PWC predicts that by 2030, AI will add 7 15.7 trillion to the global economy. When more than 90% of Fortune 1000 is written on the wall, their big data and AI costs increase. If you are a country, organization or individual that wants to be technologically relevant in the next decade, understanding AI is essential.
The good news is that you don't have to build neural networks from the ground up to participate. Understanding how to differentiate key elements and how to use AI in real business contexts (and how you can use it) is half the battle. This article will take you there.
We use "AI" as a loose term to refer to ecosystems that include computer vision, natural language processing, speech recognition, predictive analytics and other methods. Machine learning, including in-depth study, is one of the basic technologies of these applications. First let’s take a look at some of the most popular vocabulary words, feel free to skip if you already know.
Machine learning is an algorithm used to perform AI functions, often using labeled data, with numbers or group estimation rules. Deep learning is a special type of machine learning that uses multiple layers of neural networks. For example, providing an in-depth study algorithm allows security cameras to estimate images of 1,000 labeled individuals that the camera feed contains a person requesting an intrusion alert.
Natural Language Processing (NLP) is the application of ML to understand the human language. For example, an NLP algorithm provides tweets to detect emotion using positive and negative syntax and words. Using machine learning in NLP tasks.
Computer Vision is an application of machine learning to understand images or video streams. For example, the 1000 images labeled by the above individuals are an example of using machine learning to achieve computer vision.
Google Search Post-2015 (deep learning + natural language processing)
Google struggled with ~ 15% of questions never received. To minimize the number of times users think "How can I say it so Google understands it," it lists natural language processing and machine learning.
AI Improvement: Model search breaks mapping relationships between words to draw familiar phrases to the model. Tech News Publisher CIO Post-2015 User Input Provides an example of user-re-wording "What is the user's title at the top of the food chain?" Search results.
After Netflix Recommended Engine 2012 (deep learning + natural language processing + computer vision)
Netflix improves content recommendations by adding IMBD scores and self-report user data.
AI Improvement: Netflix can pull content that identifies female hero, action level, tone, language, setting, and so on. These features are converted into a keyword mapped to user preferences.
Telefonica Nika Satellite Business Development (Machine Learning + Computer Vision)
Telecom companies like Telefonica focus on high-density urban areas and the infrastructure needed to install telecom equipment. Rural areas do not have these symptoms and suffer.
AI Improvement: Satellite imaging and computer vision can be used to identify telephonica low areas (insufficient population density) and map logistic routes to these areas. This has broadened the geography that affects telephony for service.
Verizon Quality Assurance Post-2017 (Machine Learning)
Verizon's 150+ million users can't do bad service before AI / machine learning. Verizon has no choice but to rely on customer complaints and work to resolve issues.
AI Improvement: Verizon can now predict poor service quality using a number of computed real-time indicators including weather, hyper-active use, and regular checks to determine "normal" service for a particular location.
Unilever AI Recruiting (Machine Learning)
Unilever used a combination of resumes, references, and interviews to assess potential employees.
AI Improvement: In the initial screening process, ask applicants to play a 12-game series and submit their biodata online. Machine learning is used to break down keywords in resumes and scores from games to identify undesirable recruiting features. For example, the game applicant must press the balloon more w and dock them to pop the balloon. The game was used to measure risk tolerance and enabled a comprehensive screen for Unilever Recruiters, mapped to hundreds of other features expected by the candidate. Unilever reports that their AI recruitment tool Unilever saves 70,000 man-hours every year.
Thanks to AI Google users searched in the natural language, Netflix customers can enjoy their preferences without understanding, new geographic areas are affected by Telefonica, Verizon customers are automatically supported and Unilever job applicants match their exact role.