We chatted a bit with Navid Nobani (Senior Data Scientist) about innovation, artificial intelligence, and machine learning. We asked Navid how these tools are used today within the hi | habit-inspiring platform, how algorithms put people at the center, and what the concerns are—but above all, what the future prospects are for this sector.
1. At DA, you’re known as “the professor,” so as a professor of algorithms, could you explain to us (for dummies) how artificial intelligence works and what its main applications are?
Actually, I think I’m called that because my glasses look like those of a certain character from a famous TV series, haha 😊
Anyway, artificial intelligence is a multidisciplinary field; it requires expertise in computer science, psychology, and cognitive neuroscience. All of this serves a very specific purpose: to mimic the human brain. However, since this is difficult and complex to achieve, we work by “limiting” the tasks—for example, by creating an algorithm capable of identifying certain shapes or another capable of determining whether a person is creditworthy for a loan.
2. Many people—probably due to miscommunication—view AI as a threat or, at any rate, have a hard time imagining a future in which AI and automation will be present in every field. What do you think?
I agree with you that many of these opinions are likely due to a misunderstanding. On the other hand, however, in theory, it’s possible that AI-based technologies will replace most jobs as we know them today. At the same time, though, AI could create many new jobs and new professions, some of which don’t even exist today. We must also consider that this won’t be an immediate or disruptive transformation; these are changes that have happened and continue to happen with all technologies. Think, for example, of how digital technology has impacted communication, causing phone calls and text messages to lose ground.
3. Looking at hi | habit-inspiring platform, can you explain how AI, in this case, manages to put people at the center? By creating a dialogue between algorithms and behaviors?
On our habit-inspiring platform, we use artificial intelligence in various forms and at different levels: from determining the right moment to interact with the user, to the type of interaction (tips, nudges, etc.), to the creation of the content itself. For example, we consider the effectiveness of different writing styles to suit each category of users.
4. What are the future prospects for hi in terms of AI and machine learning? What are you researching?
Currently, the Advanced Analytics team is working on two main areas. The first is UX research, which aims to improve our current user experience and implement potential enhancements based on state-of-the-art (SOTA) practices. To achieve this goal, we work closely with the cognitive research and product teams. Regarding future trends, I can mention reinforcement learning, which will allow us to understand how content can perform better, given the complexity of interactions with our users.
5. Let’s talk about your experience at Digital Attitude. How did you end up at this company? Can you tell us about your career path?
Well, as an engineer, I worked in the manufacturing sector, while as a data scientist—before joining DA—I was immersed in the world of the job market. During my Ph.D. in computer science, I discovered that Digital Attitude was looking for an AI expert to implement its habit-inspiring platform. After getting to know the company and the team behind it better, I decided to become a member of this family.
6. Digital Attitude, in a word or an image, is…
An ocean! It's dynamic, you can find an endless variety of creatures, and is really powerful!
7. What is your relationship with the world of innovation? What do you think are some possible trends for the near future in the fields of AI and machine learning?
Innovation always brings with it new ideas for solving the problems we face every day. One of the current trends—which has been a hot topic for the past 5 years or so—is the field of explainable AI (XAI). XAI aims to explain the inner workings of black-box models ( which make up most ML methods) to a wide range of users. So, going back to the example I gave at the beginning, not only do we have a model that can identify shapes, but we also have an explanation that tells us exactly why that machine learning model decided to classify a shape as a rectangle rather than a square.
Thank you to Navid for taking the time to speak with us for this interview.
.svg)
.png)