AI vs Machine Learning: What Are Their Differences & Impacts?

AI vs machine learning and deep learning. These words conjure visions of decision-making computers replacing whole departments and divisions — a future many companies believe is too far away to warrant investment.

But the reality is that artificial intelligence is here, and here to stay. Particularly at the enterprise level, a growing number of companies are tuning in to the data science, productivity, and promise of machines that can think for themselves.

AI Growth Will Create New Jobs

Recent data from the National Venture Capital Association shows that 1,356 AI-related companies raised $18.5 billion in 2019 in the US, up from the $16.8 billion in 2018.

Despite scaremongering projections that millions will need to switch occupations as robots and algorithms take over specific tasks once done by humans, most analyses project job gains as a result of AI, machine learning, and deep learning. Gartner predicts that in the US AI will create two million net-new jobs by 2025, as companies expand to absorb the new productivity.

With all that in mind, how do you dial back the AI vs machine learning hype? And how should you be thinking about what cognitive computing can do for your business? Let’s take a closer look.

Read more: AI Software Trends for 2021

Venn diagram showing relationships of artificial intelligence, machine learning and deep learning.

What Is Artificial Intelligence?

Artificial intelligence is a computer system designed to think the way human intelligence does. That means more than just doing a specific task well, like say, Alexa, who responds to your voice command to play your favorite song. True artificial intelligence has the ability to parse data, make decisions, and learn from data science to create something new.

AI has been famously used to tackle big problems, like testing drug compounds for curing cancer. Alibaba uses artificial intelligence not just for predictive advertising on their sites, but also for monitoring cars and creating constantly changing traffic patterns, or helping farmers monitor crops to increase yield.

True artificial intelligence has the ability to parse data, make decisions, and learn.

Amazon Go is using AI algorithms to rethink the future of retail, creating unmanned convenient stores that monitor your shopping experience and charge you automatically when you walk out the door with an item.

Experimental machine intelligence has written novels (badly), played chess against world masters (very well), and parsed the world’s medical literature to help doctors make better and more complete diagnoses (and saved lives).

With artificial intelligence platforms like Microsoft Azure, Google Cloud, and many others, developers now have the resources they need to think creatively about AI for their own businesses. Further, AI in the cloud significantly reduces a company’s infrastructure costs for the massive computing capacity AI needs to be most useful.

What Is Machine Learning and Deep Learning?

Sometimes, machine learning is used interchangeably with artificial intelligence, but that’s not quite correct. Machine learning is actually a subset of artificial intelligence, and deep learning is a subset of that.

A machine learning algorithm is a computer program which does one task really well by parsing and analyzing historical data over time via a neural network. Deep learning combines machine learning neural networks with complex algorithms modeled with training data based on the human brain to parse huge amounts of labeled data.  

Machine and deep learning are only as good as the data flowing into the system. However, examples of machine learning and neural networks and deep learning are all around us.  Machine learning programs fuel Alexa on our tabletops, the dynamic pricing that goes up or down on a website based on your personal information, the email that gets automatically filtered to your inbox, and the chatbot that responds when you ask a question on a website. 

Read more: Data Analytics vs Data Science: What’s the Difference?

Putting AI to Work

The fields of AI, machine learning, and deep learning have promise, and are becoming more feasible for companies to incorporate into their systems. Sitima Fowler, Vice President of Marketing for Iconic IT, recommends most companies start small.

“AI is trendy right now, definitely. But the reality is, most companies will be starting with machine learning, such as bots that parse their user traffic, for instance, to mine data. They might use it for chatbots on their website to direct consumer inquiries to the right information.”

‘Most companies will be starting with machine learning, such as bots that parse their user traffic.’

“From there, many companies can use the AI development tools available in the cloud from services like Amazon and Microsoft to develop AI that powers their consumer-facing apps, and so much more,” Fowler said. “We’re all very excited about the future of where artificial intelligence can take us. But it’s important to take it one step at a time, so the rest of your systems can integrate and keep up.” 

“For example, at Iconic IT, we use AI to prevent cyber security breaches. Just simply installing an antivirus and email spam filter on your computer isn’t enough. The bad guys have figured out ways around this software,” she added. “So we incorporate artificial intelligence on top of these software so it looks as the person’s normal behavior and interactions with other people. Over time it learns a user’s email habits, communication styles, contacts to determine if a particular email is legitimate or potentially harmful.”

Adopt AI to Stay Competitive

Deep learning models like this are advancing the emerging field of behavioral biometrics, showing that we’ve only just begun to see the security capabilities of this technology. On the other side, natural language processing is improving customer service and IT management. And deep neural networks are using image recognition to improve search for a variety of industries.

The broad applications of artificial intelligence, machine learning, and deep learning are why data scientists and machine learning engineers are in high demand at many companies. Turning unstructured data into actionable insights is now key to remaining competitive.

Read next: Top Big Data Tools & Software for 2021

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