AI and ML use-cases in Industry

With all the buzz around artificial intelligence, and machine learning (ML), enterprises are now becoming curious about the applications and benefits associated with machine learning in business. ML is a data analysis process that uses ML algorithms to iteratively learn from the existing data and help computers find hidden insights without being programmed for. Some of the most common applications are Spam detection for email, and Image or Face tagging done by Facebook. While Gmail recognizes the selected words or the pattern to filter out spam, Facebook automatically tags uploaded images using the image (face) recognition technique. Business benefits of AI and ML are numerous.

What is Artificial Intelligence(AI)?

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs, and perform human-like tasks which are achieved by giving intelligence to the machines. The main goal is to create smart machines with human intelligence. Four different approaches have historically defined the field of AI:

Thinking humanly

Thinking rationally

Acting humanly

Acting rationally

Why is artificial intelligence important?

AI automates repetitive learning and discovery through data — AI is used to perform frequent, high-volume, computerized tasks reliably and without fatigue.

AI adds intelligence — AI is something that can be sold as a single entity rather it can be integrated into an existing application to improve its capabilities like Siri was added as an extra feature to a new generation of Apple products.

AI adapts through progressive learning algorithms — AI finds structure and regularities in data so that the algorithm acquires a skill, it basically tries to find ou the pattern based on which it learns an algorithm which thus becomes a classifier or a predictor.

AI analyzes more and deeper data — They are using neural networks that have many hidden layers. For example — building a fraud detection system with five hidden layers was almost impossible years ago. As it requires deep learning models which get trained from the data. The more data you can feed them, the more accurate they become.

AI achieves incredible accuracy — AI techniques from deep learning, image classification, and object recognition can now be efficiently used to find cancer on MRIs with the same accuracy as highly trained radiologists which is achieved with the help of neural networks.

AI gets the most out of data — Because these algorithms are self-learning, the data itself can become intellectual property.

Different types of AI?

At a very high level, artificial intelligence can be split into two broad types: narrow AI and general AI.

  1. Narrow AI — This kind of artificial intelligence operates within a limited context and is a simulation of human intelligence. These machines are designed to perform single or few bunches tasks under constraints and limitations than even the most basic human intelligence.

A few examples of Narrow AI include:

  • Google search
  • Image recognition software
  • Siri, Alexa, and other personal assistants
  • Self-driving cars
  • IBM’s Watson

2. Artificial General Intelligence (AGI) — AGI is a machine with general intelligence and, much like a human being, it can apply that intelligence to solve any problem. for Example — robots from Westworld.

Machine Learning(ML)

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. It is one of the branches of artificial intelligence based on the idea that systems can learn from data(historical data), identify patterns, and make decisions with minimal human intervention. It uses various methods from neural networks, statistics, operations research, and physics to find hidden insights/patterns in data without explicitly being programmed for where to look or what to conclude. It is based on the study of historical data. We actually feed in DATA(Input) + Output, run it on the machine during training and the machine creates its program(logic), which can be evaluated while testing. We test the best logic by determining its accuracy which is calculated by the loss. The dataset is simply split into halves training and testing datasets. The machine determines the logic with the help of the training dataset and tests its accuracy with testing. The model is created with this process. The more data we feed into the model, the higher the chances of better accuracy. But at some point, even the higher data creates stagnation then we opt for neural networks or deep learning.

Artificial Intelligence & Machine Learning

AI and ML are getting Widespread overuse in marketing have managed to thoroughly confuse the meanings of these words. AI is a broad science of making machines intelligent. ML is a subset of AI that focuses on training the machines how to learn. Machine learning model looks for pattern and data and try to find conclusions. AI has a very wide range of scope. It aims to create an intelligent system that can perform various complex tasks whereas machine learning is working to create machines that can perform only those specific tasks for which they are trained.

Applications of AI-ML

Chatbots — AI-powered chatbots in enterprises will also see an influx of people get more comfortable with how AI can benefit businesses. It is used for delivering smart and flexible analytics through conversations on mobile devices using standard messaging tools and voice-activated interfaces. It reduces the time to collect data for all business users, thus accelerating the pace of business and streamlines the way analysts use their time, preparing companies for the growing data needs of the near future.

eCommerce — It provides a competitive edge to e-commerce businesses and is becoming readily available to companies of any size or budget. AI is using its algorithm to enable shoppers to discover associated products whether it is size, color, shape, or even brand.

Improve Workplace Communication — Business communication is overloaded with content, channels, tools, depriving individuals (and companies) of hitting targets while also harming work-life balance. Artificial Intelligence will help businesses improve communication internally and externally by enabling individual personalization for each professional, allowing for enhanced focus and increased productivity.

Healthcare — AI presents opportunities for the health-based mobile applications to take the data we have gathered from patients and be able to clinically innovate to improve patient outcomes to an even greater extent. AI improves reliability, predictability, and consistency with quality and patient safety.

Machine Learning Industry Use cases

Pinterest — Machine learning is quite critical to Pinterest’s core business. Pinterest is a curated bulletin board of inspirational content which has the ability to identify pins that the user doesn’t even know they will like is critical for Pinterest’s long-term success. The tailored content not only allows Pinterest to increase user engagement and customer retention but also enhances Pinterest’s recommendation power. The machine learning models are only as good as the data they receive so as the user becomes more active the model can better suggest related content that would be beneficial to the consumer. Pinterest not only utilizes machine learning for their pin recommendations but also throughout their entire organization to run a more efficient business. For example, the black ops team uses machine learning to detect spam content, the monetization team uses machine learning to develop ad performance and the growth team uses machine learning to prevent user churn.

Twitter — Machine learning is used in Twitter to drive engagement, surface content to the relevant users according to the tweets they have interacted with, and promote healthier conversations. The ML platform provides tools that span the full ML spectrum, from dataset preparation to experimentation to deploying models to production. After examining TensorFlow is here to stay. It supports HDFS out of the box, has lots of documentation, and a large community. During experimentation, model metrics can be easily visualized using TensorBoard. These aspects were also strong arguments in favor of TensorFlow. Twitter’s choice data format is the data record. It has a long history of use for ML tasks at Twitter. DeepBird v2 recognizes data saved using this format.

Walmart — They are always ready to transform retail operations and customer experience by using machine learning, the Internet of Things (IoT), and Big Data. In recent years, its patent applications, a position as the second-largest online retailer, and investment in retail tech and innovation. Walmart was an early adopter of RFID to track inventory and has a tech incubator. Recently, Walmart launched Pick-up Towers in some of its stores that are 16 x 8-foot self-service kiosks conveniently located at the entrance to the store that retrieves online orders for customers. Customers can just scan a barcode on their online receipt and within 45 seconds the products they purchased will appear on a conveyor belt. They have received positive feedback from the customers regarding Pick-up Towers as an improvement over the store’s traditional pickup process. The new approach they want to adopt is Scan and Go Shopping. Customers in the pharmacy and money services areas will be able to use the Walmart app for some aspects of the checkout process instead of waiting until they reach the counter and then will be able to bypass the main queue to get in and out of the store more quickly. This is a step in the direction of being able to bypass the checkout process entirely with the use of computer vision, sensors, and machine learning as used at the Amazon Go concept store. Walmart already uses machine learning to optimize the delivery routes of their associate home deliveries.

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