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What Is Artificial Intelligence?

Source- https://aws.amazon.com/machine-learning/what-is-ai/

Artificial Intelligence (AI) is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. Artificial Intelligence, often abbreviated as "AI", may connote robotics or futuristic scenes, AI goes well beyond the automatons of science fiction, into the non-fiction of modern day advanced computer science. Professor Pedro Domingos, a prominent researcher in this field, describes “five tribes” of machine learning, comprised of symbolists, with origins in logic and philosophy; connectionists, stemming from neuroscience; evolutionaries, relating to evolutionary biology; Bayesians, engaged with statistics and probability; and analogizers with origins in psychology. Recently, advances in the efficiency of statistical computation have led to Bayesians being successful at furthering the field in a number of areas, under the name “machine learning”. Similarly, advances in network computation have led to connectionists furthering a subfield under the name “deep learning”. Machine learning (ML) and deep learning (DL) are both computer science fields derived from the discipline of Artificial Intelligence.

Artificial Intelligence (AI) is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. Artificial Intelligence, often abbreviated as "AI", may connote robotics or futuristic scenes, AI goes well beyond the automatons of science fiction, into the non-fiction of modern day advanced computer science. Professor Pedro Domingos, a prominent researcher in this field, describes “five tribes” of machine learning, comprised of symbolists, with origins in logic and philosophy; connectionists, stemming from neuroscience; evolutionaries, relating to evolutionary biology; Bayesians, engaged with statistics and probability; and analogizers with origins in psychology. Recently, advances in the efficiency of statistical computation have led to Bayesians being successful at furthering the field in a number of areas, under the name “machine learning”. Similarly, advances in network computation have led to connectionists furthering a subfield under the name “deep learning”. Machine learning (ML) and deep learning (DL) are both computer science fields derived from the discipline of Artificial Intelligence.

Use Cases

Anomaly Detection

Identify items, events or observations which do not conform to an expected pattern or other items in a dataset.

Fraud Detection

Build predictive models that help identify potentially fraudulent retail transactions, or detect fraudulent or inappropriate item reviews.

Customer Churn

Find customers who are at high risk of attrition, enabling you to proactively engage them with promotions or customer service outreach.

Content Personalization

Provide a more personalized customer experience by using predictive analytics models to recommend items or optimize website flow based on prior customer actions.

What is Deep Learning?

Deep Learning is a branch of machine learning that involves layering algorithms in an effort to gain greater understanding of the data. The algorithms are no longer limited to create an explainable set of relationships as would a more basic regression. Instead, deep learning relies on these layers of non-linear algorithms to create distributed representations that interact based on a series of factors. Given large sets of training data, deep learning algorithms begin to be able to identify the relationships between elements. These relationships may be between shapes, colors, words, and more. From this, the system can then be used to create predictions. Within machine learning and artificial intelligence, the power of deep learning stems from the system being able to identify more relationships than humans could practically code in software, or relationships that humans may not even be able to perceive. After sufficient training, this allows the network of algorithms to begin to make predictions or interpretations of very complex data.

Use Cases

Image and Video Classification, Segmentation

Convolutional Neural Networks out-perform humans on many vision tasks including object classification. Given millions of labeled pictures, the system of algorithms is able to begin identifying the subject of the image. Many photo-storage services include facial recognition, driven by Deep Learning. This is central to Amazon Rekognition, Amazon Prime Photos, and Amazon’s Firefly Service.

Speech Recognition

Amazon Alexa and other virtual assistants are designed to recognize a request and return a response. While understanding voice is something that humans can do at a very young age, it is only recently that computers have been able to listen and respond to humans. Varying accents and speech patterns in humans make this a difficult machine task to complete utilizing more traditional math or computer science. With Deep Learning, the system of algorithms can more easily determine what was uttered and the intent.

Natural Language Understanding

Natural Language Processing seeks to teach the system to understand human language, tone, and context. This begins to allow the algorithm to discern more difficult concepts such as emotion or sarcasm. This is a growing field as companies seek to automate customer service with voice or text bots, as used by Amazon Lex.

Recommendation Engines

Online shopping often involves personalized content recommendations related to items you might like to purchase, movies you might want to watch, or news you might be interested in reading. Historically, these systems were powered by humans creating associations between items. However, with the advent of Big Data and Deep Learning, humans are no longer necessary since algorithms can now identify the items that might interest you by examining your past purchases or product visits, and comparing that information to that of others.

>> Learn about MXnet, the open source deep learning framework, and how you can get started.

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