AI is a groundbreaking technology that is ready to modernize the way federal government agencies operate. By automating tasks and optimizing workflows, artificial intelligence (AI) promises to enhance efficiency, minimize errors, and boost productivity without adding resources.

As per the Internet World Stats information report, there are 2.4 billion Internet users worldwide. Per the United Nations factsheet on persons with disabilities, around 10 percent of the world’s population has some form of disability, which means more than 27 percent of potential users could have special needs when it comes to accessing the Internet. Accessibility issues can be categorized under four categories.

Ready to implement generative AI in your business processes? Starting with the right generative AI use cases is key to your success. You’ll want to find areas where you can achieve quick wins as you grow toward your larger AI vision. In this article, we’ll highlight five use cases where you can incorporate generative AI for increased process efficiency.

Imagine you’ve just started a new job working as a business analyst. You’ve been given a new burning business question that needs an immediate answer. How long would it take you to find the data you need to even begin to come up with a data-driven response? Imagine how many iterations of query writing you’d have to go through. In this scenario, you also have reports that need updating as well. Those contain some of the biggest hair-ball queries you’ve ever seen.

Cloudera is launching and expanding partnerships to create a new enterprise artificial intelligence “AI” ecosystem. Businesses increasingly recognize AI solutions as critical differentiators in competitive markets and are ready to invest heavily to streamline their operations, improve customer experiences, and boost top-line growth.

Customer service is an art—and a science. It isn’t just a transactional function. It's also a relationship building activity that’s deeply tied to physiological responses in our brains. And the stakes are high: these interactions shape the neural architecture of customer loyalty. So can you use AI for customer service? Let’s explore that question.

The rise of generative AI (gen AI) is inspiring organizations to envision a future in which AI is integrated into all aspects of their operations for a more human, personalized and efficient customer experience. However, getting the required compute infrastructure into place, particularly GPUs for large language models (LLMs), is a real challenge. Accessing the necessary resources from cloud providers demands careful planning and up to month-long wait times due to the high demand for GPUs.

Adopting and deploying Generative AI within your organization is pivotal to driving innovation and outsmarting the competition while at the same time, creating efficiency, productivity, and sustainable growth. Acknowledging that AI adoption is not a one-size-fits-all process, each organization will have its unique set of use cases, challenges, objectives, and resources.

Without a doubt, 2023 has shaped up to be generative AI’s breakout year. Less than 12 months after the introduction of generative AI large language models such as ChatGPT and PaLM, image generators like Dall-E, Midjourney, and Stable Diffusion, and code generation tools like OpenAI Codex and GitHub CoPilot, organizations across every industry, including government, are beginning to leverage generative AI regularly to increase creativity and productivity.

ChatGPT was synonymous with Generative AI some time back, prompting me to explore its impact on the payments industry in a previous blog. However, it’s crucial to acknowledge that Generative AI is the encompassing circle in the Venn diagram, with ChatGPT being one of the circles nested within it. Today, let’s delve into the broader perspective of how Generative AI is poised to revolutionize the payments landscape.

Businesses are starting to use generative AI more and more as a result of the fast development of AI. Automating software testing is only one example of how generative AI is improving efficiency and reducing costs. Here I'll go over several ways generative AI may speed up the software development process and automate software testing.

The excitement (and drama) around AI continues to escalate. Why? Because the stakes are high. The race for competitive advantage by applying AI to new use cases is on! The launch of generative AI last year added fuel to the fire, and for good reason. Whereas the existing portfolio of AI tools had targeted the more technically minded like data scientists and engineers, new tools like ChatGPT handed the keys to the kingdom to anyone who could type a question.

Not long after graduating college, in the late 2010s, I had a data analysis job that scarred me for life. It involved reading through hundreds of documents—some of them handwritten—and meticulously, painstakingly entering data into spreadsheets. Who among us hasn’t been scarred by a similar experience? I remember thinking, as I spent mindless hours copy-pasting rows of text from one screen to another, that there had to be a better way. Mercifully, there is.

Following the popularity of ChatGPT, AI (Artificial Intelligence) chatbots have become a trend in various fields. The application of these chatbots for organizations to provide immediate and accurate solutions is limitless. One such field is healthcare, where these health bots would play a vital role in helping patients by providing real-time virtual assistance whenever and wherever needed.

