With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages. However, to date there has been no systematic analysis of the quality of these publicly available datasets, or whether the datasets actually contain content in the languages they claim to represent. In this work, we manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4), and audit the correctness of language codes in a sixth (JW300). We find that lower-resource corpora have systematic issues: at least 15 corpora are completely erroneous, and a significant fraction contains less than 50% sentences of acceptable quality. Similarly, we find 82 corpora that are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-speakers of the languages in question, and supplement the human judgements with automatic analyses. Inspired by our analysis, we recommend techniques to evaluate and improve multilingual corpora and discuss the risks that come with low-quality data releases.

So I am currently doing a customer segmentation project which I am trying to learn and visualise data using 3d scatter graphs based on basket analysis, a friend told me K-means algorithm was the most optimal algorithm for this task, but out of curiosity I produced results using a hierarchical algorithm but not sure what the results pertain? Comparing both results, I am struggling to understand what is actually happening? In the k means algorithm I can see the 3rd cluster show higher annual income but less spending which means alternative methods such as advertisements can help encourage this demographic to spend more, but the hierarchical algorithm just seems random, not sure what is happening here? The colour of the clusters just seem like they have changed positions (not sure if this matters or if I did something wrong) compared to k means and clusters look bigger however the clusters remain in the same position/pattern as that of k means but less accurate reads, not sure what is happening here?

Historically, the financial services industry has been one that approaches technology with caution. Feasibility, regulation and privacy have all been barriers to tech adoption over the years. But that's changing--a move brought on not only by choice but necessity. Financial institutions are drowning in text data, from compliance reports and contracts to news stories and even social media musings. The pace of information has accelerated exponentially over the past decade, and traditional processes just can't keep up.

Vonage (Nasdaq: VG), a global leader in cloud communications helping businesses accelerate their digital transformation, has announced that it has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status in the new Applied Artificial Intelligence (Applied AI) category. This designation recognizes that Vonage has demonstrated deep experience and expertise in building or integrating ML solutions on AWS. AWS Partners recognized as part of the AWS Machine Learning Competency expansion help customers take advantage of intelligent solutions for the business, from creating, automating, and managing end-to-end ML workflows to modernizing applications with machine intelligence. By working with AWS, Vonage is bringing AI into the contact center through AWS Contact Center Intelligence (CCI), with tools for Speech Recognition, Natural Language processing, Machine Learning and Text-to-Speech to deliver personalized, flexible and integrated customer experiences. With Vonage for AWS Contact Center Intelligence, Vonage is delivering the power of AWS cloud native services for AI and Machine Learning through the Vonage API Platform, enabling brands to access the capabilities of AWS AI and ML engines within any existing contact center environment.

In this post, we will continue where our last post left off and tackle the next phase of the full machine learning product life cycle: getting an initial dataset and performing exploratory data analysis. As a quick refresher, remember that our goal is to apply a data-driven solution to a problem taking it from ideation through to deployment. The phases we will conduct include the following: 1) Ideation, organizing your codebase, and setting up tooling 2) Dataset acquisition and exploratory data

The entire process of drug discovery and development is being re-imagined by Valo Health.

The Global Artificial Intelligence in Supply Chain Market focuses major leading industry players with information such as company profiles, product …

Artificial intelligence has transformed the business environment drastically. What began as a rule-based automation system is now capable of simulating human interaction. Artificial intelligence is not only exceptional because of the human capabilities. As compared to human equivalents, an advanced AI algorithm provides better speed and capacity at a much lower price. Today, we are already related to AI in some way or the other, whether it is Siri, or Alexa, thanks to technological innovations.

Maybe you rely on Siri or the assistant built into your phone, but you likely have a full-fledged AI assistant in your home, too. Alexa, built into the Amazon Echo, is everywhere. If you have an Echo, there's a good chance Alexa has driven you up the wall a time or two with the follow-up questions. Tap or click here for 5 quick fixes to Amazon Echo annoyances. While you're poking around the settings, don't ignore security.

The study on the Artificial Intelligence And Machine Learning market presents a comprehensive assessment of economic, social, and policy factors …