Chattermill, a platform that helps companies unlock insights by analyzing customer feedback data from across myriad digital channels, has raised $26 million in a Series B round of funding. Founded out of London in 2015, companies such as Uber and Amazon use Chattermill to unify all their customer data, integrating with social networks, customer feedback and support tools, online review sites and more to establish a "single source of customer truth," as the company puts it. Meshing the data is only part of Chattermill's promise, though. Given the typically unstructured nature of customer feedback and conversations, Chattermill has developed its own deep learning models for extracting meaningful insights from the aggregated data. This could mean identifying ways to improve the overall customer experience, spotting relatively minor issues before they snowball and tracking the efficacy of new initiatives that were designed specifically to improve customers' experiences.

Are you a data scientist looking for a job in non-software enterprises? Do you need to extract insight from a large dataset in a short amount of time? Do you want to evaluate whether your idea is solvable by artificial intelligence? If your answer to any of the above questions is "Yes", this article may help you. I recently got interested in the new wave happening in the AI world named "No-Code AI". In the AI community, we still do not have a consensus on how to precisely define the no-code AI technology, though.

The detection of anomalies in time series data is a critical task with many monitoring applications. Existing systems often fail to encompass an end-to-end detection process, to facilitate comparative analysis of various anomaly detection methods, or to incorporate human knowledge to refine output. This precludes current methods from being used in real-world settings by practitioners who are not ML experts. In this paper, we introduce Sintel, a machine learning framework for end-to-end time series tasks such as anomaly detection. The framework uses state-of-the-art approaches to support all steps of the anomaly detection process. Sintel logs the entire anomaly detection journey, providing detailed documentation of anomalies over time. It enables users to analyze signals, compare methods, and investigate anomalies through an interactive visualization tool, where they can annotate, modify, create, and remove events. Using these annotations, the framework leverages human knowledge to improve the anomaly detection pipeline. We demonstrate the usability, efficiency, and effectiveness of Sintel through a series of experiments on three public time series datasets, as well as one real-world use case involving spacecraft experts tasked with anomaly analysis tasks. Sintel's framework, code, and datasets are open-sourced at https://github.com/sintel-dev/.

Computer vision is a field of AI that focuses on giving computers the ability to see and interpret the world around them in the same way that humans do. Computer vision involves teaching computers to observe the physical world, analyze data, and extract insights from visual inputs. Computer vision is one of the most promising areas of research in artificial intelligence and computer science, and it offers great benefits to businesses today. Basically, image processing involves altering one image in order to produce a new image with improved characteristics. The image might be resized, the brightness and contrast adjusted, the image cropped, blurred, or any number of other digital transformations performed.

In this code pattern, learn how to extract speaker diarized notes and meaningful insights reports using IBM Watson Speech To Text, Watson Natural Language Processing, and Watson Tone Analysis when given any video. In a virtually connected world, staying focused on work or education is very important. Studies suggest that many people lose their focus in live virtual meetings or virtual classroom sessions after approximately 20 minutes. Therefore, many meetings and virtual classrooms are recorded so that an individual can watch it later. It might help if these recordings could be analyzed, and a detailed report of the meeting or class is generated by using artificial intelligence (AI). This code pattern explains how to do that.

In 2017, we launched Amazon Transcribe, an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to any application. Today, I'm very happy to announce the availability of Amazon Transcribe Call Analytics, a new feature that lets you easily extract valuable insights from customer conversations with a single API call. Each discussion with potential or existing customers is an opportunity to learn about their needs and expectations. For example, it's important for customer service teams to figure out the main reasons why customers are calling them, and measure customer satisfaction during these calls. Likewise, salespeople try to gauge customer interest, and their reaction to a particular sales pitch.

PUNE Have you ever paid for damages for a rented car? Or had your insurance claim for the said damages rejected? Devesh Trivedi, an IIT Delhi graduate, faced a similar problem a few years back, but he did not let it go. He decided to find a solution, using technology, and reducing human intervention. Trivedi, along with Sanchit (who only goes by his first name) founded Inspektlabs, an inspection-as-a-service software startup in 2018. Their software uses artificial intelligence and machine learning to automate photo and video-based inspection of assets like cars.

Machine learning (ML) and data science are often mentioned in the same breath – and for good reason. The two complement each other. However, understanding how they work – and work together – is important. Machine learning is a branch of artificial intelligence (AI) that empowers computers to self-learn from data and apply that learning without human intervention. Data science, on the other hand, is the discipline of data cleansing, preparation, and analysis.

With a new year upon us, let's take a fresh look at the current state of the data science puzzle. What are the most important constituent concepts of the data science landscape? How do they fit together? Which of these have been elevated in importance since the previous installment, and which are less important? As a few years have passed since I last treated this particular topic, it might be worth having a look at this out of interest, and for comparison.

Sentiment Analysis or opinion mining is important for organisations, irrespective of industry. It helps organisations extract insights from social data and understand their customer base -- what they feel about the products and services and what else they expect from the company. Simply put, it tries to analyse the feelings of the customers hidden behind the words and it is able to do that by making use of a technology called Natural Language Processing (NLP). Today, to make the work a little easier for organisations and gain an overview of the wider public opinion behind certain topics, there are several tools available. And in this article, we are going to take a look at some of the tools one can use for sentiment analysis.