Artificial Intelligence (AI) has become a hot topic in everyday discussions and a pivotal innovation across all industries. But what exactly is AI?
Artificial Intelligence: the term refers to the capability of a machine, computer, or software platform to mimic human intelligence, such as logic and thought patterns, enabling it to take actions and initiate workflows based on pre-programmed stimuli. AI technology optimizes workflows—remarkably, without continuous human intervention. Artificial Intelligence leverages computers, data, and sometimes machines to mimic the problem-solving and decision-making capabilities of the human mind.
AI models are crafted by data scientists who use algorithms and other intelligent programs to process vast amounts of data—volumes so large that humans alone cannot manage. Advanced AI technologies possess the unique ability to learn and adapt based on the input they receive. Consequently, AI revolutionizes user experiences by reducing the burden of repetitive, manual, and time-consuming tasks.
AI-driven solutions automate and optimize workflows, fostering data-driven decision-making across all sectors. The more sophisticated the AI, the more it can operate independently from human oversight—even from the programmers who built it.
Before we delve deeper into AI's history and its place in today’s world, let's explore some examples of how businesses currently utilize AI.
Industries across various sectors employ chatbots to facilitate customer interactions on their websites. Agricultural and insurance companies use AI to analyze weather patterns and predict future forecasts. Financial institutions harness AI to gauge customer insights and determine investor risk tolerance. Marketing teams leverage AI-powered tools to target ads to specific audiences and measure digital content performance across platforms.
The History and Evolution of Artificial Intelligence
Though the concept of Artificial Intelligence has intrigued scientists since the early 1950s, it remained conceptual for at least another decade.
As computers evolved over the subsequent decades, they became less expensive and capable of processing increasing amounts of data. Machines, once limited to universities and well-funded institutions, began gaining traction in various organizations. With this increased exposure and potential to store more commands, scientists began turning the concept of Artificial Intelligence into a reality during the 1960s.
One of the earliest AI innovations was the interactive ELIZA program, developed by Joseph Weizenbaum at the Massachusetts Institute of Technology (MIT). ELIZA could converse in English independently, without real-time, third-party programming for responses.
Experts and government entities were fascinated by AI's potential to transcribe and translate languages. In the 1970s, the Defense Advanced Research Projects Agency (DARPA) funded AI research at several institutions, and others followed suit over the next two decades.
The 1970s marked significant progress with Natural Language Processing (NLP), which involves computer programs and human language interactions. Through NLP, machines could analyze vast volumes of documents to identify themes and draw conclusions.
1964-1967
ELIZA
A machine that could carry out intelligent dialogue independent of real-time programming
Significance:
Demonstrated a machine’s ability to react to language stimuli and “think” on its own
Implications:
Showed scientists the power of machines to recognize and process language
1970s
Natural Language Processing
A program to process and analyze large volumes of documentation to identify themes and overarching ideas
Significance:
The first program that required no human interaction to reach intelligent conclusions
Implications:
Precursor to more sophisticated AI, like Machine Learning and Deep Learning
1997
Deep Blue
IBM’s chess playing computer game
Significance:
The first machine to beat a world chess champion and grand master (Gary Kasparov of Russia)
Implications:
Showed the world that a machine could react to real-time actions and mimic human decisions – even those of the most brilliant minds
Machine Learning: The Next Phase
Throughout the 1990s and 2000s, the world witnessed the emergence of more sophisticated Artificial Intelligence through Machine Learning—a subset of AI that uses data and algorithms “to imitate the way that humans learn, gradually improving ... accuracy.”
Today, AI is often synonymous with Machine Learning, where programs can enhance their functioning and output data from the data they ingest. Machine Learning permits computers to derive learning from data, eliminating the need for programmers to provide explicit instructions for every task.
While Machine Learning is a component of AI, it doesn't fully embrace the idea that machines can operate precisely like autonomous humans. Instead, Machine Learning targets teaching machines to perform specific tasks and deliver accurate results by identifying patterns.
The Last Decade of AI Innovation: Big Data’s Crucial Role
Artificial Intelligence is inextricably linked to data.
AI enables humans to understand better and utilize the massive quantities of data available, determining relevance in various scenarios.
We can't discuss AI without mentioning Big Data, which, according to Gartner, comprises “high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight, decision-making, and process automation.”
Big Data transcends typical datasets, encompassing large volumes of complex, dynamic assets that require sophisticated tools for access and processing. The advent of cloud computing in the mid-2000s extended the scope of Big Data, as machines began exchanging information without physical connections, opening endless possibilities for data gathering, storage, and exchange.
The success of modern businesses hinges on Big Data. The ability to mine extensive volumes of information for crucial insights has fueled the rise of Artificial Intelligence (AI). This synergy enables the processing and analysis of Big Data, making AI, particularly Machine Learning, essential for businesses across industries.
Organizations that need to maximize data for business purposes require sophisticated machines, leveraging technologies that employ human-like logic to quickly and comprehensively sift through massive amounts of structured and unstructured data. AI-driven tools satisfy these needs, producing accurate conclusions that facilitate intelligent decision-making.
When AI is applied to Big Data, the transformation of businesses is profound. Machine Learning can:
Detect deviations in data
Determine the probability of future outcomes
Discover patterns within data structures that are too big for humans to comprehend
AI Today… and Beyond
AI has experienced substantial growth over the past decades and will continue to evolve. Now is the time to assess AI's potential role in your organization’s digital strategies—both today and in the future.
In the forthcoming sections of this eBook, we will explore the complexities of AI further, including its applications in the mortgage industry, from borrower to lenders.
