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Artificial Intelligence: From Science Fiction to Alexa

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For science fiction fans, Artificial Intelligence (AI) has always fired up the imagination. As a field of study, AI has been a part of academia since the mid-1950s.

Since then, AI has been hyped as the key to our civilization’s future, and panned as nothing more than entertainment for nerds.

However, over the past few years, AI has started to gain real traction. A lot of this has to do with the availability of powerful, cheaper and faster computing capability, the emergence of the Internet of Things, and the explosion of data generated as images, text, messages, documents, transactions, mapping and other data.

Many companies are aggressively adopting AI, for instance, to free up highly-skilled workers from routine, repetitive, low-skilled tasks. Learn more.

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Artificial Intelligence: From Science Fiction to Alexa

  1. 1. Artificial Intelligence: From Science Fiction to Alexa
  2. 2. Artificial Intelligence (AI) has always fired up the imagination. As a field of study, AI has been a part of academia since the mid-1950s.   Since then, AI has been hyped as the key to our civilization’s future, and panned as nothing more than entertainment for nerds. Artificial Intelligence
  3. 3. Many companies are aggressively adopting AI, for instance, to free up highly-skilled workers from routine, repetitive, low-skilled tasks. International Data Corporation is forecasting spending on AI and machine learning will grow from $8B in 2016 to $47B by 2020. Adopting AI
  4. 4. Back to the Basics – What is Artificial Intelligence? John McCarthy, one of the fathers of AI, defined AI as In other words, it's a way of making either hardware or software think intelligently, similar to the way humans think. „The science and engineering of making intelligent machines, especially intelligent computer programs”.
  5. 5. What is Intelligence? According to Intelligent Systems, the definition of intelligence is: “The ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generalize, and adapt new situations”.
  6. 6. When we talk about intelligence in a very simplified form, we are actually involving several very complex functions: Functions Learning Reasoning Problem Solving PerceptionLinguistic Intelligence
  7. 7. Machine Learning in its most basic form is about designing “models.” Models are composed of algorithms that use data, learn from it, and produce an “inference” or prediction about something. Machine Learning
  8. 8. Neural Networks are based on our interpretation of the connections between neurons in our brains. While real neurons can connect to any other neuron close enough in any direction, artificial neural networks have specific layers, connections, and directions in which data travels. Deep Learning – Neurons in layers
  9. 9. It’s all about the data. AI’s Deep Learning power comes from its ability to learn patterns from large amounts of data. This makes understanding data sets critical. AI runs on data. Lots of data
  10. 10. You can divide the typical AI process in a sequence of steps: Getting your hands dirty Data Collection Data Preparation Choosing the Model Parameter tuningEvaluation Training the model Inference or prediction
  11. 11. TensorFlow is probably the most popular deep learning framework today.  It is an open source library originally developed by the Google Brain Team, and the TensorFlow team has created a large number of models, many of which include trained model weights. Tools and Frameworks
  12. 12. There are other powerful frameworks, of course, like Caffe, PyTorch, and BigDL. Also, simulators, such as Digital Twins, allow developers to accelerate development of AI systems and the reinforcement learning libraries that integrate with them. Tools and Frameworks
  13. 13. In general terms, we can catalog the primary machine learning tasks in four groups: Machine learning tasks Supervised Learning Unsupervised Learning Semi-supervised Learning Reinforcement Learning
  14. 14. Over the years, different algorithms have been developed to resolve different types of use cases, including: Algorithms Decision Tree Learning Inductive Logic Programming and Many OthersBayesian Networks and Clustering Reinforced Learning
  15. 15. AI is increasingly being integrated into business processes across a number of different areas. To name just a few: Applications of AI Sales and CRM Applications Payments and Payment Services ManufacturingCustomer Recommendations Logistics and Delivery
  16. 16. Companies in all industries are rapidly finding use cases where AI can be successfully applied. In the short term though, it seems applications and use cases that have the highest rate of success and adoption are those that have a direct, measurable impact or return on investment. What’s next
  17. 17. Learn More appian.com twitter.com/appian facebook.com/AppianCorporation linkedin.com/company/appian-corporation youtube.com/user/appian instagram.com/appiancorp

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