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In 2015, Alphabet (Google's parent company) acquired DeepMind, an artificial intelligence (AI) research company. Founded and headquartered in London in 2010, DeepMind is known for its significant contributions to AI research and development. The company's main focus is in the field of machine learning and deep learning.
The company's fundamental goal is to create AI that can learn and reason like humans. This way, the result could be more intelligent systems capable of solving complex problems.
In this article, we will delve a little deeper into the Google DeepMind and what are their fundamental contributions to AI.
Google DeepMind and its contributions
1. Deep learning and convolutional neural networks (CNNs):
DeepMind pioneered the successful application of convolutional neural networks (CNNs) for object recognition in images.
This has led to a significant advancement in the field of computer vision, allowing machines to identify objects, faces, and patterns in images with unprecedented accuracy.
CNNs, or Convolutional Neural Networks Deep neural networks are a specialized type of deep neural network architecture. They are designed to process and analyze data that has a grid structure. This includes images and time series data. They are especially developed for computer vision tasks, where feature detection and extraction in images are essential.
They were inspired by the organization of the human visual cortex, where different regions of the brain respond to specific parts of the visual field. In this sense, these networks are capable of automatically capturing hierarchical and complex features, such as edges, textures and patterns, at different levels of abstraction, allowing an effective representation of objects in images.
These features have been essential in the field of computer vision, and thus play a fundamental role in many applications, such as object recognition, face detection, and medical image analysis.
2. AlphaGo and board games:
Perhaps one of DeepMind’s most famous achievements was the development of AlphaGo. The program gained fame in 2016 when it challenged world Go champion Lee Sedol to a series of matches. AlphaGo won four of the five matches, demonstrating the AI’s ability to overcome highly complex and unpredictable challenges.
Go is an ancient board game originating from China, known for its strategic complexity and vast number of possibilities. On the other hand, unlike other games, such as chess, where the number of possible moves is relatively limited, Go presents an almost unimaginable number of positions and moves.
AlphaGo was designed to master the game, a notoriously difficult challenge for traditional AI approaches due to its complex nature and high number of possible combinations.
AlphaGo's approach involved:
Convolutional Neural Networks (CNNs):
The program used convolutional neural networks to evaluate the position of pieces on the board and identify strategic patterns.
Reinforcement learning:
AlphaGo was trained using reinforcement learning, where it played millions of games against itself. In this way, it learned to improve its strategies based on the results of these games and the rewards it obtained.
Monte Carlo Search Tree (MCTS):
AlphaGo used the MCTS technique to explore and evaluate possible moves in greater depth, helping it make more informed decisions.
AlphaGo’s success has had a significant impact on the field of artificial intelligence and has inspired new research and advancements. In doing so, it has also opened the door to applications in areas such as medicine, scientific research, and more. These are areas where AI in particular can be used to solve complex problems that previously seemed insurmountable.
3. AlphaZero and matrix multiplication
Another notable achievement of DeepMind was the development of AlphaZero. In this sense, it is an evolution of AlphaGo, and became notable for its reinforcement learning ability, learning and mastering games through self-learning. In this way, the application does not depend on human input or pre-programmed moves to act.
In addition to its achievements in board games, AlphaZero has also demonstrated the ability to accelerate the solving of complex problems such as matrix multiplication.
Matrix multiplication is an essential calculation for a variety of applications, ranging from displaying images on a screen to simulating complex physics systems. In addition, it is also essential in machine learning.
In this way, AlphaZero surprised by showing that its reinforcement learning and self-learning approach could be applied to accelerate matrix multiplication, breaking the record that had stood for more than 50 years.
And to leave no doubt about its capabilities, the new version, called AlphaDev, has further accelerated calculations and increased the solution for organizing items in a list by 70%. In addition, it has accelerated a fundamental algorithm used in cryptography by 30%.
AlphaZero has not only revolutionized the way AIs learn and play games, but has also demonstrated its ability to generate insights in other areas, accelerating computationally intensive processes.
4. Health and science:
Beyond gaming, DeepMind is also focused on applying its technologies to domains such as healthcare and science.
In this way, DeepMind has developed AI algorithms capable of analyzing medical images and assisting medical diagnoses, as well as modeling complex molecular interactions to advance scientific research.
5. Ethics and safety:
DeepMind has also demonstrated a commitment to AI ethics and safety. The company has contributed to the development of guidelines for responsible AI research exploring ways to mitigate potential risks associated with the advancement of artificial intelligence.
In short…
Google DeepMind is revolutionizing artificial intelligence through its achievements in a variety of fields, from gaming to medicine to ethics. As such, its research has the potential to shape the future of AI by making it more powerful, efficient, and accountable.
Marcel Castilho is a specialist in digital marketing, neuromarketing, neuroscience, mindfulness and positive psychology. In addition to being an advertiser, he also has a Master's degree in Neurolinguistic Programming. He is the founder and owner of Vero Comunicação and also the digital agency Vero Contents.
