Demystifying Artificial Intelligence and Machine Learning: Understanding the Trends and Differences

13 minutes read

Terms like Artificial Intelligence (AI) and Machine Learning (ML) have become more than buzzwords, they are integral components of our technological landscape, not to mention our daily lives. From powering virtual assistants to driving predictive algorithms, these technologies are reshaping industries and redefining possibilities. 

AI tools have become commonplace as you navigate your personal lives or look to leverage them during workday. Perhaps you’re one of the more than 180 million unique ChatGPT users, or you’re one of the 77% of consumers using an AI-powered device. Regardless of your industry, role or region, AI-powered solutions have infiltrated your world. 

But amidst the excitement, there’s still plenty of confusion and many questions to be answered. What exactly are AI and ML? Why is there confusion surrounding these concepts? And what trends should the technology industry be watching? 

To demystify these topics, we sat down with TD SYNNEX Data and AI Solutions Consultant Shannon Murphy to learn more about AI and ML. To watch the full interview with Shannon, click here.

What’s the difference between AI and ML? 

Artificial intelligence essentially, is a broader term, and it is the science behind trying to incorporate human cognition, or the way that a human would think. Machine learning is using algorithms to train or to make models to enable the machine to think. The one factor that differentiates AI and ML is that ML is an algorithm that instructs how you are going to have that machine learn from the data that is being put in, and the artificial intelligence is the result. 

Why do you think there’s so much confusion around the terms AI and ML? 

There’s a lot of confusion between AI and ML, including what they mean independently and what they mean together. I think the confusion really came because more and more people are talking about artificial intelligence. Since Open AI has become commonly known, we use those solutions in greater ways. With more people talking about AI and ML, the terms are often confused and you’ll see them almost put together as if it is the same thing, or as if AI and ML are one thing. And I think they’re going to become even more confused as ML evolves and becomes more intelligent and autonomous.

How do you see artificial intelligence and machine learning being integrated into the IT channel? 

The way that TD SYNNEX integrates this technology into the channel is by focusing on the needs of our partners as they deploy AI and ML solutions to end users. One of the first ways is through automation, and if you have ever been on an app and you’ve had a problem, or you try and reach out to customer service, immediately a chat bot comes up. That’s an AI-powered automation solution. 

For example, just the other day my delivery from Walmart did not include the almond milk that I ordered. I went on my app and within 60 seconds, without human intervention, my $2.44 was refunded. So automation is definitely something that enterprises are demanding to help with operational efficiency. 

We’re also seeing supply chain optimization within the channel, and this is a huge way that businesses can leverage AI. Through supply chain optimization, we can manage inventory levels, as well as manage and forecast what the demands are going to be based on season, day of the week, weather, or some other factor. Whatever those patterns are, using those machine learning algorithms to train and to predict what’s going to happen in the future is a way that enterprises and the channel are using AI and ML.

How do you see this relationship between AI and ML evolving as these solutions become more advanced and intelligent? 

As AI and ML become more intelligent, we’re going to see these evolving in ways that really are going to expand how they are used. There is one term – explainable AI, or XAI – that is evolving right now and it is really asking AI to explain how it came up with that prediction or that conclusion, which can help us better understand how to work with these tools and this technology. XAI is going to be crucial as these technologies evolve, and it’s going to allow the user to trust AI in more meaningful ways.

The second way is continuous learning. As these machine learning algorithms become more effective, and as they are learning from unstructured and structured data and doing supervised and unsupervised learning, those machine learning algorithms are going to be able to be more autonomous. They’re going to be able to continue to learn and to adapt to the information that it continues to consume.

What are 3 trends that resellers and vendors need to know about AI and ML? 

I’ll start with the thing that is important to me. In December 2023, the New York Times sued OpenAI for copyright infringement. Questions were being asked, like “Where’s your data coming from?”, “Where is it going?”, “Who’s using it?”, “Why are they using it?”, “What are they using it for?”, and “What is the output of that training or the learning?”.

We call this data governance and data governance has been around for a while. But the trend that I see is data security and privacy within data governance, which is going to be crucial to businesses, to consumers, and really to everyone, because as we use tools like ChatGPT, we need to know where that information and data is going and who has access to it. 

Another trend is customization and personalization. We have subscription boxes where we can sign up for almost anything and it’s delivered to our door, whether it be for our pet, for our clothes, for our food, for our cleaning products. You can have subscription boxes for anything that’s also being used in different ways, because all that data around our behavior and activities is being captured and we may not even be aware of it. And so the customization and personalization for our lives and businesses is really going to become apparent and be a trend, especially with us giving more information and more data out.

The third trend that I see happening is automation. With businesses having to do more with less, or wanting to beat the competition or be the most innovative, automation is going to be very important. Automation is about making things more efficient, and when I talk to business owners it really is about exploring different ways to think about resources. It may not be a need to reduce staff, but perhaps it’s about upskilling your staff to provide an opportunity to move somebody who is doing something very manual and giving them other opportunities and ways that they can still be valuable and contribute to the organization, but just in different ways.

What’s the biggest mistake you see people make with AI and ML? 

One of the biggest mistakes I see are people being afraid of the solutions, or more importantly, afraid to learn about these technologies. They’re afraid to talk about it and afraid to implement it. Enterprises look at all the bad things that could happen or from a data security standpoint. How I talk to partners and end users about their fear is first of all, let’s go back and talk about your data governance and data strategy. 

By identifying what data these machines are going to be looking at, how they’re going to be analyzing it, and what the output is, that’s going to reduce that fear. It also increases knowledge and knowledge is power. Additionally, with that data strategy in place, it’s going to reduce the fear so that they can implement those AI solutions that are going to improve operational efficiency, make the customer experience better, and make the workplace safer.

Looking at the intersection of AI and ML in the channel, where should enterprises focus over the next three to five years?

Enterprises really need to be looking into the future on how machine learning is going to become even more automated. It’s going to be the continuous learning of machine learning. Earlier I mentioned the supervised learning and unsupervised learning. Supervised learning is when you tell a machine, “I want you to look at this data, and I want you to tell me some defined piece of information.” 

For unsupervised learning, you say, “I’m going to give you this data and you tell me what you think.” Then the machine is going to find patterns or anomalies, which is part of that continuous learning, because we keep feeding machines data based on experiences, behaviors and anything that we’re doing and they are continuously learning, and enterprises need to think about that. 

Whether it’s data that is public data or data from other places that you can purchase, there’s data that’s not within your enterprise and those organizations need to be thinking outside of the box to ask what other data is there to help make these data-driven decisions. And that’s what enterprises need to be focused on, especially to remain innovative and to have that differentiation between them and their competitors.

It’s evident that AI and ML aren’t just buzzwords but catalysts for innovation, reshaping industries and augmenting human capabilities. As these technologies evolve, TD SYNNEX will continue to harness the power of AI and ML as we forge a path into the future with initiatives like the Destination AI program. 

Be sure to check out more of the TD SYNNEX Demystifying Series, and get the latest technology industry trends and news on the TD SYNNEX LinkedIn and Instagram pages.

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