Tag: Generative Ai

Generative AI – a technology wonder of modern times – has revolutionized our ability to create and innovate. It also promises to have a profound impact on every facet of our lives. Beyond the seemingly magical powers of ChatGPT, Bard, MidJourney, and others, the emergence of what’s known as RAG (Retrieval Augmented Generation) has opened the possibility of augmenting Large Language Models (LLMs) with domain-specific enterprise data and knowledge.

RAG and its many variants have emerged as a pivotal technique in the realm of applied generative AI, improving LLM reliability and trustworthiness. Most recently, a technique known as Graph RAG has been getting a lot of attention, as it allows generative AI models to be combined with knowledge graphs to provide context for more accurate outputs. But what are its components and can it live up to the hype?

What is Graph RAG and What’s All the Fuss About?

According to Gartner, Graph RAG is a technique to improve the accuracy, reliability and explainability of retrieval-augmented generation (RAG) systems. The approach uses knowledge graphs (KGs) to improve the recall and precision of retrieval, either directly by pulling facts from a KG or indirectly by optimizing other retrieval methods. The added context refines the search space of results, eliminating irrelevant information.

Graph RAG enhances traditional RAG by integrating KGs to retrieve information and, using ontologies and taxonomies, builds context around entities involved in the user query. This approach leverages the structured nature of graphs to organize data as nodes and relationships, enabling efficient and accurate retrieval of relevant information to LLMs for generating responses.

KGs, which are a collection of interlinked descriptions of concepts, entities, relationships, and events, put data in context via linking and semantic metadata and provide a framework for data integration, unification, analytics and sharing. Here, they act as the source of structured, domain-specific context and information, enabling a nuanced understanding and retrieval of interconnected, heterogeneous information. This enhances the context and depth of the retrieved information, which results in accurate and relevant responses to user queries. This is especially true for complex domain-specific topics that require a deeper, holistic understanding of summarized semantic concepts over large data collections.

To Know More, Read Full Article @ https://ai-techpark.com/graph-rags-precision-advantage/

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Have you encountered a bad situation that was made worse by something that is meant to help? Here’s a recent example of mine – I had to take my son to an emergency room while vacationing in Asia but the most frustrating part was dealing with insurance when we got home. The agent who initially processed my claim put me (and my money) in limbo – no external or internal follow-up communication, inaccessible and invisible in the client portal – because they didn’t follow the process for handling non-English documents. This poor customer service was entirely preventable and, though I’m not an insurance industry expert, I’m going to tell you how.

I started this article with my personal experience because all service providers need to consider customer impact when designing their AI adoption. Unfortunately for me, health insurance is a relatively inelastic service. The insurance company – let’s start to see ourselves in their position now – has many customers locked in for the year irrespective of individual satisfaction. It also means that customer acquisition is relatively fixed. Insurance companies are not alone in having profit margins that are won and lost in processes. They’re also not alone in having a customer base that includes stubborn engineers who will spend above-average time investigating problems to discover a root cause (hi, that’s me). Even though I can’t switch medical insurance, the original agent’s mistakes followed by my persistence led to an undesirably high touch time for the insurance company (getting personal again, I digress…)

Whether your organization manages insurance claims, manufactures automotive components, or facilitates the food and beverage supply chain, profitability is influenced by how well your people, processes and systems are harmonized. Fortunately, some of the up-and-coming solutions embedded with AI have started to measurably improve the balance with people, processes and, ultimately, profit. One of the solutions with a high yield potential from relatively low effort is called Process Mining. Gartner defines it as “a technique designed to discover, monitor and improve real processes (i.e., not assumed processes) by extracting readily available knowledge from the event logs of information systems”. What gives process mining the potential for high yield with low effort is that it leverages information that your business processes already generate but traditionally ignore outside of IT troubleshooting. Process mining users are provided with unprecedented visibility of process flows and deviations. Analysis of those deviations turns into data-driven continuous improvement with the possibility of incorporating process improvements that were already proven through execution even though they weren’t pre-planned.

To Know More, Read Full Article @ https://ai-techpark.com/ais-role-in-process-mining/

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Can you tell us about your journey as CEO and co-founder of Corti?

