DEEPMIND: SPECIAL AI TECHNOLOGY
Anyone can define DeepMind based on the problem it can solve. In general, DeepMind is all about Artificial Intelligence technology. It uses machine learning and neural network to solve a wide collection of problems; ranging from protein folding to analytical games.
DeepMind can also be defined as an Artificial Intelligence technology that uses machine learning to solve technical problems that a normal computer has not traditionally solved. This modern technology has already taken over almost all the different kinds of industries in real-life applications.
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Origin Of DeepMind
DeepMind started as a London-based technology company in 2010 by Demis Hassabis, Shane Legg, and Mustafa Suleyman. In 2014, it was acquired by Google. And today, it has become a subsidiary of Alphabet Inc.; the parent company of Google.
DeepMind is the big brain behind the development of general-purpose artificial intelligence (AGI) technology. And this technology has made the work of humans easier. In September 2022, a group of scientists who were identified as DeepMind’s officials won a $3 million astonishing prize for their discovery on the protein-prediction program (AlphaFold). This prize was the Breakthrough of the company.
Why Do You Need DeepMind For Your Business?
Over the past few decades, humans put in so much effort to reach their goals in different industries and organizations. But in today’s world, everything has evolved in the use of technology. And this technology can convert little work input into mind-blowing output. Here, we will be looking at how DeepMind has been helpful to humanity and why we should adopt such technology into our day-to-day businesses.
Firstly, we can use raw pixel data from DeepMind as input and this technology will learn and understand the input data to produce an output. The DeepMind AI uses deep learning to understand the input data on a convolutional neural network accompanied by a model-free reinforcement learning technique usually referred to as Q-learning.
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The method of operation of general-purpose AI has been seen to be very controversial. In this light, Google has been able to create and improve its AI technology to solve a wide range of problems. DeepMind technology can learn any game on its own. For instance, when you give DeepMind AI the task to play Atari games, it first learns how to play the game before playing it. Once it studies how to play the game well, it could play it much better than humans with more efficiency. Now, in the case of business, it could study and understand the business pattern and suggest better ways to improve that business. This could pave way for the business to boom with so much strategy from the owner of the business. This simply allows the DeepMind AI o do the job for you.
When it comes to the peak of AI, DeepMind could task itself to defeat any human on a board game like Go. The board game Go is highly logical and computational. DeepMind AI finds it more challenging because of the complexity of choosing the best move from numerous possible moves in the game. In such a scenario, DeepMind AI already developed a special project known as AlphaGo. This project is a computer program that is specifically designed to play any board game. In 2017, AlphaGo was the best-supervised learning AI and the No. 1 board player worldwide.
Aside from playing games, DeepMind can also be used to develop efficient and sufficient power in an already optimized data center at Google. DeepMind has been of great use in developing Google Assistant. It also helps users to recommend good Apps on Google Play Store. Through this, users can easily get a good and reliable app to develop their business without much stress.
Some other operations DeepMind AI can perform include the following:
- DeepMind AI can find a new way to multiply numbers and speed up a computer’s operation.
- DeepMind AI, with the help of digital humanoids, can learn to play football on its own.
- DeepMind AI can beat humans at playing logical games.
- DeepMind AI can predict the numerous ways in which proteins transform themselves into different functional shapes.
- DeepMind AI can compute data and optimize phone battery life.
- DeepMind’s protein-folding AI can solve the biggest problem in biology.
- DeepMind AI can learn chemistry, physics, mathematics, and other related subjects by watching videos that do not make sense to humans.
- DeepMind AI can make the job of a software developer easier than when the developer is working with his human brain. And this makes it a bigger threat to humans.
- DeepMind AI can accurately predict if there will be rain in the next 90 minutes.
- DeepMind AI can learn how to choose different colors to beautify a design.
- DeepMind AI can take IQ tests to analyze its ability to think abstractly.
- DeepMind AI can picture the whole world in a single picture.
- DeepMind AI can self-develop direction-finding neurons to solve a maze problem like us.
- DeepMind AI can learn to understand the thinking of others. (Similar to mind reading).
How Does DeepMind Work?
DeepMind AI system has been clearly stated to be an artificial neural network. This means that it is as organized as the network of nodes, replicating neurons-connection as the brain cells. DeepMind exclusively uses a complex neural network, which is similar to the human visual cortex. The human visual cortex is the part of the brain that helps in processing visual information. This gives more advantages to the network because the system can easily choose a specific desired feature from a series of filtered, large amounts of training data. For example, in image recognition, specific nodes in the neuron-connection structure become clever to recognize a particular feature. We can see a real-life application of this principle in the human eye system or audio data.
Deep neural network; a subset of DeepMind, performs this task by passing large data through a series of layers called “hidden layers.” Each of these layers is assigned to these data, to pick and choose what and where the network should focus more.
Now that we know about the hidden layers of DeepMind technology, we will discuss each layer and how they affect businesses.
