It's not often I blog about someone else's work, but these stats on Tesla's meteroric rise, are too good NOT to share. Credit has to go to Tom Randall (@tsrandall), Senior Reporter at Bloomberg for the awesome pics and stats in this blog post.
But first I wanted to share my awesome experience of the latest version 9 Autopilot, that I tried out in Brisbane, a few months back,
Boy....has there been some serious improvements. Last time I took a Tesla Model S for a test drive, was around 1.5 years ago. And since then, the 'stay in lane' feature has come on leaps and bounds. I used the feature for around 5 minutes on a stretch of highway. The car in front was doing 70kmph in a 90kmph zone, and the Tesla slowed down gracefully. It kept perfectly in the centre of the left lane, as the road curved, left, then right and sharply left again.
At the next slip road, the vehicle in front exited. Once it was clear of the Model S, the car gracefully accelerated up to the speed limit of 90kmph without a hitch and without any input. All I had to do was to keep my hands on the wheel, so the car knew I was still alive. The 'hands-on wheel' feature was brought in by Tesla after a couple of accidents in the US, where drivers had totally relied on the Autopilot and were not paying attention to the driving. This feature aims to prevent such occurences.
All I can say it that is absolutely amazing, and I'm sure the experience translates precisely into the Model S and Model X cars. Unfortunately you can not test drive a Model 3 in Brisbane, so here is the closest I got:
In Q3 of 2018 you can see that the production of the latest Model 3 car increased exponetially. And it's likely this curve will continue through 2019 and 2020, as Tesla forfil a backorder in excess of 500,000 Model 3 orders.
It took Tesla 10 years to see 0.5 million cars, which includes significant amounts of research and development time and money. This was to hone the battery technology, the look of the various cars, and ensuring that all the components integrated seamlessly, and costs effectively. This includes the following models:
It looks like it will take only 15 months to reach the first 1 milion cars. Check the stats below:
You can see from the stats below that the Model 3 is the 5th Best-Selling Sedan in the US.
Not bad for a car that still costs around $55,000 USD.
Let's now take a look at the value, known as market capitialisation, of the world's most valuable automotive makers:
Now this next graphic shows the progression of Tesla's cash flow. This will likely lead to a positive $837millionUSD, as opposed to spring 2018, which was a negative at $795millionUSD:
Mmmmm...maybe I should invest in Tesla stock???
There is often a lot of confusion around the differences between machine learning and deep learning. Both are classed as techniques to enable artificial intellgence or AI. But what is AI?
AI is the ability to create a program or computer system that can fool a human into thinking it is another human. There is a simple test for this called the Turing Test, developed by Alan Turing. Turing is a famous computer scientist who is potrayed in the film 'The Imitation Game'. He was the UK's secret weapon in the 2nd World War.
The test is very simple. There are 3 actors. A computer, person B and our interrogator C. Each actor is placed a separate room. If the interrogator is unable to determine which actor is the computer, then the computer is determined to be intelligence, albeit it's artificial. Here is a simple diagram outlining this concept:
Coming back to the machine learning and deep learning techniques, let's define those in turn. We can use either or both techniques to fool our interrogator into thinking our computer is intelligent.
Deep Learning - is the process of applying neural network theory to help a computer learn. Neural network theory strives to mimic our brain function. Our brains are made of neurons and pathways, known as neural networks. With deep learning we setup virtual neurons and virtual gateways in our system and use similar biological rules to allow the network to start learning. In order to understand neural networks in more detail, you'll need to cover some psychobiology theory that outlines how the brain works. Here is a simple video on how neurons work:
The diagram below shows some of the many possible neural networks that you can choose from:
Machine Learning - is the process of applying mathematical models to help a computer learn. It does not attempt to mimic the brain in terms of structure, but instead provides a process for allowing a computer to learn via mathematical techniques. There are 100's of mathematical methods to enable machine learning. Some examples include: random forest, regression and dedcision trees. Here is a great example of a decision tree:
And finally to put things in context, we can see how AI, Machine Learning and Deep Learning has evolved over time in the diagram below. This is also a differentiator between machine learning and deep learning. As you can see deep learning is a newer technique, inspired by human biology, whereas machine learning is an older technique, inspired by various mathematicians:
Check out the Nvidia blog that accompanies the picture...BTW....they provide the deep learning framework for Tesla cars....
As you can see, the biggest challenge that a data scientist has to content with, is which deep learning or machine learning technique to use. That's of course once we have a clearly defined business requirement and/or outcome we're looking for and we've probably spend days or months trying to obtain clean data. Oh the joys of data science.....
Paul Colmer is a digital coach for ALC training and consulting, with a real passion for learning and applying disruptive technologies. Paul has responsibility for building and delivering ALC's digital architecture strategy and the development and execution of a number of cloud courses, including Cloud Security (CCSP), Amazon AWS, DevOps, Microsoft Azure and Office 365.