Artificial intelligence (AI) has firmly settled in the headlines of business publications and newsfeeds. Amazon went from machine learning (ML) algorithms that boost sales by improving recommendations to the quest of making the AI-powered Alexa speaker a ubiquitous device. Airbus is using artificial intelligence powered software to improve production with a self-learning algorithm that identifies patterns in production problems. Meanwhile, Coca-Cola is planning a location-sensitive vending machine with an assistant powered by artificial intelligence based technology.
The list will continue to grow, and it will grow fast. Paired with Big Data, AI-based software solutions are bringing precise automation and valuable insights to the majority of industries and business departments.
Still, the definition of artificial intelligence remains vague and, in many cases, complex.
As Intellectsoft has been creating AI-based software solutions, we are starting a series of posts about AI.
This post will answer the following questions: What is artificial intelligence? What is machine learning and what are its types? Finally, what are the three cornerstones of implementing machine learning algorithms?
What is Artificial Intelligence
In a broad sense, artificial intelligence is a field of study that uses a wide array of scientific, mathematical, and engineering methods to understand what is required for a machine to exhibit intelligence.
Concurrently, the much talked about machine learning and deep learning are essentially self-learning algorithms of different complexity.
They are part of the field of study of AI, but referring to them as “AI” is not accurate. The field encapsulates too many complex concepts; assigning the term to each separately creates confusion.
This is true for other general terms commonly used with self-learning algorithms — “software” and “technology.” These terms create further confusion by drawing associations with software development and everything that can be called a physical or digital technology.
Leaving AI out of the name won’t be accurate either. After all, machine learning and deep learning algorithms are autonomous, coming up with their own decisions based on Big Data. More so, deep learning essentially mimics the neural networks of a human brain.
As machine learning, deep learning, and adjacent algorithms are commonly used in the context of IT, the most accurate way to encapsulate them in one term would be to call them AI-based software solutions (or AI-based solutions).
This term covers all the self-learning algorithms applicable in the enterprise without touching concepts like Artificial Super Intelligence (think Skynet and Elon Musk’s presentations about dangerous robots) and questions like AI ethics and values learning problem.
Meanwhile, “machine learning software” has also gained traction in 2017; it is also correct, applying to software solutions driven by machine learning algorithms.
Machine Learning Definition and Types
Machine learning is a type of data analysis that uses self-learning algorithms to analyse vast amounts of data, learn from the data, and then present a solution to a problem, provide insight, or make a prediction.
There are two types of machine learning algorithms.
In supervised learning, an algorithm runs on labeled data. This type is used to solve a problem or make a prediction.
Let’s look at an example:
Your enterprise wants to use machine learning to check the validity of your top managers’ signatures on contracts to avoid fraud.
You gather ten documents with signatures for each top manager (for a spot-on result, as every subsequent signature differs from the previous signatures and the ones that follow), label them with the corresponding names, and then run this data through a machine learning algorithm.
Having established the connection, the software that employs ML will analyze the contracts by itself, and let you know if someone tampered with a signature immediately.
In unsupervised learning, an algorithm runs on unlabeled data.
This type of ML is mostly used to derive insights by clustering data into groups by similarity and compressing it in size and dimensions.
For example, Netflix uses an unsupervised learning algorithm to deliver nuanced recommendations based on Big Data — what the viewers searched for, rated; the time, date, and device; as well as the browsing and scrolling behavior.
Eventually, four-star Oscar movies will be side-to-side with two-star comedies in the “Recommended for You” section. Carlos Gomez-Uribe, VP of product innovation and personalization algorithms at Netflix, provides a spot-on explanation for the machine learning algorithm in a Wired interview:
People rate movies like Schindler’s List high, as opposed to one of the silly comedies I watch, like Hot Tub Time Machine. If you give users recommendations that are all four- or five-star videos, that doesn’t mean they’ll actually want to watch that video on a Wednesday night after a long day at work. Viewing behavior is the most important data we have.
Thus, unsupervised learning avoids the “intuition failure” (recommending a four-star movie on a page of other four-star movies), taking advantage of vast amounts of raw data, and allowing for valuable insights. Gomez-Uribe and his colleague Xavier Amatriain (engineering director at Netflix) wrote an article on the topic where they assert that machine learning algorithms save Netflix one billion dollars a year.
Finally, what about custom machine learning algorithms?
There’s an array of common algorithms that can be applied to solve the majority of data problems. If no existing algorithm fits your problem, you can create a your own.
What you should consider is that the accuracy of the final result depends on the following:
- Effectiveness of the chosen algorithm;
- How you apply it;
- And how much useful data you have.
These are the three cornerstones that will kickstart a successful implementation of algorithm.
We hope our post has helped you understand AI and ML better, and that your project will be on the lists of essential machine learning examples in the future.
In the next posts, we will look at deep learning and outline how to ensure an impactful and full-fledged implementation of AI-based solutions in the enterprise.
If you can’t postpone the implementation of your machine learning software or other AI-based software, get in touch with us. We will help you create a comprehensive solution, from algorithm to implementation.