To help you cut through the hype, in this article I’ll explain what AI is and what it is not. In addition, find out how recent technology advances have allowed AI to be commercialized, and concise examples of Artificial Intelligence and how it is changing our lives.
Artificial Intelligence – What Is it and What It Is Not?
Basically, Artificial Intelligence (AI) is machine and computer program intelligence. Specifically, the definition of artificial intelligence is “…the application of rapid data processing, machine learning, predictive analysis, and automation to simulate intelligent behavior and problem solving capabilities with machines and software”.
Artificial Intelligence – A Short History.
With the advent of computer technology in the twentieth century, Artificial Intelligence (AI) had small beginnings. At first It was thought of as a novelty. Consequently, for many decades AI stayed for the most part within the research community. Now AI is revolutionizing our lives. This is because of the unprecedented advancements in computer technology, the internet, and our access to huge data sets. Below are the key events that got AI started.
1950s – From Theory to Reality.
In the second half of the twentieth century, computer technology became a reality. Also, this is when the idea of artificial intelligence transitioned from a theory to something real and tangible. In 1950, Alan Turing (famous for breaking the Nazis’ Enigma code) published the groundbreaking paper, Computing Machinery and Intelligence. In this book Turing proposed to answer the question “can machines think?” and he introduced the Turing Test. Specifically, he designed his Turing test to determine if a computer could demonstrate the same intelligence as a human.
1980 – 1990s – Commercial Success of Expert Systems.
In the following decades AI research and development continued, but AI developers did not have much success at commercializing their AI software. Again, to the general public AI was considered a novelty. As an example, what the public saw in AI was limited to such things as computer chess games that had limited entertainment value. Finally in the early 1980s, AI research was revived by the commercial success of expert systems. Namely, these AI software programs simulated the knowledge and analytical skills of human experts. Consequently, by 1985 the market for AI had reached over a billion dollars.
2012 to Today – the AI Revolution.
The challenges for AI developers to get AI out of the laboratory were many. Specifically, the bottom line was that AI software did not have enough computing power or cheap data storage to meet its full potential. By 2012, this all changed. With the advent of the internet, faster computers, machine learning techniques, advanced robotics and access to large amounts of data, AI could now be commercially viable. Hence, the technology was now available to power AI. Advanced information technology enabled data-hungry deep learning methods and large language models (LLM) to be applied across a wide range of human endeavors and interact with the general public.
See Wikipedia’s Artificial Intelligence and DeepAI’s Artificial Intelligence for more on the beginnings and historical development of artificial intelligence.
10 Examples of Artificial Intelligence By Type.
In order for AI developers to implement human intelligence in machines, they need to create systems that understand, think, learn and behave like humans. There are two AI development approaches or goals to do this. The first is the human approach: create systems that think like humans or act like humans (ex. Alan Turing’s intelligent machines). The second is the ideal approach: create systems that think rationally or that act rationally. See below for examples of artificial intelligence applications that are in use today
1. Strategic Games.
These are games where the human plays against a computer such as chess, poker, tic-tac-toe, etc. In these examples, AI developers create software to think through a large number of possible positions based on heuristic knowledge.
2. Expert Systems.
AI developers basically copy an expert’s decision-making process. The end result of these software programs provide an explanation or advice to the users. Today, AI expert systems are in wide use. For example, these commercial AI applications include: medical diagnosis, chemical analysis, credit authorization, financial management, oil and mineral prospecting, genetic engineering, automobile design and manufacture, and airline scheduling.
3. Intelligent Robots.
AI developers create AI software to enable robots to perform human-like tasks. The manufacturing and logistics industries use robotics extensively, especially repetitive tasks. These robots have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. Also, see my article, Robotics In Logistics, for more details and types of intelligent robots that are in use today.
4. Internet of Things (IoT).
With the interconnection via the internet of computing devices embedded in everyday objects, AI developers create software to leverage data for a variety of reasons. This includes traffic management, environmental monitoring, smart buildings and smart homes, and supply chain management to name a few. For more details on IoT, see my article, Internet Of Things Examples – Hidden Technology Automating Logistics.
5. Speech Recognition.
This AI capability is also known as automatic speech recognition (ASR), computer speech recognition natural language processing, or speech-to-text. Example of this include voice assistants such as Amazon Echo, Siri, Google Assistant, Google Home, Amazon Alexa. Also, customer service applications use speech recognition extensively for online chatbots that replace human agents. Additionally, software developers are now further enhancing speech recognition technology with large language models (LLM). See my article on AI chatbot technology for more details.
6. Content Creation.
AI is now effectively creating art, music, verbal communication, and the written word. For example, AI can act as a writing assistant (like an AI-powered word processor) to help writers be more productive and help improve the quality of their content. Surprisingly, these AI content generators can also almost be autonomous churning out product descriptions, blog postings, e-mail, and responding to user product reviews. Additionally, AI developers are commercializing AI image generators to assist businesses with a variety of creative tasks.
7. Computer Vision.
This AI technology enables computers to derive meaningful information from digital images, videos, and other visual inputs, and then take the appropriate action. For example, AI computer vision includes autonomous vehicles, Google translate app, facial recognition apps, medical diagnostic apps to analyze X-rays, agriculture apps to analyze grain quality, predictive maintenance apps, and quality control apps.
8. Decision Support / Recommendation Engines.
AI are great recommendation engines for personalized use. For, example, it can use past-observed human behavior data and real-time AI algorithms to help to discover data trends in the areas of ecommerce, entertainment, and social media. For example, AI recommendation engines include: Facebook — “People You May Know”, Netflix — “Other Movies You May Enjoy”, Amazon — “Customer who bought this item also bought …”, and Waze — “Best Route”.
With the growth of Large language Models (LLM) such as ChatGPT, this generalized AI is able to provide decision support to improve knowledge-based tasks. What is exceptional about LLM is its ability to interact with non-technical users and provide knowledge-based support across many industries. See SC Tech Insights’ AI Impact On Business Decisions – Know How To Best Apply To Get The Most Benefits for a detailed explanation and examples.
9. Autonomous Stock Trading Automation.
AI developers create AI software to optimize stock portfolios. Today, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention. For more information on automation and AI autonomous automation, see my article on What Is Automation?
10. Augmented AI Automation For Fraud Detection.
Banks and other financial institutions can use machine learning to spot suspicious transactions. For more information on automation and AI augmented automation, see my article on What is Automation?
What Is Automation? High Tech Ways To Better Replicate, Apply AI To Business Processes. In today’s fast-paced business world, technology is crucial for streamlining processes through automation. Do you recall when technology was simpler and understanding automation was more straightforward? As computers and IT emerged, automation became more complex and challenging to implement. Now, with the rise of advanced Artificial Intelligence (AI), the rapid evolution of business automation can be perplexing. This leaves many of us wondering, “What is automation, exactly?”
Let me clarify this concept for you! Click here to delve into various types of automation, including process automation, virtual assistants, augmented AI, and autonomous AI. We will also discuss the benefits of incorporating automation into your business and present some real-world examples. Finally, I’ll highlight emerging trends in the world of automation: hyperautomation and intelligent automation. Prepare for an enlightening journey into the realm of business automation!
For more examples and details on Artificial Intelligence, see Microfocus’ What is Artificial Intelligence (AI)?, IBM’s What is Artificial Intelligence, TutorialsPoint’s Artificial Intelligence – Overview, and IEP’s Artificial Intelligence.
For more information from Supply Chain Tech Insights, see articles on AI, data analytics, and robotics.
Writer and Supply Chain Tech Expert. Passionate about giving actionable insights on information technology, business, innovation, creativity, and life in general.