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| Photo: Vlad Tchompalov |
It was the first computer program to defeat a professional human Go player, much less a world champion. Later that year, Google introduced AlphaGo Zero, an even more powerful iteration of AlphaGo.
Anyone wanting to understand the difference between artificial intelligence and deep learning can start by understanding the difference between AlphaGo and AlphaGo Zero. With AlphaGo, Google trained the original AlphaGo to play by teaching it to look at data from the top players, said Avi Reichental, CEO of XponentialWorks. Within a short period of time it was able to beat almost all standing champions hands down, he said. But with AlphaGo Zero, instead of having an algorithm look at lots of data from other players, Google taught the system the rules of the game and let the algorithm learn how to improve on its own, Reichental said. The end result, he said, is a computational power unparalleled in speed and intelligence.
Without a doubt artificial intelligence is becoming more common in our daily and business lives. It is making appearances in voice assistants and chatbots, as well as in complex business applications. As it does, it is important to learn to distinguish among the different types of AI, such as deep learning.
Defining AI and Its Many Iterations
Starting with the basics, AI is a concept of getting a computer or machine or robot to do what previously only humans could do, said Mark Stadtmueller, VP of Product Strategy at Lucd. Machine learning is a type of AI where algorithms are used to analyze data, he continued. “Machine learning analysis involves looking for patterns within the data and creating and refining a model/equation that best approximates the data pattern. With this model/equation, predictions can be made on new data that follows that data pattern.”
Neural networks are a type of machine learning in which brain neuron behavior is approximated to model many input values to determine or predict an outcome, Stadtmueller said. When many layers of neurons are used, it is called a deep neural network. “Deep neural networks have been very successful in improving the accuracy of speech recognition, computer vision, natural language processing and other predictive capabilities,” he said. When using deep neural networks, people refer to it as deep learning, Stadtmueller said. “So deep learning is the act of using a deep neural network to perform machine learning, which is a type of AI.”
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Source: CMSWire
