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This is the Kodak Moment for the Auto Industry

Plug-In Drivers Not Missin' the Piston Electric vehicles are here to stay. Their market acceptance and growth will continue. Why?...

Thursday, June 4, 2020

2020s The Decade Of The EV


The decade* has not gotten off to a good start: a pandemic, giant killer hornets, racial strife, Ebola outbreak, Michigan dam breaches, Puerto Rico earthquakes, Australian bushfires, Cyclone Amphan, Cyclone Harold, Taal volcano eruption, Brazilian floods & mudslides...

Some of these disasters could leave an indelible mark on this decade; and while I hope that we learn our lessons from these tragedies and improve our society, that's a topic for another forum. This blog is about electric cars and EVs are sure to leave their mark on the 2020s.

Technologies frequently limp along for 10 or 20 years before the stars align and they suddenly become an "overnight success". This decade will be the one where EVs hit this overnight success tipping-point and become the norm. By the end of the decade, new car sales will be dominated by electric vehicles. When you are car shopping in 2029, considering a gas-powered car would be like considering a flip-phone in today's smartphone world.

Source: BloombergNEF
Why do I make this assertion?
  • First, EVs are more fun to drive (they are quieter, smoother, quicker) 
  • Gas prices are volatile and change with the whims of politics, saber-rattling, hurricane refinery outages... Electricity prices are far more stable and you can even generate it yourself from your own roof.
  • Battery prices have and will continue to drop. Batteries are the most expensive component in electric cars today and their price of manufacture has continued to drop. More battery factories are being built today than ever before in history.
  • EVs will be more affordable than gas cars by 2026. Today, if you consider fueling and maintenance, EVs are cheaper from the long term total cost of ownership perspective. However, for many people today, the initial sticker shock drives them away from an EV purchase. Following on the trend of battery costs, the sticker price for EVs will continue to drop. 
  • Charging speeds will increase. As battery tech improves, the causes of battery degradation will be mitigated and batteries will continue to toughen up and become tolerant to higher charging rates and more heat. 
  • Ranges will increase. As battery tech improves, more energy will fit in the same space with less weight. This will be driven by both the technology improvements and the cost reductions.
  • Charging infrastructure will continue to proliferate. Unless you drive an EV, you are likely unaware of all of the charging infrastructure that already exists. Take a look at the map on plugshare.com, there are many places you can plug-in. And as more people start driving EVs, more infrastructure will be deployed at businesses that want to attract EV drivers and by utilities that want to sell electricity.
  • Electric fuel is cheaper. As I write this, gasoline prices are cheaper than they have been in decades. However, even at $1 per gallon, charging overnight at offpeak rates, I'm paying ~70% less per mile than a similar gas-powered car (25MPG @ $1 per gallon compared to $0.05 per kWh @ 4 miles per kWh). 

Monday, May 25, 2020

What is Tesla's Project Dojo?


Tesla has made significant investments in artificial intelligence (AI). AI is the key to Tesla's full self-driving (FSD) future. Yet, Elon Musk has also called AI humanity's “biggest existential threat.” How do you reconcile this dichotomy? The answer is simple, Narrow AI vs General AI. A narrow AI is trained for a particular task such as playing a particular game or language processing. These narrow intelligences are not transferable. A narrow chess AI will not know anything about checkers despite the two games sharing a board. Whereas, a General AI (sometimes called Strong AI or Artificial General Intelligence(AGI)) is the hypothetical ability of a system to learn any intellectual task that a human could learn. Skills an AGI learned in one arena could be applied in new areas and an artificial superintelligence could quickly develop. An artificial superintelligence may find humans are irrelevant or worse, a threat. This is the “existential threat” that concerns Musk. 

So Tesla's FSD system will be a narrow AI, able to drive your car and you'll even be able to tell it where you'd like to go. You won't, however, be able to chat with the FSD AI about your day, but at least you'll know it won't decide that the best way to reduce traffic accidents is to kill all humans. 


Tesla's AI investments to date include creating an AI software development and validation team, creating a data labeling team, and creating an FSD hardware team to design their own custom neural network inference engine. Next on Tesla's AI investment list is "Project Dojo."


