Artificial Intelligence (AI) has been a key topic in the technology industry and continues to trend in the media with the adoption from a variety of sectors and disciplines. From banking and financial services to healthcare and life services, the implementation of AI continues to prosper as AI assists in increasing efficiency, improving customer experience and growing revenue.
With this in mind, it is interesting to consider how methods of data analysis can further increase opportunities and lower risks for all industries on a global scale. The composition of AI is incredibly detailed and so for this particular blog post, we will be focusing on one specific subset of AI – machine learning (ML).
So, what is machine learning?
Machine learning is a method of data analysis that essentially trains a machine how to solve a problem.
ML models function by looking for patterns in data and attempting to draw conclusions independently based on large amounts of hand-crafted, structured data. Once the algorithm becomes successful at drawing the right conclusions, it applies that knowledge to new sets of data. Thus, learning and acting independently (like humans!) as it feeds from information in the form of observations and real-world interactions.
For optimum performance and accuracy, AI and ML requires three specific elements to function efficiently;
- Granular data (extremely detailed data)
- Large volumes of data
- Extremely diverse data sources
Each element is needed to be able to find the patterns within data and learn from it. As models are exposed to new data, they are able to adapt autonomously, learning from previous conclusions in order to continuously produce reliable decisions and results.
A key benefit of implementing a machine learning algorithm is its ability to quickly highlight or find patterns in large quantities of data that would have otherwise been missed by human beings. Therefore, with AI and ML, industries benefit immensely as they are able to analyse larger, more complex data and deliver faster, more accurate results. We are living in the cognitive era where AI and cognitive systems both learn and adapt from the conclusions made through ML.
In this cognitive era, machines are provided with the ability to form and build relationships using ML to imitate human interaction. For example, ChatBots use ML to converse with humans, learning language and inflections to adapt to a conversational style with the user in real-time. The ML model has access to a continuous flow of transactional data, scraping websites and databases to collect answers and then combining that data with ‘human-like’ jargon to convey the answer in a more informal format. With the ability to reason and understand the subject matter, the ChatBot simulates real human interaction and is particularly useful in customer service, especially for out of hours’ queries. With the integration of ChatBots in your business, it ensures that your brand continues to be viewed as highly responsive to customer needs.
Not only is the ChatBot used in customer service to provide tailored experiences and answer customer queries, it is also useful as a tool to help with mental health. For example, the “Replika” ChatBot offers emotional support through the app as it encourages mindfulness and self-enquiry in an “AI-powered journal”. From the very first ChatBot ELIZA; created by Joseph Weizenbaum in 1966 to one of the most recent virtual assistants; Amazon Alexa, innovation persists for AI in all areas of daily life from business, relationships and in the home. There is a growing consumer demand for a smarter home which can be obtained with the Amazon Alexa virtual assistant as it links with apps to connect to smart home devices such as central heating and security/alarm systems.
Challenges and limitations
There are, of course, challenges and limitations with machine learning. For example, one major challenge in the initial stages is finding people with the technical ability to understand and implement ML technology. In order for the machine learning tool to provide accuracy and high performance, it requires large amounts of hand-crafted, structured training data which can be expensive and time-consuming. It’s important to have this information to increase the accuracy of drawn conclusions and performance efficiency.
Success and innovation
Despite the few issues listed, the incorporation of machine learning across the spectrum of industries speaks volumes in publicised success stories. For example, an article on techburst.io discusses cases of machine learning in the banking industry. Five of the largest and most influential banks in the US, including Bank of America and Wells Fargo, are investing heavily into the facilitation of Artificial Intelligence and ML with existing services.
“The Machine Learning use cases are many – from sorting the email using Natural Language Processing (NLP) and automatically updating the records in the Customer Relations Management (CRM) solution, to providing efficient assistance through customer self-service portals and up to predicting the stock market trends in order to ensure successful trading.” (ref: Techburst)
When scrolling through Facebook or searching on Google, have you noticed the targeted advertising – usually something you have looked at and considered purchasing then changed your mind and closed the tab? This is a perfect example of how ML is applied in marketing efforts and how it plays a role in appropriately matching advertising requests to the right audience at scale, whether on mobile, desktop or different devices and media.
As previously mentioned, the accuracy of the ML intelligence vastly depends on the data and information initially inputted which means that the accuracy and value of the data can be fallible. It’s important to research all the facets of ML to ensure that its integration with your strategy will result in success and provide valuable data.
A recent article by Forbes determines that machine learning can transform advertising efficacy, allowing marketers to reconcile data and use these insights to fine-tune campaigns for optimal efficiency, engagement and ROI.”
In consideration of ChatBot integration in customer service, existing articles, the bloggers at Ecrubox Digital believe that companies can save money using ML as ChatBots offer basic support conversations; responding to customers basic frequently asked queries in real time operating on a 24/7 basis and ensuring that their customer service team are using their time in the best way possible – with more complex issues and complaints – ensuring a higher level of customer service overall and higher customer satisfaction rate across the board.
ML in business exceeds the familiar affiliation with online shopping and ChatBots. It is much more widespread in the case of facial recognition and virtual reality. It is developing year-on-year and will be the engine of global growth, changing expectations and longstanding practices. Ultimately, the machine learning tool works autonomously without human intervention to provide insights on how to identify profitable opportunities and avoid potential or unknown risks.
Whatever your field of expertise, there are endless opportunities for innovation, prosperity and growth with the implementation of AI and machine learning.
Are you eager to utilise a tool which will help to solve problems and generate solutions or find out how ML can integrate with your strategy? Get in touch with one of our experts to see how Codeminers can advise and help you with your upcoming project.