Artificial Intelligence Will Only Replace Low-paying Jobs

The rise of automation and artificial intelligence is greeted positively by many within the tech industry. Artificial intelligence software is designed to processes greater amounts of data in less time than any human possibly could, which speeds up decision-making and improves accuracy. On another level, vending kiosks are poised to replace fast food workers. Indeed, automated cars could replace taxi and Uber drivers. In one respect, this is frightening. Fewer jobs will be available, but as pointed out in a report to Congress in 2016, workers earning less than $20 per hour can expect an 83% chance that their job will be transferred to an AI powered robot by 2021. Those earning $40 or more per hour will only face a 31% chance that a robot will replace them.


In a recent Huffington Post article, Anurag Harsh points out that technology has been replacing humans since the onset of the Industrial Revolution. He also brings to light the fact that tractors were once expected to take away all agricultural jobs. In reality, the invention and adoption of the tractor forced generations of would-be farmers to attend and complete high school. Once educated, they moved on to higher skilled, and better paying jobs, in factory towns and urban areas. A way of life changed completely, but the need to earn a living did not.


Considering the historical precedent that has been set, it is imperative that younger generations position themselves for higher paying jobs. Those positions could include engineers who write the code that run the robots, or innovators who conceive new strategies for robotics, or it could be an influx of physicians, lawyers and other creative thinkers who can conceive of patterns and perceive nuances that AI-driven devices will not be able to replicate. So while robots flip hamburgers, chefs will still be in demand. In this sense, robots are helpful in more ways than simply doing drudge work. They challenge us to raise the bar, to receive better educations, to hone greater skills and to earn more money. As the adage goes: work smarter, not harder.


Using Deep Learning, Driverless Cars Will Soon Start Learning How To Interact With The Environment

The phenomenon of driverless cars is taking the automobile industry by storm. In fact, scientists and software engineers are still trying to decide how they should make the driverless cars more intelligent. Perhaps, a California startup, has the answer. The new company will use deep learning as part of ongoing tests to make driverless car learn from their environment.

Deep learning is an advanced form of artificial intelligence. Actually, its not new because researchers at the Stanford University are already using it to test different technology. However, this is the first time that a company will use deep learning as a tool to train driverless cars.

Interestingly, the company only reveled its plan to test the driverless vehicles because it had to get the test license from the Government of California. According to sources, the license will allow to use driverless car for the training purposes. Accordingly, the company has recently hired high-profile automobile executives that includes former General Motors director Steve Girsky. It is also able to obtain nearly $12 million in funding, which indicates future potential.

According to president and co-founder Carol Reiley, deep learning in automobiles will work by allowing the care to decide from millions of test cases. For instance, engineers can offer thousands of different scenarios for the driverless car to learn in matter of days. The amount of and data that modern computers are able to store will help the computer to evolve with the passage of time. As more unique circumstances are presented, the artificial intelligence will reach new levels of coordination. Carol also revealed that deep learning is not limited to driving the car. Instead, the technology will enable the computer to make instant decision on what is best for other drivers, pedestrians and environment. Overall, it means that is building a computer that will not only save the driver, but is also concerned about the safety of others. If true, other companies can also learn from

As of now, the deep learning will continue to be in a Beta mode. As co-founder of the company, Sameep Tandon, sees it, deep learning requires continuous learning process. The learning cycle continues because it is impossible to make a guide for a computer that is always facing new scenarios.