Dynamic price optimization is becoming increasingly popular among retailers. Machine learning has exponentially increased their ability to process data and apply this knowledge to real-time price adjustments. But the necessary software is still relatively expensive. So how can it really help businesses? And, most importantly, what’s the return on investment at the end of the day?

Adobe developed a tool that automates the process of selecting headlines and images. Its AI gathers data and learns how to personalize content for different audiences. Will it improve how content is targeted?

This year, an international diploma program used by thousands of high schools canceled exams that students rely on for college admissions, replacing them with a grade determined by an algorithm. The result was lower-than-expected grades for many students, who are now left to deal with the consequences. What went wrong and what can be done to prevent it from happening again?

A bot is a computer program that automates a task. Chatbots are, therefore, programs that automate conversation, both voice and text. In essence, a chatbot can simulate an intelligent conversation with a human. They generally make use of machine learning to gather conversational rhythm, beat and flow, and thus mimic human conversation.

The buzz surrounding GPT-3, the most powerful language model ever built, has been pretty spectacular. Some experts warn us of the dangers AI technology can introduce, such as more lifelike generated content slipping into the already chaotic online news and social media spaces. But what is GPT-3? Is it really dangerous and does it deserve the hype that’s generated around it? Let’s find out.

The use of chatbots has exploded in recent years. Companies are taking advantage of these AI-powered virtual agents to automate routine processes and give consumers round the clock attention in areas like customer service, payments, and marketing. And now, chatbots are set to make a mark on the healthcare industry.  According to analysis by Crunchbase, more than $800 million has been invested across at least 14 known startups developing a health chatbot offering.

It has been two years since we started our data science team. At the beginning, we have built a strong group focused mostly on machine learning. In the meantime, the team has naturally evolved to its present form of two subgroups: data engineering and machine learning. Both focused on the two ends of successful AI adoption, working hand to hand.

The key to the successful implementation of digital healthcare solutions is patient engagement. Without mutual trust between healthcare providers and patients, even the most sophisticated technological solutions won’t improve patients’ medical outcomes. We gathered experts from across digital healthcare – both from dynamic startups like Keto-Mojo, as well as enterprises like Roche – and with this patient-centered approach in mind, discussed the essential aspects of patient engagement:

The age of generic services is dropping off. Today's customers expect more, and they want you to produce software tailored to their needs. This can be done with machine learning. Its solutions are a revolution happening right before our eyes, and those late to the party might find themselves at a huge disadvantage.

Rapid advances in technology allow us to observe our environment on a larger scale and in greater detail. Due to this fact, the popularity of hyperspectral imaging is increasing, especially in agriculture, ecology and environmental monitoring, urban planning, and many more. Applications are limitless, such as detecting concentration of greenhouse gases, monitoring crop irrigation levels to prevent crop waste, or detecting and classifying pollution of water bodies.