If what you know about AI comes from movies like Ex Machina and I, Robot, you are not alone.
AI in Business
Artificial intelligence in business is a subject that is both complicated and broad. No one knows all there is to know about AI (even Elon Musk recently accused Mark Zuckerberg as having a “limited” understanding of the subject).
Advances in AI have only scratched the surface, but current applications of AI technology are already changing many facets of work and personal life.
Here is your basic guide of AI terms to know:
Artificial Intelligence refers to a collection of technologies enabling machines to solve problems, make decisions, or perform tasks that are typically easy for humans, but are difficult for computers. So when you hear “machine learning,” “deep learning,” or “natural language processing” — all of these terms fall into the AI category.
Today’s AI can be leveraged for specialized applications – single tasks such as playing chess, making sales predictions, or automating the lead qualification process.
According to Harvard Business Review, “[Companies] are using AI much more frequently in computer-to-computer activities and much less often to automate human activities. Machine-to-machine” transactions are the low-hanging fruit of AI, not people-displacement.”
Machine learning the subset of AI that enables machines to receive and adapt to new information. According to NVIDIA, machine learning is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.
This is the technology that makes the computer system “smart,” in the sense that it can dynamically shift recommendations based on the data it receives.
A common application utilizing machine learning is personalized marketing. Much like how Netflix provides show recommendations and Facebook customizes your news feed, businesses can leverage the wealth of information they have to provide customers with personalized experiences throughout their buying journey.
Natural Language Processing (NLP) deals with the computer system’s ability to understand and interpret human language the way that it is written or spoken. This also includes the ability to draw insights from data contained in unstructured material such as emails, social media, health records, and videos, to name a few.
A simple example of NLP technology is Google Search. When you misspell a word, Google responds with, “Did you mean ….?” Other examples of NLP technology include Google Translate, Amazon’s Alexa, and Conversica’s sales assistant.
NLP aims to bridge the gap between the way humans communicate and what machines understand. Current capabilities of this technology – recognizing natural language, extracting meaning, and providing answers – has broad implications for the way businesses and customers interact with each other.
Natural Language Generation
Whereas NLP aims to understand language, Natural Language Generation (NLG) deals with producing textual content. According to Phrasee, natural language generation is the process of developing a learning machine capable of sorting through variables and putting them together into natural, human-sounding sentences, statements, or paragraphs without intervention from the handler.
A simple way to tell the difference between NLP and NLG? NLP ‘reads’ while NLG ‘writes.’
At Conversica, NLP technology is used to read and interpret written business conversations, while NLG technology is used to craft human-like responses that are tailored to the topic of discussion. By applying these and other AI technologies to our platform, routine conversations in sales, marketing, customer service, and more can be automated to enable businesses to operate more effectively.
AI in Business Takeaways
AI technologies are already in use by many companies to gain a competitive edge in the market, automate specific business functions, and augment employee performance.
If your company is not yet leveraging AI technology, it is a good idea to explore possible applications to improve upon existing workflows.