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Introduction to the Artificial Intelligence (AI) & Machine Learning Industry, Business and Industry Trends Analysis

Artificial intelligence (AI) spending worldwide was estimated at $154 billion for 2023 by analysts at IDC and is expected to grow to $300 billion by 2026.  This is an estimate on a broad basis that includes spending on software and services.  Plunket Research estimates the U.S. market for AI at $175 billion for 2023.  Researchers at Gartner estimated that the global business value derived yearly from AI had already reached $3.9 trillion by 2022, while analysts at McKinsey Global Institute estimated that AI’s contribution to the global economy could soar to $13.0 trillion by 2030.
In many ways, artificial intelligence (AI) is a logical extension of recent technology trends.  A combination of ever-more-powerful computer chips, cloud computing, continued miniaturization of devices such as sensors, and the growth of the big data/data analytics sector are major enablers of AI on what is now a cost-effective basis.
Artificial intelligence (AI) and machine learning will create vast changes in nearly all segments of business and industry over the mid-term.  The effect of AI on consumers and households is already in broad evidence, although the people benefiting from such technologies may not be aware of the process or the significance of what’s going on around them.  For example, utilizing machine learning, pioneered the development of advanced software that learns from a shopper’s actions online and then makes product recommendations tailored to the individual.  In its early years, Netflix famously offered a $1 million prize to anyone who could engineer an algorithm that would learn from a subscriber’s movie rental habits in a manner that would increase the accuracy and usefulness of its online recommendation engine by 10%.  The more that Amazon or Netflix can display perfectly curated products for individual shoppers, the happier the consumer and the greater the amount of sales completed.  (Yes, Netflix paid off on this Progress Prize offer, selecting the work of a team of engineers that called themselves “BellKor’s Programmatic Chaos.”)
Search engines like Google and Bing utilize similar technology to serve up billions of dollars’ worth of online ads weekly to carefully targeted readers of news, entertainment and data online.   These recommendation engines run in the background 24/7; they learn more and more as time goes by and interactions with consumers increase; they benefit from frequent, incremental improvements made by software engineers; and they make the owners of these technologies highly efficient, effective and profitable in their business operations.  
Today, Amazon’s incredibly popular cloud computing subsidiary, Amazon Web Services (AWS), offers the “SageMaker” tool to enable companies of all sizes to quickly build machine learning tools.  AWS also offers easy-to-deploy AI-based tools for speech recognition, image recognition personalization engines, face recognition, forecasting and much more.  Many other firms also offer rapid-deployment AI platforms so that institutions and corporations of all types to easily put AI to work.
Consider the implications of machine learning for critical industrial processes.  For example, airlines around the world spend hundreds of millions of dollars monthly on fuel.  Imagine the benefit, both financially and in terms of reduced carbon emissions, if the air transport sector can reduce fuel usage a mere five percent through the utilization of machine learning—determinizing the most efficient air routes in light of current weather, setting the optimum engine speeds for fuel efficiency and assigning the most efficient flight paths in and out of airports by computer-aided air traffic controllers.  Airlines will thereby reduce both total time in the air and total fuel burnt.  This is but one possibility from tens of thousands of potential applications—virtually all factory, supply chain and transportation sectors can benefit through such uses of AI.

Technologies that Have Deep Synergies with Artificial Intelligence
Autonomous Vehicles
Big Data and Data Mining
Cloud Computing
Digital Assistants (Siri, Alexa, etc.)
Electronic Games
Energy Management and Conservation
Fraud Prevention Systems
Generative Chat-- Text/Images/Code Creation
Insurance and Credit Risk Modeling
Internet of Things (IoT)
Internet Search
Investment Modeling
Pharmaceuticals Research
Predictive Analytics
Predictive Marketing
Robotic Process Automation/Customer Service
Robotics and Automation
Sensors and Wireless Networks
Voice, Image and Facial Recognition
Source: Plunkett Research, Ltd.

How AI works:  Simply put, AI and machine learning work by finding patterns in data.  The larger the pool of data, the more observable the patterns and the better the accuracy and outcomes of the machine learning (ML) process.  To some extent, AI often simulates human brain-like functions such as learning, problem-solving, reasoning and perception.  This means that this technology can greatly speed up human or robotic tasks, by completing or enhancing work, and do it at blinding speed.
ML is a key component of AI and involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed in how to do so.  ML algorithms are exposed to large datasets and then discern patterns from the data, enabling the algorithms to improve their performance over time.  For example, ML might study a dataset containing the results of treatments of specific types of medical patients.  By observing those results, the machine will see patterns and can be programmed to predict outcomes of one type of treatment compared to another type.  There are several varieties of ML, including supervised learning, unsupervised learning and reinforcement learning.  This study of datasets during ML is considered to be “training” the AI system involved.
Technologies commonly used in AI include neural networks, which are computational models inspired by the structure and function of the human brain.  Neural networks consist of interconnected nodes (neurons) organized into layers that are capable of learning complex patterns from data.  Deep learning is a subset of neural networks that involves training deep AI architectures with many layers, enabling them to learn hierarchical representations of data.  Other AI technologies include natural language processing (NLP), which focuses on enabling computers to understand and generate human language; computer vision, which involves teaching computers to interpret and analyze visual information; and robotics, which combines AI with mechanical systems to create intelligent machines capable of performing physical tasks.
In summary, AI works by simulating human intelligence through techniques such as ML, neural networks, and other technologies.  ML, in particular, plays a crucial role in AI by enabling algorithms to learn from data and improve (train) their performance over time.  The main point is that AI software can be trained by being constantly fed data, queried as to its meaning and receiving feedback as to the accuracy or usefulness of its responses.  It is essentially training a machine to respond correctly to data of a given nature or to data within a given set of circumstances.

Industry Sectors with Significant Benefits from Artificial Intelligence and Machine Learning
(The higher the amount, recency and frequency of data available, the more useful the outcomes from applying AI to such data.  Health care is a perfect example, with vast amounts of patient and outcome data captured daily on a global basis.)
Health Care
=         Disease diagnosis and analysis of scans, samples and symptoms
=         Pharmaceuticals Research
=         Recommendations for optimum treatment and patient care
Internet and Digital Tools
=         Internet search and online advertising
=         Writing, responding to emails
=         Summarizing online meetings
=         Enhancement of precision Agriculture, for efficient planting, irrigation and harvesting
=         Prediction of weather
=         Providing traffic flow management
=         Enabling self-driving (autonomous) cars and trucks
=         Optimizing operations for aircraft, truck fleets, railroads and ships
Energy Efficiency and Production/Environmental Controls
=         Developing technologically advanced “smart cities” and green buildings
=         Improving energy efficiency in air conditioning, lighting and other systems
=         Improving operations and outcomes at all types of energy production operations, from selecting better sites for drilling oil wells to gaining optimum output from windmills
=         Enhancing air and water quality monitoring and control
=         Reducing plant downtime
=         Increasing efficient use of materials and personnel
=         Optimizing actions of robotic equipment
Financial Services
=         Better analyzing risk for insurance underwriting
=         Analyzing optimum investments for specific goals
=         Approving loans and controlling credit risk
Supply Chain
=         Optimizing timing of orders and shipping
=         Reducing inventory wastage and delays
Entertainment and Publishing
=         Writing/research/content creation
=         Graphics and video creation
=         Editing and production
=         Providing the ability to rapidly analyze and react to potential cybersecurity threats
=         Providing advanced techniques for user identity and digital log-on to accounts
Source: Plunkett Research Ltd.

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