Generative AI, a field of artificial intelligence, has existed since the 1960s. However, since November 2023, when the first iteration of ChatGPT was launched, the generative AI surge has been unstoppable. 

In the realm of data-driven decision-making, the adage 'garbage in, garbage out' is overwhelmingly true. We examine the top 10 culprits responsible for eroding the quality of data that organizations rely upon.

Clean and accurate data is crucial for seamless business operations, particularly when it comes to managing shipping orders and deliveries. Companies that depend on unreliable data can swiftly encounter problems for both themselves and their customers.

Artificial intelligence (AI) is being used to power our technology and the services we use every day like banking, retail, entertainment and shipping. Major corporations spend billions on AI solutions to make their companies more efficient, profitable and cyber safe.

In today's fast-paced digital landscape, businesses constantly seek ways to stay ahead of the game and provide exceptional customer experiences. One technology transforming the way businesses interact with customers and optimize internal processes is the answer engine.

Standards Development Organizations (SDOs) have become increasingly interested in recent years in digitizing their published standards.

Prioritizing data quality can help organizations in multiple ways. It ensures reliable data for informed decision-making, reduces the time and effort required to work with data, enhances customer experience, avoids penalties and provides a competitive edge.

Paint manufacturing is a complex process that involves blending different raw materials and chemicals to create a finished product. One of the key factors that can affect the quality of the final product is the quality of the data used to guide the manufacturing process.

The "1-10-100 rule" is a concept in data quality management that suggests the cost of addressing a data quality issue increases as the issue moves through different stages of the data lifecycle.

One of the main products we’ve been focusing on in the last four (4) years is our Data Quality Engine. DQE helps companies clean their data with a combination of business rules and AI/ML models. DQE helps companies climb the data quality ladder to achieve AI-readiness.

Data is the lifeblood of the modern enterprise. However, bad data can be poisonous, leading your teams off in the wrong direction and causing you to lose thousands of dollars in revenue. That’s why quality is critical.

Companies are working hard to keep on top of an ever-increasing compliance burden. Since the policies are constantly changing, it's always challenging to keep up and ensure that every process complies.

Since its release in November 2022, ChatGPT has been getting a lot of traction, which is great for AI and applications of Chat and QA (Question Answering) tools.

Data quality is one of the biggest challenges facing companies today. Many companies have access to large quantities of unstructured data (whether public or private), but lack the proper tools to understand it. Poor quality data can lead to waste and inefficiency.

Data plays a critical role in ensuring smooth and optimal operations for a company. By having quality manufacturing, operations, supply chain and products data, companies can build good AI/ML models to streamline manufacturing operations and make the best decisions possible.

Organizations across the legal and compliance industry have to deal with complex unstructured documents like contracts, invoices, agreements, etc., daily. AI can help.

Aviation companies have been collecting a lot of unstructured data, like documents related to historical defects, aircraft manuals, safety reports, and troubleshooting approaches. Managing this unstructured data is essential, and AI is the answer.

Institutions and organizations are inundated with text files, tables and lots of unstructured data. If only it was easy to organize all this information and find the right answer to your question or query.

Using only the structure of a molecule, it is now possible to generate high-accuracy and near-instantaneous property predictions for a wide variety of small molecule drug candidates.

As artificial intelligence becomes more widely used, banks are investing more heavily in AI systems. According to a recent International Data Corp. report, banks worldwide are expected to spend an additional $31 billion on AI embedded in existing systems by 2025.

The collaboration and joint venture will develop AI solutions for the legal industry.

The performance of the Question Answering Model highly depends on the performance of both the Retriever and Reader models. Retriever and Reader are loosely coupled models and can be independently evaluated.

Transportation and technology go together better than most people may think. After all, getting goods from one location to the next involves more than just hopping in a truck and heading out on the road.

As we continue making progress on our product strategy, the banking industry is emerging as a great fit for our products. Read on for some of the areas in which our technologies will make a significant difference in banking.

