Artificial Intelligence Definition, Opportunities and Challenges

Transcription

1 Artificial Intelligence Definition, Opportunities and Challenges Patrick Hosein and Inzamam Rahaman The University of the West Indies Commonwealth ICT Forum / 20

2 Some Definitions Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. (Wikipedia) Big Data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. (Wikipedia) Business Intelligence (BI) comprises the strategies, processes, applications, data, technologies and technical architectures used by enterprises to support the collection, analysis, presentation and dissemination of information. (Wikipedia) Machine Learning (within the field of data analytics) is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. (Wikipedia) 2 / 20

3 Factors for Surge in Artificial Intelligence? AI has become practical because of increased computing power/storage and data from: Social Media (FB), Online Retail (Amazon), Online Entertainment (Netflix) Health Sector, Business Processes, Manufacturing Internet of Things (Smart Homes, Smart Cities, Smart Grids, Wearable Devices) Connected Sensors (Agriculture, Transportation, Manufacturing, Security) 3 / 20

4 Big Data (growth in video) 4 / 20

5 Big Data (growth in IoT sources) 5 / 20

6 Business Intelligence (displaying big data) 6 / 20

7 Data Analytics (making sense of data) Computer Vision - giving machines the ability to understand and react to visual stimuli (self-driving cars) Natural Language Processing - giving machines the ability to communicate, understand, and manipulate human language (chatbots) Voice Recognition - giving machines the ability to understand human speech and respond to its content (Siri, Alexa) Face Recognition - giving machines the ability to detect and identify objects (e.g. faces) in images (Facebook, iphotos) Recommender Systems - giving machines the ability to make product recommendations based on historical and peer data (Netflix, Amazon) 7 / 20

8 Crash Course in ML (prediction with limited data) 8 / 20

9 Crash Course in Machine Learning (prediction with big data) 9 / 20

10 Crash Course in Machine Learning (training a neural network) 10 / 20

11 Neural Network Training Process Feed pixels of photos of a cat (or dog) to train ANN Modify weights to better achieve correct output (e.g. if image is cat then probability of cat should be high in output). Repeat for all training images (possibly multiple times) When trained, given any image of cat (or dog) ANN should produce output in which probability of cat (or dog) is high and probability of dog (or cat) is low. 11 / 20

12 Result: Image Classification Given an image of some object detect the main object in the image 12 / 20

13 Data Analytics Problem Domains 13 / 20

14 Data Analytics Techniques include Clustering (K-means, Hierarchical) Prediction (Regression, Neural Networks, Naive Bayes) Dimensionality Reduction (Principal Component Analysis) Data Processing (Search, Sort, Merge, Compression, Encryption) Graph Algorithms (Shortest Path, Max-Flow, Spanning Tree) Optimization: {Linear, Non-Linear, Dynamic, Stochastic, Integer} Programming 14 / 20

15 Some Practical Aspects of Data Analytics 15 / 20

16 Some Perils of Data Analytics Data Quality: Some companies do not use collected data so validation and consistency checks are not performed. Past data must be scrubbed, holes filled and standards developed for future data collection Privacy: Customer privacy is important and their data must be kept secured (maybe anonymized) when performing analytics Security: Keep company data secured especially when using cloud-based and third party solutions. Keep backups. 16 / 20

17 Emerging Jobs and growth (Source: LinkedIn) 17 / 20

18 Machine Learning Applications in Telecommunications Customer service chatbots (routing service inquiries, etc.) Speech (recognition) services for customers (e.g., Alexa, Siri) Predictive Maintenance (cell towers, power lines, etc). Reduce Customer Churn (increased retention) Automatic Parameter Tuning (e.g., beamforming in 5G) Personalized Product Recommendations Self Healing, Self Optimization and Self Learning for Wired/Wireless Networks Data Monetization (use subscribers data to sell services) Cybersecurity (monitor and instantly react to intrusions and hackers) 18 / 20

19 Some Challenges to Artificial Intelligence Data may not be easily accessible, poorly-organized, or unlabeled Significant computing power for some tasks (need GPUs) Significant creativity required to determine best method, architecture required (e.g. for Deep Neural Networks), etc. Algorithm cannot provide justification for result (as would a human) 19 / 20

20 Thank You 20 / 20