As we continue our digital transformation journey and just headed into 2020, we will go over each Technology Trend that is already impacting our lives or soon will. Machine learning („ML“) is one of those digital trends that will become more and more relevant to us all. So, how will Machine Learning in 2020 drive our digital transformation?
Machine Learning – An introduction
Machine Learning is an application of Artificial Intelligence („AI“) with the objective to search and find relevant patterns within different data-sources with the use of sophisticated mathematical models. When such patterns are detected, these results can be used for multiple purposes in AI applications or use cases. Machine Learning provides results on the basis of historical data, which gets more and more real-time as sensors from Internet of Things („IoT“) fuels large datasets in milliseconds. The quality – and the performance – of these patterns (models) is highly dependent on both the quality and quantity of the data that was used to ‚train‘ it. Side note: don’t rely on Machine Learning to change bad or inconsistent data too useful data.
The current state of Machine Learning
To date, Machine Learning has been used in multiple industries with different use cases. For example, image processing, predictions, classifications, and learning associations. According to independent research, the global Machine Learning market is expected to grow at a CAGR of +48.3% to reach $19.40 billion by 2023, during the forecast period of 2018-2023. More and more organizations are starting ML-centric projects to gain market share with new business models driven on ML outcome.
Examples of Machine Learning
Pinterest uses Machine Learning to find patterns in pictures to enhance their spam moderations and content discovery for advertising. This application of ML and AI enhances the end-user experience and drives additional revenue to Pinterest.
Allot of websites have already implemented chatbots to enhance their user experience. Chatbots are the small ‘pop-up screens’ that make you believe that you are directly talking to a service representative, instead, you are ‚interfacing‘ with an AI that uses ML to provide you with accurate answers or solutions. If for whatever reason, the solution cannot be found or processed, you could be connected to a service representative. I need to say ‘could’ as some websites already 100 percent rely on chatbots. This allows organizations to lower personal costs whilst in parallel increase user experience as for example, no waiting times apply for ‚talking‘ to a service representative.
Twitter is using the ML patterns to protect users from spam and evaluates each tweet in real-time to ‚score‘ them according to various metrics. Ultimately, the Twitter algorithms then display tweets in your feed that are likely to drive the most engagement. The decisions, which show up in your feed, can be adjusted on your individual preferences resulting again in higher engagements.
Salesforce, a global Customer Relationship Management („CRM“) software company, uses AI (which they named ‚Einstein‘) to predict new leads and/or when you should follow up on a specific email or phone call. Lead prediction and scoring are among the greatest challenges for sales staff and with Einstein, Salesforce is supporting its users to make the right decisions at the right time. Ultimately, this results in higher engagement with customers and higher sales results.
Future of Machine Learning
As we enter 2020, and data is becoming more and more available to feed Machine Learning and AI, the use cases will continue to grow and outcomes more precise. Despite the rapid adoption of ML, most organizations ‚take it for granted‘ and don’t further invest or evaluate new possibilities. I want to encourage you to take a step back and evaluate what new data-sources your organization has gained in the last months (years?) and what use-cases can be created with the use of ML and AI to accelerate in 2020.
State of Machine Learning in 2020
To date, Machine Learning has been used in multiple industries with different use cases. For example, image processing, predictions, classifications, and learning associations. According to independent research, the global Machine Learning market is expected to grow at a CAGR of +48.3% to reach $19.40 billion by 2023, during the forecast period of 2018-2023.
Examples of Machine Learning in 2020
3- Salesforce Einstein