Tesla FSD v12 Rollout - FIRST THOUGHTS! (Ep. 752)
Summary
TLDRThe video discusses Tesla's new FSD software version 12, which completely shifts to a neural net approach for full self-driving capabilities instead of relying on rules-based coding. Early testing results from a non-Tesla employee show impressive performance, but also times when FSD gave up control due to low confidence. The key challenge now is improving FSD through massive amounts of data, compute power, and enhancements so it rarely lacks confidence to handle complex situations. If Tesla can achieve major leaps in FSD capabilities this year, it would be an exciting advancement towards unsupervised autonomous driving.
Takeaways
- 😲 Tesla rolled out FSD version 12 software to select non-employees, marking a major milestone
- 🧠 FSD v12 shifts from rules-based to neural net end-to-end approach for entire planning process
- 🚗 This is monumental and ambitious, as driving is very complex
- 🤔 FSD v12 needs to drive as well or better than prior versions with guardrails to be safe
- 👍 Early FSD v12 results are promising enough for limited external testing
- ❓ Current weaknesses seem to be lower confidence in more complex situations
- 🌄 Goal is to improve confidence across most driving scenarios
- 📈 Rapid improvements expected as more data and compute is added
- 🔬 FSD team laser focused on data, compute, model improvements this year
- 😃 Exciting to see how fast FSD v12 abilities advance throughout 2022
Q & A
What is Tesla's FSD version 12?
-FSD version 12 is Tesla's upcoming rewrite of their full self-driving software. It shifts their approach from rule-based coding to an end-to-end neural net.
Why did Tesla previously rely on rule-based coding for FSD?
-The rules provided guard rails and instructions on how to drive safely. Without the rules, the car could potentially do something dangerous.
What are the challenges in moving from rule-based to neural nets?
-The neural nets need to handle unguarded situations and new scenarios without thousands of hardcoded rules. They need to drive better and safer than with the rules.
When did Tesla decide to go all in on the end-to-end approach?
-Around spring last year, after early testing showed promising results with the end-to-end neural net approach.
Why does FSD version 12 need massive amounts of data and compute?
-To train the neural nets to handle driving safely without relying on hardcoded rules, which requires analyzing millions of video clips.
What are some initial weaknesses observed in FSD version 12?
-It sometimes gives up with red hands when confidence drops below a threshold, rather than trying to push through low confidence situations.
How can Tesla improve FSD version 12?
-By feeding more data to improve the neural nets, adding more compute power, and iterating to increase driving ability and confidence.
What is the goal for FSD version 12 this year?
-To improve it dramatically, so it rarely gives up control and can confidently handle complex driving situations.
When could Tesla potentially reach full self-driving capabilities?
-It's unclear, but rapid improvements this year could demonstrate the path and timeline to unsupervised, full self-driving.
Why is it exciting that FSD version 12 was given to non-employees?
-It shows Tesla has enough confidence in the initial version to let people test it, despite still needing major improvements.
Outlines
🏎️ Tesla FSD Rewrite Overview
Paragraph 1 provides background on Tesla's full self-driving software version 12, which completely rewrites it to use neural nets instead of rules-based coding. This is a monumental shift allowing the car to figure out driving on its own. Tesla let a non-employee test drive it, showing confidence in the rewrite. The challenge is getting the neural nets to drive better without relying on hardcoded safety rules.
😕 Current Weaknesses and Path Forward
Paragraph 2 discusses version 12's current weaknesses and path to improvement. It still sometimes lacks confidence to handle complex situations. Tesla needs to train the neural nets with more data and compute power to dramatically improve driving ability over time. The goal is to increase confidence levels so it rarely needs human intervention.
Mindmap
Keywords
💡Full Self-Driving (FSD)
💡neural net
💡end-to-end
💡planning
💡rules
💡confidence threshold
💡data
💡compute
💡improvements
💡robotaxi
Highlights
Tesla rolled out its FSD version 12 software to a very small number of non-tesla employees.
FSD version 12 is Tesla's upcoming rewrite of their full self-driving software.
In version 12 Tesla shifted their approach to an end-to-end neural net approach.
Tesla started to experiment with an end-to-end full self-driving approach using only neural nets.
Early results were promising so Tesla decided to go all in on the end-to-end neural net approach.
The challenge is getting to a first version that's good enough for customers to actually use.
Version 12 needs to drive better and more safely than previous versions with hardcoded rules.
Version 12 still has some guard rails like handing control to the driver when confidence drops.
Tesla needs to improve neural nets through more data and compute to boost confidence.
Tesla needs to improve FSD performance through data, compute, and neural net enhancements.
FSD 12 handled many situations amazingly but sometimes gave up with red hands.
Tesla needs to improve FSD confidence so it rarely gives up control.
Tesla's challenge is to quickly improve FSD to handle complex situations confidently.
Hopefully we'll see fast improvements in FSD version 12 performance this year.
It will be exciting to see FSD version 12 progress and improvements this year.
