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While the most well-known area of self-learning systems are undoubtedly self-driving cars, there are other areas where self-learning algorithms can be applied:
Completely automated chatbots
Personalized offers to customers
Multivariate website testing
Intelligent content curation
Robotics/automation powered by AI
AI-powered recommender engines can make recommendations based on tens of thousands of data points. This means highly relevant results for the end user.
This area of machine learning is especially relevant to aviation, retail and telecom industries where it’s used for:
Personalized marketing and sales offers
Recommendations based on recent/past activity
Recommendations based on real-time activity
Matching people with interests or with other people
Time-series predictions allow making predictions about future outcome by taking into consideration one extra constraint – the order of dependence between observations. This area of data science helps to:
Identify trends and seasonality in your business data – Find out what are the low seasons in sales or production.
Predict changes in your company’s inventory – Avoid recurrent shortages or surpluses through intelligent predicitons.
Predict your company’s future sales – Be prepared for high- and low-demand seasons, and serve your clients without any friction.
Predict the future reactions on the market – Predict how your new product will be received on the market and when’s the best time for launching new campaigns.
Predict demand – Predict increase/decrease in demand based on measurable things: weather data, air quality, etc.
AI and machine learning have the potential to make many business and manufacturing processes faster and more cost-efficient.
To give you some examples:
Model discovery – Find unproductive patterns in your workflow, and make them more efficient.
Next step prediction – Make use of your data to help machines and people decide real-time what’s the next best step to take.
Problem detection – Identify issues in the early production phase and find out what the root cause is.
Production optimization – Use machine learning to detect the optimal ways to maximize your production line’s output.
Most of the chatbots you see today are not powered by artificial intelligence. They follow a simple built-in flow of commands.
However, machine learning does enable building chatbots that are truly intelligent and learn based on customer interaction. Building these chatbots requires more data and time, but can potentially automate a substantial share of your communication with customers.
AI-powered text analysis has most value for companies that handle large volumes of emails, customer feedback or a specific type documents on a daily basis. Popular text analysis use cases include:
Sentiment analysis – Run a keyword analysis on positive and negative sentiment words/sentences to discover why people like or dislike your brand/product.
Trend preditcion – Analyze large corpuses of texts on social media, review sites, and in online publications to predict relevant trends and gain competitive advantage.
Text categorization – Automate the filtering and categorization of messages across your companies’ emails, documents, etc.
Customer service routing – Use text analytics to route specific requests to appropriate customer service representatives
Problem detection – Examine company-wide emails, customer complaints or social media feedback to identify issues real-time.
Intelligent image recognition systems are used by security organizations, retailers, and manufacturers alike to automate important parts of their work.
Security agents use image recognition to automate security checks and identify people from images.>
In retail, machine learning can be used to identify specific products from images, predict trends, and help people find relevant objects they want to buy.
Brand exposure measurement can be automated by tracking the brand logos that appear on social media or in video footage for sponsored events.
Large manufacturers apply image recognition to automate quality assurance and detect flawed objects on the production line.
In medicine, image recognition can be used to give more accurate diagnosis to patients.
Companies also use intelligent algorithms to detect and classify objects in images or spot faulty items on the production line.
Anomaly detection helps to spot problematic areas/processes early on and prevent costly disasters. This area of machine learning is especially relevant to companies in the manufacturing, retail, and finance industries.
Quality assurance – Ensure that your production output won’t include low-quality objects.
Find anomalies in your technical logs, and detect errors early on.
Detect irregularities in your business processes, find blockages, and remove them.
Enhance security by detecting abnormal activity in real-time video footage.
Intrusion detection – Identify unusual patterns in network traffic that could signal a hack.
Novelty detection – Uncover new patterns in your data that didn’t exist a while ago.
From banks to telecoms, machine learning is used to detect fraudulent transactions and lower the rate of fraud. AI also helps to reduce false positives by avoiding human error and lower the headcount in companies’ compliance departments. Potential use cases include: