Tesla Full Self Driving BETA 2020.40.11 vs Roundabouts (All the BEST Clips)
“Full Self Driving Beta” In depth review & reaction | Tesla Software 2020.40.11 | Tesla Autopilot Full Self Drive HW3 Model 3 Performance version.
Stop/Start At TRAFFIC LIGHTS, STOP SIGNS & GIVE WAY! 🇬🇧 Tesla Autopilot 2020.36.10 Full Self Drive | Tesla Autopilot Full Self Drive HW3 Model 3 Performance version
Tesla Autopark Ultimate Test | *SECRET* 2020.28.1 Update shows PARKING SPACES & does it park FASTER? | Tesla Autopilot Full Self Drive HW3 Model 3 Performance version
Tesla follows a similar format to semantic versioning for organizing software updates. In the version “2020.12.11.1”, 2020 is the year and 12 is the week a specific update began development, 11 is a major update, while 1 is a minor update or maintenance (bug fixes).
Developing novel applications based on deep tech (ML, AI, HPC, quantum, IoT) and deploying them in production is a very painful, ad-hoc, time consuming and expensive process due to continuously evolving software, hardware, models, data sets and research techniques.
After struggling with these problems for many years, we started the Collective Knowledge project (CK) to decompose complex systems and research projects into reusable, portable, customizable and non-virtualized CK components with the unified automation actions, Python APIs, CLI and JSON meta descriptions.
Our idea is to gradually abstract all existing artifacts (software, hardware, models, data sets, results) and use the DevOps methodology to connect such components together into functional CK solutions. Such solutions can automatically adapt to evolving models, data sets and bare-metal platforms with the help of customizable program workflows, a list of all dependencies (models, data sets, frameworks), and a portable meta package manager.
CK is basically our intermediate language to connect researchers and practitioners to collaboratively design, benchmark, optimize and validate innovative computational systems. It then makes it possible to find the most efficient system configutations on a Pareto frontier (trading off speed, accuracy, energy, size and different costs) using an open repository of knowledge with live SOTA scoreboards and reproducible papers.