Transcripts
hey it's Dave so yesterday there was big
news for Tesla FSD or full stelf driving
FSD version 12 if you don't know already
is Tesla's upcoming rewrite of their
full self-driving software previously
Tesla relied on over 300,000 lines of
code to largely put guard rails around
FSD and give instructions on how to
drive but in version 12 Tesla shifted
their approach to an endtoend neuronet
approach which means rather than
dictating how the car drives by
instructions Tesla is letting a neuronet
AI determine how to drive on the Fly
I'll explain more on this and why it's
important but last night Tesla rolled
out its FSD version 12 software to a
very small number of non-tesla employees
and one of them was Omar from hullar's
blog who covers Tesla on X in this video
I wanted to share my reaction and
thoughts to his initial drives with
fsd2 all right so first off it's a huge
accomplishment for Tesla to give a
non-employee fsc2 it means that they are
confident enough with the current
iteration to let people see it drive and
now this is a big deal because of how
Monumental the fsd2 software rewrite is
in fact it's something that's never been
done before at this scale or ambition at
least that I know of where something as
difficult as driving has shifted from
using horis sixs or rule-based coding to
a neuronet end-to-end approach this is
impressive and in order to understand
why we need to understand why the
heuristic code was there in the first
place now over the years Tesla shifted
their perception code to neuron Nets and
it's already been running almost all
neuron nets for a while perception means
the part of the code that perceives the
world or in other words analyzing mostly
video along with how the car is moving
but there's another big part of driving
and that is planning even if you see the
world around you you still need to
figure out how to navigate that world
and Tesla was trying to cautiously move
over that part of the code to more
neural Nets and they've been doing so
it's just that it's really hard to get
rid of all those rules you put in place
and it's because the rules are there for
a good reason without those rules the
car might do something really dangerous
and get into an accident and there are a
lot of rules countless and for a ton of
situations here's my speculation as
Tesla tried to move rule-based coding to
neural Nets with planning they
encountered a lot of challenges and it
was slower than what they hoped for take
away some rules but then it introduces
these unguarded situations that neuron
Nets need to handle but what if they
can't handle those situ
or there's new situations that are
created by the absence of the rules that
you took away it's all extremely
complicated to say the least but we know
from isaacson's biography of Elon Musk
that over a year ago Tesla started to
experiment with an endtoend full
self-driving approach forget coding all
the rules just use neural Nets not just
for perception but also for the complete
planning process in other words get rid
of all the guard rails and see if neuron
Nets by themselves can figure out how to
drive safely and better than any
previous FSD version as Tesla tested
this out the early results were
promising and the more they tested they
realized that this was the way to go
they decided to go all in earlier last
year my guess was probably sometime
around spring I think but then you got
this Monumental task of building and
training a massive neuron net with the
right data to make sure it could drive
safely the video data they need to do
that is just massive and the compute
required is massive too that's why Tesla
increased their compute goal last year
is so that they can go all in with
endtoend neuron nets for FSD now the Big
Challenge in my opinion is to get to a
first version that's good enough for
customers to actually use the reason why
this is so hard is because of how big
the jump is from rule-based FSD planning
to purely neuronet based planning the
jump is huge and the new neuronet
planning approach needs to be as good as
the old code-based one or else it's not
safe to roll out to customers here's
another way to look at it you've got
thousands of rules R in place to make
sure the car doesn't get into accidents
and if you take them away then the car
really needs to know how to drive by
itself without those rules all the
crutches come off and then you're left
with the true driving ability of the AI
but that's hard because why did Tesla
need all of those rules in the first
place it's because it needed those rules
to prevent dangerous mishaps so in order
to get rid of all those rules then FSD
needs to go through a huge jump in
ability probably the biggest jump in its
history it needs to somehow get to a
place where it can drive without
thousands of hardcoded rules and figure
out driving from millions of video clips
and figure it out in such a way that I
can drive better and more safe than it
did with all those rules it almost seems
like an impossible task that's why any
customer roll out of FSC version 12 is
such a big deal even though the first
version rolled out to Hol Mar's blog and
a few others is far from perfect it
shows that Tesla has figured out its
endtoend neural network approach enough
where it can handle most situations now
the question is what is version 12's
current weaknesses and how long will it
take to fix those and ultimately how
long will it take to get to unsupervised
FSD and robotaxi now on the second
question I think we need to see more of
FSD version 12 progress over the next
several months to get a better feeling
of how fast Tesla is able to improve
version 12's driving abilities my hunch
is that we're going to see sizable leaps
and abilities as Tesla brings on larger
compute clusters for training I think
they'll need all the compute training
that they're bringing online this year
and probably even more but how about
current weaknesses in Omar's tweets he
did share how FSD handled a lot of
situations amazingly however there are
times where it would just stop with red
hands and basically give up now I
interpret that as there's a confidence
level where the neuron Nets have a
certain level of confidence to handle
situations and if that confidence level
drops below a certain threshold then
it's taught to basically give up and
hand control over to the human it's a
safety feature where they don't don't
want the neural Nets to hallucinate and
just make up a path to drive when it has
low confidence in other words there are
still guard rails with FSD it's just
that they aren't thousands of rule-based
code rather it's probably some training
to the model where when confidence level
drops below a certain threshold then it
shouldn't push it and continue in that
situation and that's a reasonable rule
especially for the time being now the
challenge is going to be to increase FSC
version 12's driving ability so it
rarely ever drops below the threshold of
confidence in other words Tesla is going
to need to feed a lot more data into the
neuron Nets and also improve the neural
Nets to the point where it can drive
dramatically better than this early
version and so much so that it's night
and day Tesla version 12 needs to get to
a point where it rarely ever holds up
red hands and gives up control to the
driver rather the neuron Nets need to
improve to a point where it's confident
in practically every driving situation
even complex ones and that's the
challenge Tesla's FSD team has this year
is to radically and quickly improve
fst's abilities through data training
and improvements with their neural Nets
it's a challenging problem for sure but
one that I think has a clear path on how
to improve it they basically need a lot
of data they need a lot of compute and
they need a lot of improvements right to
their neural Nets that's what Tesla's
FSD team is laser focused on this year
and it's going to be exciting to see the
results hopefully we'll see some fast
improvements and some amazing
performance out of FSD version 12 this
year all right take care everyone we'll
see you guys my next video
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