We are in the midst of the AI revolution. A recent report by PwC and another by McKinsey both place AI as the biggest commercial opportunity for “companies and nations” over the coming decades. 30% of businesses are already conducting AI pilots, and nearly half are using AI capabilities within at least one standard business process — up from 20% in 2017.
Even given the state of development and adoption, there is widespread confusion about the exact nature of AI. Although becoming an AI expert is easier said than done, one place to start is with the difference between AI and machine learning (ML). Although these terms get thrown around nearly interchangeably, there are real differences. Understanding these differences will help you parse some of the nuance within the market and make the best choice for your business. Let’s get started.
What is AI?
AI is the broad umbrella term. It captures any kind of software that is designed to operate with either zero or minimal human input, using algorithms for learning and problem solving — replicating human information processing. In the very broadest sense, AI is the application of computer technology to mimic human cognitive functions. It describes software that is able to acquire and apply knowledge, enabling ‘smart’ decision making.
What is machine learning?
If AI is the ability for programs to make ‘smart’ decisions, ML is one of the critical techniques enabling that implementation. Briefly defined, machine learning programs are algorithms that can contextualise their actions within success/fail parameters, and use those on-going results to improve future actions.
Effectively, machine learning is a subset of AI that describes one of the most common methods by which AI functions. Even more specifically, ML describes a subset of AI that enables AI to improve itself, rather than relying on its initial programming. Although machine learning is not the only contributing technique enabling AI functionality, the flourishing resurgence of ML since the 1990s is directly related to the growth and success of AI application.
With that said, it is possible to build AI systems without ML. Historically, AI is the older concept. The original AI systems revolved around simply inputting huge quantities of data into pre-set programs, using ‘knowledge-based’ approaches rather than algorithms.
AI and ML: better together
The real reason that AI and ML are so often talked about in the same breath is that they are better together than apart. The starting algorithm is of critical importance to an AI, but so are the initial data sets and on-going data inputs -- be that AI built with ML or not. What ML does is vastly increase the positive feedback loop of on-going data inputs and, specifically, human interaction with the AI.
When humans engage with machine learning systems, they provide on-going data and success criteria that the program can use to continually improve itself. Without ML, AI is stuck with its initial set of parameters. Effectively, AI without ML is dead inside. It cannot put to best use the vast amount of data and decision making power at its disposal — at least given current methods of information processing.
If you want an analogy, you could think about ML and AI as an engine and a car. AI is the car, the shell that holds everything together, providing purpose and capability. ML is the engine, providing the power required to get from point A to point B. Both the engine and the car are useful on their own. A car without an engine is basically a cart, and you can attach an engine to almost anything. But when brought together, they deliver the seamless ability to deliver a much-needed outcome.
What about Deep Learning?
The waters of AI and Machine Learning are muddied slightly when we add the prospect of Deep Learning to the conversation. Deep Learning is best thought of as a subset of machine learning. It uses a similar iterative process to learning, contextualising actions and using outcomes to alter algorithms and hone better results.
The main difference between standard ML and Deep Learning is the application of numerous layers of these ML algorithms, each addressing the data flows in slightly different ways. These algorithm layers are known by another buzzword: neural networks, or neural nets. This creates a multi-layered system that is used to fuel a more flexible and ‘deep’ ML improvement process.
Although it is more complicated than this, it’s simplest to think of Deep Learning as the more advanced version of ML that is improving on the capabilities and increasing the speed at which complexity can be addressed.
A real-world business example of AI and ML in action
To put this in context, let’s look at chatbots — one of the most commonplace deployments of AI today.
Chatbots use relatively simple algorithms which replicate the responses a human customer service agent might generate to a customer query. Chatbots vary in sophistication but generally are used as a filtering mechanism to handle low-complexity customer queries to ease pressure on frontline employees.
The AI element of the chatbot is the decision-making process — the autonomous ability to look at a query, make a determination about what the best response should be, and then taking action. Obviously, this can be done by a Heuristics system of rules, with no model training involved. Then the ML comes into play. Modern chatbots learn from historical conversations, contextualising each request and response within success/fail criteria, and then use that information to further hone and improve future responses — altering their decision-making algorithm to improve over time.
An ML empowered chatbot has a wide range of starting assumptions and capabilities. However, it then learns what a successful answer looks like based on the outcomes it records while deployed within that specific environment. This ‘learning’ is the ML. The Heuristic set of rules (which is being constantly updated by ML) is the AI -- if you want to think about the AI as a separate entity. Although distinct, this example also shows the intractable interconnection of these techniques when deployed together.
Is there a difference as far as your business is concerned?
When it comes to modern applications of AI, most systems pull on elements of machine learning and Deep Learning. Although you could technically devise an ML system that does not have the decision-making power of AI, this is not very common.
As far as your business is concerned, you should be looking for AI-based solutions that deploy ML as a critical component within that system. The real question more accurately revolves around your application’s need for more advanced Deep Learning solutions. The value of Deep Learning basically revolves around complexity and data availability, quality and quantities.
These more specific questions are ones that you need to answer within the context of your own business, the types of data you can access and the outcomes you are looking to achieve. Start by thinking about your goals, then work backwards to find a solution.
An AI consultancy is a good option for helping you get off the starting line and head in the right direction. Although AI adoption rates have never been higher, it’s worth pointing out that not every business or every task is suited to AI or ML. A lot of that comes down to assessing your data streams and modelling. If you invest too much upfront on a project that you should have never started, it can damage your ability to invest in the right project. By partnering with experts in the field, you can get past that first treacherous step and deploy AL and ML in a way that will let you thrive in a new, smarter future.
However, one thing’s for sure: if you’re not taking advantage of this technology, your competitors most likely are!
To find out how you can leverage AI for your business, get in touch with our team.