Project Dojo

We've been given a few hints about Dojo: Musk talked about it in the 2019 financial call and Tesla's Director of Artificial Intelligence and Autopilot, Andrej Karpathy, has talked about it at multiple AI conferences. We'll discuss how neural nets work and then move into some wild speculation; but first, we have to acknowledge the Dad Joke that is the name Project Dojo. We know that Project Dojo is intended to vastly improve the Autopilot Neural Network training. If you want to train, where do you go? A Dojo, of course. 



Before we get into Dojo we need to cover a few basics about neural networks. There are two fundamental phases to neural networks (NN): Training and Inference.

Training

NNs have to be trained. Training is a massive undertaking. This is when the digital ocean of data that is the training dataset must be digested. It takes terabytes of data and exaflops of compute to train a complex NN. Through training the NN forms "weights" for nodes. When the training is complete, the resulting NN is tested. A test dataset that was not part of the training dataset, where the expected results are known, is thrown at the resulting network and if the NN is properly trained, it infers the correct answer for each test. Since Project Dojo is all about training, we'll dig more into this later. Depending on the use case, there may be several stages of simulation and testing before the NN is deployed. Deploying the NN leads us to our next phase, Inference.

Inference

When a neural network receives input, it infers things about the input based on its training; this is known as “inference.” These inferences may or may not be correct. Compared to training, the storage and compute power needed for inference is significantly lower. However, in real-time applications, the inference needs to happen within milliseconds; whereas training can take hours, days, or weeks.

Unlike training, inference doesn't modify the neural network based on the results. So when the NN makes a mistake, it is important that these are captured and fed back to the training phase. This brings us to a third (optional) phase, Feedback.

Feedback

You may have heard the phrase "Data is the new Oil." Nowhere is this more applicable than AI training datasets. If you want an AI that performs well, you have to give it a training set that covers many examples of all of the types of situations that it may encounter. After you have deployed the AI, you have to collect the situations where it did the wrong thing, label it with the expected result, and add this (and perhaps hundreds or thousands of examples like it) to the training dataset. This allows the AI to iteratively improve. However, it means that your training dataset grows with each iteration and so does the amount of computing horsepower needed for training.


Tesla's Autopilot Flywheel 

Now that we've ever so briefly covered AI basics, let's look at how these apply to Tesla's FSD.

Let's start with Deploying the Neural Net. Every car that Tesla makes today is a connected car that receives over-the-air updates. This allows the cars to receive new software versions frequently. When a new version of Autopilot is deployed, Tesla collects data about its performance. The AI makes predictions such as the path of travel, where to stop, et cetera. If Autopilot is driving and you disengage it, this may be because it was doing something incorrectly. These disengagements are reported back to Tesla (assuming you have data sharing enabled). The report could be a small file that only has the data labels and a few details or it could be streams of sensor data and clips of video footage depending on the type of disengagement and the types of situations that Tesla is currently adding to their training set.

Even if Autopilot is not engaged, it is running in "shadow mode." In shadow mode, it is still making predictions and taking note when you, the human driver, don't follow those predictions. For example, if it predicts that the road bends to the left, but you go straight, this would be noted and potentially reported back to the mothership. If Autopilot infers that a traffic light is green but you stop, this data would again likely be noted and potentially reported back.

Tesla has about a million vehicles on the road today collectively driving about 15 billion miles each year. The bulk of these cars are from Tesla's Fremont factory. Tesla now has a second factory, Giga Shanghai, putting cars on the road. Soon Giga Berlin and Giga Austin (or will it be Tulsa?) will join them. All of this will result in a large amount of data for the training dataset.

The bigger the training set, the longer it takes to process. However, with a system like this, the best way to improve it is to quickly iterate (deploy it, collect errors, improve, repeat). If training takes months, this slows down the flywheel. How do you resolve this? With a supercomputer dedicated to AI training. This is Project Dojo: make a training system that can drink in the oceans of data and produce a trained NN in days instead of months.


