Robert Napoli | February 16th, 2022
“Am I speaking to a human?”
It would have seemed odd hearing that phrase even just 50 years ago. Still, it has become a common and understandable query when navigating automated telephone menus or online customer service chats.
In 1950, British mathematician Alan Turing famously proposed a test to measure the ability of computers to display intelligence and human characteristics. According to the prophetic “Turing Test,” one wouldn’t be able to tell the difference between a sufficiently intelligent machine and a real person in a blind conversation.
The fact that we now find ourselves occasionally unsure of whether we are communicating with a human being is a testament to the rapid advancement of artificial intelligence (AI). While these AI “bots” that we directly interface with give us firsthand a sense of AI’s power, the majority of AI has been put to use in more subtle and less visible ways.
AI is rapidly becoming embedded in technology at all levels of society, business, and government. It can deal with massive data sets at a speed that would require many humans to match – something that could be an advantage for small businesses with limited funds and personnel. Estimates claim that around 6 billion labor hours are saved annually by AI.
You may be wondering if it’s time for your business to begin integrating AI into your activities. What you might not know is that it’s likely a part of your daily routine already.
AI and Machine Learning
Artificial intelligence is a broad and often confusing term. This is partly due to its evolution and the emergence of nesting methodologies such as machine and deep learning. But this is also partly due to disagreements about what “intelligence” is in the first place. It’s generally accepted that AI is the attempt to mimic or simulate human thought or behavior. As AI has transitioned from theory to practice, it has branched into different subdisciplines according to application, complexity, and methodology.
One of the characteristics of humans and other intelligent animals that sets them apart from simple machines or programmable computers is their ability to spontaneously learn from trial and error, a capability that one AI methodology, machine learning, attempts to address.
Machine learning AI uses large data sets to train algorithms to perform tasks that have a wide range of unpredictable inputs and outcomes. One of the chief reasons “Big Data” has become big business in recent years is that there is a constant demand for new data to improve the accuracy of machine learning models.
Pattern and object recognition tasks achieved through machine learning include facial and other biometric recognition, natural language processing, image processing, financial prediction, and behavioral analysis, to name a small sample. These and other learning processes now underlie many technological applications.
AI under the hood
AI serves as a backend process for tools and applications that people use regularly.
Internet searches are enhanced by predictive algorithms, trained by machine learning, that attempt to anticipate the information needed based on other users’ search terms.
Social media and entertainment platforms make content recommendations based on viewing habits and patterns.
The text message autocomplete feature on mobile phones (which sometimes famously gets things wrong with hilarious results) and word processing grammar checks are other ubiquitous examples of AI.
Businesses also benefit from AI, with intelligent components built into common tools. Modern cybersecurity software relies heavily on AI to detect and prevent costly attacks. Algorithms are constantly retrained with new data to help keep up with new threats.
Website analytics provide valuable information to businesses about who uses their products and how. Intelligent analytics tools help improve customer engagement, streamline outreach efforts, and enhance the customer experience.
Organizations that use business intelligence (BI) tools are likely using AI as well. BI software analyzes relevant data about customers, competitors, and general business trends, organizing it into visual, actionable formats for users. BI analytic methodologies operate on large data sets, and the most accurate models are usually AI-driven.
Beyond the common AI elements that are under the hood, there are plenty of ways businesses can more deliberately integrate intelligent technology. From automated customer service agents to human resources and recruiting to custom-built analysis tools, businesses are adopting AI to save time and money and stay competitive.
Businesses considering an investment in AI-based initiatives have a few factors to consider. Will it add value in the short and long term? If AI replaces labor hours, can the time and personnel be put to more productive use?
The advantage in memory capacity and computational speed that computers have over humans is undeniable, and the gap is likely to continue widening. There is ongoing debate as to whether a machine can approach certain abilities of the human brain.
What’s not debatable is how much progress has been made in AI and how it has gradually and quietly become an integral part of business and society. Full disclosure: this article was written by a human.