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.

The Coming AI Regulations

Advances in artificial intelligence offer both promise and peril. Regulation is essential to the ethical and responsible development of AI. This article is an overview of the current and future regulations surrounding artificial intelligence.

AI Risk Management

Like e-commerce, smartphones, and cloud computing, new technology will often present novel and, in some cases, serious risks. In response to the coming artificial intelligence revolution, enterprise security departments should commit to identifying - and mitigating - known AI risks and anticipating emerging risks based on business, societal, and technological trends.

ChatGPT Cyber Risks

Overview of the cyber risks associated with Chat GPT. Those risks include exposing sensitive data, generating dangerous malware, aiding phishing attacks, and more.

Deepfake and AI Generated Security Threats

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.

AI Risk Management Frameworks Overview

Hopefully, to avoid a repeat of today's Internet nightmares, risk professionals have begun the process of developing artificial intelligence risk management frameworks - frameworks inclusive enough to encompass the evolving role of AI, including AI and cloud computing, AI and edge computing, AI and the Internet of Things (IoT), as well as AI and finance, medicine, transportation, and so-called "knowledge work."