Four Ways AI Is Shaping the Future of Business

Machine Learning, Robotics, Natural Language Processing, Computer Vision, Generative AI… Artificial Intelligence is destined to change business as we know it. Keep reading and find out how AI capabilities are shaping our future.

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By this point, you’re probably aware of just how industry-changing AI is going to be, but you might not know the ways that it is actively shaping business today. In this article, we’ll be going over all of the different branches and types of AI, as well as four ways they’re shaping the future of business.

Read on to learn more.

How many branches of AI are there?

Certain terms might seem synonymous with artificial intelligence, but there’s a bit more to this lexicon than meets the eye. The various different branches of AI are applied in specific ways and industries in order to solve certain problems. 

We hope that by the end of this segment, when you hear words like “machine learning” and “neural networks,” you know exactly what it does and how it’s used.

Machine Learning

Okay, let’s get the most popular buzzword out of the way first. Machine learning (ML) is a form of AI that bases itself on training an algorithm to recognize certain text, code, speech, or images. This has been applied to things like facial recognition, self-driving cars, and speech-to-text.

This form of AI requires an extensive amount of data in order to function, and the algorithm is only as good as the data you feed it, meaning that high data quality is crucial to its success. These programs also need to be trained in order to reinforce successful recognition.

This training process can be completed in a variety of different ways:

Supervised Learning: 

In this form of ML, data experts feed labeled data to the algorithm, defining variables for what’s in the data, as well as providing it with the desired label for the output (what the machine recognizes). This is best implemented in programs that need to accurately register exactly what it is they’re looking at.

Unsupervised Learning: 

In this form of training, the data fed to the ML algorithm isn’t labeled. Instead, the algorithm attempts to group the data into bundles that appear to be most similar to one another. Data experts will then alter the program to incentivize certain groupings over others. This is particularly useful in cluster analysis algorithms.

Reinforcement Learning: 

This form of learning is very similar to the way in which we train dogs, believe it or not. Data experts use positive and negative reinforcement in order to train the ML algorithm. This is particularly useful when designing a program to complete a multi-step process and requires the most intervention out of any method.

Robotics

While traditional robotics might not implement the use of artificial intelligence, the robotics of today certainly does. This branch of AI is incredibly niche and focuses solely on programming AI to automatically operate machines in large factories. 

For the most part, things like visual recognition aren’t always employed when it comes to programming robotics. The automotive industry, for example, can simply program assembly lines to complete repetitive tasks, providing the exact same starting conditions for each step so that the machine is not required to alter any of its programmed movements.

Other, more complex applications of robotics do make use of AI algorithms. This is more common in processes where a single robot has to complete a series of tasks where the starting condition is different each time.

Neural Networks

This is perhaps the most complex form of artificial intelligence. It takes inspiration from biology and organizes a set of nodes into a connected web. These individual components form the basis of a neural network, each influencing the other in order to store and analyze complex data.

Nodes each contain what is known as a weight and a threshold. When a node receives enough data to trigger its threshold, it activates, passing that information onto the next node and applying its weight to the data, a node that does not have its threshold reached will not activate. 

While the inner workings of neural networks are probably best left to data experts, just know that the more node layers, the more complex the network. Technically a branch of machine learning, they are a powerful tool in AI’s arsenal and are key to creating deep-learning algorithms.

Expert System

Originating in the 1970s and improved upon since then, expert systems are perhaps the first form of AI model ever created. They base themselves on a collection of information and use that data in order to provide suggestions in an if-then configuration. The larger the information base, the better the model is at providing suggestions.

Expert systems are most commonly used in programs such as Google Search in order to provide suggestions for spelling errors in user search queries. These are complex models designed to be extremely fast, responsive, reliable, and easily understandable.

Natural Language Processing

Natural language processing, or NLP, is an AI model that is specifically designed to understand and recognize human speech and text. Modern NLP models make extensive use of sentiment analysis as well, allowing them to determine, through candor and intonation (for voice) as well as word groupings (for text), the emotional tone of the person it’s conversing with (either positive, negative, or neutral).

NLP has been a very tough nut to crack for years, which is why the current application of it in programs such as ChatGPT created such a stir among data experts. If you’ve ever used the program yourself, you’ll know that NLP allows ChatGPT to not only clearly understand text but to reproduce it with frightening accuracy.

Fuzzy Logic

Fuzzy logic focuses on determining the degree to which a condition is true or false. While most other forms of digital logic will function on a binary system of true (a value of 1.0) or false (a value of 0.0), fuzzy logic allows for a range of values between the two.

