Far from being the stuff of science fiction, artificial intelligence (AI) and machine learning (ML), are becoming increasingly common sights in today’s world where AI-enabled tools are being used to transform many areas of everyday life.
Therefore, what is AI, why and when should we use AI if we want to deliver an operational efficiency?
In the following article, we aim to answer all these questions.
What is AI?
In a simple definition, AI as a concept refers to building software machines that, with the help of machine learning, will be able to essentially think for themselves, and make decisions based on the data they are being fed, without constant human supervision.
The new advances in artificial intelligence are largely driven by machine learning, which automates analytical model building.
AI systems can be hugely complex and powerful, with the ability to process extensive volumes of information – advanced analytics based on statistics, data mining, graph, and various predictive approaches – very quickly in order to come to an effective conclusion.
Predictive technologies, such as AI and ML are key to modern preemptive business tactics and they can transform a business by putting the vast amounts of data collected to good use.
The Implications of AI in Project Management
AI is focused on the development of intelligent processes, which incorporate the development streams of software and constantly evolving machine learning models. The application of cognitive technologies leverages the emerging capabilities of machine learning.
A project management methodology takes into account the various data-centric needs of AI while keeping in mind the application-focused uses of the models produced during an AI lifecycle’s stages: solution definition, development, deployment, production, and updates, similar to other development projects in its life cycle, but with distinct requirements for each stage, as illustrated below.
AI life cycle stages
Deliverables in a Machine Learning project
|The ML model is an enabler of a deliverable, not providing any functionality in and of itself. It refers to the model artifact that is created by the training process that provides an ML algorithm – a learning algorithm – with training data to learn from.||The learning algorithm finds patterns in the training data that map the input data attributes to the target – the answer that you want to predict – and it outputs an ML model that captures these patterns.|
If you dig deeper into machine learning models, what exactly is in the model?
You can use the ML model to get predictions on new data for which you don’t know the target. For example, let’s take the case where you want to train an ML model to predict if an email is a spam or not spam. You would provide an ML tool such as Amazon with training data that contains emails for which you know the target.
Then you would train an ML model by using this data, resulting in a model that attempts to predict whether a new email will be spam or not.
Expectation management is key to success
It’s important to set expectations straight, understand the ML life cycle and how data requirements vary across life cycle stages from the beginning of any AI project. The success of each ML stage depends on getting just the right data in the right condition into data platforms that are helpful to data analytics.
| ||The Global AI On Tour in Bucharest, Romania is a free event on 22 May 2020 from 09:00 – 18:00.|
About the event
Join the Bucharest edition of Global AI Community on Tour for hands-on sessions in applied Machine Learning. From beginner to advanced workshops, we’ll cover five different applications in one engaging day packed with information on Azure Machine Learning and more.
Details on the agenda coming soon!