The ability to process large volume of data has enabled deep learning professionals to quickly ascertain the real value of information and applications in business decision making. But, this ability is limited to a handful of human beings, and that too, to those who are capable of applying human intelligence to design highly powerful machine learning algorithms.
In the last 3 decades, the nature of analysts’ jobs has drastically changed. But, we know the origins of deep learning professionals for sure!!! When IBM was looking to build the most powerful and fastest computing system, it hired a team of world’s best scientists to design what we call world’s first supercomputer. And, the responsibility to assemble the world’s first supercomputer fell on Seymour Cray of Colorado US. Today, he is known s the “Thomas Edison of Supercomputer industry.”
Since then, the computer industry has grown leaps and bounds. A deep learning professional today works with a bay or battalion of supercomputers that 2000x times powerful than the supercomputer Cray and his associates built in the 60s and the 70s.
It is easy to point out that Deep Learning has two major branches:
This was the distinct division of components driving any computer. But, in the last few years, we have seen hardware moving to more sensor-based applications, enabling the rise of a third branch called “The Internet of Things”. These IoT devices are the new medium of Connected Devices that can be connected to any computer for generating, ingesting, and analyzing data related to human bodies, locomotion, fitness tracking, and so on.
When my 7 year old nephew asks which is the best IoT device I saw as a kid, I tell it was the CD, or Floppy Drive that helped me move information from one system to another. Obviously, deep learning professionals may not look into that era anymore, but the data transport and its evolution from USB, to Fiber optics, and now to Bluetooth-enabled and Cloud architecture really needs to keep the race open for innovations and disruptions.
Since we are already done and dusted with the decade that has witnessed phenomenal rise of AI and Machine learning applications, along with specific IoT and Drone capabilities, it’s empirical for every data scientist to think about the future of Neural Networking.
In 2020-2029, what holds for the careers of Deep Learning?
In the next 10 years, we will see the transformation of deep learning jobs toward more strategic and data privacy-related assignments. From protecting human data to keeping machines at the helms of detecting and ‘prosecuting’ data breaching individuals, we may see Data Scientists becoming the ‘Police Force’ of the internet and computer industry through this roles:
With data science training course, applying for these roles is easy.
