With technology shaping our future at an impressively fast pace, computer processing power and data storage have evolved dramatically due to artificial intelligence and similar processes such as machine learning. But should we have a machine learning vs AI discussion, or are these technologies the same?
We’ve already seen how AI is technically a broad term with our Cognitive Computing vs AI: What’s the Difference? blog, but not necessarily all-encompassing. Machine learning enables AI, and it’s quickly becoming AI’s fastest growing component.
For AI to progress, however, we would need higher performing computers like quantum computers – but that’s a different story for a different day (or blog!).
What is Machine Learning?
Machine learning is a subfield in AI, helping computers to learn by themselves by identifying patterns in data, building models, and predicting events. They do this without explicit, pre-programmed models and rules through a specialised algorithm.
The Future of Humanity Institute state in a report that researchers think that, in the next 45 years, AI has a 50% chance of outperforming humans. This is called a singularity, in which AI can improve itself – but that is still a good few years away!
The hierarchy of machine learning translates to:
- Artificial Intelligence
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Machine Learning
Supervised Learning
Supervised learning relates to the existence of input variables (X) and the existence of output variables (Y). An algorithm learns the mapping function from these input variables to the output variables, which is represented in the equation below:
Y= f(X)
The objective is to be able to predict output variables for specific data from the approximation of the mapping function, when there are new input variables. As this process is similar to a teacher supervising a learning process, it’s thought of as supervised learning.
The algorithm will iterate predictions based on training data, to which we know the correct answers to and proceed to ensure the algorithm is corrected. Until the algorithm achieves the perceived acceptable performance level, learning continues.
From this method there are two further issues that can arise: classification, when the output variables are illustrated into categories such as “blue” or “red”, and regression, when the output variables are real values such as “weight”.
Unsupervised Learning
Unsupervised learning corresponds to the existence of input variables (X) only, without corresponding output variables. The objective of this process is to model the distribution or the underlying structure of data so that we learn more from it.
Without any correct answers or “teacher”, unsupervised learning consists of algorithms finding and presenting data structure by themselves. From this method, issues that can arise are grouped into: clustering, where inherent data grouping want to be found like “purchasing behaviour”, and association, in which rules need to be found to describe large amounts of data such as customers who buy both “A” and “B”.
Reinforcement Learning
Similarly to supervised learning, reinforcement learning also makes use of mapping between both inputs and outputs. The difference, however, is that reinforcement learning doesn’t receive feedback from a “teacher” – it uses punishments and rewards to know what are positive and negative behaviours.
The objective is to identify suitable action models that can maximise the total cumulative reward. Put simply, reinforcement learning has a focus on performance based on elements such as state, environment, value, policy, and reward.
Machine Learning vs AI
Although AI brings intelligence to the table, machine learning is what will make it possible for machines to process data and learn by themselves without the need of constant supervision. With breakthroughs in the AI field and the invention of the internet, it’s clear that machines need to be able to “think” for themselves so they can analyse the massive amounts of data available.
Machine learning has provided us with things such as effective web search and practical speech recognition. Google’s machine learning algorithms help users when they make typos on searches; that message “Did you mean…” that appears on the search engine is a result of these algorithms.
Using Machine Learning to Improve Customer Journey
Google’s RankBrain algorithm uses machine learning to help determine which search results are more relevant to users’ search engine queries. The company teaches the algorithm by giving it data from a variety of sources, which translates to it then teaching itself over time and making calculations so that it can order rankings.
But just how is RankBrain improving the customer journey?
Better Semantic Understanding
Since Google’s investment on this machine learning algorithm, search results demonstrate a better understanding of the semantic connection between the search questions and the contents in the results.
Relevance Over Optimisation
Google can now identify and display with more accuracy the pages that provide the answers to the question’s intention. The search engine doesn’t just display the highly-optimised pages – it now relies on what is more relevant for the users.
Assuming the Best Fit
The algorithm uses mathematically mapped relationships to assume which results match queries the best. These results are refined as users continue to interact with the machine learning algorithm, providing better matches between user search intent and displayed results.
And how is machine learning used to improve marketing strategies?
- Improving UX. Alongside marketing automation, chatbots are becoming the main point of contact with customers.
- Predicting Churn. AI can spot the signs and predict churn by analysing behaviour and historical data alongside triggering actions to prevent it.
- Improving Marketing Campaigns.
- Better Personalisation. Machine learning algorithms can provide users with personalised ads and campaigns, down to headers, taglines, and formatting.
- Better User Segmentation. More dimensions can be added to analyse customer data through clustering algorithms, which are more responsive by constantly adapting to more data.
- Dynamic Pricing. Businesses become more agile and can quickly respond to fluctuations in supply and demand.
Machine learning has the potential to enhance various other components of marketing strategies. As the technology continues to be in refinement stages, businesses can expect to see its impact on ROI.
