Master data management deals with the accuracy and consistency of master data in any organization. Master data is the core data of any business and represents critical entities, including customers, products, providers, employees, equipment, locations, and cost centers. All these entities are efficient in their business management and functions. While creating master data assets, similar records are created and maintained as golden copies.
Traditional queries help find duplicate records or records that differ from each other. But the traditional queries cannot support the grouping of variations in similar records. In this case, machine learning helps the MDM system organize the master data in a better way.
Do you want to use artificial intelligence and machine learning technologies on your WordPress site?
Many WordPress plugins are using AI and machine learning to offer smarter features. There is a chance that you might already be using one of these plugins on your site.
In this article, we will share some of the best WordPress plugins that use artificial intelligence and machine learning.
What are Artificial Intelligence and Machine Learning?
If you follow the news, then you must have heard the words artificial intelligence or machine learning being mentioned.
Artificial intelligence and machine learning are computer technologies that offer smarter solutions in computer devices, apps, GPS devices, smart TVs, home assistance devices, and more.
Artificial intelligence gives computer programs able to make choices without human intelligence.
Machine learning helps the software learn from experiences, data, and user activity to improve artificial intelligence and make better choices.
For example, voice recognition, search patterns, translations, image recognition, and more are currently being used by most modern mobile phones and smart devices.
What’s the difference between artificial intelligence and machine learning?
Artificial intelligence can make decisions based on programmed intelligence.
Machine learning further enhances artificial intelligence by self-learning through user activity and collecting information. This makes artificial intelligence smarter and allows it to make more accurate decisions.
What about machines taking over the world?
Many prominent scientists, engineers, and entrepreneurs have expressed concern over the possible threat to human society from artificial intelligence and self-learning machines.
However, it also has great potential to improve the lives of billions of people. That’s why even the most prominent AI skeptics encourage its careful usage for the greater good.
What Can Artificial Intelligence Do for WordPress Websites?
It can provide a smarter and much better user experience for your users.
For example, it can improve WordPress search, grammar and style checking, show related content, improve conversions, boost eCommerce sales, and more.
Your website becomes smarter, faster, and more personalized for your users. This saves you time which allows you to do other things to grow your business.
That being said, let’s take a look at the WordPress plugins that use AI and machine learning.
Akismet is one of the only two plugins that come pre-installed on every WordPress website. It uses artificial intelligence and machine learning to detect spam comments.
Used by millions of websites, Akismet has improved over the years to detect almost all spam submitted by bots. It can also detect spam comments manually submitted by actual human users by learning from their mistakes and recognizing patterns across millions of websites.
For more details, see our Akismet guide for beginners with instructions on how to set it up on your WordPress website.
The WordLift plugin integrates its web-based service to your WordPress website. It offers SEO tips to improve your content with the help of its knowledge graph.
It analyzes your content to offer facts, figures, and media to match and build contextual relationships among your articles which results in better content recommendations. It builds a knowledge graph for your own website, which becomes smarter as you add more content.
Grammarly is one of the most popular WordPress plugins. It is an artificial intelligence-powered grammar-checking tool that analyzes the readability of your content to help you write better.
It supports American, British, Canadian, and Australian English languages. It is also available as a Google Chrome addon as an alternative to the JetPack module.
Google Language Translator lets your users easily translate your website content without creating a multilingual WordPress site. It brings Google’s AI and machine learning translation engine to your WordPress website.
You can simply select languages you want to display on your website. After that, you can add the translator to your website using the sidebar widget or shortcode.
MyCurator is a cloud-based content curation service. It allows you to add an artificial intelligence-powered content aggregator, which shows content relevant to your selected topics and improves results by learning from what you add or vote on.
You can create a curated content section on your website by fetching the best bits of content relevant to your audience. You can add any article you like to your website with an excerpt and a link back to the source.
WebsiteVoice is a free WordPress plugin that converts your text content into high-quality audio. This helps improve user engagement and save time for users to listen to your articles while they’re busy with other routine work.
You’ll need their website token to activate the text-to-speech option on your site. These tokens are free initially, but you have to upgrade to a premium plan to continue using their service.
Quttera Web Malware Scanner allows you to scan your WordPress site for malware and malicious code. This is particularly useful if you suspect that your WordPress site is hacked or blacklisted by search engines.
The plugin uses artificial intelligence to learn from false positives and improves scan results based on usage. While it helps scan an infected website, it doesn’t protect your website from getting affected.
For better security, see our ultimate WordPress security guide to protect your website against hacks, DDOS attacks, and malicious code.
ChatBot provides AI-powered virtual chat agent for your website. It uses machine learning to improve the live chat experience and send accurate responses.
You need to sign up for a free account on the plugin’s website. After that, it will start learning as you use it. You can also train it on what to say and how to respond to user queries. This can be handy in keeping users engaged on your website and generating more leads.
LinkWhisper is a free WordPress internal linking plugin powered by Artificial Intelligence. It learns from the content on your site and suggests powerful internal linking opportunities to improve your search rankings and grow your traffic.
