A few decades back Artificial Intelligence was tasked as the technology of the future. Fast forward to today, it is quickly transitioning from the much-hyped future technology to surrounding us and affecting our daily lives. Right from predicting the next word to type in a text message to taking Instagram perfect pictures, Artificial Intelligence is being fused into products and services that we use on a daily basis.
In the Manufacturing sector, AI has been at the forefront in driving transformation. In particular, this technology has disrupted the car manufacturing industry spawning the era of self-driving cars. To date, it is estimated that more than 200 tech companies are creating software solutions that will facilitate the transition from manual driving to the automotive revolution of autonomous driving.
And since the self-driving cars are premised on the fact that they won’t require human intervention, the concept has elicited a number of concerns from the general public and stakeholders. For instance, will these autonomous cars be safe? What is their level of judgment call? Are we risking by creating intelligent machines that will end up colonizing us?
In this article, we are going to address all these concerns. We are also going to look at how AI can help improve autonomous cars, the role AI developers in this revolution and the future of Artificial Intelligence in the Automotive industry.
Self-driving Cars and Their Mode of Operation
Self-driving cars are vehicles that utilize a combination of AI, sensors, cameras, and radar to commute without the help of a human operator. For a vehicle to qualify for the fully autonomous tag, it must autonomously operate without any human intervention from a specific location to a predetermined destination using a set of preconceived parameters.
Basically, they operate under a very basic principle whereby AI developers develop self-driving car systems, fuse them with Machine Learning, neural networks, and image recognition systems, to create complex systems that can autonomously drive the cars.
Using neural networks, AI systems are able to identify patterns like traffic lights, street signs, curbs, pedestrians, trees, and other objects from the imaging system. This data is then fed to Machine Learning algorithms and collated to create parameters within which the autonomous cars operate.
According to a 2019 report by the US Department of Transport, the total number of autonomous vehicles on the US roads is estimated to be 1,400. Although this number may seem small, more than 80 top Artificial Intelligence companies in the country have heavily invested in making autonomous driving a reality.
For an industry set to hit a market valuation of $36.8 B, it would only make sense to assume that the demand for AI developer skills has already outpaced the supply. As a result, it’s a wise idea to learn Artificial Intelligence today. But before we look at what it takes to become an AI developer, here is a brief overview of the current AI labor market.
Brief Overview of the AI labor Market
Apparently, there is a huge shortage of Machine Learning and Artificial Intelligence professionals. Industry estimates show that there are more than 500,000 AI-related vacant jobs due to skills shortage. Multinational tech companies in Silicon Valley are armored with tons of resources and have been able to attract the best talents to build their solutions. However, the small scale companies find it hard to source even junior developers.
According to a report by PWC, AI is set to eliminate close to 1.8 million jobs by 2020. This against the 2.3 million jobs that the technology itself will create. AI Engineers, Machine Learning Engineers, Computer Vision Engineers, and Data Scientists are some of the most sought after professionals within the AI ecosystem.
Necessary Skills to Learn as an AI Developer in Self-driving Cars Industry
Being a relatively new field, autonomous driving requires a wide array of both existing and yet to emerge skills if it is to fully actualize. If you are looking for ways on how to become an AI developer for the Automotive industry, here are some of the skills you need to learn.
On average, a single autonomous vehicle requires over 250 million lines of code within its hardware. These multiple lines of code are responsible for making the car “intelligent” enough to understand what’s around it in the real world. Furthermore, it takes a countless number of different programs and platforms to design, construct and operate an autonomous vehicle.
As an aspiring AI developer or a practicing developer looking to venture into this space, it’s important to have a complete understanding of coding and how the different elements in the system work and interact with each other. In particular, extensive Python, C++, and Linux experience are non-negotiable as they form a major part of the Autonomous vehicles industry.
Another must-have skill for an AI developer in the autonomous vehicles is ML. As earlier indicated, self-driving cars work through algorithms. As new problems continue to emerge, these algorithms need constant updates and refinement. Such improvements can only be done using Machine Learning by analyzing operational data collected from the entire autonomous fleet and creating solutions depending on the pre-set parameters.
As an AI developer, having ML skills and data set preparation in particular then becomes a necessity. You see, no matter how big your information repository is, AI is nearly useless if not harmful, if you can’t make sense of your data records. There are three commonly used data sets in AI development;
- Training Set: Basically, this data set is used to train algorithms to understand different concepts like neural networks as well as learning and producing results.
- Test Set: Used to determine how well your algorithm is trained using the training data set.
- Validation Set: Used to select the final algorithm model.
- Test Set: Used to evaluate the performance of the final algorithm.
Considering self-driving vehicles are in the early development stages, it will take a bit longer to see massive deployment of these vehicles on our urban traffic-filled roads. This is because even the slightest flaws during the designing and development stages could prove to be fatal. However, through technologies such as Computer Vision, chances of such incidences are significantly minimized.
