Technology

The Future of Automotive Innovation

Published

6 years ago

February 15, 2018

Jeff Desjardins

The Future of Automotive Innovation

Since the invention of the internal combustion engine, there have been many incredible innovations made in the auto industry.

Manufacturers created new body styles and market segments, automatic transmissions and power steering were introduced, and safety features such as airbags made passengers much safer. Computers were even added into cars to optimize performance and provide GPS for navigation purposes.

In short, vehicles got cheaper, lighter, stronger, safer, cleaner, faster, and more luxurious.

But despite this, there is a strong case that the biggest innovations in the auto industry are yet to come.

A New Era of Automotive Innovation

Today’s infographic comes to us from Evolve ETFs and it explains the many forces shaping the future of automotive innovation.

Unlike past periods of innovation in the industry, the coming years will be particularly interesting because many of the changes will come from outside of the traditional workings of a car.
Automation and the shared economy will change how the entire commuting model works. Meanwhile, an increased penetration of EVs will have an impact well beyond the engine, as charging infrastructure needs to be added, battery supply chains need to be created, and as legacy auto parts become obsolete.

While these transitional changes take place, the auto market is expected to jump from $3.5 trillion (2015) to $6.7 trillion (2030) in total size – and a whopping 30% of the revenue will come from new services that don’t even exist today.

The ACES Framework

The future of automotive innovation will hinge on four major technologies: automation, connectivity, electric power, and the shared economy.

This can be simplified into the acronym “ACES”:

A: Automation
Perhaps the most obvious and fundamental change facing the auto sector is the rise of autonomous cars.
Not only does this technology have implications on major manufacturers and suppliers to the auto sector, but giving the cars the ability to self-drive will have an impact that extends well beyond it, as well.

The passenger economy, which will come from relieving people from the driver’s seat, is expected to be a $7 trillion industry alone by 2050.

C: Connected
New cars are already taking advantage of increased connectivity today, and it will soon be the norm even in lower-end vehicles. This added networking unlocks new features such as infotainment, enhanced safety features, and diagnostics and analytical tools.

E: Electric
In just seven years since its IPO, Tesla was able to leapfrog Ford in market valuation. Yet, this is still the very beginning of the EV revolution.

Many countries have announced regulations to curb gas or diesel fueled vehicles, and EVs are expected to hit 41 million global sales by 2040.

S: Shared
The shared economy is the result of technological factors, but also societal ones. However, when combined with automation, sharing presents a fundamental shift to how commuting and transportation will work in the future.

With autonomous and shared cars, current commuter inconveniences such as traffic and parking will be reduced considerably – and it’ll make catching a ride to your destination far cheaper, as well.

Related Topics:batteries automation autos vehicles evs sharing Cars

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AI

Charted: The Exponential Growth in AI Computation

In eight decades, artificial intelligence has moved from purview of science fiction to reality. Here’s a quick history of AI computation.

Creator Program

Published

4 days ago

September 18, 2023

Pallavi Rao

A cropped version of the time series chart showing the creation of machine learning systems on the x-axis and the amount of AI computation they used on the y-axis measured in FLOPs.

Charted: The Exponential Growth in AI Computation

Electronic computers had barely been around for a decade in the 1940s, before experiments with AI began. Now we have AI models that can write poetry and generate images from textual prompts. But what’s led to such exponential growth in such a short time?

This chart from Our World in Data tracks the history of AI through the amount of computation power used to train an AI model, using data from Epoch AI.

The Three Eras of AI Computation

In the 1950s, American mathematician Claude Shannon trained a robotic mouse called Theseus to navigate a maze and remember its course—the first apparent artificial learning of any kind.

Theseus was built on 40 floating point operations (FLOPs), a unit of measurement used to count the number of basic arithmetic operations (addition, subtraction, multiplication, or division) that a computer or processor can perform in one second.

ℹ️ FLOPs are often used as a metric to measure the computational performance of computer hardware. The higher the FLOP count, the higher computation, the more powerful the system.

Computation power, availability of training data, and algorithms are the three main ingredients to AI progress. And for the first few decades of AI advances, compute, which is the computational power needed to train an AI model, grew according to Moore’s Law.

Period	Era	Compute Doubling
1950–2010	Pre-Deep Learning	18–24 months
2010–2016	Deep Learning	5–7 months
2016–2022	Large-scale models	11 months

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

However, at the start of the Deep Learning Era, heralded by AlexNet (an image recognition AI) in 2012, that doubling timeframe shortened considerably to six months, as researchers invested more in computation and processors.

With the emergence of AlphaGo in 2015—a computer program that beat a human professional Go player—researchers have identified a third era: that of the large-scale AI models whose computation needs dwarf all previous AI systems.

Predicting AI Computation Progress

Looking back at the only the last decade itself, compute has grown so tremendously it’s difficult to comprehend.

For example, the compute used to train Minerva, an AI which can solve complex math problems, is nearly 6 million times that which was used to train AlexNet 10 years ago.

Here’s a list of important AI models through history and the amount of compute used to train them.

AI	Year	FLOPs
Theseus	1950	40
Perceptron Mark I	1957–58	695,000
Neocognitron	1980	228 million
NetTalk	1987	81 billion
TD-Gammon	1992	18 trillion
NPLM	2003	1.1 petaFLOPs
AlexNet	2012	470 petaFLOPs
AlphaGo	2016	1.9 million petaFLOPs
GPT-3	2020	314 million petaFLOPs
Minerva	2022	2.7 billion petaFLOPs

Note: One petaFLOP = one quadrillion FLOPs. Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

The result of this growth in computation, along with the availability of massive data sets and better algorithms, has yielded a lot of AI progress in seemingly very little time. Now AI doesn’t just match, but also beats human performance in many areas.

It’s difficult to say if the same pace of computation growth will be maintained. Large-scale models require increasingly more compute power to train, and if computation doesn’t continue to ramp up it could slow down progress. Exhausting all the data currently available for training AI models could also impede the development and implementation of new models.

However with all the funding poured into AI recently, perhaps more breakthroughs are around the corner—like matching the computation power of the human brain.

Where Does This Data Come From?

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

Note: The time estimated to for computation to double can vary depending on different research attempts, including Amodei and Hernandez (2018) and Lyzhov (2021). This article is based on our source’s findings. Please see their full paper for further details. Furthermore, the authors are cognizant of the framing concerns with deeming an AI model “regular-sized” or “large-sized” and said further research is needed in the area.

Methodology: The authors of the paper used two methods to determine the amount of compute used to train AI Models: counting the number of operations and tracking GPU time. Both approaches have drawbacks, namely: a lack of transparency with training processes and severe complexity as ML models grow.