Tesla’s vision on their future

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October

2021

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It is generally agreed upon that Tesla has been leading the race towards autonomous driving. Having eight surrounding cameras with a combined 360-degree view and twelve ultrasonic sensors, their detection system is very complicated. Ever since 2016, all new Tesla vehicles are built with the necessary sensors and hardware for full self-driving, showing their capabilities and planning towards a long-term goal.

Concluding from the 2021 AI day, it became clear that Tesla wants to have a future where their cars are trying to create a predicted vector space by just using cameras and getting rid of sensory data (lidar technology). The main reason for this is that creating and maintaining these infrastructures (lidar maps) is unscalable and the technology should be perfected in figuring out where lanes, traffic lights, crosswalks, etc. are in real-time without having predetermined data like a map giving it a prediction.

In addition to these long-term focuses, they want to create intermediate features such as neural network architectures that can do a search through an action space such as a parking lot (searching for an optimal parking space) and aligning data from different vehicles in real-time to reconstruct roads for labeling.

In parallel to these projects, they are starting/continuing the development of the DOJO computer, a powerful supercomputer that can be used firstly for the neural network training of autonomous driving. However, the capabilities of the computer are not limited to driving and roadmaps, they could in theory be used as an ‘AI as a service’ principle where external companies could use its powerful computing power for their own neural networks projects.

It’s clear that Tesla is out of the beginners’ stage when it comes to autonomous driving, and is focussing more on optimization of the systems in place to create a safe future that includes mass self-driving vehicles. Besides that, they are slowly starting new projects that could focus on real-world problems that could/should be resolved by AI.

sources: https://europe.autonews.com/automakers/musk-shows-self-driving-progress-humanoid-robot-tesla-ai-day

https://venturebeat.com/2021/07/03/tesla-ai-chief-explains-why-self-driving-cars-dont-need-lidar/

https://www.tesla.com/AI

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How long can a tail get?

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October

2021

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One question that is for sure on the minds of CEOs, strategists, analysts and maybe comes up on everyone’s mind every so often, is what is the future of business? The longer you think about it, the more models come up in your head that would describe modern-day business models. These models are relatively new and there is no certainty that they will be successful in the future. One of those models, the long tail strategy, is one you see in all large-scale businesses nowadays. For example, Amazon will recommend you a book about mountain climbing that was published in 1988 (Touching the Void by Joe Simpson), just because it is similar to a book that’s trending right now (Into Thin Air by Jon Krakauer). This results in an increase in sales of Simpson’s book (Wired, 2004).

The mountain climbing book example shows the value of the long tail. Amazon can provide you with something that typical customers wouldn’t even get close to searching for, thus offering niche markets that still provide value. More companies embrace this strategy, Amazon is simply used as an example. Think about Netflix and Google, what if you had a weird knob on the very edge of your 4th little toe, the first thing you might do is ask your parents or lover to take a look at it, they have no clue what it might be. Going to the doctor for it might be not an option for you (shameful), so you Google it. And guess what, Google interprets your Google search and tries to find the most useful sites for you, resulting in an effective click rate that further leads you to ads.

The practicalities of the long tail are increasing, but where lies the end of an exponential curve, is it infinity or are we going to hit a wall in the future?

Reference: https://www.wired.com/2004/10/tail/

https://www.calltheone.com/en/consultants/the-long-tail-chris-anderson-explanation-examples

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