AI & Machine Learning

Time Series Forecasting is an important area of machine learning with the goal to predict things that involve a time component. It is often neglected because the involved time component makes it a bit harder. It basically allows us to forecast any variable that can be tracked and collected over time. Examples are a stocks closing price, annual population data or sales figures. A Time Series Forecasting model is just using the collected data to forecast future values.

The vast amount of data generated and to be processed in connected devices through internet of things is expected to be cumbersome. Artificial intelligence based solutions will make IoT more efficient, thus planting the seed for the global AI in IoT market. The coming years are expected to be the turning years for this market. The AI in IoT market will be influenced by AI’s capacity to provide tools and frameworks for automating processes and real time decisions.

Some of the excitement around RPA is just plain hype, while a bit of it has some substance. RPA has potential – but it does not fix bad processes. Before automating small parts of processes it makes sense to see the big picture. For optimal results leaders need to think about is how an organization’s end to end processes are performing – for both customers and the company – and where RPA may provide the greatest value. That’s how organizations can put process back into RPA.

AI will enable organisations to leverage data and embed smartness in every process and customer touchpoint. When you put smartness to work, it will empower your employees and customers and make your organisation more humane. In today’s organisations, a lot of employees have to deal with a lot of administrative tasks and bureaucratic processes. However, in the organisation of tomorrow, such tasks and processes will be managed by AI. Within the organisation of tomorrow, humans and AI will work together. 

Thanks to advances in deep learning, AI algorithms have become capable to automate text-related tasks that previously required the skills of human operators. Many companies completely rely on AI algorithms to process text content and make important decisions. But deep learning algorithms are also vulnerable to their own unique type of security threats. With AI becoming more and more prominent in tasks such as filtering spam, detecting fake news, processing resumes and analyzing the sentiment of social media posts, it’s important that we understand what these threats are and find ways to deal with them.

With RPA, there is a lot of focus on the tech and efficiency savings, and little consideration is given to the people whose lives would be affected by the changes. There must be a way to achieve efficiencies with RPA and to look after the people within an organisation. Organisations need to stop viewing RPA as a point solution to deliver singular short-term savings, and instead look at how automation affects the entire organisation. Organisations can get so much more out of RPA if they connect the dots, focusing on the humans that are affected by RPA as well as technology.

Traditionally, pipelines involve overnight batch processing, i.e. collecting data, sending it through an enterprise message bus and processing it to provide pre-calculated results and guidance for the next day’s operations. Whilst this works in some industries, it is really insufficient in others, and especially when it comes to ML applications. When developing a model, data scientists work in some development environment tailored for Statistics and Machine Learning (Python, R etc) and are able to train and test models all in one ‘sandboxed’ environment while writing relatively little code.

Organisations that want to prepare for an automated future should have a thorough understanding of AI. However, AI is an umbrella term that covers multiple disciplines, each affecting the business in a slightly different way. Artificial intelligence can be divided into three different domains consisting of the seamless integration of robotics, cognitive systems, and machine learning. The objective of machine learning is to derive meaning from data. Therefore, data is the key to unlock machine learning. There are seven steps to machine learning, and each step revolves around data.

AI is giving us a unique opportunity to rethink how work is done, and how people use their skills and talents to complete critical and differentiating tasks. AI works by applying pattern recognition to categorize structured and unstructured data, to flag anomalies and make recommendations. It can take care of the repetitive tasks so that all employees — from the back office to the CEO — can focus on higher-value projects that help them stay competitive.

Organizations are using data and algorithms based on that data to drive critical and automated decision-making at unprecedented scale.  But what if the data entering the AI algorithms has been compromised along the way or the algorithms themselves altered? Companies need to know that they are using pristine data in their AI systems. They must be able to stand by the integrity of the data and algorithms used by AI. This might be called Verifiable AI — when an organization can provide immutable proof that the data used by their AI systems is unaltered.

Machine learning is already becoming a commodity. Companies racing to simultaneously define and implement machine learning are finding, to their surprise, that implementing the algorithms used to make machines intelligent about a data set or problem is the easy part. There is a robust cohort of plug-and-play solutions to painlessly accomplish the heavy programmatic lifting, from the open-source machine learning framework. What’s not becoming commoditized, though, is data. Instead, data is emerging as the key differentiator in the machine learning race. This is because good data is uncommon.