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Machine Learning

Machine Learning and Deep Learning

While Machine Learning and Deep Learning(ML/DL) is often seen as a sub-category of Artificial Intelligence (AI), it would be correct to think of it as the current state-of-the-art of AI – ML is currently showing the greatest potential in providing tools to industries and societies to drive change. Machine Learning is an approach to AI showing great potential when it comes to developing autonomous, self-learning systems which are revolutionising and disrupting many industries.

In the simplest way possible, a Machine Learning algorithm is trained from a specified ‘training set’ of data which is then used as the basis to solve a given problem. An example of this would be when a computer is given a training set of photographs, some which say “this is a flower”, and some which indicate “this is not a flower.” Next you would show the computer a series of new images and it would start to distinguish which photos contain flowers.

Machine Learning continuously adds to its date set by identifying every picture (correctly or incorrectly), essentially becoming ‘smarter’ and more efficient at completing tasks over time. It is, in effect, learning.

Deep Learning (DL), on the other hand, can be considered as the ‘cutting-edge of the cutting-edge.’ Essentially, DL fuses AI’s core ideas and revolves them around solving real-world problems with deep neural-networks designed to mimic our own decision-making. It is easiest to describe Deep Learning as a sub-set which focuses on narrower tools and techniques, applying them to solve just about any problem which requires ‘thought’ – human or artificial.

Deep Learning

How we use Machine Learning

Data Modelling

Data Modelling

Looking at existing data to predict what is likely to occur and what is going to occur

Process Automation

Process Automation

Abolish monotonous tasks in a cost-effective way to optimise work-flow

Computer Vision

Computer Vision

Classifying data in a method that is otherwise hugely time consuming and often complicated

Why is Machine Learning and Deep Learning Being Used?

From our experience, Deep Learning is being constantly implemented into businesses at a significant rate over the last few years which we believe could be to its outstanding ROI capabilities and insights. We come across many shapes and forms of DL on a daily basis. For example, Deep Learning is used by Google in its voice and image recognition algorithms, used by companies such as Netflix and Amazon, to help their users in deciding what to watch or purchase next, and even by researchers at MIT to predict future economic occurrences. By being able to provide powerful and unrivalled insights, predictions and decision making in potentially any industry, ML and DL are considered as invaluable and irreplaceable by many organisations.

Machine Learning & Deep Learning in Industry

 

Natural-Language Processing (NLP)

NLP is being implemented into a wide variety of new and exciting applications throughout many disciplines. Machine Learning algorithms centralising around natural language can be used in place of customer service agents and efficiently direct customers to the information they need or problem to be resolved. NLP is now also being used within the legal sector. NLP is tasked with translating obscure legalities used within contracts and documents into plain language and assist attorneys in sorting through large volumes of data prior to preparing for a case.

 

Smart Cars

It is expected that around 75% of cars on the road by 2025 will be considered as Smart Cars. Smart Cars not only integrate into the Internet of Things (IoT) but also continuously learn about their owner and surrounding environments; adjusting internal settings (temperature, audio, seat position) automatically based on the driver’s preferences as well as becoming autonomous and offer real-time traffic, road and weather conditions.

 

Financial Trading

Humans do not possess the capacity to consume vast quantities of data on a continuous basis and execute a trade at an instant speed whilst analysing statistics simultaneously – many people are eager to be able to predict what the stock markets will do at any given moment in time and humans simply cannot compete with machines and their instantaneous and multi-tasking capabilities. From our understanding, many leading trading firms are using proprietary systems to predict and execute trades at high speeds and high volumes. The outcomes produced rely on probabilities, although, even a trade with a relatively low probability, at a high enough volume/speed, can turn over huge profits for traders and firms.

Healthcare

Machine Learning can be used to understand risk factors for diseases in large populations. These algorithms can process information and identify and analyse trends better than their human counterparts. For example, Computer Assisted Diagnosis can review early mammography scans of women who over time developed breast cancer. This leads to over a 50% chance of identifying cancers as much as a year before the women were officially diagnosed compared to other methods.

 

Marketing Personalisation

Essentially, the greater the understanding of your customers, the better you can serve them, therefore the greater the likelihood of your products being purchased- the foundations of marketing personalisation. For example, how many times have you browsed products on particular site but didn’t decide to purchase, and then you see digital ads across social media and other sites showcasing that exact product you browsed a few days ago? This level of personalisation increases the level of trust amongst consumers and can lead them to foster brand/product recall and recognition, ultimately leading them to the purchase of your product. The same could be done to through special offers targeted towards particular buyer personas. ML essentially associates ideal products towards a particular person based on their buyer behaviour and associations, displaying them recommended products.

 

Fraud Detection

Machine Learning is constantly learning and becoming more effective at identifying potential cases of fraud across many different fields. For instance, PayPal is utilising ML to eradicate money laundering. The company possesses tools which compare and analyse millions of daily transactions and can distinguish between legitimate and fraudulent transaction between different parties.

Recommendation Engines

Amazon and Netflix can be held as prime examples as the successful use of ML. Intelligent Machine Learning algorithms analyse user’s activity and compare it to millions of other users to predict what products you would most likely want to buy or which series you might binge-watch next. These recommendations are getting smarter with time, recognising purchasing habits. For example, recognising that family members have different TV preferences or recognising that you are purchasing certain items or gifts for others rather than yourself.

Online Search

Every time we perform a Google search, the engine observes our reactions to the search results. For example, if you were to click on a top result and browse the web page, Google assumes that the user found what they were looking for and the search was a success. On the other hand, if you continue to browse the list of search results and type in various search strings, intelligent algorithms summarise that the engine did not produce the appropriate results, hence, the program learns from its mistakes to deliver better results in the future.