Accelerating AI Development With Synthetic Data

Synthetic data applications are at the intersection of some of the most meaningful developments in enterprise AI. Synthetic data techniques represent some of the earliest manifestations of generative AI and predate the widespread adoption of language models. In fact, employing synthetic data is one of the foremost methods of building—and fine-tuning—language models and foundation models in general.

AI Enhances Customer Service in Several Ways

Although the initial seeds of generative AI (GenAI) were planted in the 1960s, for the most part it didn't come into full bloom until the introduction of ChatGPT in November 2022. Before that, GenAI had all but the most nascent use in business. As the technology entered the public consciousness, it spread across industries from entertainment to manufacturing to healthcare for a wide variety of uses, including customer service.

The Homepage of the Future: Evolution, AI, and the End of Navigation

The evolution of web homepages reflects the internet's transformation during the past 3 decades. From simple, text-based pages to sophisticated, AI-driven interfaces, homepages have adapted to meet changing user needs and technological advancements.

The Rise of GenAI and LLMs

In 1950, Alan Turing suggested a test to determine if computers could mimic human intelligence well enough that an impartial observer could no longer tell the difference. We are still talking about the Turing Test almost 75 years after its inception.

The AI-Driven Transformation of Enterprise Data Architecture

The past year has been an exhilarating one with AI and more specifically, generative AI (GenAI), quickly emerging as a transformative force, reshaping how businesses will operate, innovate, and interact with customers. As AI continues to gain prominence, its impact on enterprise data architecture is becoming increasingly apparent.

Taming the Data Quality Issue in AI

Data quality is the showstopper of AI. Many enterprise leaders who were hot on the business potential of AI are realizing that their efforts will be dead in the water if the data they are employing to train and populate their AI models is inadequate, inaccurate, or not timely.

Developing a Strong AI Governance Framework

The exponential advancement of AI technologies necessitates the implementation of robust governance frameworks to guide their responsible development and deployment.

Transforming Customer Service with AI and Knowledge Management

Customer service and customer support operations are no strangers to AI technologies. AI has long been embedded in the interactions between companies and their customers.

Adding Meaning to Data: Knowledge Graphs, Vector Databases, and Ontologies

This article discusses the importance of context in enterprise data for generative AI (GenAI), and, in fact, for any AI initiative.

Elevating Enterprise Search With AI, ML, NLP, and LLMS

Information retrieval, now more commonly known simply as search, is one of the earliest manifestations of using AI. Consequently, enterprise search is inseparable from AI and one of the most meaningful beneficiaries of the exponential advancements recently achieved by machine learning (ML)—the most accomplished expression of statistical AI.

The Future of Work Chatbots, Voicebots, and Virtual Assistants

Enterprises increasingly rely on chatbots, voicebots, and virtual assistants to service more customers without adding to their staff count. These devices can be connected by a single underlying software platform, although there are several companies that use these devices independent of each other.

Upskilling and Reskilling in the Age of AI

The AI revolution is upon us. A tidal wave of AI technology is sweeping across businesses, fundamentally reshaping how employees, managers, and AI system developers approach their work.

Artificial Intelligence (AI): Top Ten Trends

Owing to its potential, AI is one of the most active areas of technological development, particularly in process automation, revolutionizing how we work. In a fast-paced environment where one development, like OpenAI's ChatGPT, can impact the world literally overnight, many enterprise leaders are understandably curious - even anxious - about current and coming AI trends, hoping to leverage artificial intelligence platforms to improve their productivity, profitability, and competitiveness.

Deepfakes: Overview and How to Spot Them

A deepfake is a form of synthetic content, such as images, audio or video, that purports to be real but is actually false and misleading. The word itself is a portmanteau of deep learning, a form of AI, and fake. In addition to manipulating existing content, the purveyors of deepfakes can create entirely new content where an individual is represented in a false manner, portrayed as doing or saying something that he or she has not done or said. An example of this is a deepfake video from 2022 that appears to depict President Zelenskyy of the Ukraine instructing Ukrainian troops to surrender to Russian aggressors. The primary aim of deepfakes is to spread false information so that it appears to come from trusted sources.

Generative AI: Market Overview, Trends, and Enterprise Use Cases

One of the latest and most controversial developments in the field of artificial intelligence, generative AI (GAI) refers to programs that can generate original content, literally creating new digital images, video, audio, text, and code.… While generative AI may be troublesome for those in the creative community, it offers great opportunities for enterprise adopters.

Generative AI Risk Management: Frameworks and Best Practices

Many enterprise risk leaders are busy developing generative AI risk regimes: policies, protocols, and procedures designed to ensure that gen AI implementations are safe, secure, and effective.