Artificial Intelligence

AI Vs Automation: What’s The Actual Difference In 2024?

March 20, 2024
7 minutes

Artificial intelligence has become a buzzword in the tech industry. Companies are eager to present themselves as “AI-first” and use the terms “AI,” “machine learning,” and “deep learning” abundantly in their web and marketing copy. Why? Because it’s cool.

Cool people at cool dinner parties talk about cool things as though they’re second nature. Summers in the Greek Islands, photo safaris to Africa, paddling the Amazon in a kayak. Cool people just know and do more cool stuff than the rest of us.

These days, every tool is “AI-powered” or “AI-enabled” and people talk at length about the “transformative” nature of AI or the “pervasiveness” of big data: “It’s the future, you know. And the future is here”.

For the record, I’m including myself in the definition of “people”. At swivl, while we have built an AI product, utilizing the term AI in every other sentence doesn’t help our community. It just creates more confusion. So what is “AI”?

What Is Artificial Intelligence Really?

Back in the 1950s, when the term was coined by people who aspired to build computing machines that possessed human-level intelligence.

I often think about this excellent quote from Michael I. Jordan, explaining why today’s artificial intelligence systems aren’t actually intelligent:

“People are getting confused about the meaning of AI in discussions of technology trends—that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans," he says. “We don't have that, but people are talking as if we do."

It might be easier to start with what is not artificial intelligence. The simplest defining factor is that if it doesn’t learn, it’s not AI.

It’s easy to get confused about the difference between automation and AI. In some cases automation will use AI; however, most automation utilizes traditional software to move data from one place to another.

The difference between AI and automation is that AI aims to simulate human thinking. Put another way; automation works with data — AI ‘learns, understands, and interprets’ data.

Automation Vs AI Vs Machine Learning

Is It AI Or Automation?

Automation is deeply ingrained in our daily lives. It's used extensively in industries like e-commerce, banking, and telecommunications. Take a simple example: when you book a doctor's appointment and receive a confirmation message or email reminder, that's an automated response.

These systems are undeniably useful, but it's important to remember that this isn't true artificial intelligence. Automation follows a set of pre-programmed rules, executing tasks without intelligent adaptation.

Artificial Intelligence (AI), on the other hand, is designed to mimic human thought processes. It can learn and evolve, making decisions based on the data it analyzes. Machine Learning (ML), a subset of AI, takes this further. ML systems start with a blank slate and develop intelligence over time, adapting to new information and patterns independently.

“ML” & “AI” Are Often Used Interchangeably

In most cases, including with swivl, when the term “AI” is used, we are really referring to ML. With machine learning, usually called neural nets, a system can look at lots and lots of data and learn from it. The more examples of how to do something right you show the ML system, the more it refines its algorithm to get better. Here’s a fun analogy…

It’s like training a puppy. You ask them to sit and when they get it right, we provide positive feedback. Eventually, the puppy grows into a dog that reactively puts its tush on the floor when the word “sit” is commanded. The idea is about the same with neural networks.

What Does AI Include?

When we talk about AI, we’re essentially referring to technologies with a wide range of capabilities. Under today’s AI umbrella, this includes machine learning, deep learning, natural language processing, text analytics, voice recognition, speech recognition, and computer vision.

Rather than thinking about AI as a binary concept, it’s more useful to imagine it as a range, or a spectrum, with assisted intelligence at one end and autonomous intelligence at the other.

The Facade Of Being “AI-First”

When companies talk about being “AI-first,” their goal becomes to somehow integrate the latest and greatest advances in AI research into their products (or at least pretend to do so). When this happens, the company starts with the solution and then tries to find a problem to solve with it.

The media often portrays large language models as versatile problem-solvers in natural language processing. While these models are undoubtedly remarkable, it's essential to acknowledge that they aren't a one-size-fits-all solution. In reality, when faced with a well-defined problem, simpler models, or even traditional rule-based programs, might prove more dependable than the powerful GPT-based models.

Are You Really Using AI?

Everyone likes to talk about AI chatbots, but few are backed by neural nets that allow the bots to actually learn and improve. Siri, Alexa, and Google Assistant are three examples that utilize Natural Language Processing (NLP) to understand natural human language. Natural Language Processing or NLP is a branch of AI that aims to provide machines with the ability to read, understand and infer human language. 

Most of the bots you talk to on the phone or in web chats aren’t AI. They’re just taking your input and matching it to some known phrases. There’s no learning going on. That’s not AI.

Take the following sentences as an example:

  • “My husband is French”
  • “Excuse my French”

Both sentences use the word French - but the meaning of these two examples differ significantly.

Most analytics today aren't machine-learning-based. It’s just really complex programming that likely also uses probabilities and pattern matching of some sort, but the algorithm doesn’t self-improve, so again, there isn’t any learning going on. While it might be a powerful tool, it’s not artificial intelligence.

Does swivl Offer Real Artificial Intelligence?

swivl utilizes NLP technology similar to Siri and Alexa. Through Natural Language Processing techniques, computers are learning to distinguish and accurately manage the meaning behind words, sentences, and paragraphs. This enables us to do automatic translations, speech recognition, and a number of other automated business processes.

What Is Human-In-The-Loop (HITL) Technology?

Specifically, the AI system we’ve built utilizes Human-In-The-Loop (HITL) training technology. This type of system requires human interaction. In the context of machine learning, HITL is used in the sense of humans aiding the computer in making the correct decision in building a model. HITL improves machine learning over random sampling by selecting the most critical data needed to refine the model. 

We chose to build this type of system because of the large benefits such as being extremely effective for the purposes of training because it allows the human trainee to immerse themselves in the event or process effectively contributing to a positive transfer of acquired skills into the real world.

Similarly, ChatGPT’s language model underwent a fine-tuning process, benefiting from a combination of supervised learning and reinforcement learning techniques. 

OpenAI employed this innovative approach, where human AI trainers engaged in conversations, playing both the roles of the user and the AI assistant, enriching the model's understanding and capabilities.

Automation Vs AI Vs Machine Learning: Closing Thoughts

Distinguishing between artificial intelligence (AI) and automation is essential. While automation follows predefined rules, AI, including ML, tres to mimic human thought processes and evolves from data. At swivl, real AI is deployed through techniques like Natural Language Processing (NLP) and Human-In-The-Loop (HITL) training. Understanding these differences is crucial for harnessing the possibilities of AI.

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