Part 1: What is Artificial Intelligence?
by Nathan Whittacre
As a business leader, you understand the importance of efficiency and innovation. Like many of us trying to balance life's demands, such as my own challenge of reducing time spent on social media, businesses must optimize their resources. This story isn't about personal productivity hacks; it's about transforming your business operations through the power of Artificial Intelligence (AI).
Integrating AI Into Daily Life and Business
One of my personal goals is to reduce my time on Facebook. It’s a guilty pleasure to check on friends, post a few pictures of my own, and see what others are doing in a few outdoor and adventuring groups that I belong to. I’ve decided to limit my time on Facebook to 15 minutes a day through a setting on my app. Of course, I’m undeterred by one app limiting my wasted time, so I’ve started scrolling through other social media apps instead.
A few weeks ago, I was winding down for the day, mindlessly scrolling through Instagram for a few minutes when an ad popped up. It was a video for an Artificial Intelligence telemarketing service, where the AI agent was making phone calls to schedule appointments for their live sales representatives. I was intrigued and scheduled a call with the company to discuss the service. They told me that the company developed the AI telemarketing service in house to solve their need to generate more leads. After 18 months of development, they were able to improve their appointment setting rate by 160% all while reducing their sales labor force by 90%. If their numbers are real, that is a huge improvement in production for a fraction of the cost (software development aside).
"Is this what AI means for business in the future?"
That got me to wonder, “Is this what AI means for businesses in the future? Are we going to be able to be more productive in our companies with far less resources? Even in personal interactions, like direct sales, can AI improve the outcomes of the business for a fraction of the cost? Is this really the next revelation in business?” The answer seems to lean heavily towards 'Yes!'. My mind was racing with the possibilities after seeing the AI telemarketing system in action.
A recent global survey of business leaders found that the majority of respondents were more excited about AI than concerned about the negative effects. The survey respondents felt that generative AI “saves time and increases efficiency while relieving humans of mundane and tedious tasks.” Most of them were already using some form of AI in their personal or business lives. Only 15% of respondents had not yet used the technology.
The Power of AI In Business
Just like the study, most of the business leaders I know are either using AI in some form or are interested in incorporating it into their business soon. As with most technology integrations, the idea of integrating a disruptive change to business process into an organization can seem overwhelming. To make matters worse, this is a new technology that is still being developed, has major legal and societal implications, and can affect all areas of the business.
I think the biggest question that is plaguing business leaders isn’t when to incorporate AI into their company, but is “how?” In this multi-part blog series, I’ll discuss the background of AI, the current landscape of AI in business, why it matters to your company today, and finally give you some ideas and methods on how to get started with AI in your company.
What is Artificial Intelligence?
Discussing AI requires a whole new vocabulary. You’ve probably heard several terms surrounding discussions of AI that seem quite foreign. To best understand AI systems, let’s set a basis for these key terms.
Artificial Intelligence (AI):
A computer system that allows it to think and learn like a human. It can do tasks that usually need human intelligence, such as understanding languages, recognizing pictures, solving problems, and making decisions.
Big Data:
Extremely large sets of information that are too complex and vast to be handled by traditional data processing software. These datasets can include everything from numbers and text to images, videos, and more. The challenge with big data isn't just its sheer volume, but also the variety of data types and the speed at which it needs to be processed.
Machine Learning:
A part of Artificial Intelligence. It's a way for computers to learn and get better at tasks by studying examples, rather than being directly programmed to do something. In the past, computer scientists would have to write many lines of code to develop a program. With machine learning, the computer can learn to program itself after a base set of rules for the program is created. Just like you learn to improve at a game by practicing, the computer learns from data to get better at what it's programmed to do.
Deep Learning:
A subset of machine learning that uses algorithms inspired by the structure and function of the brain's neural networks. As a type of machine learning, deep learning allows computers to learn from data through layers of processing—each layer calculates the probability of a feature being present and passes the output to the next layer for further refinement. This method enables deep learning models to handle very large, complex datasets and perform tasks like image and speech recognition, language translation, and decision-making with increasing accuracy.
