What is Machine Learning (ML)?
ML can be simplified as the subset of Artificial Intelligence (AI) focused on computer algorithms which improve automatically through experience, meaning the more data they handle, the better the insights which they can generate.
Before ML, in order for a given autonomous digital entity (AI), such as a robot or an application, to work properly across its operation scenarios, humans would have to write in code all the possible scenarios. These developments compose most of the traditional Artificial Intelligence technology (AI), which has brought us robots, devices and autonomous applications we have been using for decades.
Machine Learning is the realm which provides AI with the ability to learn and evolve autonomously. AI can work 24-7 and process and correlate volumes of structured and unstructured data faster, cheaper and with less error-proneness than humans. ML can build mathematical models based on such sample data and make predictions or decisions. This means humans no longer have to code endlessly, AI is able to learn and “program itself” through experience. It is one of greatest technological achievements at the dawn of Industry 4.0.
To really set the difference between AI and ML, let us consider a traditional electronic calculator. Anecdotally, it can be considered a very simple form of AI, a robot with a set of commands which it executes autonomously, on-demand and always the same way. All its outputs derive from the logic which is coded in its circuits.
ML would be providing the electronic calculator with the ability to correlate multiple factors, such as the calculi performed since the beginning of the semester, the speed in which the user is currently typing (e.g. one would type faster at a a test or examination), how many times a given equation is inserted (e.g. typing faster may generate more errors and the need to repeat the calculations) and the type of calculus being used (e.g. the quadratic equation, trigonometry or Newton’s laws of motion), and generate insights such as alerting when the user may have entered incorrect equation parameters, or propose formulae when it detects the user is stuck around the same calculations.
How is ML being used today?
- Virtual Personal Assistants and chatbots
- Traffic Predictions
- Social Media Services
- Email Spam, Malware Filtering, Online Fraud Detection
- Online Customer Support
- Search Engine Result Refining
ML is also being used for chronic disease prevention and monitoring – ML models are being used to predict the development of chronic diseases by correlating clinical data, electronic health records (EHR) and genomic data.
What can ML do for your business?
Any organization which handles Big Data can be helped by Machine Learning algorithms. Several banks, insurance companies and network operators across the globe are already leveraging machine learning and AI in their daily routine, namely in:
- Providing customer support – critical aspect where machine learning can automate the 24/7 customer support that customers request;
- Fraud Detection in real-time – ML can detect fraud in real-time to prevent loss and to provide security and accuracy for the transactions in real-time;
- Customer data management – most banks support online transactions which generates a lot – a lot – of data. Data scientists can identify important information using machine learning;
- Risk modeling for investments – using machine learning, banks can provide suggestions and predictions for investment in various fields;
- Marketing and Customer Segmentation – machine learning models can be trained to choose and target the correct audience for different products and services, by clustering and segment the audiences;
How to combine new ML applications with existing AI solutions, such as RPA?
In recent years, many companies have adopted RPA in its processes and operations. For the long run, it is essential to zoom out, from the quick-win and process-centric lens which has driven many contemporary RPA implementations, to a broader view of an automation ecosystem. While RPA is primarily implemented to increase productivity in specific, already-existing and mostly predictable processes, it can allow for the creation of new processes and smarter, more integrated solutions, by using Machine Learning, ChatBots and more. RPA can only achieve its maximum potential through a holistic approach to automation, considering all levels of organizations, from business, to governance, operations and technology, and how the multiple available technologies can work together.
Our product Gen.Flow can facilitate a broader integration between multiple traditional and novel automation technologies, such as smart contracts and distributed ledger technologies (DLT). We call this full-stack automation.
If you are looking to know more about ML technology and how to further automate your business and operations, get in touch and let’s talk!