Artificial Intelligence, Machine Learning and Deep Learning @GenesisBlockch
Gothenburg, Sweden - May 13, 2019 (Investorideas.com Newswire) During past days I've heard a lot, more than usual, to people close to me talking about how technology is helping and at the same time invading their routines. All of them mention these seemingly new and cool buzzwords: machine learning, deep learning and artificial intelligence.
But how does one word encompass the other? How does the "regular Joe" know?
When words such as these reach the 'common ground' it means that technology is evolving quickly, putting these words and resources in the hands of the every day man and woman.
Call it a next step where technologies and and its core terms find access to an individuals' day to day activities. For example, heard some thoughts like: how can Siri interact as if it knew what the best choice for its owner is or how is it that Netflix guesses what the user would like to watch?
Defined more or less as an intelligent agent capable of perceiving its environment and making decisions to maximize the chances of achieving its goal. Ai includes different subfields, among them vision, robotics, machine learning, planing, and natural language processing.
The Artificial Intelligence revolution is generating applications and solutions after a considerable amount of research and beta-testing. Advancements in AI can now diagnose treatments before the patient even gets sick. There are new developments and new tasks being accomplished by machines previously done by humans. Not long ago, the word AI brought us 'cool ideas' about sexy robots (Bladerunner) or omnipotent digital hive minds (The Matrix). These days, artificial intelligence is all around us. And it's not a sexy robot (or is it?).
Traditional CPU data centers will encounter obstacles trying to fulfill AI demands, something else will be innovated, like an advanced or custom distributed ledger technology (Blockchain).
Machine learning gives computers the ability to learn without being explicitly programmed. It is divided into 3 different subsets Supervised Learning (classification, regression), Unsupervised Learning (Clustering, dimensionality, reduction, recommendation), and Reinforcement Learning (reward maximization). It's important for people to know where the hype around machine learning ends and where practical applications begin. For me, I see blockchain technology as the enabling infrastructure that will allow machine learning to reach its full potential.
Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to 'deep learning' because the neural networks have various (deep) layers that enable learning. Just about any problem that requires "thought" to figure out is a problem deep learning can learn to solve.
Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. The more deep learning algorithms learn, the better they perform.
A quick sample is the case of facial recognition:
Deep learning is being used for facial recognition not only for security purposes but for tagging people on Facebook posts and we might be able to pay for items in a store just by using our faces in the near future. The challenges for deep-learning algorithms for facial recognition is knowing it's the same person even when they have changed hairstyles, grown or shaved off a beard or if the image taken is poor due to bad lighting or an obstruction.
The more experience deep-learning algorithms get, the better they become. It should be an extraordinary few years as the technology continues to mature.
Keep in touch with these three terms, whenever you feel tempted, the internets has vast amounts of information where you will find education. Our society is becoming an integral part for tests technologies to evolve, not the other way around.
Keep an eye on them.
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