ChatGPT has increased the awareness and utility of artificial intelligence (AI). It’s proven that a human can talk to a machine, the machine understands what is said and gives a response that mimics human speech.

Two things that happen from that. The large model is one. First, it allowed us to communicate with machines in natural language. This is normal language, which is how we speak and write. It’s unstructured. You can say it may interpret language and execute various actions. After those actions are complete, it can give you an update or report of what took place. Second, AI will perform intelligently like humans in so many different ways. That means you can apply it now in business. So now AI can do all the redundant tasks that you would hire resources to do. Now you can speed up productivity or improve a process with your laptop and phone.

Not only can AI help with automation, but it can also aid in:

• better, more accurate product recommendations;
• answering questions from employees about work policies and benefits;
• marketing campaigns;
• fraud detection; and more

According to IBM, 54% of global organizations are seeing benefits from using AI to automate IT, business or network processes, including cost savings and efficiencies. No matter the type of company you’re running, there are practical applications if you know where to look.

The purpose of this article is to explore AI and its business applications. There is plenty of information online about AI, so we won’t get too heavy with definitions here. Our glossary can be your quick reference guide at the end of this article. As competition increases in the AI space, all businesses must adopt AI.

What Is Artificial Intelligence?

Artificial intelligence (AI) has numerous terms associated with it. AI is the overarching category in computer science, whereas machine learning and deep learning are algorithms and mathematical concepts for creating models. AI imitates human cognition to help us solve everyday problems and optimize our work.

The most recent advancement in the field is the large language model (LLM), which relies on statistical models to comb through vast amounts of data. Some of the most well-known language models like BERT and LLaMA are open source, enabling developers and small companies to create and leverage AI applications without the high price tag. Computer scientist Mark Riedl wrote, “A language model only trained on medical documents might respond very well to inputs related to medical contexts, but be quite bad at responding to other inputs like chitchat or recipes.”

What Are Machine Learning and Deep Learning?

Machine learning is a subset of AI that uses algorithms to analyze patterns and make predictions. For example, Netflix can recommend new shows for streaming customers based on their viewing history. It might even create music someday. You could have the AI create music and lyrics for you and mix two genres, like Jay-Z and Rachmaninoff. And the AI will produce new songs instantly, never playing the same song twice.

Deep learning models observe patterns in data and need more data points than machine learning models. For example, you can classify tree species by their characteristics and identify new species. 

What Is a Language Learning Model?

A language learning model is a statistical process that predicts what word comes next in a sentence. Suppose you want to write an article and change its comprehension level or translate it into other languages. Changing an article’s comprehension level without using an LLM could take four months, whereas LLMs can do the work in minutes.

In another example, Amazon’s Kindle could leverage AI and take every single eBook and turn them into multimedia for the masses. They could create five different books for five different age ranges. They could add illustrations or turn them into a coloring book. The book and its variants can be created in four to five months instead of the four to five years it would take in the book publishing industry. Books will transform from 2D to 3D. That’s the power of generative AI.

Or imagine creating a movie. With AI, you can take a movie and rerun it, this time with a completely different tone to it. You can rerun it from different characters’ points of view, translate the movie into Spanish or flip the main characters. You could make five different versions of a movie or you could take one movie and turn it into a series.

The possibilities become endless with the application of AI.

What Are Companies Building Right Now?

According to IBM, about 35% of businesses globally are using AI and another 42% are exploring it. Wix, a website builder, is a notable company building its AI arsenal with style. Wix announced an AI website generator that allows entrepreneurs to build their professional site or blog in minutes. It will even create marketing and social media collateral, if prompted. It eliminates the friction of designing and developing a website from scratch.

Venture capitalists are asking companies to invest in AI. Committees are asking the question, “How can we apply AI?” They know they need it but aren’t sure how to proceed. In the first half of 2023, Pitchfork data revealed that venture capitalists invested $15.2 billion into generative AI companies globally.

Companies use AI to address their specific needs. For example, a cybersecurity company might leverage AI for anomaly detection and other intrusion detection type of software. Another company might use AI to understand what their customers are asking and want to interpret it better. AI might listen to calls and identify upset people, categorizing and prioritizing them. So, there are many ways to use AI and there’s a confusing myriad of available utilities. An agency like Ventive can help identify those needs and resources.

‍

How Can Companies Start Building AI Applications?

Building an app using generative AI requires some understanding of logic and application development. You need basic software development knowledge and should brace yourself for security holes. You also need to check compliance with industry standards if you’re in the finance, medical, or engineering industries.

