So, What is an Algorithm?
22nd Annual SCIP European Summit in Cascais, PortugalOctober 18, 2017
Comintelli’s CEO Jesper Martell to Speak About Artificial Intelligence and Knowledge Management at KMWorld ConferenceOctober 24, 2017
- Artificial Intelligence
- Augmented Intelligence
- Business Translator
- Cognitive Computing
- Competitive Intelligence
- Deep Learning
- Information Discovery
- Knowledge Management
- Machine Learning
- Machine Learning Implications for Intelligence and Insights
- Predictive Analysis
- Time to Insights
”The twenty-first century will be dominated by algorithms. “Algorithm” is arguably the single most important concept in our world. If we want to understand our life and our future, we should make every effort to understand what an algorithm is. “ (Yuval Noah Harari, author of Sapiens and Homo Deus)
Since algorithms are at the core of Machine Learning, it is important to understand how they work because this is definitely an area were intelligence professionals can apply their expertise. Machine Learning algorithms are based on a set of rules used to calculate and predict an outcome.
Y= Predicted outcome (validation, approval)
F = Prediction function (model)
X = Input variables (data)
Thus, in technical terms, an algorithm is a sequence of instructions telling a computer what to do. Below is a concret example of an algorithm that could be used by a computer for playing tic-tac-toe, with the likeable property that it never loses!
If your opponent has 2 in a row, play on the remaining square.
Otherwise, if there is a move that creates 2 lines of 2 in a row, play that.
Otherwise, if the center square is free, play there.
Otherwise, if your opponent has played in a corner, play in the opposite corner.
Otherwise, if there is an empty corner, play there. Otherwise, play on an empty equare.
However powerful and intelligent that algorithms may be, it Is still essential to understand that they also have limitations. For example – being based on probabilities and data from the past, which isn’t necessarily representative of the current state. They also rely heavily on being supplied with relevant data to provide results of high quality.
This was part four in our new blog series, based on the article “Machine Learning Implications for Intelligence and Insights”, written by Jesper Martell, Comintelli, and Paul Santilli, Hewlett Packard Enterprise.