Introduction to the Artificial Intelligence (AI) & Machine Learning
Artificial intelligence (AI) spending worldwide was estimated at $37.4 billion for 2019 by analysts at IDC and is expected to grow to $100.1 billion by 2023. This is an estimate on a broad basis that includes spending on software and services. Stanford University estimates the U.S. market for AI on this basis at more than $17.6 billion for 2019. Researchers at Gartner estimated that the global business value derived yearly from AI had already reached $2.6 trillion by 2020, while analysts at PwC 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 benefitting 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, Amazon.com 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.
that Have Deep Synergies with Artificial Intelligence
Big Data and Data Mining
Digital Assistants (Siri, Alexa,
Imaging and Facial Recognition
Robotics and Automation
Sensors and Wireless Networks
The Internet of Things (IoT)
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 process.
Amazon, for example, not only uses AI broadly in its online services, it
is successfully applying it in physical retail stores. Amazon operates several AI-assisted, brick
and mortar convenience store called Amazon Go in major U.S. cities. Customers may pick up drinks, snacks and
prepared meals. Shoppers scan an app on
their smartphones when they enter the store, so that they can be properly
identified as individual shoppers.
Cameras throughout the store track shoppers and note which products they
have selected while totaling the cost. The store runs without cashiers, utilizing electronic checkout and payment, while sensors based on AI determine which products were removed from the shelves by which customer, facilitating both checkout and restocking. There is the potential for a very large rollout of these Amazon Go stores worldwide, with a UK launch possible in the near future. Technologies refined in the Amazon Go stores may show up in stores at Amazon’s Whole Foods subsidiary, and in its specialty stores that sell books and electronics. Based on the
experience of Amazon and a few other pioneers, AI will have a very significant
effect on the way we shop in stores.
One of the more promising advancements is called “deep learning.” In 2014, Google spent nearly $600 million to acquire UK-based DeepMind, an intensive learning research group. Deep learning is sometimes referred to in conjunction with phrases such as “machine learning” and “neural networking.” The main point is that software can be trained by being constantly fed data, queried as to its meaning, and receiving feedback to 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.
Sectors with Significant Near-Term Benefits from Artificial Intelligence and
(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.)
Disease diagnosis and analysis of scans, samples, symptoms and
Recommendations for optimum treatment
Enhancement of the “Precision Agriculture” trend, for more effective irrigation, planting and harvesting
Prediction of weather and rain
Providing traffic flow management
Enabling self-driving cars and trucks
Optimizing operations for aircraft, truck fleets, railroads and
Efficiency and Production/Environmental Controls
Developing technologically-advanced “smart cities” and green buildings
Improving energy efficiency in air conditioning, lighting and
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
Better analyzing risk for insurance underwriting
Analyzing optimum investments for specific goals
Approving loans and controlling credit risk
Optimizing timing of orders and shipping
Reducing inventory wastage and delays
Plunkett Research Ltd.