Artificial Intelligence (AI) stakes a claim on productivity, corporate dominance, and economic prosperity with Shakespearean drama. AI will change the way you work and spend your leisure time and puts a claim on your identity.

First, an AI primer.

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SwissCognitiveWhat is Intelligence?

Let’s define intelligence, before we get onto the artificial kind. Intelligence is the ability to learn. Our senses absorb data about the world around us. We can take a few data points and make conceptual leaps. We see light, feel heat, and infer the notion of “summer.”

Our expressive abilities provide feedback, i.e., our data outputs. Intelligence is built on data. When children play, they engage in endless feedback loops through which they learn.

Computers too, are deemed intelligent if they can compute, conceptualise, see and speak. A particularly fruitful area of AI is getting machines to enjoy the same sensory experiences that we have. Machines can do this, but they require vast amounts of data. They do it by brute force, not cleverness. For example, they determine the image of a cat by breaking pixel data into little steps and repeat until done.

Key point: What we do and what machines do is not so different, but AI is more about data and repetition than it is about reasoning. Machines figure things out mathematically, not visually.

What is AI?

AI is a suite of technologies (machines and programs) that have predictive power, and some degree of autonomous learning.

AI consists of three building blocks:

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  • Massive data
  • Fast computations
  • Smart algorithms

An algorithm is a set of rules to be followed when solving a problem. The speed of the volume of data that can be fed into algorithms is more important than the “smartness” of algorithms.

Let’s examine these three parts of the AI process:

  • Data pipeline
  • Fed into machine learning (ML) models
  • Applied to business applications

Big Data

The raw ingredient of intelligence is data. Data is learning potential. AI is mostly about creating value through data. Data has become a core business value when insights can be extracted. The more you have, the more you can do. Companies with a Big Data mind-set don’t mind filtering through lots of low value data. The power is in the aggregation of data.

  • Data pipeline
  • Fed into machine learning (ML) models
  • Applied to business applications

Building quality datasets for input is critical too, so human effort must first be spent obtaining, preparing and cleaning data. The computer does the calculations and provides the answers, or output.

Machine Learning

Conceptually, Machine Learning (ML) is the ability to learn a task without being explicitly programmed to do so. ML encompasses algorithms and techniques that are used in classification, regression, clustering or anomaly detection.

ML relies on feedback loops. The data is used to make a model, and then test how well that model fits the data. The model is revised to make it fit the data better, and repeated until the model cannot be improved anymore. Algorithms can be trained with past data to find patterns and make predictions.

Key point: AI expands the set of tools that we have to gain a better grasp of finding trends or structure in data, and make predictions. Machines can scale way beyond human capacity when data is plentiful.


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