Artificial Intelligence will change the future. You’re here to read this post so you probably know it already has. There is so much talk about how data science will change/have changed the way we shop, or how robotics is going to change medicine. In this week’s post, we’re here not to tell you how AI changes the world, but how AI itself is going to change in the coming years.
Continue reading to get acquainted with four important ideas and technologies that -in our opinion- will define the path of artificial intelligence in the next ten years.

Explainable AI

When the results are machine learning models are applied in critical areas such as healthcare or strategic planning, it is of crucial importance to understand how the output of the models is produced by the data fed to them. Otherwise, artificial intelligence may not help but harm the transparency, fairness, accountability, and reliability of the system it is placed into. This is what we call the explainability problem, and what caused the Idea of Explainable AI (or XAI) into being.
To be eligible for being used in health care, law, or autonomous driving, explainable AI should be able to answer a lot of wh questions: How did it get these results? Why did it choose that step? Where did it find that pattern? To What extent they are interpretable?. Every classification, object detection, and planning program must provide answers here. The need for explainable AI is recognized more in recent years and will definitely affect how AI will progress in research, society, and industry.


Management of an ML project, keeping it working, precise and up-to-date consists of so many repetitive tasks: managing datasets, monitoring models, and Shaping repeatable and reliable processes for a corporate’s ML projects take a good portion of the development process of any big-enough data science project.
MLS is about how to make the geeky beautiful ML programs into real, practical, reliable, and scalable software that can be used in the industry. Despite the recent progress in data science and deep learning algorithms and techniques, MLOps is still a challenge for many companies, and with the rapid expansion of AI in every area of their work, it’s getting harder by the day.
With the growing immigration of ML development to the cloud, Google and other cloud services are now providing professional tools for the data science teams to enable ML project management at the highest automation level. This takes a huge load off the developer team’s shoulders and lets it freely focus on making their models work.

Contrastive Learning

Contrastive Learning is a form of self-supervised representation learning. The main idea behind this ML technique is learning from the differences and similarities between unlabeled examples and finding the general similarity patterns. The reason it’s called self-supervised is that this technique does not need any labels or annotations on the data.
Contrastive learning plays a big role in making future progress in data science, because in so many cases, e.g. medical image segmentation, manual labeling of data is a tedious task that may take a lot of time from professionals. Facebook’s MoCo and Google brain’s SimCLRv2 are two of the most important instances of deep contrastive learning networks.

The Internet of Behaviors

From the moment a person wakes up by her phone’s alarm clock ringing and maybe save your sleep info in your health app which is connected to her smartwatch; to when she drives back home using a GPS-based routing app, she is making behavioral data.
The International Data Corporation (IDC) predicts that by 2025, every human connected to the Internet will make one digital data interaction about every 18 seconds on average. The billions of Internet of Things (IoT) devices out there are expected to generate over 90 zettabytes of data by 2025.
The behavioral data produced from three important kinds of user experiences, including the patient experience -PX-l the employee experience -EX-, and the customer experience (CX) will be one of the pathmakers for the progress of data science in the third decade of this century.