One of the best ways to stay current in the fast-evolving field of artificial intelligence and machine learning is by following thought leaders, evangelists, and influencers in the industry. In this article, we’ve selected 10 of the most influential thought leaders (listed alphabetically) that are helping drive the field forward.

2020 was quite a year of innovation for Qlik analytics. We delivered key new augmented analytics capabilities with big updates to Insight Advisor, we integrated intelligent alerts fully into Qlik Sense in less than a year, we continued to expand our visualization capabilities to make it easier to showcase your data in exciting and compelling ways, and we made it even easier to execute analytics in the cloud.

With the speed of change in artificial intelligence (AI) and big data, podcasts are an excellent way to stay up-to-date on recent developments, new innovations, and gain exposure to experts’ personal opinions, regardless if they can be proven scientifically. Great examples of the thought-provoking topics that are perfect for a podcast’s longer-form, conversational format include the road to AGI, AI ethics and safety, and the technology’s overall impact on society.

When it comes to machine learning (ML) in the enterprise, there are many misconceptions about what it actually takes to effectively employ machine learning models and scale AI use cases. When many businesses start their journey into ML and AI, it’s common to place a lot of energy and focus on the coding and data science algorithms themselves.

Although the issue of silos in IT and data management are well known, companies appear to be falling back into this trap by not distributing their artificial intelligence (AI) and machine learning (ML) capabilities across their business. New research from Qlik and IDC revealed that just 20 percent of businesses widely distribute these capabilities across the organization.

Deep learning has evolved in the past five years from an academic research domain, to being adopted, integrated and leveraged for new dimensions of productivity across multiple industries and use cases, such as medical imaging, surveillance, IoT, chatbots, robotic,s and many more. From NLP to computer vision, deep learning has been breaking the barriers of SOTA algorithms and providing results that were, otherwise, impossible to achieve.