Artificial intelligence (AI) definition suffers from incurable case of vagueness. Along with the progression of machines’ ability to show human-like intelligence, the expectation from AI keeps changing. Some things that machines can do are not treated as AI. Despite suffering from vagueness, we may define AI as a machine’s capability to do jobs that require human-like intelligence, such as sensing, perceiving, learning, deciding, and implementing decisions. We may call it artificial general intelligence (AGI) definition. To bring fussiness in the definition of AI, we may refer to other classes. On the other hand, machine learning (ML) refers to the ability to learn from observing, teaching, and experiencing reality. The current machine learning algorithms rely on learning from training samples. Hence, artificial intelligence and machine learning differences refer to terming machine learning as a sub-domain of artificial intelligence. However, the performance of machine learning itself shows progress in building AI capability in software and machines.
Types of AI—adds confusion in making the difference between AI and ML
Here are seven levels of artificial intelligence, reaching the state of singularity.
- Reactive Machines–capable of responding to external stimuli in real-time;
- Limited Memory–able to store knowledge and use it to learn and train for future tasks;
- Artificial Narrow Intelligence–designed to complete very specific actions;
- Artificial General Intelligence–capable of learning, thinking, and performing at similar levels to humans;
- Artificial Superintelligence–able to surpass the knowledge and capabilities of humans.
- Theory of Mind–can sense and respond to human emotions;
- Self-aware—able to recognize others’ feelings and his sense of self and human-level intelligence;
Such classification confuses defining specifications for building AI machines and separating them from ML tools. Hence, we should focus on one specific type, which is deliverable. Therefore, let’s focus on AGI to understand the difference between AI and ML.
Examples of artificial intelligence and machine learning difference
Let’s assume that we have autonomous vehicles. Despite the vagueness of the definition of AI, we may term autonomous vehicles as AI machines. Of course, it needed machine learning exercises to learn a few things like recognizing traffic signals, detecting obstacles, and understanding people’s gestures. However, such learning exercise is not enough to have AI automobiles—robot cars—to offer drive to destinations. Additionally, this autonomous vehicle needs a set of rich sensors, software to process sensor data for extracting information, decision-making ability to respond to obstacles, and the ability to communicate with neighboring vehicles and pedestrians.
The next example could be an artificially intelligent medical doctor. Of course, machine learning will help to map symptoms to the cause of sickness. However, that learning alone is not sufficient to perform a job as an AI doctor. There is also a need to know the history, feeling and other health issues to decide what medication should be prescribed.
On the other hand, ML, as predictive analytics, plays a valuable role in millions of operational decisions. For example, the role of ML in predicting which customers are most likely to cancel could be highly beneficial in targeting incentives to those customers so that they stick around. Similarly, real-time predicting of which credit card transactions appear fraudulent can help a card processor disallow them.
As these examples clarify, the difference between artificial intelligence and machine learning is in the scope. Machine learning is just a building block of an AI machine or system; practical use cases of ML to improve the efficiencies of existing business operations are relatively straightforward. The purpose of ML is to issue actionable predictions.
Lack of clarity of artificial intelligence and machine learning differences leads to confusion and distraction
Often, even experts tend to mingle AI and ML in expressing what next-generation machine capability can deliver. Instead of using AI or ML separately, they refer to them together using AND. For example, an expression like this: “Humans must be able to interpret the collected data from AI and ML to make decisions and find solutions to world problems” makes it confusing to make the difference between artificial intelligence and machine learning. Is not an AI machine supposed to do the jobs autonomously? Hence, such an expression creates confusion about the meaning of AI. It seems the person wanted to refer to the role of ML and the importance of humans taking advantage of it.
Recent splashes like OpenAI’s ChatGPT and other generative extensive language-based tools are termed AI tools. Perhaps more rational naming should have been ML tools. Due to their high performance in knowledge compilation and summarization, there has been hype about taking over the jobs of knowledge professionals like teachers, journalists, and lawyers. However, do good teachers reproduce what is just written in the books? Do they play a role in connecting with learners’ minds and motivating them?
On the other hand, do journalists just report what they see? Of course, in performing their jobs, white-collar professionals perform knowledge compilation jobs. Hence, ChatGPT-4, like ML tools, will help improve productivity. However, knowledge professionals must deliver services beyond what ChatGPT-like tools can accomplish. Therefore, they must focus on sharpening their Innate abilities to add value beyond the available written or Codified knowledge base.
There are issues like artificial intelligence (AI) vs deep learning (DL) vs machine learning (ML). DL is an ML technique, and AI machines require ML capability. Hence, DL, , and AI are not the same.
Lack of clarity about artificial intelligence and machine learning differences distracts companies
Perhaps our technology core is insufficient to roll out AI machines to make human intelligence obsolete. AI possibilities suffer from pervasive uncertainties and a high risk of getting caught in the chasm. For example, autonomous vehicles are yet to appear despite hype and an investment of $80 billion in R&D. Instead, we should focus on leveraging ML’s knowledge compilation ability for productivity enhancement tools. For this reason, perhaps WEF’s Future of Jobs Report 2023 has lowered expectations about the role of AI in jobs. The expectation is augmenting humans’ cognitive capability instead of taking over it. This is about migrating from AI to leveraging ML.
Terming ML possibility as AI inflates expectations. As a result, it distracts from the practical way ML will improve business operations. Overstating ML’s performance in inflating AI expectations may risk creating hype and suffering from its fading. Therefore, it’s time to have a clear line between artificial intelligence and machine learning differences.