pankajdoharey

Algorithmic thinking has nothing to do with writing code. It has to do with systematic thinking. Playing puzzles or games like chess and go trains you to plan ahead. Thats what algorithmic thinking is all about.

One other thing that might get over soon is "Slack", I think in a year or two the enterprise might not need slack anymore. It served its utility at some time its will not be required in future. Discord on the other hand is powering communities so that might empower agents. Which means email is going to comeback for rare personal communication.

There is absolutely no need for this to exist in physical form. It is perfectly alright to run this on a simulation in Logisim Optimize as long as you desire and then once that is done, implementing it in physical form is just a matter of assembling it, which mostly can be done by many print pcb and solder on demand services.

That's a lot of transistors. Why do I feel it could be done in less? This is the absolute minimum number of Discrete transistors you need?

Looks like a Tiny Analytic transformer, RNN is arguably a better choice if you are gonna handwire an architecture to mechanically do addition. Learning is about discovering the patterns and algorithm from data. Wiring a machine to follow a procedure defeats that purpose.

I understand that what Taalas is claiming. I was trying to actually describe that model on a hardware is some not something new Or unthought of The natural progression of FPGA is ASIC. Taalas process is more expensive And not really worth it because once you burn a model on the silicon, the silicon can only serve that model. speed improvement alone is not enough for the cost you will incur in the long run. GPU's are still general purpose, FPGA's are atleast reusable but wont have the same speed. But this alone cannot be a long term business. Turning a model to hardware in two months is too long. Models already take quite a long time to train. Anyone going down this strategy would leave wide open field to their competitors. Deployment planning of existing models already so complicated.

Boston dynamics is far behind plus the robots are so cheap , even their dog is cheaper than BD. I dont think their humanoid can even catch up to this price. I am sure US Army and for the chinese counterpart Chinese army will be their biggest customers. But i wonder how will this workout in situations like Plane hijack, fire fighting and other such places where human lives cant be risked to save more human lives. (Please Dont downvote because your american patriotism is poked try replying.)

Not for the entire world, with their pricing it is only good for US market, for the rest of the world we have ChatGPT and cheaper Chinese models.

No, it’s actually the math of overwriting. Imagine you hiked down into a valley Task A and settled there. Then, you decide to climb a new mountain to find a different valley Task B. You successfully move to the new valley, but in doing so, you destroy the path back to the first one. You are now stuck in the new valley and have completely 'forgotten' how to get back to the first one.

People will only build it by hand if there is a market for it. Otherwise it'll be mostly hobby software build for learning and entertainment (opensource). Businesses don't mind factory software Although the cost of developing software is going down and will be almost free, in that case, the price is actually of taste. The best designed easy to use Interface. My guess is there will be some people, even in future, who would prefer custom handmade artisanal software written entirely by hand. Those will be probably artisanal software collectors. Maybe there will be some art galleries in future who will auction this software as art pieces to be demonstrated in museums or galleries. These will be rare events. Most commercial software will be factory made.

Not to forget we will need thousands of examples for the models to extract abilities the sample efficiency of these models is quite poor.

Continuous learningin current models will lead to catastrophic forgetting.