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#AI

First steps towards success

What is the AI adoption success formula?

We are in the middle of yet another wave of digital transformation with AI at its core. Business leaders’ skills are put to the test here, as transformation is never an easy task. Let’s look at the common hurdles of AI implementation.

Volume, velocity, and variety

Decoding the 3 potential advantages of big data

Big data has been present in the industry for quite some time now. These huge data chunks helps enterprises keep a clear track of the information regarding their customers, products, environment and about themselves.

Flatten the curve with AI

Could AI solve the COVID-19 crisis?

Artificial intelligence could help us fight the coronavirus crisis. AI can, for example, already identify pneumonia on a CT scan in seconds with a high degree of accuracy. See what other things it can do to help flatten the curve.

The inevitability of AIOps

How AI will earn your trust

We’re going to need high dimensional probability and statistics and model it in high dimensional geometry. This is why AIOps is inevitable. This article examines four of the reasons why people distrust AI and what properties define big data.

ML at the heart of business

3 global manufacturing brands at the forefront of AI and ML

How can machine learning and artificial intelligence change the landscape of manufacturing? According to reports by Deloitte and McKinsey, machine learning improves product quality and has the potential to double cash flow. Let’s take a look at three global manufacturers who are already on board.

Bringing AI to video and images

Using AI for managing images and videos at scale

#AI

AI is finding a wide variety of uses for images and video, including auto-cropping and resizing, automating image tagging, and creating video auto-previews. This article examines five examples of how AI is used to enable image and video management at scale at Cloudinary.

The ethics of AI and ML

What data should AI be trained on to avoid bias?

Humans are introducing their own biases and prejudices into machine learning. As advanced as AI can be, having been built by humans, it can still share some of our own ethical shortcomings. The usage of proper databases during training is one of the ways to help prevent biases from developing within artificial intelligence.