The plugin looks into your website’s content, including pages and posts. Then it offers suggestions in the WordPress block editor to add internal links while you’re writing and editing an article.
ImageSEO is a free WordPress image optimization plugin. It uses artificial intelligence to optimize the alt text and title of your images to grow your organic traffic.
The plugin also generates social cards for Twitter, Facebook, and channels. After activating the plugin, you can upload images on the WordPress website, and ImageSEO will write the name, title, and tag for the images automatically. It seamlessly works on single and bulk image uploads.
We hope this article helped you learn about WordPress plugins using artificial intelligence and machine learning. You may also want to see our guide on how to improve your WordPress website speed and performance.
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Every successful interaction between you and your favorite app results from the combined efforts of a quality assurance (QA) team. These tireless problem-hunters ensure that every aspect of the apps that mobile device users worldwide depend on for their everyday needs works without a hitch through every single release and update.
When you wake to the tones of your morning alarm clock, check the weather, or message a loved one, we have to thank these often unsung heroes.
In 2022 the news about artificial intelligence (AI) and automatic learning (Machine Learning or ML) have skyrocketed and are expected to accelerate in 2023.
Many claims that these technologies will be the most disruptive and transformative ever developed. Sundar Pichai, CEO of Google, claims that the impact of AI will be even more significant than fire or electricity on humanity; "It will fundamentally change the way we live our lives, and it will transform healthcare, education, and manufacturing," says Sundar. Well, it's hard to really imagine its impact, but one thing is for sure: In 2022, trends in AI and ML will continue to make headlines everywhere. The need for automation in the enterprise, coupled with advances in AI/ML hardware and software, is making the application of these technologies a reality.
JAX is up and coming in the Machine Learning space with ambitions to make machine learning simple yet efficient. JAX is still a Google and Deepmind research project and not yet an official Google product but has been used extensively internally and adopted by external ML researchers. We wanted to offer an introduction to JAX, how to install JAX, and its advantages and capabilities.
What Is JAX for Machine Learning?
JAX is a Python library designed for high-performance numerical computing, especially machine learning research. Its API for numerical functions is based on NumPy, a collection of functions used in scientific computing. JAX focuses on accelerating the machine learning process by using XLA to compile NumPy functions on GPUs and uses autograd to differentiate Python and NumPy functions as well as gradient-based optimization. JAX is able to differentiate through loops, branches, recursion, and closures, and take derivatives of derivatives of derivatives with ease using GPU acceleration. JAX also supports backpropagation and forward-mode differentiation.
Machine learning (ML) is a powerful force fed by data to become more proficient at performing assigned tasks to execute predictive modeling. In conjunction with artificial intelligence (AI), the two could help humans create solutions never understood because of an extensive backlog of historical data and an infinite amount of novel, incoming information. There are sometimes inaccuracies or changes due to this volume, so what happens at that point?
What Is Concept Drift in ML?
Concept drift in ML is when outdated or inaccurate data influences the creation of predictive models. ML often generates its determinations based on mapping that doesn’t consider instances where past data could inaccurately represent future predictions.
Modern machine learning algorithms are revolutionizing our daily lives. For instance, large language models like BERT are powering Google Search, and GPT-3 is powering many advanced language applications.
Today, building complex machine learning algorithms is easier than ever. However, no matter how complex a machine learning algorithms get, it falls under one of the following learning categories:
Software testing is straightforward — every input => known output. However, historically, a great deal of testing has been guesswork. We create user journeys, estimate load and think time, run tests, and compare the current result with the baseline. If we don't spot regressions, the build gets a thumbs up, and we move on. If there is a regression, back it goes. Most times, we already know the output even though it needs to be better defined — less ambiguous with clear boundaries of where a regression falls. Here is where machine learning (ML) systems and predictive analytics enter: to end ambiguity.
Kubernetes and machine learning (ML) are a perfect match. As the leading container orchestrator, Kubernetes' scalability and flexibility make it the perfect platform for managing extract-transform-load (ETL) pipelines and training ML models. That's why there's a thriving ecosystem for running ML tasks on Kubernetes. Let's look at how Kubernetes is uniquely suited for supporting machine learning, how you can use it to make your ML pipelines run faster and better, and some of the most popular Kubernetes tools for ETL and ML.
The results achieved by advanced machine learning algorithms may seem mind-blowingly mysterious to outsiders, but careful data set programming makes them possible. They involve things like understanding how the finished algorithm would ideally work, sourcing appropriate information, and preparing it to remove errors. Here are some critical steps to take when creating a data set to program an effective machine learning algorithm.
1. Take Time to Understand and Define the Problem or Question
People normally develop machine learning algorithms because they need to solve a problem or answer a pressing question. Consider an example where an e-commerce retailer wants to know which products will most likely prompt shoppers to rebuy an item. In that case, the machine algorithm would likely include data about consumers’ past purchases and any other notable buying trends.