Our recently released predictions report includes a number of important considerations about the likely trajectory of cybercrime in the coming years, and the strategies and tactics that will evolve in response. Every year, the story is “Attackers are getting more sophisticated, and defenders have to keep up.” As we enter a new era of advanced AI technology, we identify some surprising wrinkles to that perennial trend.

Discussions about AI could be heard throughout the 7th annual Appian Government event, held November 29, 2023 at Capital One Hall in Tysons, Virginia. At breakfast, a customer sitting next to me mentioned how he’s looking forward to hearing how AI can help mine his historical data. “And securely,” another added. In a lunchtime conversation, yet another customer told me about the productive meeting they had with Appian on incorporating AI into their processes.

In 2023, generative AI took the spotlight, emerging as the most talked-about technology of the year. This content creating powerhouse can do everything from text, image, and video generation to answering questions through natural language queries. And its potential uses are vast. While many industries are still in the experimental phase, the insurance sector is poised to benefit significantly from the integration of artificial intelligence into its ecosystem.

Customer services now constitute one of the key distinguishing factors of banks in an ever-changing environment. With increasing customer requirements, banks must adopt and incorporate novel technological methods to provide customized services. A very promising technology in this regard is generative AI. Generative AI in core banking systems will enable banks to transform their customer services by increasing efficiencies and building meaningful customer relationships.

This tutorial shows how to use ClearML to manage MONAI experiments. Originating from a project co-founded by NVIDIA, MONAI stands for Medical Open Network for AI. It is a domain-specific open-source PyTorch-based framework for deep learning in healthcare imaging. This blog shares how to use the ClearML handlers in conjunction with the MONAI Toolkit. To view our code example, visit our GitHub page.

Natural language processing and large language models to help BigQuery process dataframes.

Visionaries from Capgemini, Databricks and Fivetran lay out the data quality imperative for implementing enterprise AI applications.

The rise of generative AI and the massive popularity of OpenAI’s ChatGPT has led to widespread recognition that software applications are about to fundamentally change. Generative AI offers the potential to both deliver breakthrough new application capabilities and transform the way people interact with software.

In the ever-evolving landscape of software testing, the advent of Artificial Intelligence (AI) has not just been a game-changer; it’s been a paradigm shift. Test automation, once a static process, has metamorphosed into a dynamic and intelligent entity, reshaping how we approach quality assurance.

The current state of AI, despite the relevant infancy of the tools, showcases promising potential. While human assistance is still needed, the convergence of chat UI and large language models allows users to ask for what they want in a natural language, and the technology is growing intelligent enough to respond or even take action.

New, game-changing technologies have emerged in the ever-changing field of software engineering as a result of the relentless search for efficiency and creativity. Platform engineering, AI coding assistants, and AI-augmented software engineering (AIASE) are predicted to achieve widespread acceptance in the next 2-5 years, according to the Gartner, Inc. Hype Cycle for Software Engineering, 2023. When it comes to Quality Assurance, software testing is one area where Chat GPT is predicted to thrive.

GenAI depends on data maturity, in which an organization demonstrates mastery over both integrating data – moving and transforming it – and governing its use.

In recent years, governments across the globe have recognized the transformative potential of artificial intelligence (AI) and have embarked on initiatives to harness this technology to drive innovation and serve their citizens more effectively. These government-led efforts have had a profound impact on the development and adoption of AI solutions in the public sector, paving the way for a future where data-driven decision-making and automation are the norm.

Commercializing an AI-based product requires turning technology into a marketable product, navigating challenges from development to market entry. Appealing to enterprise buyers is crucial for sustainable, continuous growth, as their interest not only validates your product’s value but also lays the foundation for long-term success and scalability. More specifically, the larger the businesses of your potential customers are, the more of a monopoly your product likely has in the market.

The misconception that product led growth implies a business neglects sales couldn’t be further from the truth. In reality, product led growth (PLG) involves integrating sales later in the customer journey, placing the purchasing power back in the hands of your users. This self-service approach is highly effective, especially when paired with insightful user data to streamline and target growth. For AI-centric companies, this product-led strategy serves as a fundamental pillar for success.