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Artificial Intelligence (AI) is a field of data science, which includes machine learning.
Machine Learning
Machine learning is a field of data science within “Artificial Intelligence” that refers to the ability of technology to read and understand data (pattern recognition). Examples of technology that use machine learning include Netflix and Amazon, which use machine learning algorithms to suggest videos or shopping suggestions. Another more popular machine learning algorithm is Google ads, which suggest ads based on browsing behavior. There are two types of learning: supervised and unsupervised.
In the supervised learning environment, “training data” is used to provide the program with a baseline for how to process the information. This training data includes both the inputs as well as the desired outputs. The learning occurs when the program recognizes the relationships that it is expected to create and suggests those groupings going forward.
In the unsupervised learning environment, only inputs are provided and the program is expected to sort and structure the data based on commonalities that it finds.
Deep Learning
Deep learning is a class of machine learning that operates in layers. The first layer of learning may create 25% of the picture, the next adds another 35% and the last layer adds another 40% of information to complete the picture.
Natural Language Processing (NLP)
Natural language processing (NLP) is the field of Artificial Intelligence that deals with the interactions between humans and computers. An example of a type of NLP that has become prevalent is calendar scheduling. In the “olden” days, one would have to undergo the tedious task of opening the day at the time a meeting should be set and opening the invite field.
Now, NLP has made it such that you can type in a few words such as “meeting with mark at 5pm this friday” (no caps required) in the “Add New” field of your calendar and the invite will be created for you on the day at the time you selected with Mark.
Insight
The concepts of Artificial Intelligence will only become more common across traditional non-software heavy industries as technology continues to enhance our daily lives.
While IBM’s Watson is a super-computer that relies heavily on machine learning, natural language processing, information retrieval, knowledge representation and automated reasoning for its Question Answering (QA) computing system, it has yet to achieve sentience – the quality that most who think of AI consider to be the defining trait of Artificial Intelligence.
Therefore, while AI in the scary sense that most people are familiar with through the sensationalized Hollywood movies may not be a near-term reality. The reality is that we exist in a world in which algorithms can and do understand natural language inputs and are able to learn from previous interactions (supervised and unsupervised) to organize large sets of data presents all leaders across all industries with opportunities for changing the way they approach customer or business challenges.
The question that should be asked is how.
By: Kiran Chin
FEATURED INSIGHTS
Data Analytics & Reporting
