Machine Learning & Deep Learning In Industry
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.
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.
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.
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.
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.
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.
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.