Corti was founded in 2016 in Copenhagen by Lars Maaløe and me. Lars, with his PhD in machine learning, and my experience as a multi-entrepreneur in AI from Scandinavia to Silicon Valley, has made for a solid partnership. We’ve always been driven by a belief in AI’s transformative potential for healthcare. We started Corti as a research company, with a bold thesis that Generative AI would become an integral part of every patient interaction in real time. At the time, this was a radical idea, but it has since proven to be viable. Initially, we focused on emergency medicine, assisting in detecting cardiac arrests and managing COVID-19 calls. Today, we are building the most reliable and effective Generative AI platform tailored to healthcare’s unique needs, scaling globally to enhance real-time consultations across healthcare and reducing the margin for error by up to 40%.

What inspired you to focus on healthcare technology, and how does your personal connection influence Corti’s mission and innovation?

From day one, we’ve been driven by the desire to reduce disparities in healthcare. Our vision is that everyone, everywhere, should have access to medical expertise. The uncomfortable truth is that millions of healthcare professionals are missing, and this gap is widening. From a young age, I saw firsthand the impact of overburdened healthcare systems on patients, which inspired me to seek a solution. That’s what drives me every day – to think that, thanks to Corti, a patient somewhere might get more time with their doctor, or avoid a misdiagnosis that could have been life-altering.

Could you provide an overview of Corti’s solutions?

Corti offers an AI-powered platform that enhances decision-making across the entire patient journey. Our AI provides a “second opinion” and integrates seamlessly into nearly any system, enabling healthcare professionals to quality assure, journal, code, nudge, prompt, and document every patient interaction. This drastically improves care, documentation, and revenue through expert guidance and support. We know there is no one-size-fits-all in healthcare, so Corti is fully customizable. It fits effortlessly into any workflow, effectively becoming the easiest employee you’ve never hired – no downtime, no breaks. With Corti, healthcare professionals save an average of two hours of documentation time per day and up to 80% on administrative tasks, freeing them to focus more on what truly matters: patient care.

What are the core values that Corti is built upon, and how do they guide the company’s objectives and mission in revolutionizing healthcare?

At Corti, we believe that everyone is a patient at some point, and we strive to support those who care for us when we need it most. Working at Corti means being committed to making the healthcare system better for all. Our team is driven by this belief – that we can and must do more to support caregivers. This mindset fuels us to go above and beyond, embracing challenges, staying curious, learning from failure, and waking up every day ready to try again.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-andreas-cleve/

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Sebastian, can you start by sharing your background and what led you to your current role as VP of Engineering at Storyblok?

My journey in the tech industry began with a deep interest in software development and a passion for creating innovative solutions. Over the years, I have held various roles in engineering and management, which have provided me with a broad perspective on technology and its applications.

Before joining Storyblok, I worked with several startups and established companies, focusing on building scalable and secure software solutions. My experience in these diverse environments has been instrumental in shaping my approach to engineering and leadership. With Storyblok, I was drawn to the company’s vision of transforming content management and the opportunity to lead a talented team in driving this innovation forward.

In what ways can generative AI be utilized to create malicious content such as phishing emails and social engineering attacks?

Generative AI can produce highly realistic and personalized phishing emails by analyzing vast amounts of publicly available data about potential targets. This allows attackers to craft messages that are more likely to deceive recipients into divulging sensitive information. Similarly, AI can generate fake social media profiles or impersonate trusted contacts, enhancing the effectiveness of social engineering attacks. The ability to produce high-quality, contextually relevant content at scale means that these AI-generated threats can bypass many traditional security filters designed to catch generic phishing attempts.

The current cybersecurity measures seem adequate. What specific measures do you believe are most effective against AI-driven attacks?

While current cybersecurity measures provide a foundation, they need to be enhanced to effectively counter AI-driven attacks. Key measures include advanced threat detection where AI and machine learning are used to detect and respond to threats in real-time, behavioral analytics, which is the monitoring of user behavior to identify deviations that may indicate compromised accounts. Zero Trust Architecture is also important which involves implementing a model where verification is required for every access request, regardless of its origin.

Keeping staff informed about the latest threats and best practices to mitigate human error are also key measures in reducing the threat of AI-driven cyber attacks as is Multi-Factor Authentication (MFA) where an extra layer of security is added to verify user identities.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-sebastian-gierlinger/

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Paige, kindly let our readers know how Seismic perceives the role of AI in evolving customer experiences and go-to-market (GTM) processes based on recent data.

