We’ve talked to our NDSML Summit speakers about AI and ML trends to watch for

We’ve talked to our NDSML Summit speakers about AI and ML trends to watch for

The disruptive year that passed left businesses with the urgent need to transform and adapt to the new reality. The crisis not only acted as a catalyst for rapid digitalisation and digital transformation, but also made organisations dependent on advanced technologies such as AI and ML in order to facilitate remote operations. Considering the high stakes and investment in these technologies, we’ve talked to some of our NDSML Summit speakers about ML and AI trends they expect to see throughout 2021 and beyond.

Some of the AI and ML trends that emerged at the beginning and will continue to accelerate through the rest of 2021 are:

  1. Automation of processes and more intelligent machines
  2. AI-driven real-time analytics
  3. Greater use of AI in healthcare
  4. NLP-powered human-machine conversation
  5. Graph machine learning
  6. Causality in machine learning
  7. Smart systems for intelligent decision making
  8. AI for smoother WFH experience
  9. Utilising computer vision and NLP for improvements around video/audio/text understanding
  10. Reinforcement learning and smart simulations for robotics and self-driving vehicles
  11. ML applications in e-commerce and social media for better user experience
  12. Transformers for language and image interpretations, and wider recommender system applications.
  13. Stronger regulation on data utilisation.

Below, we will look at how the speakers see these trends rolling out in the near future.

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Smriti Mishra, Head of AI at Earthbanc, says that after the pandemic, we have seen so many remote options for almost everything come around based on AI. Organisations are becoming more and more dependent on AI, and it will be interesting to see how it further transforms our future. 

Smriti outlines some of the trends we can expect to see in 2021 and beyond:

1. Automation of processes and more intelligent machines will be around more. Tasks like data-collection and analysis might be done using technologies such as Robotic process automation (RPA). This will give us more time for tasks like data analysis and modelling itself. Also, most organisations are migrating their infrastructure to cloud. Automation will become more and more popular in 2021 and beyond. 

2. Businesses can analyse their results in real-time based on AI. Generating real-time reports using machine learning and NLP and providing the businesses quick and detailed insights. Also, it could be connected with something like Siri, which could help people out with searching important details in the automatically generated reports, or even read them out loud if required. 

3. A lot of improvements using AI in healthcare. Some use cases could be analysing how the surgery would perform even before doing it. Also, the AI models doing this will be more transparent and interpretable so that people can understand and trust the technology. 

4. NLP powered human-machine conversation is another trend that will be huge in 2021 and beyond, Smriti emphasises. This technology has several use cases and fields of applications – in retail, healthcare, improving customer and employee experiences, etc. This will save time, be cost-effective and provide better insights from customers.

Dan Saattrup Nielsen, Former Machine Learning Consultant at the Danish Business Authority, currently Research Associate in Machine Learning at the University of Bristol, highlights that the two most pronounced ML trends we could see are graphs and causality.

5. Graph machine learning is really in its infancy still, almost exclusively using methods that were developed in NLP, so Dan states that it would be surprising if they did not find their own graph-specific algorithms in the near future. But even algorithms aside, the mere use of graphs as feature extractors is something that can be highly useful in many areas of industry. The interest in graph data analytics will only continue rising in the next coming years, affirm Dan.

6. As for causality, this is even more in its infancy, at least when it comes to the connection between causality and machine learning. All machine learning models today are good at prediction correlations. If you always bring your umbrella with you when it rains, a machine learning model would simply be able to predict that umbrellas are correlated with rain. It would have no idea whether bringing an umbrella would cause it to rain, or whether it is the other way around, adds Dan. This sort of knowledge would help give more transparency to the predictions of machine learning models, as well as help things like reinforcement learning algorithms often used in robotics.

Nastaran Ghadar, Engineering Manager at Twitter, explains that this year due to COVID, there are some trends that a lot of folks are investing in

7. Many industries and factories took a hard hit. It became necessary to have all the way smart systems where you can build smarter, but you can also make intelligent decisions such as predicting when supply chains may get disrupted or when downtime may happen. 

8. Another strong trend is how to use AI to ease and improve work from home experience.

9. One hot trend Nastaran points out is augmented reality and improvements around video/audio/text understanding (such as utilising computer vision, natural language processing). 

10. Robotics and self-driving cars are still going strong (such as the use of reinforcement learning and smart simulations), and the need is even more during this pandemic. 

11. E-commerce and Social media ML applications also are going very strong, Nastaran adds, as the number of users is growing rapidly and having a better experience is key.

Alberto Barroso, Global Head of Decision Science at Tetra Pak, sees several important trends emerging from a technical and ethical point of view.

12. From a technical point of view, he sees Language and Image interpretations growing exponentially. For instance, transformers have enabled a significant improvement in classification accuracy. In the short term, the application to other recommendation engines will be on the rise, Alberto adds.

13. From an ethical perspective, seeing the challenge of regulation, it seems that we are still living in the wild west when it comes to data monetisation, argues Alberto. He states that we will see the application of a stronger regulatory framework that ensures individual freedom and enables market competition.

Final words

Almost halfway through the year, some of the above AI and ML trends are already showing promising outcomes, while others are in their infancy and we’ll just have to see how the scene plays out. However, we can certainly expect that AI and ML will become even more intertwined in various business functions and industries, transforming our lives on a larger scale and introducing innumerable innovation opportunities.

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