This can be extremely useful when designing AI models that mimic human thought since we tend to identify concepts and logic within a spectrum. Fuzzy logic has been implemented within machine learning algorithms in order to increase the sophistication of these systems.

Computer Vision

Computer vision is one of the most popular forms of artificial intelligence on the market today. It focuses specifically on creating models that can accurately interpret digital images and is responsible for the creation of facial recognition algorithms, and is a crucial element of image-generation programs like Midjourney Dall-E. 

The challenge with computer vision is that the data fed to these machines is 2D, making it difficult for them to understand how certain shapes can appear from different angles, like hands and feet. We understand that while photographs are two-dimensional, the objects they depict are 3D and that they will change in shape depending on the angle of the observer.

What about Generative AI?

Four ways AI models are shaping the future of business

Now that we have extensively covered all of the terminology within the AI lexicon, let’s dive into how these various forms of AI are being used to shape the future of business.

1. Recommendation Systems

This is perhaps one of the uses of AI that you are most familiar with, and it’s present in every streaming service out there. Recommendation systems are ubiquitous with streaming services for the simple reason that they work. Netflix has stated that they would likely lose USD $1 Billion annually to users leaving the streaming service if they did not implement the recommendation system.

The way they chose to categorize this statistic is interesting, they didn’t say that the recommendation system “makes” money for Netflix, but rather that they would lose money were it not to be used. This means that recommendation systems aren’t meant to make money directly but rather are a tool for customer retention.

This all plays into how streaming services differentiate themselves from one another. Obviously, a big part of what draws users to one platform over another is the exclusive content that each one of them has available, but the second half of this customer retention medallion is the customer experience.

Through the use of machine learning algorithms, recommendation systems are able to identify what shows within their catalog you are most likely to enjoy watching. This makes for an incredibly user-friendly experience. Using Netflix as an example, with 3,600+ movies and 1,800+ TV shows available, it can be incredibly difficult for users to find that next piece of entertainment.

But these recommendation systems go beyond just streaming services, retailers like Amazon make excellent use of AI to recommend products based on what users have searched for or purchased in the past.

2. Chatbots

If you’ve ever had to deal with terrible customer service bots then you know just how frustrating it is not to be able to speak to an actual human being when you need assistance. With the advent of natural language processing, these awful customer experiences will be a thing of the past.

While people do generally prefer to speak to a flesh and blood human when it comes to solving their computer problems, even these services can feel cold or unhelpful. NLP can offer users human-like responses instantly and 24hs/day, drastically improving the customer experience when it comes to technical support.

These programs aren’t limited to tech support either. Just like recommendation systems, chatbots can be put to use in a variety of different industries, like medicine and education.

We’ve already seen how well received ChatGPT was, due to its uncanny ability to mimic human writing. Now just imagine this level of interactibility in your average online customer assistance chatbot.

3. Sentiment Analysis

Alongside NLP, sentiment analysis has been used to great effect within customer service automation. One of the main reasons that users prefer human customer service operators over machines is precisely because of their ability to interpret emotions over the phone. 

Sentiment analysis can provide chatbots and other automated customer service options with an air of humanity, providing users with the right information in the right way. Combined with NLP, sentiment analysis can provide chatbots with even more accurate human-like responsiveness.

Don’t be surprised if all customer support interactions become completely automated within five years’ time. This complete industry automation will not only save the company’s money but the user’s time as well.

4. Open AI Tools

Open AI was the first to really spread the word about recent developments in Artificial Intelligence. Tools like ChatGPT and Dall-E are not only fun to play around with but are being adapted to function within already pre-existing business models.

This is already being adopted by a variety of different companies in a multitude of industries, from entertainment and customer service to healthcare and education.

Case Study: Adapting ChatGPT For The Classroom

Case Study: Adapting ChatGPT For The Classroom

MJV was recently approached by Conexia, an ed-tech company that is part of the SEB Group, a global education group present in more than 30 countries. They reached out to MJV to create an AI-powered virtual teaching assistant.

The challenge was the adaptation of the ChatGPT tool for use in an educational environment, creating a virtual assistant integrated with the existing platform to enhance the kids’ learning experience. If you want to take a look at the full story, check out our case study here.

Artificial Intelligence might seem incredibly complicated, and in truth, it is. But that doesn’t mean that gatekeepers are baring average users and small companies from taking advantage of it. If you plan on still running a business in the next 10-15 years, you’ll have to get accustomed to the terminology used by the AI industry.

If you’re thinking about implementing artificial intelligence into your business, then why not reach out to one of our consultants? MJV has extensive experience with adapting AI into new innovative applications for their clients. It might not be the simplest transition you’ve ever had, but remember, you don’t have to go it alone.

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