What is Machine Learning?
Machine Learning is a branch of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being specifically programmed. Machine Learning can learn from data, identify patterns and make decisions with minimal human intervention. The basic premise of Machine Learning is to build algorithms that can receive input data and use statistical analysis to learn from that data, then make predictions and decisions.
Timeline of Machine Learning
Machine learning happens in three stages — data processing, model building and deployment & monitoring. In the middle we have the core of the pipeline, the model, which is the machine learning algorithm that learns to predict the given input data.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data — repetitively and at faster speeds — is a more recent development.
Here are a few widely publicized examples of machine learning applications you may be familiar with:
- Self-driving vehicles: the essence of machine learning.
- Machine learning applications for everyday life: online recommendation offers such as those from Amazon and Netflix.
- Fraud detection: one of the more obvious, important uses in our world today.
Who is using Machine Learning?
Machine learning is becoming more and more beneficial to the world around us. Here are some of the industries (but not limited to these alone) that are being transformed by machine learning.
Like self-driving cars, the manufacturing industry collects a great amount of data from sensors attached to every aspect of the production line. Machine learning can be used to drive collaborative robots in factories that learn by observing the production line and data streams. These robots are able to smartly optimize the production process to lower production costs and speed production cycles without the time and financial costs of a human having to analyze the data.
Retail and Consumer Goods
Retail and consumer goods companies are seeing the applicability of machine learning (ML) to drive improvements in customer service and operational efficiency. For example, the Azure cloud is helping retail and consumer brands improve the shopping experience by ensuring shelves are stocked and products are always available when, where and how the consumer wants to shop.
Some of the main achievements of Machine Learning in Retail is:
- Inventory optimization through SKU assortment + machine learning ensures shelves are stocked and best products are always available for purchase.
- Visual Search focuses on customer-centric search with device-friendly capabilities.
- Sentiment Analysis can help companies improve their products and services by better understanding their consumers and the impact on the consumers.
- Fraud Detection can automatically detect anomalies and other errors that signal dishonest behavior.
- Demand Forecasting by pricing optimization to meet consumer demand related by creating a demand forecast at various price points and business constraints to maximize potential profit.
- Personalized Offers improve the customer experience by offering relevant information which in turn provides retailers with improved data about the customer’s brand engagement and a higher ROI (Return on Investment).
There are already a lot of great machine learning applications that are bringing value to the financial services sector.
The term “robo-advisor” was essentially unheard of just five years ago, but it is now much more common to hear in the financial sector. This term is misleading and doesn’t involve robots at all. Rather, robo-advisors are algorithms built to calibrate a financial portfolio to the goals and risk tolerance of the user.
Users enter their goals (for example, retiring at age 60 with $750,000.00 in savings), age, income and current financial assets. The advisor (which would more accurately be referred to as an “allocator”) then spreads investments across asset classes and financial instruments to reach the user’s goals.
With origins going back to the 1970’s, algorithmic trading (sometimes called “Automated Trading Systems,” which is, arguably, a more accurate description) involves the use of complex AI systems to make extremely fast trading decisions.
Algorithmic systems often making thousands or millions of trades in a day, hence the term “high-frequency trading” (HFT), which is considered to be a subset of algorithmic trading. It is believed that machine learning and deep learning are playing an increasingly important role in calibrating trading decisions in real time.
As the internet is becoming a part of our everyday lives, with an increasing amount of valuable data us being stored online, which requires higher security and fraud prevention. This is where machine learning comes in to play. Using machine learning, systems can detect unique activities or behaviors and flag them for security teams. Some security AI executives believe these learning systems will be a necessity in the next five to ten years.
Demands in transportation are increasing due to trends in population growth, emerging technologies and the increasing globalization of the economy.
Here are a few ways that machine learning is benefiting transportation:
- Monitoring and managing transportation system performance
- Autonomous vehicles and connectivity within the car
- Freight transportation operations
- Air traffic control
- Predictive analytics for smart public transport
- Vehicle safety monitoring
- Object detection and traffic sign recognition
- Analysis of the traveler’s behavior