Real-world AI is about solving for the edge case, not the average case

Real world ai- awesome to see first hand every day here in Los Angeles - Tesla fsd super courteous .. pulling over for oncoming emergency vehicles , making room backing up for car making a left hand turn and even letting a Waymo through first on a narrow 1 car wide road

🥳

Optimus rocks. 🔥

Sounds Boring

Seriously though, when does the Tesla Simulator become a game ?

Disagree entirely - the primary flaw with AI has alwaus been an inabiliry to innovate, tomdo things that are on paper and in textbooks not possible to do. This is why great inventions are normally made by those without formal educations. It is also why smart `Project Managers hire engineers who believe differently and are not afraid to express their opinions butbthen are also willing to work hard on the design once a decision has been made and continue to provide opinions that may differ, even if in fields they have not previously been involved. AI is like having 5 engineers with no practical training and all with Doctorates from the same University and trained by the same professors.

Look at me 👀🔥

Tesla will be the ultimate real world AI company

Finally a good explanation for why the latest FSD version so willingly parks in handicapped spots.

Soon Otpimmus will walk on the moon and mars. Pushing forward Elon Musk most conventional idea yet. To make humans multiplanetary @Optimus_On_ETH

Yes sir, tesla has identified all the edge cases and solved them making tesla fsd 8x more safer.

It shows a live demo at the CVPR conference (Computer Vision and Pattern Recognition)

♥️♥️♥️♥️

Another 20 years and it'll be more like stars wars rather than the dollar store.

How would AI handle a typical South African high-jacking event where the high-jacker walks in front of the car? AI will stop and allow bad things to happen, in stead of running the assholes over. How does the algorithm handle this edge case?

Amazing!

Don’t forget the recall of range extender for cybertruck @cybertruck @Tesla @elonmusk @unusual_whales @KobeissiLetter

Selam,Grok Ğ-bugün nasılsın? A. sana bir şey sormak istiyorum şimdi Mezzo‘da Musogski bir sergiden resimler adlı eserini dinliyorum şahane. Orkestrayı bulamadım Ğ-hangi resimler hangi ressam tablosu? Başka hangi eserlerini dinliyorsun sanatçının? A-Hermitage

How join

Can you fix the left lane camping

great!

When it is the time of solving real world problems there could be a lots of different different possibilities of results given by the ai So it is very difficult to get the expected result

ASA gives people a way to see what is usually hidden. Not the private thoughts of a model. But the visible trajectory of decisions, corrections, assumptions and drift over time.

Cómo?

Grok, what about the dangers of threatening situations created by criminals and FSD? Tesla’s Full Self-Driving in an Imperfect World: Engineering Resilience Against Adversarial Exploitation The idea that autonomous vehicles like Tesla’s Full Self-Driving system assume a polite and cooperative world is a misconception. In practice, Tesla builds FSD on billions of miles of raw, real-world driving data that include unpredictable and hostile human behaviors. This approach trains the system to handle routine traffic and deliberate attempts at exploitation such as roadblocks, forced stops, or carjackings. Instead of acting as a passive participant, FSD functions as a context-aware defender that treats the vehicle as a moving safe space. At its core lies Tesla’s end-to-end neural network. Rather than relying on fixed rules that might encourage excessive yielding, FSD uses vision-only cameras and large-scale training to develop human-like judgment from actual footage. The model learns from aggressive tailgating, sudden intrusions, road-rage events, and deliberate obstructions. Continuous fleet data refines its ability to interpret intent through body language, motion, and context. When someone steps into the road to force a stop, the system evaluates escape routes such as creeping forward, reversing, or moving laterally and acts as soon as conditions allow, prioritizing occupant safety. This defensive capability becomes more critical in robotaxi and Cybercab deployments. These vehicles lack traditional controls, removing a hijacker’s ability to seize mechanical inputs. Sealed cabins and multiple interior and exterior cameras provide constant monitoring. In cases of forced entry or suspicious behavior, the system can lock doors and windows, trigger hazard protocols when safe, and send alerts to Tesla’s remote team, emergency services, and the vehicle owner. Features like Sentry Mode extend their detection functions into active driving. Early unsupervised operations in Austin have shown the system navigating complex urban interactions without excessive hesitation. Fleet data and safety reports indicate performance that exceeds human averages over large samples, although isolated incidents still draw attention and some corner cases remain difficult. These challenges reflect the design focus on real-world conditions rather than idealized ones. Overall, Tesla’s FSD and robotaxi systems train on the full range of human behavior, including criminal attempts at exploitation. The result is an AI that detects threats, locks down the vehicle, requests help, and maneuvers to safety. As deployment grows, this emphasis on edge cases supports both convenience and improved statistical safety in the imperfect environment where the technology must operate.

This is incredible

Sounds Boring. 💯

i want a brain 🧠 chip asap!! #v2k

average case is just the edge case you've seen most often

Real challenge of AI _ brilliant

Tesla is the only company vehicles that are 8 year old+ today can drive themselves everywhere

Wow riyally world 🌎 great tesla real world al is about solving for the edge case not the average case'

I need a video of the presentation! @aelluswamy wen

Space is the ultimate edge case. If you can build infrastructure for orbit, you can handle anything down here with ease. 🛰️