A Cerebras Wafer Scale Engine

Cerebras

At the start, I promised some speculation. As promised, here it is.

The size of the chips used for AI training has been increasing every year. From 2013 to 2019, AI chips increased by about 50% in size. A startup called Cerebras saw this trend and extrapolated it to its natural conclusion of 1 chip per wafer. For comparison, the Cerebras chip is 56 times bigger than the largest GPU made in 2019, it has 3,000 times more on-chip memory, and it has more than 10,000 times the memory bandwidth.

This wafer-scale chip is an AI training accelerator and my conjecture is that a Cerebras chip will be at the heart of Project Dojo. This wafer-scale chip is the biggest (literally and figuratively) breakthrough in AI chip design in a long time.

There is one (albeit tenuous) thread that connects Tesla and Cerebras, both are part of ARK Invest's disruption portfolio. ARK has investments in both companies and meets with their management teams. When there are two companies that could mutually benefit working together and it would benefit their mutual investor, ARK, you can bet that introductions would be made.

Thursday, January 16, 2020

10 Years of Trading Tesla (TSLA)



Tesla's stock has been on a tear recently. I've been buying (and occasionally selling) the stock since its IPO in 2010. Below is a brief history of my trading activity.

Of course, I have no way of knowing what the stock will do tomorrow, so don't take this as stock advice.

I bought my first shares soon after the IPO. The stock opened at about $20 and had a dip over the next few weeks. In late June and early July of 2010, I bought at $18, $17.84, and (the best price I picked some up was) $16.01 per share.

I held these shares for nearly 6 years, until early 2016. Why did I sell them then? Two reasons. First, after a stock has had a good run (from $18 to $249 (or ~1400%) in this case), I like to take out my initial stake so that no matter what happens to the stock after that, I will always be net positive. The second reason I sold was that we were going to buy a new car in 2016. I didn't sell all of my shares.

My timing to sell was great. The stock dipped later in 2016 and I was able to buy the shares back at a lower price.

After taking delivery of the car in the fall of 2016, my view of the company changed. This was not my first EV (it was my 3rd actually). I knew that EVs were the future of personal transportation, but Tesla was lightyears ahead of everyone else. There was no other car that could compare. After owning a Tesla, all other cars (electric or not) seem like relics from a bygone era. They did unlock as you walked up to them, you had to push a button or turn a key to start it and stop it, they had tiny screens, they didn't have vast free Supercharging networks, they didn't have 200+ miles of range, they didn't receive firmware updates over-the-air...

Based on this two-pronged belief (1: EVs are the future. 2: Only Tesla has cracked the code), throughout 2017 and 2018, I was buying TSLA whenever the price dropped below $300. At the end of 2018, I sold a portion of my shares at $375. The reason we sold this time was once again, to buy a Tesla.

Again my sell timing was lucky. We sold near a local maximum. Soon after we sold, the SEC became concerned with Musk's infamous 420 tweet. This, and other concerns, drove the stock price down in the first half of 2019. This allowed me to buy shares back in the $200s, I even picked up some in May of 2019 for $185 per share. I had just sold for $375 and now I was able to buy it at half that price. How great is that? I understand that an investor would not be happy if they had bought at $375 and saw their investment halved. I, on the other hand, was convinced that this slump in the stock price was temporary. Issues like this get resolved and Tesla still made the best vehicles in a fast-growing category.

Now, it's early 2020 and the stock is over $500 per share. Again, I am taking some profits for the same 2 reasons I did initially. One, to remove my seed funds. Doing this allows me to sleep soundly at night. TSLA is a volatile stock. If it goes up, I still own shares and I'll share in the rewards. But if it goes down, I'm not concerned. By removing the money I initially put into it (plus a little), I am guaranteed, that (even if the stock goes to zero) I've made money on my Tesla trades. And the second reason is to again buy a Tesla product. This time we are getting Powerwalls installed on our home. More on that in later posts.

It only seems right that after making money on their stock that I should share the profits with them by buying their products. I've certainly done the same with Amazon, Netflix, and Google.

I'm still holding TSLA, I'm long the stock.

http://ts.la/patrick7819