AI & Machine Learning

Just as computing dramatically altered workplaces in the 1980s, the development of artificial intelligence and robotic process automation will usher in a new period of dramatic change in 2020 and beyond. Enticed by the potential for the technologies to streamline workflows and improve customer service, organisations will be undertaking deployments in ever-increasing numbers. At the same time, the capabilities of the technologies will continue to grow at a breakneck pace. During 2020, five key trends will shape the field of AI and RPA.

Over the past two years, we’ve seen robotic process automation (RPA) emerge as one technology for making this automation-first mindset a reality. However, introducing automation into an organization is a massive undertaking that can be difficult to effectively implement if you don’t execute it correctly. Even after addressing challenges such as organizational resistance, concerns over job losses and identifying the best automation technology for a business, it can be difficult to establish an automation program that operates at full efficiency. 

During 2020, we will increasingly see a more predictive approach being offered by applications. Driven by the addition of factors such as artificial intelligence and sensors, applications will be able to better provide support and services. They’ll shift from being reactive business processes to proactive business applications. Predictive and proactive business applications will become much more widespread. Predicted trends such as these will guide that change and help organisations take advantage of opportunities as they appear. Here the top five trends to watch out for in 2020.

The reaction to the phrase artificial intelligence was mixed. Did it really explain the technology? Was there a better way to word it? Well, no one could come up with something better–and so AI stuck. Since then, we’ve seen the coining of plenty of words in the category, which often define complex technologies and systems. The result is that it can be tough to understand what is being talked about. So to help clarify things, let’s take a look at the AI words you need to know.

Why are black-box models so in-vogue? A model can be a black box for one of two reasons. The function that the model computes is far too complicated for any human to comprehend, or the model may in actual fact be simple, but its details are proprietary and not available for inspection. It’s hard to argue against the tremendous recent successes of deep learning models, but we shouldn’t conclude from this that more complex models are always better. There has been a lot of research into producing explanations for the outputs of black box models. 

Facial recognition isn’t as basic as taking two pictures and seeing if they match. Facial recognition algorithms create a mathematical representation of a human face called a face template by identifying landmarks on the face such  as the nose and eyes and calculating the distance between those landmarks. It is, at its basic form, computing geometry.  These equations represent the face which is then compared to other mathematical representations to find a match or a similarity score.

Whether you are leading a small upgrade project for the latest software update or spearheading a companywide ERP redeployment initiative, identifying opportunities for automation and process improvement is critical for future success. Project managers play an integral part in driving these initiatives and making sure that each phase of the project life cycle is consistently adhered to. It is imperative to create a repository of historical data used in each project. Without the historical data and lessons learned, RPA and other advanced technologies will be challenging to implement and adapt.

If machine learning and statistics are synonymous with one another, why are we not seeing every statistics department in every university closing down or transitioning to being a ‘machine learning’ department? Because they are not the same! A major difference between machine learning and statistics is indeed their purpose. However, saying machine learning is all about accurate predictions whereas statistical models are designed for inference is almost a meaningless statement unless you are well versed in these concepts.

Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. The practice showed a lot of promise in the early decades of AI research. But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside. Being able to communicate in symbols is one of the main things that make us intelligent. Therefore, symbols have also played a crucial role in the creation of artificial intelligence.

Machine learning is still in its early stages. Indeed, both software and hardware components are constantly evolving to meet the current demands of ML. Deploying Machine Learning is and will continue to be difficult, and that’s just a reality that organizations are going to need to deal with. Thankfully though, a few new architectures and products are helping data scientists. Moreover, as more companies are scaling data science operations, they are also implementing tools that make model deployment easier.

Machine Learning comes with challenges that the Software Engineering world is not familiar with. Building experiments represents a large part of our workflow, and doing that with messy code doesn’t usually end up well. When we extend scikit-learn and use the components to write our experiments we make the task of maintaining our codebase easier, bringing sanity to our day-to-day tasks. Learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.