Nowadays, applications are not just made for mobile devices but also for e-readers, gadgets, and other Internet-connected devices. There is a vast number of available Android devices, and as you have probably realized, apps are becoming smarter as well.
QA professionals often test such features using browser-independent and cross-platform software testing, which makes use of machine learning (ML) and artificial intelligence (AI).
Why and How to Use Multiple GPUs for Distributed Training
Data Scientists or Machine Learning enthusiasts training AI models at scale will inevitably reach a cap. When the datasets size increases, the processing time can increase from minutes to hours to days to weeks! Data scientists turn to the inclusion of multiple GPUs along with distributed training for machine learning models to accelerate and develop complete AI models in a fraction of the time. We will discuss the usefulness of GPUs versus CPUs for machine learning, why distributed training with multiple GPUs is optimal for larger datasets, and how to get started training machine learning models using the best practices.
Why Are GPUs Good for Training Neural Networks?
The training phase is the most resource-intensive part of building a neural network or machine learning model. A neural network requires data inputs during the training phase. The model outputs a relevant prediction based on processed data in layers based on changes made between datasets. The first round of input data essentially forms a baseline for the machine learning model to understand; subsequent datasets calculate weights and parametersto train machine prediction accuracy.
Technology continues to evolve. Who would have dreamt of smartphones, Alexa, electric cars, and all the modern technology we see today back in the 90s. It’s incredible to see devices around us with intelligence sometimes surpassing the human minds.
You may ask, what made this possible? The answer is Artificial Intelligence. You must have heard about Machine Learning, Deep Learning, and Artificial Intelligence before, probably thousands of times.
IT modernization and innovative new technologies change the healthcare industry significantly. This blog series explores how data streaming with Apache Kafka enables real-time data processing and business process automation. Real-world examples show how traditional enterprises and startups increase efficiency, reduce cost, and improve the human experience across the healthcare value chain, including pharma, insurance, providers, retail, and manufacturing. This is part five: Machine Learning and Data Science. Examples include Recursion and Humana.
Blog Series - Kafka in Healthcare
Many healthcare companies leverage Kafka today. Use cases exist in every domain across the healthcare value chain. Most companies deploy data streaming in different business domains. Use cases often overlap. I tried to categorize a few real-world deployments into different technical scenarios and added a few real-world examples:
Machine learning (ML) is a domain of computer science that allows computers to learn without having to be programmed directly. Machine learning is one of the most intriguing technologies ever discovered.
Machine Learning in Security
Thanks to machine learning (ML), computers may learn without being explicitly programmed. Machine learning works with computers to learn as humans do: by trial and error. The topic of artificial intelligence encompasses machine learning as a subset.
This article is an excerpt from the book Transformers for Natural Language Processing, Second Edition. This edition includes working with GPT-3 engines, more use cases, such as casual language analysis and computer vision tasks, and an introduction to OpenAI's Codex.
Ever trained a new model and just wanted to use it through an API straight away? Sometimes you don't want to bother writing Flask code or containerizing your model and running it in Docker. If that sounds like you, you definitely want to check out MLServer. It's a Python-based inference server that recently went GA, and what's really neat about it is that it's a highly-performant server designed for production environments. That means that, by serving models locally, you are running in the exact same environment as they will be in when they get to production.
This blog walks you through how to use MLServer by using a couple of image models as examples.
Machine learning models are not static pieces of code but, instead, dynamic predictors that depend on data, hyperparameters, evaluation metrics, and many other variables; it is vital to have insight into the training and deployment process to prevent model drift predictive stasis. That said, not all monitoring solutions are created equal. These are the three must-haves for a machine learning monitoring tool, whether you decide to build or buy a solution.
Complete Process Visibility
Many applications involve multiple models working in tandem, and these models serve a higher business purpose which may be two or three steps downstream. Furthermore, the model's behavior will likely be dependent on data transformations that are multiple steps upstream. Thus, a simple monitoring system that focuses on single model behavior will not capture the holistic picture of model performance related to the global business context. More profound knowledge of model viability only comes from complete process visibility – having insight into the entire data flow, metadata, context, and overarching business processes on which the modeling is predicated. For example, as part of a credit approval application, a bank may deploy a suite of models that assess creditworthiness, screen for potential fraud, and dynamically allocate trending offers and promos. A simple monitoring system might be able to evaluate any one of these models individually, but solving the overall business problem demands an understanding of the interlocution between them. While they may have divergent modeling goals, each model rests upon a shared foundation of training data, context, and business metadata. Thus, an effective monitoring solution will take these disparate pieces into account and generate unified insights that harness this shared information. These might include identifying a niche and underutilized customer segments in the training data distribution, flagging potential instances of concept and data drift, understanding the aggregate model impact on business KPIs, and more. The best monitoring solutions can also work not only on ML models but also on generic, tabularized data, allowing the monitoring solution to be extended to all business use-cases, not just those involving an ML component.