Someone with zero experience working with generative software could use a no-code platform like Wix. These platforms make it much easier to interact with the generative AI. Someone could use AI to get an idea of the type of an application they want to build and then an app development agency can build it for them.

Here is an example. If you want to generate content using your data, you need to create your own model. The model interprets your input to generate data output. To create a model, you need lots and lots of usage examples. Say you have 1,000 pictures of stick figures. You can then use machine learning to train a model on those pictures. You could tell the machine to create a walking stick figure or a stick figure with his hands out. The model interprets your words and creates an image based on the examples. In general, the more data and examples you provide, the better. So AI makes inferences based on the examples. One thing AI currently cannot tell you is HOW it arrived at its answer.

Using AI isn’t necessary for every business process. Instead, focus on what gives you the biggest bang for your buck. For example, to help employees submit time-off requests, you could build a feature in your company’s calendar software instead of using AI. It’s far easier and more efficient.

Risks of Implementing AI (and How to Mitigate Them)

There are few downsides for companies wanting to join the AI gold rush. From fake court cases to book citations, generative AI has a problem with hallucinations. These models (think ChatGPT, Bard, etc.) are compelled to respond, programmed that way, to give you the best answer they can. And the output may not be truthful or accurate. This fact can make people doubt that AI is a legitimate tool for work or pleasure.

But you can train models using internal data to prevent hallucinations from happening. Suppose you’re trying to create a legal document to present in court. You can provide context—have the AI only analyze a set of real cases and respond based on those cases. Then the model will create a far more reliable output that you can use in your research.

“Large Language Models do not have any sense of truth or right or wrong. There are things that we hold to be facts, like the Earth being round. An LLM will tend to say that. But if the context is right, it will also say the opposite because the internet does have text about the Earth being flat. There is no guarantee that a LLM will provide the truth. There may be a tendency to guess words that we agree are true, but that is the closest we might get to making any claims about what an LLM “knows” about truth or right or wrong.” —Mark Riedl

AI also has known security and legal risks. You could create an app that has logical, abstract holes in your product. For example, you build a product where users can upload pictures, but you failed to add some checks to ensure you wouldn’t receive explicit images. Then you release the app. People are uploading explicit images, the website gets taken down, and lawsuits pour in. Now you’re in court because the AI provided an app that violates state and federal laws.

Additionally, costs are another barrier to entry especially if your company has a small budget. The costs of making an AI application will vary widely depending on what you are trying to do. You could start by reducing your friction points or bottlenecks in your processes. Perhaps an easy solution is available but you have to first determine what’s needed, analyze your budget, and understand the benefits of using AI. You won’t know until you research it. AI that is thoughtfully implemented will be cheaper and more scalable over time.

You will be slower if you do not invest in AI. AI is very cost effective when used correctly. The trick is identifying which uses are ‘correct’.

The Bottom Line

AI isn’t a fad. It is disrupting what we create and how we work, accelerating the speed of innovation. We can be far more efficient, helpful, and precise with machines trained to use our internal data. Meet with Ventive and we will help you put AI to use in a model that makes sense for your business.

Glossary

Algorithm: Abundant in digital products and services, algorithms solve specific problems efficiently using mathematics. Think of them as replicating a process like following a recipe or deciding what clothing to wear.

Artificial Intelligence: Computers or robots trained to complete tasks that require human intelligence. Face and voice recognition, chatbots, and credit card fraud detection are some examples.

Deep Learning: A subset of machine learning that helps automate tasks using algorithms. They can process and interpret unstructured data like text, images, or audio, helping humans with classification or transcription tasks. And fraudsters manipulate images, called deepfakes, using deep learning.

Hallucination: A large language model’s tendency to give false or incorrect information in its output due to its programming. An LLM like ChatGPT is trained to respond with confidence, not give 100% accurate responses.

Large Language Model: Trained on billions of parameters, LLMs are deep learning algorithms adept at writing, editing, poetry, image creation, and more.

Machine Learning: Recognizes patterns and mines massive data sets to help solve problems like navigating to a destination, finding a new TV show to watch, and buying a pair of shoes.

Natural Language Processing: To communicate with machines, we must speak the same language. Trained machines understand the way people speak and write and will respond to them. Some examples of NLP include autocomplete, voice assistants, and customer service chatbots.

‍

Want to explore AI for your business? Contact Us to get started.