Neural Networks:
Systems in computers that are designed to recognize patterns. They are structured a bit like the human brain with lots of neurons, which are like tiny information stations. These networks learn from large amounts of data by adjusting the connections based on what works best, like how you learn from your mistakes and successes.
Natural Language Processing (NLP):
A part of artificial intelligence that focuses on helping computers understand, interpret, and respond to human language in a way that is both valuable and meaningful. Think of it like teaching a computer to understand and use human languages (like English or Spanish) so it can perform tasks like translating languages, responding to spoken commands, or even detecting emotions in text. For example, when you talk to voice-activated assistants like Siri, write a query into a search engine, or speak to a AI telemarketer, NLP is what helps these technologies understand what you're saying or typing and then figure out how to respond.
Generative AI (GenAI):
A type of artificial intelligence that is designed to create content. It can generate text, images, music, and other types of media that are new and original, based on the patterns and information it has learned from the data it was trained on. Think of it as an advanced artist that can make its own paintings or write its own stories after looking at many examples and learning what they should look like or sound like. This technology is used in tools like chatbots, which can generate human-like text in conversations, or in programs that can create realistic images from descriptions.
A Brief History of Artificial Intelligence
This isn’t a discussion about the schooling of your dead-beat brother-in-law. The creation of AI systems like ChatGPT, Gemini, Dall-E, or the AI sales representative example above, have been built upon many years of development of the underlying systems that allow for the complex construction of Generative AI systems. It seems like the AI systems suddenly sprang up in 2023, but they have been in development for more than a half a century. All the pieces have come together in the last decade, and we now have the computing power and deep learning capability to make them useable today by everyone, not just by those with deep pockets and big budgets.
The First Days of AI
Artificial Intelligence began to take shape in the middle of the 20th century when scientists started thinking about making machines that could think like humans. The name "artificial intelligence" was first used by John McCarthy in 1956 at a conference where he and others discussed the possibility of making computers perform tasks that required human-like intelligence. He was one of the creators of the LISP, which was a recursive programming and the choice for AI development in the 1960s. (I had to learn LISP in college and, ironically, was one of my least favorite classes – mostly because I didn’t get along with the professor.) This early stage of AI included the development of simple programs, like one that could play chess and another called the Logic Theorist, which could solve math problems, showing early on that computers could handle complex tasks.
Early Advancements in AI
During the 1980s and 1990s, AI grew more advanced with improvements in machine learning and neural networks, which are systems designed to work like the human brain. This period introduced the backpropagation algorithm, which helps neural networks learn from data by adjusting themselves. AI also saw the creation of expert systems, designed to make decisions like a human expert would. Although interest in AI dipped for a while because these early systems had their limits, they laid the groundwork for even smarter AI applications. I think most people can remember the big news when IBM’s Deep Blue was able to beat a world champion at chess. The systems that could do these types of computations required entire buildings of computing power and were not accessible by most people or businesses.
AI in Today's World
Today, thanks to more powerful cloud computers, access to huge amounts of data (big data), and better learning algorithms, AI has become a crucial tool in business. It helps companies operate more efficiently, improve customer service, and discover insights from data that help make better business decisions. AI is now used in a wide range of applications, from running automated customer support and tailoring marketing to individual needs, to conducting sophisticated data analysis that helps predict future trends. AI’s role in business continues to grow, making it an essential part of modern industry.
What's to Come
Next week, we're diving into the current state of AI for businesses, uncovering the most dynamic and effective systems available today. Don't miss our comprehensive review of these revolutionary AI solutions, each handpicked for their potential to transform how you operate and compete. Join us as we explore practical ways to integrate cutting-edge AI technology into your company, ensuring you stay ahead in a rapidly evolving digital landscape. Make sure to tune in – this is information you won’t want to miss!
Next Steps
Are you interested in using AI in your business? Stimulus Technologies can assist you in deciding which AI systems make sense for your business, putting a game plan together for the implementation of AI, and making sure your IT infrastructure and cybersecurity is ready for the new technology. Click here to schedule a time with me to discover the possibilities.