At Seismic, we believe enablement is a mission-critical function that turns strategy into reality, and generative AI is creating an industry-defining moment for GTM and enablement teams. It is changing everything about the sales process, from prospecting to meeting preparation, content and presentation development, follow-up, training and performance tracking.

In fact, an overwhelming majority (93%) of enablement tech users acknowledge AI as the driving force behind their future investments. Based on this data, it’s evident that organizations neglecting to integrate AI into their GTM processes risk lagging behind and losing competitiveness in today’s industry, a sentiment echoed by 73% of respondents in our research.

What’s more: our customers know this to be true. In a Seismic customer survey, 65% of respondents cited AI as a primary reason for increased enablement investment. Specifically, they view Sales Content Generation & Optimization as the most valuable use cases to explore and implement for their teams. Over half (52%) are currently using or evaluating AI-powered tools within sales enablement processes, with 61% sharing that they are familiar with these tools to varying degrees.

Your report suggests a significant impact on both internal processes and customer experiences for businesses leveraging AI. How does Seismic observe this impact in terms of tangible ROI and enhanced customer engagement?

In addition to the clear revenue growth teams have witnessed, GTM leaders predict an average of 23% of that growth will be directly attributed to AI utilization over the next five years. In fact, 63% believe that AI is the primary force behind evolving customer experiences today. AI solutions are poised to touch nearly every corner of customer engagement.

For context, GTM leaders in the United States are leveraging AI-powered enablement tools for three primary functions: 53% utilize them for content analytics, 50% for content distribution, and 48% for learning and coaching. Businesses are experiencing significant benefits, with 91% of those who have implemented AI tools reporting an increase in customer satisfaction since integrating AI into their enablement processes.

How is Seismic addressing the gap in understanding how AI is used in GTM processes, and what educational initiatives or tools are being introduced without delving into specific numbers?

At Seismic, we work to consistently update our AI product offerings and tools to ensure they are meeting the needs of our customers and empowering them to build more strategic relationships, effectively engage with buyers, and speed up the entire buying process.

In fact, just last year we introduced an AI customer community, which is dedicated to sharing best practices, knowledge and strategies to seamlessly integrate AI into revenue teams’ operations. Additionally, we offer both the Seismic Advocacy Program and Seismic Community – two spaces for our customers to hear more directly from Seismic leadership about our products, as well as share with each other what they’re learning and doing with AI-powered enablement at their company.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-transforming-gtm-processes/

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Generative AI has swiftly become popular among marketers and has the potential to grow to a $1.3 trillion industry in the next 10 years. OpenAI’s ChatGPT is just one growth example—rocketing to over 100 million users in just two months of its release.

Many have hailed generative AI as a process-changing tool that can quickly produce swaths of content with minimal human intervention, drastically scaling content production. That’s the claim anyway. But as AI becomes more prevalent, its use in content production opens several questions — does generative AI actually produce quality content? Can it match what human marketers can produce?

With the digital landscape already saturated with content, marketers in the AI era need to fully understand the strengths and weaknesses of current generative tools so they can build (and protect) high-quality connections with their audiences.

Human-generated content beat out AI-generated content in every category.

Though the AI tools had strengths in some areas, no one tool mastered multiple criteria across our tests. When it comes to accuracy, readability, and brand style and tone, the AI tools could not reach the level of quality that professional content writers provided. It also lacked the authenticity of human-written content.

The lesson: Brands and marketers must keep humans at the center of content creation.

Unsurprisingly, AI is not the end-all-be-all solution for creating content that truly connects with human audiences.  

Yes, AI is an efficient and capable tool that marketers can leverage to supercharge specific content tasks. Using AI for tasks such as research, keyword analysis, brainstorming, and headline generation may save content creators money, time, and effort.

Even so, marketers should prioritize humanity in their writing. AI can only give us an aggregate of the staid writing available across the internet. But highly skilled human writers are masters of contextualization, tapping into the subtleties of word choice and tone to customize writing to specific audiences.

As some have pointed out, quantity can never win out over quality.

In the race to adopt AI tools, we must remember what makes content valuable and why it connects with human audiences. The online marketing landscape is becoming increasingly competitive, and brands can’t risk the ability to build trusting connections with consumers in their rush to streamline workflows. Ultimately, humans must remain the central focus as brands invest in unique and authentic content that connects.

