Systems | Development | Analytics | API | Testing

April 2024

What Are the Security Risks Associated with AI-Generated Code? | Vandana Verma | #QonfX 2024

In this session, Vandana Verma explores the intersection of AI and security in software development. Vandana delves into the potential of AI-powered coding tools like GitHub Copilot and ChatGPT to accelerate project delivery while highlighting the inherent security challenges they pose. Through a hands-on demonstration, Vandana showcases the construction of a demo app using GitHub Copilot, followed by the identification and exploitation of vulnerabilities within the AI-generated code.

Unveiling the 'Black Box': How Explainable AI Transforms Decision-Making in Business

There is an increasing demand for openness and accountability in Artificial Intelligence (AI) decision-making processes. Explainable Artificial Intelligence (XAI) is a developing idea as organizations attempt to comprehend and trust AI’s suggestions and insights. Gartner expects that explainable AI will reach the pinnacle of expectations. Over the next fifteen years, XAI will enter the decisive stages of development.

Analyze documents in BigQuery with Document AI

Turn your documents into actionable data! This video explores the power of BigQuery's integration with Document AI. Learn how to transform unstructured documents – think invoices, contracts, forms – into neatly structured tables within your data warehouse, unlocking smarter insights using familiar SQL syntax.

What are LLMs? An intro into AI, models, tokens, parameters, weights, quantization and more

To keep up with everything happening in the world of artificial intelligence, it helps to understand and grasp key terms and concepts behind the technology. In this introduction, we are going to dive into what is generative AI, looking at the technology and models they are built on. We'll discuss how these models are built, trained, and deployed into the world.

3 Ways Logi Symphony Leverages AI for Actionable Insights

Table of Contents April 25, 2024 insightsoftware is a global provider of reporting, analytics, and performance management solutions, empowering organizations to unlock business data and transform the way finance and data teams operate. In the rapidly-evolving world of embedded analytics and business intelligence, one important question has emerged at the forefront: How can you leverage artificial intelligence (AI) to enhance your data analysis?

Will AI Replace Software Quality Assurance Roles?

My horse recently had an injury. Initially, we suspected an issue with his hind leg due to prior swelling, but our vet suspected something else. To confirm her assumptions she also used an AI tool called Sleip.AI during the examination, and confirmed the problem in the front leg instead. This AI tool didn’t replace her expertise but accelerated her diagnostic process, showcasing how AI augments rather than replaces professional judgment.

Introducing Testim Copilot: Accelerate test creation through generative AI

We are thrilled to announce the arrival of Tricentis Testim Copilot, the first of many Tricentis Copilot solutions. Tricentis Copilot solutions are a collection of advanced generative AI capabilities available as add-ons across our products that help customers boost their efficiency throughout the entire testing lifecycle. With Testim Copilot, we’ve added new advanced generative AI capabilities to Testim, an already AI-driven test automation platform.

Harnessing Generative AI for Flood Insurance Risk Evaluation and Mitigation

Floods are the most common natural disaster worldwide. A flood is demarcated as an overflow of water that submerges land that is usually dry. Flash flooding befalls when heavy rainfall over a short period hits areas with inadequate drainage. Other causes of flood events comprise high tides, storm surges, river overflow, and snow melt. Each year, floods extinguish assets worth billions of dollars. In the last five years alone, monetary losses caused by floods worldwide were estimated at 299 billion U.S.

Snowflake Arctic: The Best LLM for Enterprise AI - Efficiently Intelligent, Truly Open

Building top-tier enterprise-grade intelligence using LLMs has traditionally been prohibitively expensive and resource-hungry, and often costs tens to hundreds of millions of dollars. As researchers, we have grappled with the constraints of efficiently training and inferencing LLMs for years.

Integrating LLMs with Traditional ML: How, Why & Use Cases

Ever since the release of ChatGPT in November 2022, organizations have been trying to find new and innovative ways to leverage gen AI to drive organizational growth. LLM capabilities like contextual understanding and response to natural language prompts enable the development of applications like automated AI chatbots, smart call center apps, or for financial services.

Have We Already Exceeded AI's Capabilities?

Are we in an AI bubble? We can't stop talking about AI in tech. It's at every conference and in every startup pitch. But is the rest of the world as enamored as we are? In this conversation, we explore AI’s impact beyond the echo chamber of the tech industry. We look at attitudes toward AI in other spaces, from healthcare to finance, weighing the risks and benefits of its application. We also look to the future, questioning whether we’ve reached the limits of AI given compute power constraints.

The AI Financial Bubble Point of View

Are we in an AI bubble? We can't stop talking about AI in tech. It's at every conference and in every startup pitch. But is the rest of the world as enamored as we are? In this conversation, we explore AI’s impact beyond the echo chamber of the tech industry. We look at attitudes toward AI in other spaces, from healthcare to finance, weighing the risks and benefits of its application. We also look to the future, questioning whether we’ve reached the limits of AI given compute power constraints.

