Introduction to AI & ML
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The document introduces artificial intelligence, machine learning, and deep learning. It discusses supervised, unsupervised, and reinforced learning techniques. Examples of applications discussed include image recognition, natural language processing, and virtual assistants. The document also notes that some AI systems have developed their own internal languages when interacting without human supervision.
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Introduction to AI & ML
- 1. Artificial Intelligence & Machine Learning Introduction & Business Use Cases Nov 2017 Mandy Sidana
- 2. Artificial Intelligence, Machine Learning & Deep Learning
- 3. Artificial Intelligence is the ability of computer systems to perform tasks that normally require human intelligence, such as visual perception, speech recognition & decision-making. A field of study that seeks to explain and emulate intelligent behaviour in terms of computational processes Artificial Intelligence is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving” Artificial Intelligence
- 4. Learning denotes changes in a system that enable a system to do the same task more efficiently the next time Machine learning is programming computers to optimize a performance criterion using example data or past experience Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Machine Learning
- 5. Supervised Learning • To map a logic when input and output is given to a computer Unsupervised Learning • No labels are given to the learning algorithm, leaving it on its own to find structure in its input Reinforcement Learning • A computer program interacts with a dynamic environment in which it must perform a certain goal Machine Learning Classification
- 6. Neural Network Architecture. A Brain modelled. In between the input units and output units are one or more layers of hidden units, which, together, form the majority of the artificial brain. Most neural networks are fully connected, which means each hidden unit and each output unit is connected to every unit in the layers either side. The network allows self development hidden layers and assign weights to these layers from inputs to match with output layers. This is called supervised learning
- 7. Architecture in Reinforced learning During unsupervised and reinforced learning, the outcome being a positive or negative indicator, reinforces the behavior the network to promote or demote a node
- 8. Can machine brains dream? In a famous experiment by Google, allowing neural nets trained to identify images to run a continuous feedback loop by linking output to input, thereby creating a dream like state for the neural net, resulted in these remarkable images.
- 9. Bots developing their own languages ● Google translate neural net developed its own artificial language to translate context of complete sentences ● Facebook neural bots assigned with task of learning negotiation eventually learned to lie by themselves and also developed their own language. Facebook eventually shut down the program. ● In both the situations, humans were incapable of understanding these artificial languages created by bots for their own communication.
- 10. Emerging Applications of Artificial Intelligence
- 11. AI as a service Application Business Context Vision Image processing algorithm to identify , caption and moderate pictures Knowledge Map complex information and data to solve tasks such as intelligent recommendation and semantic search Language Allow apps to process natural language with pre-built scripts and learn how to recognize what users want Speech Convert spoken audio into text Search Search APIs to your apps and harness ability to comb billions of webpages, images, videos Examples: Microsoft Cortana IBM Watson used in domains: • Retail • Financial Services • Education Sector • Health Sector Every Google application: • Google Search • YouTube • HDR+ • Google Drive
- 12. AI transformation opportunities for companies
- 13. Investments in the Field of AI in 2016
- 14. Natural Language Processing ● Ability of machines to understand and interpret human language the way it is written or spoken ● Applications in solving business problems by using NLP in Big Data ● Application in Log Analysis and Log Mining to extract useful information and knowledge
- 15. Communication Applications Skype Translator for real time language interpretation Automatic Speech Recognition & Text To Speech applications in Search Engines Customer Review Improve customer satisfaction by analyzing large volume of customer reviews Suggest and target more relevant products by Big Data analysis through NLP Virtual digital assistants Online Purchases, Music Streaming, Providing Surroundings Information Apple’ Siri, Google Assistant, Amazon Alexa, Microsoft’s Cortana Business Applications NLP
- 16. Image Recognition Use Cases • Emerging field of AI that analyses images and retrieves information about them real time • Camera based Google translate • Face detection and object identification for sorting photo libraries • Google Photos • Assistance for Online Shopping • Point & Shop in Amazon Flow
- 17. References • http://www.turingfinance.com/misconceptions-about-neural-networks/ • https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html • https://medium.freecodecamp.org/the-mind-blowing-ai-announcement-from-google-that-you-probably-missed-2ffd31334805 • https://www.newscientist.com/article/2114748-google-translate-ai-invents-its-own-language-to-translate-with/ • https://techcrunch.com/2016/11/22/googles-ai-translation-tool-seems-to-have-invented-its-own-secret-internal-language/ • https://www.accenture.com/lv-en/_acnmedia/PDF-33/Accenture-Why-AI-is-the-Future-of-Growth.pdf • https://www.mckinsey.com/lv-en/_acnmedia/PDF-33/Accenture-Why-AI-is-the-Future-of-Growth.pdf