To Know More, Read Full Article @ https://ai-techpark.com/ai-vs-human-content-quality/

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The rapid advancement of AI has revolutionized industries worldwide, transforming the way businesses operate. While some organizations are still catching up, AI is undeniably a game-changer, reshaping industries and redefining enterprise operations.

Estimates from Goldman Sachs suggest that AI has the potential to increase global GDP by approximately 7% (almost $7 trillion) over the next decade by enhancing labor productivity. Even with conservative predictions, AI is poised to drive significant progress in the global economy.

The Importance of Training and Development

Training and development also play a critical role in this AI-driven evolution. Recent data showed that 66% of American IT professionals agreed it’s harder for them to take days off than their colleagues who are not in the IT department, which has serious implications for burnout, employee retention, and overall satisfaction. This makes AI integration more important than ever before. But first, proper training is essential.

As IT professionals are beginning to leverage AI’s power, emphasis must be placed on cultivating skills in data analysis, algorithm development, and system optimization. Especially as organizations embrace closed-loop AI systems, considerations around data security, ethics, and workforce upskilling become imperative.

AI companions are becoming increasingly essential to ensure efficient IT operations. Luckily, innovative solutions are emerging with capabilities like ticket summaries, response generation, and even AI solutions based on device diagnostics and ticket history to help streamline daily tasks and empower IT professionals to focus on higher-value issues.

Integrating Closed-Loop Systems to Supercharge Your AI Integration

The evolution of AI technology and closed-loop systems is set to revolutionize enterprise operations. As businesses navigate this future, embracing these advancements responsibly will be crucial for staying competitive and efficient. AI’s ability to enhance decision-making, streamline processes, and drive innovation opens new avenues for growth and success.

By integrating closed-loop systems and prioritizing responsible AI, enterprises can create more responsive and adaptive environments, ensuring continuous improvement and agility. The future of enterprise technology is here, and those who adapt and leverage these powerful tools responsibly will undoubtedly lead the way in their industries.

To Know More, Read Full Article @ https://ai-techpark.com/ai-evolution-enterprise-future/

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IT companies and consulting firms are on a relentless quest to stay innovative in a rapidly evolving digital world. Industries worldwide are embracing the digital landscape, using AI to help transform their operations and adapt to new challenges.

Digital transformation integrates digital technologies into all operational areas, streamlining processes, enhancing customer interactions, fostering a forward-thinking work culture, and improving overall strategic planning. By embracing digital transformation, companies have the potential to save money while maximizing efficiency.

A Grim Reality: Economic Challenges and Layoffs

In response to economic challenges, including significant layoffs in the tech sector, companies must innovate and adapt swiftly. Digital transformation, especially through AI, provides a lifeline.

In 2023, 1,186 tech companies laid off 262,682 employees and this year alone, 168 tech companies have laid off 42,324 employees. Major consulting firms are also at risk. This is forcing them to stay ahead of the curve and innovate before it is too late.

Why Digital Transformation Matters

Digital transformation, especially when incorporating AI, can be a strategic solution for the challenges in the tech sphere. Imagine this: a mid-sized IT company experiencing fast-declining revenues and an increase in operational costs integrates AI into its workflow. AI acts as a catalyst to streamline processes and reduce manual errors while freeing up time for employees to focus on more strategic tasks. This results in increased productivity, efficiency and profitability. This is what companies need to stay ahead of competition.

By 2027, AI tools are expected to be used for digital transformation to cut process costs in half and reduce modernization expenses by 70%.

But despite its potential, digital transformation is much more difficult for companies to adopt than it seems. Only 35% of businesses have successfully adopted digital transformation efforts which highlights a pressing issue: many organizations are not fully prepared to embrace digital change and integration.

Digital transformation is the future of business. By embracing it now, companies can turn challenges into growth opportunities and thrive in the evolving digital landscape. IT companies looking to protect and evolve their operations can rely on this approach to ultimately tackle economic challenges and layoffs. By investing in skill building, promoting innovation and planning accordingly, organizations can turn the challenges they face to opportunities of growth. While adopting digital transformation strategies may be difficult now, it is the future of business. Companies who embrace it can thrive in the evolving digital landscape.

To Know More, Read Full Article @ https://ai-techpark.com/ai-drives-digital-evolution/

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Hello Hussein, can you share with us your professional journey and how you became involved in the field of AI and technology, leading to your role as co-founder of Momentable?