Computer Vision helps autonomous vehicles in the following ways:
- 3D Mapping: Computer Vision is the secret behind real-time visual data capture in autonomous vehicles. The cameras fitted in the autonomous vehicles are able to record live footage, curate it, and develop 3D maps. With these maps, self-driving cars are able to better understand their environment by spotting obstacles crossing their designated paths and consequently seek alternative routes.
- Object Detection: Computer Vision is also used by self-driving vehicles to detect and profile different objects. Using cameras and LiDar sensors to measure distances, the collected data is then combined with the 3D maps to locate objects like vehicles, traffic lights, and pedestrians. As a result, self-driving vehicles are able to instantly process this data and make real-time decisions like braking to avoid collisions.
- Training Algorithms: Computer Vision through the help of sensors and cameras is an excellent way to collect large data volumes. By collecting crucial information like location information, road maintenance or traffic conditions, self-driving vehicles are able to develop awareness and reach crucial decisions quickly.
As you can see, without Computer Vision, self-driving vehicles would essentially remain a sci-fi fantasy. As an AI developer, having these skills will come in handy by making your work easier.
According to a recent report by IHS, autonomous cars sales is expected to reach 11.8 million in 2035. This is quite a large number and may pose a lot of safety concerns. However, the strides made by Radar (Radio Detection and Ranging) promises to make autonomous cars safer.
Radar works by transmitting radio waves from a source onto a surface. The surface then reflects these waves to a receiver system where it is then processed. A good example of RADAR solution is ADAS (Advanced Driver Assistance System). It is already in use and has exceptionally performed in blind-spot monitoring, collision warning, and object detection.
With autonomous driving rapidly gaining traction, regulatory bodies world over have been pushing for the mandatory inclusion of advanced driver assistance systems and other similar security features.
As a developer, familiarizing with different sensor systems such as RADAR, LiDAR, and infrared (IR) will definitely give you an upper hand.
The main aim of human-machine interfaces is to provide autonomous car drivers with multiple platforms with which they can interact with the vehicles’ features. And auto manufacturers in collaboration with tech companies are already building hot-off-the-assembly-line disruptive HMIs. Discussions have also been going around about the possible shift from the from contemporary displays to more autonomous Augmented Reality Head-Up Displays (HUDs).
This increased affinity for even more autonomy will also open new frontiers in remote accessibility of autonomous vehicle features through tactile movement using Touch HoloActive systems.
And since all these supplementary technologies will need a platform to anchor onto, it is paramount for any AI developer to learn human-machine interface technologies.
Autonomous vehicles will need to be connected to each other to facilitate a smooth flow of their operating environment generating large amounts of data. For example, it is estimated that a small fleet of autonomous vehicles can generate up to 4,000 GB per day. With AI Cloud platforms, all this data can be stored and easily retrieved whenever needed. Needless to say, creating such platforms will require some AI skills.
Artificial Intelligence Developer Salary
The salaries of Artificial Intelligence developers is rapidly spiraling so fast that the tech industry has coined a joke saying that AI salaries need to have a National Football League-like salary cap.
These huge salaries are largely catalyzed by a number of factors. The competition between Silicon Valley and the Auto industry for skilled experts. On the one hand, large tech firms like Google and Facebook are trying to use AI to solve problems like spotting offensive content and building digital assistants– and they are offering dolling salaries. On the other hand, the auto industry is looking to recruit AI developers in large numbers to help build self-driving cars. Some have even resulted to fish for these lavish professionals in academia, creating an acute shortage of developers joining the AI field.
And the AI salaries are a clear reflection of this shortage. According to Indeed.com, the average Artificial Intelligence engineer in the US earns an average of $134,135 annually while a Machine Learning engineer earns approximately $169,930 annually.
In the UK, the base pay for a Senior AI engineer is £84,000 per year, with an intern taking home up to £25,000 annually. Salaries in Eastern Europe countries are relatively low compared to other developed economies. For instance, in Ukraine, the monthly salary of an AI developer is $10,000 or roughly $120,000 per year.
If the words of Elon Musk on Artificial Intelligence are anything to go by, misuse of AI technology can turn out to be the biggest existential threat to humanity. A few years ago during an interview, he warned that intelligent machines could turn dangerous and colonize us in the future.
On the other hand, Bill Gates a big supporter of AI has said that the rise in AI technology will bring efficiency into our society. Depending on how you look at it, the two tech pundits are correct.
But so far, we have seen the benefits of AI in other areas like Healthcare, Manufacturing, and Agriculture. If this is anything to go by, the self-driving cars industry is yet to see the best of AI.
As for the AI developers, the future of AI self-driving cars industry is bright. The jobs associated with the growth and advancement of this sector may not be in existence today, but it is better to learn the skills and wait, rather than wait and learn the skills later.