What we tell people is, just be lawful and just be considerate, Ng said to Bloomberg.
But thats about where the agreements end.
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While especially efficient for classification tasks,deep learning suffers from serious limitsand it can fail in unpredictable ways.
The benefit of this model is that it can all be packed into a consumer-level vehicle.
It doesnt need any additional, costly hardware attached to the car.
To be fair, this is a model that only a company such as Tesla can perform.
Like many other things, automobiles are going through a transition ascomputation and connectivity becomes ubiquitous.
Some scientists believe that we need to think ofAI technologies beyond deep learning and neural networks.
Chief among them is light detection and ranging (lidar).
Lidar is an evolving domain and various companies are using different technologies to perform its functions.
The following video shows how the technology works.
Adding all these technologies surely make these vehicles much better equipped than Teslas computer visiononly approach.
However, this doesnt make their technology flawless.
Also, adding all that gear to a car costs a lot.
Then automobile manufacturers might move toward equipping their vehicles with the self-driving technology without dramatically raising the costs.
It basically means that if youre jaywalking and an autonomous vehicle hits you, its your own fault.
Rodney Brooks, another AI and robotics legend, alsodismisses Ngs proposition.
The same can very well happen with driverless cars.
This too has a precedent.
With the advent of airplanes, airports were created.
In cities where bicycles are very popular, separate lanes were created for bicycles.
So what is the infrastructure for driverless vehicles?
Academics from Edinburgh Business School propose inan article in Harvard Business Reviewto create smart environments for self-driving cars.
This will help them coordinate their movements and avoid collisions more accurately.
One of the challenges of this model is that vehicles live for decades.
This means that cars that are manufactured today will still be on roads in the 2030s.
So you cant expect every single vehicle to be equipped with sensors and M2M capabilities.
Also, we cant expect all the roads in the world to suddenly grow smart sensors.
When will driverless cars become the norm?
But it has become evident that overcoming the challenges is much more difficult than we first thought.
Our cars might one day become smart enough to be able to address every possible scenario.
But it wont happen overnight, and it will likely take several steps and phases at different levels.