The first layer is the convolutional layer. This layer detects the features of the input by using a filter called “kernel” to filter the input data. A combination of the input data and the kernel amplifies the features that the DeepMind AI presumes to be important.
The next layer is the pooling layer. This layer is majorly needed to reduce the complexity of the features the convolutional layer created. This makes it easier for the data to be processed.
The last layer is the fully connected layer. It uses the output from the pooling layer to predict future events. For instance, when the convolutional neural network has understudied the patterns of tumors in a set of medical training data (images), it could easily pick up new data (image) and predict if there could be any trace of tumors in the future. This is why DeepMind is very important in the medical field. With this, medical experts will save more lives.
The good thing about DeepMind is that it is a machine-learning algorithm. Just like training a child on how to eat, urinate, walk, bathe, and all that. The same applies to DeepMind. There is no specific rule for it. All you have to do is combine huge amounts of data and allow the machine to learn and identify the repeated patterns of the data. It will do the rest without the need for humans to make the output.
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What Does DeepMind Do And How Can It Develop Your Business?
The biggest advantage of DeepMind technology is its ability to be used for all fields of industries and industrial processes. The DeepMind’s algorithm can teach itself how to play Atari games and defeat humans in the Go game. Just like described above, the Go game is an attractive, complex, and strategic board game that requires deep thinking to capture an opponent’s territory on the board with the use of black and white pieces.
This game illustrates the ability of AI technology to learn a given data and master its pattern. DeepMind has been used to solve more and more real-life problems, ranging from generating pure-mathematical assumptions (which are often used in theoretical calculations) to revealing the possible structures of protein reformation.
The protein transformation work has shown that there could be a huge improvement in the field of proteomics. Proteomics is the branch of molecular biology that studies protein structure. Proteins can change from one shape to another but knowing how the change occurs internally needs careful attention because of how sluggish the process is. AlphaFold scientists had to experiment with a method known as X-ray crystallography to observe this transformation process. After several years of work, they predicted 200 million protein structures.
DeepMind AI is also used in Google’s data centers. Here, it helps to put the cooling equipment in check and at the same time reduce energy usage. WaveNet, one of the products of DeepMind, controls the voice of Google Assistant and their AI linked with YouTube controls the Ad placement and other parts of the video platform.
In 2022, scientists from the Swiss Federal Institute of Technology in Lausanne (EPFL) reported how they tested hydrogen plasma inside a fusion reactor. This is a step towards generating energy using nuclear fusion. Experts from different field of industry are already working on using algorithms learned by DeepMind AI to self-driving cars, diagnoses medical ailment, play games, analyze sports, and lots more.
Products and Technologies
- Deep reinforcement learning
- AlphaGo and successors
- AlphaFold
- WaveNet and WaveRNN
- AlphaStar
- AlphaCode
- Gato
- Miscellaneous contributions to Google
- Sports
- Archaeology
- Sparrow
- Chinchilla AI
What Records Has DeepMind Broken?
The record DeepMind had broken is far beyond discovering how to predict different shapes of protein structures. It is not the only achievement so far. In October 2022, a scientist from DeepMind broke a 50-year-old mathematical record. The record included a discovery of ways to do matrix multiplication, (multiplying two or more arrays of numbers). Usually, multiplying a 4-by-4 matrix of numbers with another structure of matrix will take 64 calculations with humans but with a DeepMind AI known as DeepTensor, the calculation will be performed in just 47 analyses.
After this record, another group of scientists published a written document that revealed that DeepTensor could reduce the number of times to calculate multiples of two 5-by-5 matrices.
DeepMind has repetitively beaten the best player of the Go game worldwide. Its victory led to the retirement of Lee Se-dol; a South Korean champion in 2019. In 2017, AlphaGo won another Chinese who was an undefeated champion of the game.
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Is DeepMind Ethical And Useful In Business?
The creators of DeepMind technology bear a heavy load of responsibility. Since DeepMind technology could drive traffic on social media, engage people on social media, pass the necessary information to people on social media, diagnose different medical conditions and use the result to either improve the patient’s life or save a patient from dying, and possibly drive people’s cars to their desired destination. Then it is indeed good and a MUST have for every business. And it is another responsibility of the creators to make more discoveries to improve their AI system.
The Problem Of DeepMind
In 2017, DeepMind introduced an Ethics & Society team to manage the right of the company but this could keep them completely out of the problems they were trying to avoid. Currently, they are faced with a class-action lawsuit in England over the scientific testing of an app known as Streams. This app was designed to detect acute kidney injury. But according to Healthcare IT News, the Royal Free London NHS Foundation gave enough patient data to test for it but it appeared that the agreement was broken because it went against the U.K.’s patient data protection law.
Conclusion
Notwithstanding if DeepMind is ethical or not. Its AI technology will always bring light and progress to the development of every business not minding its field.
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