3 Key Drivers and Considerations for AI Analytics in 2024

For independent software vendors (ISVs), creating unique differentiators and value increasingly entails adopting the latest technologies to enhance your product experience, especially in an ever-evolving competitive landscape with multiple new tech solutions. AI analytics is the latest emerging field in business intelligence (BI) solutions that offers new sophisticated capabilities for the user experience (UX) of your product’s analytics component.

How Important is AI Right Now?

Are we in an AI bubble? We can't stop talking about AI in tech. It's at every conference and in every startup pitch. But is the rest of the world as enamored as we are? In this conversation, we explore AI’s impact beyond the echo chamber of the tech industry. We look at attitudes toward AI in other spaces, from healthcare to finance, weighing the risks and benefits of its application. We also look to the future, questioning whether we’ve reached the limits of AI given compute power constraints.

AI is Being Used as More Than Just an LLM

Are we in an AI bubble? We can't stop talking about AI in tech. It's at every conference and in every startup pitch. But is the rest of the world as enamored as we are? In this conversation, we explore AI’s impact beyond the echo chamber of the tech industry. We look at attitudes toward AI in other spaces, from healthcare to finance, weighing the risks and benefits of its application. We also look to the future, questioning whether we’ve reached the limits of AI given compute power constraints.

Capturing the opportunity of AI while keeping an eye on sustainability with STaaS

The ever-growing tide of data, fueled by analytics and AI, places a significant strain on data center resources and increases energy consumption. With more stakeholder scrutiny and evolving regulations, such as the EU’s Corporate Sustainability Reporting Directive (CSRD), and U.S. regulations on the horizon, organizations are taking notice now more than ever before. As a result, organizations are prioritizing sustainability in their IT strategies.

A Look Back at the Gartner Data and Analytics Summit

Artificial intelligence (AI) is something that, by its very nature, can be surrounded by a sea of skepticism but also excitement and optimism when it comes to harnessing its power. With the arrival of the latest AI-powered technologies like large language models (LLMs) and generative AI (GenAI), there’s a vast amount of opportunities for innovation, growth, and improved business outcomes right around the corner. All of that technology, though, depends on data to be successful.

Introducing Qlik's AI Accelerator - Delivering Tangible Customer Outcomes in Generative AI Integration

At Qlik, we're witnessing a thrilling shift in the landscape of data analysis, customer engagement, and decision-making processes, all thanks to the advent of generative AI, especially Large Language Models (LLMs). The potential for transformation across all sectors is enormous, but the journey toward integration can be daunting for many businesses with many leaders wondering where to start in integrating the exciting capabilities of AI into their daily workflows.

Embedded analytics in the age of generative AI

Every company around the globe is trying to get in on the GenAI wave to simplify user experiences with natural language. And this is especially true in the realm of data and analytics. Imagine if you could enable all of your marketers to evaluate the performance of their campaigns with a simple question? Or, if you could provide all of your insurance risk managers with the ability to analyze the risk profile of their claims with the power of search and automated insights?

Are We in an AI Information Bubble?

Are we in an AI bubble? We can't stop talking about AI in tech. It's at every conference and in every startup pitch. But is the rest of the world as enamored as we are? In this conversation, we explore AI’s impact beyond the echo chamber of the tech industry. We look at attitudes toward AI in other spaces, from healthcare to finance, weighing the risks and benefits of its application. We also look to the future, questioning whether we’ve reached the limits of AI given compute power constraints.

How to Perform Database Analysis with AI

This blog explores how DreamFactory leverages its robust features to perform database analysis with AI, ensuring secure and efficient data operations. We will discuss the platform’s ability to generate dynamic APIs, provide real-time data insights, and maintain stringent security measures to protect data integrity.

Introducing Tricentis Copilot solutions

We are thrilled to announce Tricentis Copilot solutions, a collection of advanced generative AI capabilities available across our products that help customers boost their efficiency throughout the entire testing lifecycle. With Tricentis Copilot solutions, you can autogenerate manual tests from requirements, optimize your portfolio, autogenerate custom code, and get meaningful insights.

Turbocharging Your Business with (Gen)AI

If you were to stop someone walking down the street and ask them how long artificial intelligence, or AI, has been a hot topic, they might say it’s something that’s emerged mostly in recent years. But AI has been around for a long time, with the term first being coined as long ago as 1955. Generative AI however is a different beast, and one that's largely responsible for moving the topic of AI to the tip of everyone’s tongues – from consumers to enterprises alike.

Testing generative AI systems and red teaming: An introductory guide

The topic of testing AI and ensuring its responsibility, safety, and security has never been more urgent. Controversy and incidents of AI misuse have increased 26-fold since 2021, highlighting growing concerns. As users quickly find out, AI tools are not infallible; they can make mistakes, display overconfidence, and lack critical questioning. The reality of the market is that AI is prone to error. This is exactly why testing AI is crucial. But how do we test AI?

Introduction to Gemini in BigQuery

Data practitioners spend much of their time on complex, fragmented and sometimes, repetitive tasks. This limits their ability to focus on strategic insights and maximize the value of their data. Gemini in BigQuery shifts this paradigm by providing AI capabilities that help streamline your workflows across the entire data lifecycle.

