With new technologies fast in development, the different processes businesses can find themselves working with may be very different from one year to the next. The relevance, importance, and applications of tech are increasing for businesses of all sizes, and an inability or reluctance to deploy the most effective solutions could lead to significant limitations in a competitive market.
Rapid developments can vastly expand on capabilities and propel businesses into the future, but this will not be without a range of technological challenges. Reliable and flexible IT support is the answer for many small to mid-sized enterprises, and for those based in London, Computers In The City is an excellent choice.
What is Machine Learning?
Machine learning is the application of artificial intelligence (AI) to systems that can be improved by an intelligent, rather than a mechanical approach. It involves computer programmes that can access data and understand it, so they can improve on existing processes. In this way, machine learning can obviate human intervention, or at least lessen the need for it.
In machine learning, algorithms are formed from sample data to make predictions or decisions in order to achieve a certain task. These processes are related to computational statistics and data mining, and they take us closer to the independent thought of a computer. Machine learning is also referred to as predictive analytics, it can have varying degrees of human supervision and a wide range of applications in different fields.
How Can It Be Used?
1. Transport and Travel
Navigation services are used for the prediction of traffic, by saving GPS locations and velocities to a central server, then using the data to build a map of congestion at any given time. As cars do not have GPS technology installed, machine learning can be used to predict congestion based on previous trends.
Machine learning is also used by ride-sharing companies, such as Uber, who estimate prices based on busy periods. These are calculated by rider demand based on machine learning algorithms.
2. Personal Assistants
These are digital assistants that can find information for users from sets of available data. Examples of such assistants include Alexa from Amazon, Siri or Google Assistant. They are often used in conjunction with a mobile device or a smart speaker.
Though these functions only require simple access to online or stored data, machine learning is used in refining the information returned based on all previous involvement. This ensures that the results delivered are more relevant to the preferences and background of the individual user.
3. Malware and Spam Filtering
Email clients use spam filtering, which is often rule-based and can be overcome by spammers. However, filtering techniques that are powered by machine learning, such as C4.5 Decision Tree Induction or Multi-Layer Perceptron, are intelligent enough to combat persistent spam.
New malware is detected on a continual basis, and each piece of code bears a similarity to previous versions by between 90 and 98 per cent. Machine learning helps security systems to understand and predict the patterns in code so that the variations can be noticed, and new malware detected.
4. Video Surveillance
Monitoring a wide range of video cameras is a dull and repetitive task for a human, so the application of automation processes couldn’t be more appropriate. But technologies applied need to be more intelligent than simple robotic process automation (RPA), so this is where machine learning comes in.
Facial recognition is important in security systems for the same person to be recognised in different frames of videos. Machine learning can also be used to notice unusual behaviour of people captured on camera so that humans can be alerted and potentially dangerous situations avoided. When incidents are reported successfully, this can also be fed back into the machine learning process.
5. Speech Recognition
Also known as automated speech recognition (ASR), computer speech recognition or speech to text, this is the process of recording spoken language from written text. Machine learning can develop and improve the process by training the software before it goes into validation, then as part of a continual feedback process.
Because of the complexities and nuances of human speech, speech recognition still has several challenges to overcome before it’s perfected. But with promising advancements in machine learning, it won’t be long before ASR is used as a widely adopted interface.
6. Fraud Detection
As a way of bolstering the defences of IT security, machine learning algorithms can be used to provide protection through online fraud detection. Machine learning is currently employed by PayPal in protection against money laundering, by using various tools that compare records of transactions to differentiate between legal and illegal trends and activity. Machine learning can highlight unusual behaviour in cases of potential fraud, and reduce the amounts of money lost.
7. Customer Support
Customer support is the perfect example of a field in which machine learning can be successfully applied. Online chatbots have the potential to address customer queries immediately, which can provide immediate results for the customer and remove the need for a human representative.
This can improve the accuracy of answers provided by removing the potential for human error, though there are still challenges to overcome in the area of speech recognition and training software before human communication can be understood flawlessly. However, chatbots are able to continually improve by taking feedback into account and updating their practice accordingly.
8. Image Recognition
Another important application of the capabilities of machine learning, image recognition involves the identification or detection of an object or features in a digitally captured image. These techniques may be applied to facial recognition, face detection, optical recognition or pattern recognition. Machine learning is not always used for image recognition, though it can be particularly useful for extracting key features and adding them to a machine learning model.
Many of the applications of machine learning are common practice without the knowledge of the general public. Machine learning is a powerful emerging technology that has the potential to improve human practices and augment human intelligence immeasurably. The sooner we can recognise this power, the more easily we can apply it to our own lives and businesses to the most effective ends.