DeepMind gained the world’s attention courtesy their practice of using suitable algorithms on data sets in order to harness a system’s capabilities, enabling it to come up with predictions. Established in 2010, DeepMind made a habit of building efficient machine learning systems before being acquired by Google for $400 Million.
CodeForce 360, through the length of this article, explains all you need to know about Google DeepMind, its striking features, what makes it tick and its capabilities.
ABOUT GOOGLE DEEPMIND
DeepMind took the whole world by storm this year when it beat the reigning Go (game) champion Lee Seedol. Contrary to other developments in Artificial Intelligence, DeepMind runs on a much more specific branch known as Machine Learning. The process relies on the implementation of neural networks and reinforcement learning to enable the system to make predictions. All of this thrives on large sets of data in most of the cases.
Unlike other AI systems loaded with laborious lines of code, DeepMind (as mentioned earlier) relies on algorithms applied on these large data sets.
TESTED EFFICIENCY, IMPORTANCE & APPLICATIONS
In order to test the efficiency and performance, the research team at DeepMind decided to make an agent play a game on an Atari gaming system. The agent surprised almost everybody on the team as it was able to perform at the level of a human in 49 out of 57 games.
While its accomplishments in the realm of gaming are growing by the month, the science of machine learning is touted to provide cost-effective solutions in the healthcare industry like being able to predict and provide solutions on apt treatment procedures etc.
As of today, Google’s DeepMind has also been tested and implemented for identifying handwriting, speech, images, spam as well as fraud.
Apart from these developments, the platform is expected to help in multiple critical areas such as improving sales, customer satisfaction and perform well at other field-specific tasks that determine the success and failure of operations in business.
Basically, anyone with a huge set of data at their disposal with the need for predictions can make great use of the machine learning algorithms.
The science behind machine learning is no closely-guarded secret given that a great deal of code is currently open-source.
Along with this, there are a whole series of videos providing instructions on how to build your own machine learning device, from Google themselves. There are also plenty of courses up for grabs at online learning portals such as Udemy, Coursera etc. Furthermore, one can even use some of the platforms such as IBM Watson, Microsoft Azure, Big ML, Amazon Machine Learning and more.
According to CodeForce 360 the popularity of these recent developments coupled with the resources at everybody’s disposal is just going to make things better for machine learning with a future full of developments and experimentation.
Moreover, unlike other recent technologies, the results so far speak volumes about the platform’s capabilities when implemented.
The future is certainly full of amazing prospects and the best is yet to come!