I’ve always been fascinated with technology and sci-fi. AI is one of those things that sticks in your mind, and you can’t help but think about it.

I studied engineering and worked in tech, and even with all the advancements in technology we have been witnessing in the past two decades, AI was one of those things that we always thought would remain a sci-fi pipe dream for a long time.

This is not because there was nothing happening. But those who work in tech know it takes a while for the evolution of these technologies, and advancements are usually several degrees of separation from the regular user.

I’m always learning, reading, and building tech products, so AI was a field of study; however, implementing it was never accessible for early-stage products.

The status of AI has forever changed. OpenAI’s ChatGPT launch has had a remarkable impact on the field of AI and tech in general. AI is now available for regular users. People like me working in tech can now use AI in everything they are doing, which will accelerate product development and will impact the kind of products we can build and deliver to customers.

In addressing concerns surrounding AI ethics, you mentioned the importance of regulatory measures, technological transparency, and societal readiness. Could you elaborate on how Momentable approaches these areas to mitigate potential ethical dilemmas?

With great power comes great responsibility. AI is a powerful technology, and it’s very easy for those wielding it to amplify the impact of the good and the bad in the work they do.

While we, in the tech space, are doing our very best to build great products that deliver great value, we are not social scientists, psychologists, or public servants. So, we can’t be expected to regulate and supervise ourselves, nor can we evaluate the impact of these technologies and the products using them on the individual and on society.

It’s great when companies have values, codes of ethics, missions, and visions; however, those are not enough. Just like we do not rely on drivers to drive safely, we have traffic laws, signs, lights, and we make sure people driving a car are licensed and trained. We need to do the same with technologies, which, I would argue, have a massive impact on shaping our future as a species more than anything we’ve ever had in our history.

At Momentable, we are acutely aware of the impact of generative AI on our stakeholders, artists, cultural organizations, and art lovers. We engaged our stakeholders, ran several experiments where generative AI created artworks with input from artists, with their permission and consent.

In addition to using AI to enhance customer experience on our platform, we are using the learnings to evolve our product and introduce Generative AI in a thoughtful way that adds value and advances the art and culture space.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-hussein-hallak/ 

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With data-driven decision-making now the best competitive advantage a company can have, business leaders will increasingly demand to get the information they need at a faster, more consumable clip. Because of this, we’ll continue to see calls for AI to become a business-consumer-friendly product rather than one that only technically savvy data scientists and engineers can wield. It’s this vision for the future that’s driving the five trends in AI-driven analysis that we see right now:

Users demand an explainable approach to data analysis

As AI technology advances, understanding the processes behind its results can be challenging. This “black box” nature can lead to distrust and hinder AI adoption among non-technical business users. However, explainable AI (XAI) aims to democratize the use of AI tools and make it more accessible to business users.

XAI generates explanations for its analysis and leverages conversational language, coupled with compelling visualizations, so non-data experts can easily interpret its meaning. XAI will be crucial in the future of AI-driven data analysis by bridging the gap between the complex nature of advanced models and the human need for clear, understandable, and trustworthy outcomes.

Multimodal AI emerges

Multimodal AI is the ultimate tool for effective storytelling in today’s data-driven world. While Generative AI focuses on creating new content, Multimodal AI can be seen as an advanced extension of Generative AI with its ability to understand and tie together information coming from different media simultaneously. For example, a multimodal generative model could process text to create a story and enhance it with pertinent images and sounds.

As data sets become more complex and robust, it’s become difficult to comprehensively analyze that data using traditional methods. Multimodal AI gives analytics teams the ability to consume and analyze heterogeneous input so they can uncover critical information that leads to better strategic decision-making.

Across all AI-driven analytics trends, it is crucial to emphasize AI safety and ethical practices as fundamental aspects in all areas of the business. For instance, Ethical AI is essential to help ensure that AI technologies are beneficial, fair, and safe to use. That is because AI models can inadvertently perpetuate biases present in the training data. As AI becomes increasingly personalized, incorporating a wider variety of data inputs and innovations, it is crucial that responsible AI governance and training are implemented across all levels of the organization. When everyone understands both the advantages and limits of AI, the future truly becomes brighter for all.

To Know More, Read Full Article @ https://ai-techpark.com/five-key-trends-in-ai-driven-analysis/ 

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