Most technology professionals have heard about the Agile methodology and have also read about Artificial Intelligence (AI) and Machine learning (ML). It is rare to see a mention of all three words used in a business use case. This is because Agile is a software development process that can be applied to building IT application in many applicable industries. While AI and ML are technological innovations that helps many businesses create value via decision support and human augmentation.
In a world of fast tech companies, we have seen Agile as the go-to software development process for start-ups. Successful start-up companies create disruption, and major established companies must adapt or get left behind. This has prompted most established companies to go from a waterfall development process to a rapid, agile process. Long before Agile started trending in the technology industry, artificial intelligence and machine learning had taken us on a path that helped and continues to help our rite of passage to Digital Transformation.
Our current society is inundated with technology connectivity, whether it’s a smart tv or a cell phone connecting to our vehicle, we connect everything. I have likely used AI in the process of writing this article, communicating with friends, ordering my lunch, and making an online purchase. This desire to have a responsive tool at our fingertips does not all come together magically. Therefore, we need the ability to learn these different tools and able to maintain them besides human involvement. That’s where Machine Learning comes into play.
According to MIT technology review, Machine-learning algorithms use statistics to find patterns in massive amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm.
In other words, we give machines access to data and they learn from that data to make system decisions. The 3 types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. This process powers many of the services we use today – Recommendation algorithms like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri, Alexa, and so on. To learn more about ML, cloud platforms such as Microsoft Azure and Amazon Web Services (AWS) provide very good information and business use cases.
Agile has made it easy to manage and implement these new technological tools. This adaptable software process allows companies to validate their assumptions and create meaningful products by adapting to the changing marketplace, customer needs and expectations. In addition, companies will develop code efficiently, better integration with other tools, and better architecture platforms and system design. Companies will start having to build and transform their development shop with the end user in mind.
Arthur Samuels of IBM developed a computer program for playing checkers in the 1950s. Imagining a computer program that could memorize every move an opponent makes in a game of Checkers. Once learned, it can anticipate the next move or limit the moves based upon conditions of placement and open or closed spaces on a board. Now over 60 years later, intuitive tools such as maps and social media apps use machine learning for customization, product advertisement and many more. The Maps app as an example uses ML through smart applications to anticipate traffic jams or identify the fastest route to a destination.
Machine learning algorithms monitor the location of vehicles through the data transmitted by the GPS navigation system. It then finds patterns and predicts the traffic congestion levels based on its learning. GPS determines the position using the signals which are transmitted from satellites. GPS works anywhere the satellites signal reaches, and it is much more robust than other location technologies.
While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks smartly. Artificial Intelligence applies machine learning, deep learning, and other techniques to solve actual problems.
Since the human brain has not been fully mapped and reproduced, AI is our closest insight to actionable acts from machines, applications, and computers, whether you see them in cars, airplanes, robots, etc. Their programmable actions all stem from the effort of a human to create a machine that can augment the actions of a human.
Thinking about the Agile development process, our entire technology industry is being revolutionized to be intuitive and fast-paced. Companies are being compelled to think continuous integration and building products that customers anticipate while still limiting expenses and be profitable.
Using that same directional insight on improving via Agile, we are simultaneously building devices, better applications, and creating better ways of doing things. Now couple this with machine learning as the ways of doing things are being captured and translated into complex algorithms which through AI can be carried out smartly. As humans, applying the rigor of agile behind the advanced monitoring or already mapped or programmable references, creates a basis for AI to continue to grow exponentially beyond our imaginations.
Use case for AI, ML, and Agile.
A medical professional in Ireland works at a treatment facility that specializes in treating viral infections. The facility uses state-of-the-art technology to track treatments procedures and monitor patient health improvements. Machine Learning plays a major role in capturing and tracking algorithms associated with this data. This information is then made available to an AI system that is globally utilized to assist medical professionals with diagnosing patient conditions and advising on the latest treatments. The technology team that is assigned to upgrade the application utilizes the agile development process to promote transparency with stakeholders. The team submit a spike to research what can be done to improve performance speed of the algorithms and communication of those results across the medical networks. The focus is on continuous improvements to the applications and its integrated systems. Each moment of improvements to the system could translate into better data for the medical professionals. Data that used to take months is now made available as needed.
With our very existence as humans on a planet that has being engulfed by a series of virus that threaten our daily lives. The emphasis on correlations like these gives scientists, educators, and medical professionals instant information that can help solve major medical problems in the future.
AI, ML, and Agile - Working together for a better future
Written by Taiwo Ajayi, Digital Transformation Leader, Agile Product Manager