Have you ever wondered how Google shows the “About” panel when you search for a person or a place? They use Knowledge Graphs to do it! I will introduce Knowledge Graphs Question Answering (KGQA) on structured data in this article.

In the previous article, I introduced the Machine Reading Comprehension model using SQuAD Dataset, Language Models, and Transfer Learning. In this article, I will discuss how to scale the Machine Reading Comprehension model for long documents.

Conventional search engines were only capable of giving relevant websites for questions. However, due to advancements in Machine Reading Comprehension, Transfer Learning, and Language Modeling, current search engines can provide granular answers to every question.

We have completed our sixth anniversary at Cognistx. I continue to be blessed to work with our team, especially my co-founder Dr. Eric Nyberg. This post shares what has worked for our Company and how we are helping our customers with AI.

Artificial intelligence powers the world around us, fueling even the simplest tasks like selecting a movie to stream, listening to your favorite music on Spotify, and unlocking your phone to make a call.

Using contextual learning to avoid limitations of existing profile screening systems can help recruiters and managers to filter candidates for their job requirements.

The increasing quantity of data has fueled an era of data-driven innovation. Industries are adopting new technologies to become leaders in their market, yet efforts to become a data-driven company remain difficult.

Artificial Intelligence – referred to as simply AI – transforms our daily lives in ways most of us don’t even realize. Here are some common AI terms and their definitions.

Holly Weaver and Mingyi Wang chat live during the pandemic of 2020 about China and the future of artificial intelligence.

Holly Weaver and Raminder Dhiman chat live during the pandemic of 2020 about India, Engineering and solving problems with AI.

As a growing company in the exciting space of Enterprise AI, we had to reimagine our business models and strategy to scale better.

One of the most frustrating aspects of a poor customer service experience is when an agent can't solve the customer’s issue.

Poor Data Quality can result in less accurate AI/ML models and consequently incorrect insights and increased costs.

Changes in consumer demand have made managing the supply chain increasingly complex. Due to the Amazon Effect, the supply chain industry is being pressured to evolve.

Holly Weaver and Tom Bu chat live during the pandemic of 2020 about the future of Artificial Intelligence.

Customers want most things at their fingertips, including insurance services. And they want everything online, not waiting in a queue.

Holly Weaver and Francis Carayol chat about New Mexico, travel and the differences between American and West African culture.

Holly Weaver and Uxue Zurutuza chat live from Pittsburgh about the Covid-19 crisis and what it's doing to AI, along with some classic Spanish recipes.

In this conversation between Holly Weaver and Roshan Bhave, a Data Scientist explains how Artificial Intelligence affects all of us and how to adapt in the new norm of Covid-19.

Demand can change significantly due to external or internal events. The COVID-19 pandemic is a huge global external event. Can you plan accordingly?

In any company with large amounts of data, it is critical to ensure that the highest level of quality is met within the data in order to guarantee precise and worthy business decisions. Anomalous data refers to information that is inaccurate or lacking data integrity.

AI is growing in prevalence and businesses are getting more and more excited to jump on the bandwagon and start gaining business insights with Data Science. But is all the hype surrounding AI worth it?

Unstructured data is widely prevalent in business. You interact with tons of it on a regular basis, including text files, email, social media, websites, communications, and more. So where does AI come in?

Supply chain and logistics companies have a high velocity and volume of data. It is not possible to track data quality with human personnel. This makes automation essential.

With demand forecasting using AI, we are able to achieve new levels of accuracy in our predictions, enabling businesses to allocate resources optimally and bring in higher profits.

Cognistx developed SmartCourse™, an AI platform that enhances the golf playing experience for everyone involved!

Cognitive computing can dramatically enhance the retail experience for both companies and customers. Cognistx is successfully leveraging such technology for companies, big and small.

Our website will tell you we are “leading researchers in cognitive computing and machine intelligence with deep expertise across multiple industries.” Sounds like a mouthful to me. In simple terms, this means we are experts in Intelligent Information Systems (IIS).