Streamline Your AI Integration: A Deep Dive into Kong AI Gateway

Join us to learn about the AI Gateway concept and explore the rapidly evolving landscape of large language models (LLMs) in modern applications. With the surge of AI providers and the lack of standardization, organizations face significant challenges in adopting and managing AI services effectively. Kong's AI Gateway, built on the proven Kong Gateway platform, addresses these challenges head-on, empowering developers and organizations to harness the power of AI quickly and securely.

Snowflake Launches the World's Best Practical Text-Embedding Model for Retrieval Use Cases

Today Snowflake is launching and open-sourcing with an Apache 2.0 license the Snowflake Arctic embed family of models. Based on the Massive Text Embedding Benchmark (MTEB) Retrieval Leaderboard, the largest Arctic embed model with only 334 million parameters is the only one to surpass average retrieval performance of 55.9, a feat only less practical to deploy models with over 1 billion parameters are able to achieve.

LLM Metrics: Key Metrics Explained

Organizations that monitor their LLMs will benefit from higher performing models at higher efficiency, while meeting ethical considerations like ensuring privacy and eliminating bias and toxicity. In this blog post, we bring the top LLM metrics we recommend measuring and when to use each one. In the end, we explain how to implement these metrics in your ML and gen AI pipelines.

Unleashing the Power of Digital Assurance in the Age of AI and Gen AI: Charting the Way Forward

In the pursuit of excellence, Quality Assurance (QA) has embarked on a profound journey of automation. Beginning with manual testing as its foundation, QA has progressed steadily through functional automation and smart automation, culminating in its embrace of Intelligent automation and Codeless automation. This evolution mirrors our transition from traditional waterfall models to agile methodologies.

Why RAG Has a Place in Your LLMOps

With the explosion of generative AI tools available for providing information, making recommendations, or creating images, LLMs have captured the public imagination. Although we cannot expect an LLM to have all the information we want, or sometimes even include inaccurate information, consumer enthusiasm for using generative AI tools continues to build.

Have You Heard of Devin the AI Software Engineer??

Imagine a world where every piece of digital content can be verified and traced back to its source. Lindsay Walker, Product Lead at Starling Lab for Data Integrity, walks us through the emerging tools that could make this possible. While AI tools hold incredible potential for good, Lindsay also warns against threats and countermeasures needed to keep our virtual representations safe. She emphasizes the need to build provenance into tools, discusses blockchain use cases, and shares systems that implement hashes for security.

A Breakthrough AI-Powered SQL Assistant

Data is the lifeblood of modern businesses, but unlocking its true insights often requires complex SQL queries. These queries can be time-consuming to write and challenging to maintain. At Snowflake, we believe in making the power of data accessible to all. That’s why we prioritize simplicity, governance and quality in everything we build – including our AI-powered tools.

The Sliding Doors for Responsible AI

In this blog series, I have been exploring the “sliding doors” – or divergent paths - organizations can take with data and analytics. Sometimes, grabbing the wrong door means missing out on creating the most value with your data. But in some instances, it can also lead you on a more serious path of breach of compliance with regulations.

Blockchain, Deepfakes, and AI, Oh My!

Imagine a world where every piece of digital content can be verified and traced back to its source. Lindsay Walker, Product Lead at Starling Lab for Data Integrity, walks us through the emerging tools that could make this possible. While AI tools hold incredible potential for good, Lindsay also warns against threats and countermeasures needed to keep our virtual representations safe. She emphasizes the need to build provenance into tools, discusses blockchain use cases, and shares systems that implement hashes for security.

The AI API Gold Rush: How to Turn Your Models into Revenue With Moesif

So, you’ve built a cutting-edge AI model! Congratulations! With the vast array of AI capabilities coming to market, it may generate realistic product descriptions, analyze customer sentiment, or perform cutting-edge processing. Innovation with AI is helping companies to discover new capabilities and use cases. However, innovation alone isn’t enough. To truly reap the rewards of an investment in AI, companies need a strategy to monetize it.

Katalon recognized in the 2024 Gartner Market Guide for AI-Augmented Software Testing Tools

ATLANTA, April 2024 - Katalon is a leading provider of the most modern, comprehensive software quality management platform. The company announced that it was named as a Representative Vendor in the 2024 Gartner® Market Guide for AI-Augmented Software Testing Tools for the second year in a row.

Building reliable systems out of unreliable agents

If you’ve tried building real-world features with AI, chances are that you’ve experienced reliability issues. It’s common knowledge that AI makes for great demos, but… questionable products. After getting uncannily correct answers at first, you get burned on reliability with some wild output and decide you can’t make anything useful out of that. Well, I’m here to tell you that there’s hope.

Leverage Google Gemini on ThoughtSpot AI-Powered Analytics

Over the past couple of years, ThoughtSpot and Google have collaborated on a series of seamless user experiences—enabling deployments on Google Cloud Platform, creating the ability to live query entire Google BigQuery analytics catalogs, and integrating key Looker Modeling functionality just to name a few. This type of co-innovation helps mutual customers get the most value out of their data.