In this session you’ll learn how Tailwind Traders took their support ticket text and audio files, convert and extract insight metadata from each ticket using Azure Cognitive Services Text Analytics and Speech-to-Text.

They then aggregated their findings to inform their product backlog and implement improvements.

Tailwind Traders have a great website and application for customers and partners. However, they are seeing an increased amount of support tickets regarding usage of these offerings. They want to store, analyze and extract insights from their text and audio data to make better product backlog decisions and reduce their support tickets. 

     

The health emergency underway worldwide has highlighted the need to strengthen the surveillance and care of the sick at home, to avoid hospital overcrowding.

X-RAIS is an AI tool, which as a third eye supports radiologists during the reporting phase of radiological images.

Within this context, we extended X-RAIS capabilities with ALFABETO (ALl FAster BEtter TOgether).

ALFABETO has the main objective of assisting healthcare personnel in the initial triage phase at the patient’s home: using instrumental data, anamnestic data, etc.,

ALFABETO carries out an objective evaluation of the degree of severity of the pathology and a predictive analysis of the possible evolution in the short to medium term, thus providing the essential elements to decide the care strategy to be implemented (home care vs. hospitalization).

In today’s economy, financial services firms are forced to contend with heightened regulatory environments and a variety of market, economic and regulatory uncertainties.

Coupled with increasing demand from customers for more personalized experiences and a focus on sustainability/ESG, incumbent Banks, Insurers and Asset Managers are reaching the limits of where their current technology can take them with their Digital Transformation initiatives.

Earlier today, I shared Lex Fridman’s discussion on DeepMind’s recent advancement on protein folding.

Join DeepMind  Science Engineer Kathryn Tunyasuvunakool to explore the hidden world of proteins and why this discovery is a big deal.

These tiny molecular machines underpin every biological process in every living thing and each one has a unique 3D shape that determines how it works and what it does.

But figuring out the exact structure of a protein is an expensive and often time-consuming process, meaning we only know the exact 3D structure of a tiny fraction of the 200m proteins known to science.

Being able to accurately predict the shape of proteins could accelerate research in every field of biology.

That could lead to important breakthroughs like finding new medicines or finding proteins and enzymes that break down industrial and plastic waste or efficiently capture carbon from the atmosphere.