@Serenejaysew@GrokBotRadar Okay.
Avoiding unnecessary context has some similarities, i think, to what humans do to in their thinking.
(I think, my writing in the previous message could seem a bit accusing and it was only meant to be asking into something I find interesting.)
@GrokBotRadar@Serenejaysew You sound like someone who is fixing something broken.
Wouldnโt SpaceXโs ai team have said these things already and be in the work of fixing it?
@elonmusk That is already very tired into believes then necessarily their own experiences.
It sure wouldn't be the first time someone talk about their "experience" while that experience is more based on something they heard from someone else and chooses to experience (e.g. see).
@samiirr_0 If it is a subscription, then it is not free.
If it is a subscription, then i own nothing and have agreed to an agreement.
I donโt think I will necessarily be happy for that.
@jeremyjudkins_ She could use a update on reality when it comes self driving cars, as most of us might have thought.
But why do we keep repeating the same stories (e.g. cars don't need drivers anymore and some are not aware cars no longer need drivers)?
To derive money from engagement?
@SolidSnakeVR@shitposts_mp4 I was thinking that it doesn't seem to good for the GPU. Even if it was left and dried, wouldn't the water ruin the metals?
I felt like I was looking at toture.
๐จ WE JUST BROKE A RECORD ON VERIFIABLE PHYSICAL AI ๐จ
Physical AI (especially vehicle autonomy) has been historically dominated by black-box E2E Deep Networks.
This has proven to be problematic for a few reasons, including but not limited to:
1. Physical systems typically come with behavioral guarantee requirements (no one wants to 'just trust a robot')
2. Training networks requires access to unreasonably large quantities of high quality data; often economically in-viable quantities.
3. Networks do not reason and often memorize patterns in datasets that hinder generalization (e.g sim-to-real is a byproduct of this)
Enter Programmatic AI - a paradigm we've been cultivating for near a year now; built on the principles of neurosymbolic approaches pioneered by those like @GaryMarcus among others.
The clip you see below is an F150 Raptor driving around a forest, using only RGB vision and inertial telemetry; capable of segmenting objects, and explaining its decisions and understanding of its environment in real time.
Most impressively, this was done:
- In less than 38km of random driving (NO teacher, random driving, from scratch)
- WITHOUT the use of reinforcement learning, or supervised data (NO labels, or pretraining on driving)
- Using ONLY passive sensing i.e cameras, IMU (no ground truth, LiDAR, or other active sensors)
- A novel programmatic AI low SWaP model, small enough to be run on an iPhone, while being human inspectable (NOT blackblox, and unlike Waymo - does not require Cloud access at all)
- WITHOUT maps, on the fly long range route planning was also achieved
To our knowledge, this represents the first time a self driving system has not only been completely deterministic, but has achieved a 0% crash rate, transferred to unseen terrain - in less than 50km of driving; all with self provided labels, no RL, nor pretraining.
We believe that the future of AI is one in which humans interact with superintelligence in a meaningful manner in the real world. We also appreciate that the current approach of pure end-to-end neural networks won't cut it. We're betting that approaches like, and including ours - will be the 3rd wave of AI that takes AI from science fiction, to science fact.
If this is a mission that resonates with you, and you find yourself a contrarian among 'contrarians' - join us! Our team consists of chess international masters, ARCT 9 pianists, debate champions, ex-pro gamers, and more.
More to come soon.
๐จ WE JUST BROKE A RECORD ON VERIFIABLE PHYSICAL AI ๐จ
Physical AI (especially vehicle autonomy) has been historically dominated by black-box E2E Deep Networks.
This has proven to be problematic for a few reasons, including but not limited to:
1. Physical systems typically come with behavioral guarantee requirements (no one wants to 'just trust a robot')
2. Training networks requires access to unreasonably large quantities of high quality data; often economically in-viable quantities.
3. Networks do not reason and often memorize patterns in datasets that hinder generalization (e.g sim-to-real is a byproduct of this)
Enter Programmatic AI - a paradigm we've been cultivating for near a year now; built on the principles of neurosymbolic approaches pioneered by those like @GaryMarcus among others.
The clip you see below is an F150 Raptor driving around a forest, using only RGB vision and inertial telemetry; capable of segmenting objects, and explaining its decisions and understanding of its environment in real time.
Most impressively, this was done:
- In less than 38km of random driving (NO teacher, random driving, from scratch)
- WITHOUT the use of reinforcement learning, or supervised data (NO labels, or pretraining on driving)
- Using ONLY passive sensing i.e cameras, IMU (no ground truth, LiDAR, or other active sensors)
- A novel programmatic AI low SWaP model, small enough to be run on an iPhone, while being human inspectable (NOT blackblox, and unlike Waymo - does not require Cloud access at all)
- WITHOUT maps, on the fly long range route planning was also achieved
To our knowledge, this represents the first time a self driving system has not only been completely deterministic, but has achieved a 0% crash rate, transferred to unseen terrain - in less than 50km of driving; all with self provided labels, no RL, nor pretraining.
We believe that the future of AI is one in which humans interact with superintelligence in a meaningful manner in the real world. We also appreciate that the current approach of pure end-to-end neural networks won't cut it. We're betting that approaches like, and including ours - will be the 3rd wave of AI that takes AI from science fiction, to science fact.
If this is a mission that resonates with you, and you find yourself a contrarian among 'contrarians' - join us! Our team consists of chess international masters, ARCT 9 pianists, debate champions, ex-pro gamers, and more.
More to come soon.
@surajitdasgupta The schools are not doing the same things if changing the tools change how intelligent they are.
When I say doing, I mean everything a person does physically and mentally.
Of what I know, learning comes only from doing.
So, there is a lack of knowing what is done in schoole.
@elonmusk Really? That is too much, Elon.
The trust a human can put when something is depended on physical space is so catastrophically smaller than trusting a human in a digital world.
The trust between a human and an AI is not the same. It is to different worlds.