Fuzzy Logic – AI without Data Scientists

Fuzzy logic helps quickly model everyday commonsense rules used in your business to Artificial Intelligence – pretty powerful stuff!

Picture an impending tornado headed your way threatening calamity, what would you do?

  • Use a deep learning model developed at considerable expense and find out you have 20 seconds before the tornado will reach you

  • Use commonsense and seek shelter immediately

The commonsense approach makes intuitive sense but commonsense is not easy to express mathematically in a way computers understand. Fuzzy logic helps overcome this hurdle and get started with AI using simple “If Tornado is close then seek shelter” type rules using plain English and notions such as ‘Close’ can be specified as a value between lets say zero to a half mile away. Fuzzy Logic converts natural language like rules into mathematical models. The deep learning model would have required a lot of data beforehand to train a model which is both time consuming, expensive and difficult to interpret.

Humans and animals often operate using fuzzy evaluations in many everyday situations. In the case where someone is tossing an object into a container from a distance, the person does not compute exact values for the object weight, density, distance, direction, container height and width, and air resistance to determine the force and angle to toss the object. Instead the person instinctively applies quick “fuzzy” estimates, based upon previous experience, to determine what output values of force, direction and vertical angle to use to make the toss.

Infinity’s Fuzzy Logic Processor Advantages:

  • Model using near natural language

  • Fully integrated with NiFi

  • Get Started in Minutes

  • No data required to get started

  • Improve model as you collect more data

  • JSON input and output

  • Express rules with IF-THEN syntax

  • Easy to interpret model

  • Change rules fast when data changes

  • Combine processors for complex rules

  • Model real world ambiguity

  • Mimics human reasoning

  • IEC 61131 Part 7 compliant

  • Quick ROI

  • Model complex non-linear requirements

Fuzzy Logic has been adopted across domains including IoT, Healthcare, Advertising and Finance. As you might have guessed by now, Fuzzy Logic is a great for use cases where human experience can be expressed using natural languages but cannot be quantified or at least not without great difficulty using traditional programming methodologies.

Illustrative examples of fuzzy logic for Medical Applications

Predict the response to have treatment with citalopram in alcohol dependence
Aanalyze diabetic neuropathy
Detect early diabetic retinopathy
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Illustrative examples of fuzzy logic for Anomaly Detection

Predictive maintenance of industrial equipment using IoT sensor data, etc
Credit card fraud
Network intrusion
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Illustrative examples of fuzzy logic for Advertising

Spot bidding strategy for mobile apps based on advertising executive expertise
Demographic segmentation
Landing page redirects
Marketing campaign strategy

Illustrative examples of fuzzy logic for Smart Cities

Flood Monitoring
Crime Prevention
Bike share demand forecast
Employee scheduling

Illustrative examples of fuzzy logic for Industrial Automation

Crane sway control
Climate control
Robot movement control
Energy efficiency

Illustrative examples of fuzzy logic for Appliances

Detergent use in washing machines
TV picture based on ambient light
Stabilize video camera shake
Microwave auto-cook options

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