How was that credit application declined? Why was that person denied parole? How were disability benefits cut for those constituents? And why do computers learning from humans automatically see certain occupational words as masculine and others as feminine? It’s hard to explain how the most advanced algorithms make decisions. But as predictive systems proliferate, there are signs we’ve become more wary of their use in making critical decisions.

There’s a lot of hype out there about artificial intelligence (AI) and how it’s revolutionizing this or transforming that. But in this timely remix of a previously published post, AI expert Joanna Bryson (@j2bryson) calls out AI hyperbole and helps us cut through the smoke and mirrors on: Note: Bryson is Professor of Ethics and Technology at the Hertie School of Governance in Berlin where she educates future technologists and policymakers on digital inclusion and AI governance.

Artificial intelligence (AI), automation and machine learning (ML) are rapidly transforming the analytical experience for everyday business users in 2021. Whether it’s automated visualizations, continuous analysis, or reduced time-to-insight, there are many practical benefits of augmented analytics that are well documented and fully realized today.

As a result of overwhelming excitement (and pressure) from my fellow Qlikkies, I’m going to share with you the recent demo I did at our all-company annual kick-off which shows Active Intelligence in action. It was intended to be an “internal-only” demo because it mixes existing capabilities with near-term future ones, but, on reflection, I think you, too, will be just as excited.

Building machine learning (ML) and deep learning (DL) models obviously require plenty of data as a training-set and a test-set on which the model is tested against and evaluated. Best practices related to the setup of train-sets and test-sets have evolved in academic circles, however, within the context of applied data science, organizations need to take into consideration a very different set of requirements and goals. Ultimately, any model that a company builds aims to address a business problem.

We have three big announcements to our community today, and I wanted to talk to you about them: One, Allegro Trains is changing its name, two, we’re adding a completely new way to use Trains, and three, we’re announcing a bunch of features that make Trains an even better product for you! Read all about it on our blog at Clear.ml, our new website for our open source suite of tools.

Artificial intelligence and machine learning are relentlessly revolutionizing marketplaces and ushering in radical, disruptive changes that threaten incumbent companies with obsolescence. To maintain a competitive edge and gain entry into new business segments, many companies are racing to build and deploy AI applications.

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.