AI Google Deepmind

AI Google Deepmind is going to play a major role in the game world. it will also help the machine learning another major area of the tech world. just take your time to read this article on Google DeepMind. I believe you will love it.

Artificial Intelligence (AI) has multiplied and carries out various tasks that only humans could do. Furthermore, Technologies like Machine Learning carry out administrative tasks, recognize faces, play chess and even translate languages. Deep Learning learns from unstructured data to compile analytical reports or carry out tasks unsupervised by humans.

Companies like DeepMind were founded to continue developing this field. What is there to know about this company? The important things you need to know about AI Google Deepmind :

Google DeepMind’s history

DeepMind Technologies was established in 2010 in London by Demis Hassabis, Mustafa Suleyman and Shane Legg, who are all AI enthusiasts and some regard them as pioneers of deep learning. But 4 years after that, Google acquired this company. Its ownership also changes in 2015 because it was then acquired by Alphabet, Inc and since then, it has been a subsidiary of this company

DeepMind Technologies research centres are in the United States, Canada, and France. It started being recognized by many in 2016 after creating AlphaGo which beat Go’s world champion Lee Sedol.

They developed another program called AlphaZero that plays chess, shogi and go best. The game was documented and they began giving credit to the company.

DeepMind received quite large financial support because individuals like Scott Banister and Elon Musk also chipped in. That was an addition to the capital they derived from venture capital companies, Horizons Ventures, and Founders Fund. The founders of DeepMind had a solid presentation to these entities and that’s why they received the funding.

General-Purpose Learning Algorithms

DeepMind is very interesting in general-purpose learning algorithms the company that started doing so by developing systems that can play a wide range of different games.

One of the founders mentioned that human-level artificial intelligence can be reached when a program can play different games. By machines learning how to play these complex games, they will attain the capability of thinking and acting strategically.

Even though the company is keenly interested in machine learning to achieve human intelligence, it also has an objective view on the safety of using these technologies. 

DeepMind developed an open-source testbed is called GridWorld and it ensures that AI remains safe and harmless to itself, developers and other human beings exposed to it.

 AI Google Deepmind
AI Google Deepmind

Deepmind’s Deep Reinforcement Learning

DeepMind’s deep reinforcement learning isn’t preprogrammed but learns with experience just like any human being does.

For example, IBM Watson or Deep Blue was developed with a certain purpose and is programmed to function in the desired capacity only

Their systems are then tested on a variety of video games without being programmed instructions on how to play that game. 

Everything is done independently by the system and it learns how to play the video game and, after quite a few attempts, plays better than any human being.

Deep reinforcement learning removes any human error that could disturb the efficiency of the gameplay. It hasn’t been used in games only but also a variety of different useful systems that have had an impact on the healthcare industry.

Other contributions to Google

DeepMind’s AI system gathers data on your preferences and then recommends apps similar to the ones that you have downloaded before.

Very soon, users that have devices that run on Android Pie will have features such as adaptive brightness and battery. Machine learning will assist with energy conservation on these devices by adapting the brightness to current lighting conditions.

DeepMind systems have increased the efficiency of those cooling systems and Google has greater plans in store for this company. Also, it will make the operating systems generally easier to use, improving the user experience. 

Creating these systems should have been a little more complex because of the small scale of this project. Machine learning systems of this kind generally require larger computing power to function successfully.

WaveNet Collaboration

DeepMind collaborated with Google and speech-impaired individuals like Tim Shaw, who suffers from Amyotrophic Lateral Sclerosis (ALS).

There are millions of people that suffer from speech impairment and can’t get back their original voice. Text-to-speech systems often produce robotic or unnaturally sounding voices.

The objective was developing a system that sounds like the natural voice of the patient, which may first seem like mission impossible. Recreating a voice needs hours of audio recordings of that individual reading a particular script.
The results surprised them because it sounded like Tim’s voice before ALS started affecting his speech abilities. You can see the reaction for yourself on YouTube because the whole experience was filmed and uploaded.

The bottom line

Deepmind has taken on other collaborations such as WaveNet that add value to the lives of the population. The contributions that it has made to Google’s AI department are very valuable and have been used on a global scale. The peculiarity of the AI system they use, deep reinforcement learning has made them the company of choice for AI Google Deepmind.

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