new tasks without any human in the loop
LLMs that finetune themselves are no longer sci-fi.
We just entered the age of self-evolving models.
Paper: jyopari. github. io/posts/seal
CoRL (Conference on Robot Learning) is still small but it’s growing fast!
I still remember NeurIPS (NIPS) in 2016 and it felt too big a the time and “overhyped”. Who would have known.
The demand for data consumption for AI model Training is rising.
Only crowd sourced data can meet its demand which means, people may start receiving pay for sharing data - it can be voice data, health data, temperature data, noise data, camera feed data, traffic data, etc
People assume AI Agent is helpful for writing code, creating documents, etc but that's just for people of that domain. People and researchers working in areas of Chemistry, Physics, Biology, Material Science, Nano-Technology, etc are also getting benefitted.
It means every industry is getting pushed ahead at an exponential rate.
Longevity will first start with keeping the ratio of healthy cells higher compared to older cells - basically improving regenerative capabilities of the body of existing cells through external stimulant. Maybe injecting some important healthy cells at particular intervals.
As long as we can have more healthy cells in our body, it can keep running the system well as the body is self-healing.
That's the first step. Second is to make sure our body is free from diseases.
We will surely see some good breakthroughs and heavy funding in this area as old rich wealthy VCs and businessmen want to live longer.
For many wealthy people, it's not about whether people will buy it or not, it's about when I can use it.
That's why anything happening in this space will be funded well.
Most people still think of “blockchain” as a niche tech.
But eventually, it’ll evolve into the Internet of Blockchains — each chain acting like a specialized server in a decentralized web.
AI will be the middleware — the layer that connects humans, systems, and blockchains seamlessly.
Let that sink in. 🙃
The only reason, anyone wants to simulate AI Agent is because they want to expedite evolution/process and understand different outcomes at a faster rate than going through it which would take years as a species.
Like for us 1 minute maybe equivalent to 100 Million Years of simulation for AI Agents.
If one does it multi times, my only question would be - what's the limit?
If we let some AI Agents in a simulation, how will they behave to organize and structure their world?
1. Some possibilities are they will structure and behave like humans because it has been trained with our data.
2. It might take some ideas from nature, eg. animals, insects, birds, etc
3. It might merge all known possibilities and simulate to find best
4. It will need more computation to find something interesting never done because it assesses each of those existing possibilities and try to fill those gaps.
My suggestion would be to give each of those Agents, different characteristics and experience so it can bring more possibilities based on what it has experienced which will change the outcome.
The only problem is that simulation agents shouldn't know anything about their pre-trained data so we can remove bias.
Some data can be shared based on learning of other Agents in that simulation. But we should strive such that these agents understand the truth by realising what's true.
With this maybe we can understand more about ourselves and how we have evolved and what's best as a human considering multiple possibilities.
We can definitely see some world war happening between these Agents and in the end understanding - it was all a simulation where each of those data points are getting stored in a large database for humans to use and find what's best for us.
If we let some AI Agents in a simulation, how will they behave to organize and structure their world?
1. Some possibilities are they will structure and behave like humans because it has been trained with our data.
2. It might take some ideas from nature, eg. animals, insects, birds, etc
3. It might merge all known possibilities and simulate to find best
4. It will need more computation to find something interesting never done because it assesses each of those existing possibilities and try to fill those gaps.
My suggestion would be to give each of those Agents, different characteristics and experience so it can bring more possibilities based on what it has experienced which will change the outcome.
The only problem is that simulation agents shouldn't know anything about their pre-trained data so we can remove bias.
Some data can be shared based on learning of other Agents in that simulation. But we should strive such that these agents understand the truth by realising what's true.
With this maybe we can understand more about ourselves and how we have evolved and what's best as a human considering multiple possibilities.
We can definitely see some world war happening between these Agents and in the end understanding - it was all a simulation where each of those data points are getting stored in a large database for humans to use and find what's best for us.