Tag: Large Language Models

As we approach 2025, the technological landscape continues to evolve at an unprecedented pace. The rapid development of emerging technologies is poised to revolutionize industries ranging from transportation to healthcare over the next decade. Innovations like causal AI and next-generation large language models (LLMs) are set to transform traditional methods, enabling businesses across sectors to make accurate, data-driven decisions derived from experimentation and insights.

In this exclusive AITech Park article, we explore the perspective of Mridula Rahmsdorf, CRO at IKASI, on how the coming years hold immense promise for groundbreaking advancements that will redefine the way we work and interact.

Key Insights:

Integration of Causal AI in Decision-Making

The year 2025 and beyond will witness significant technological advancements as businesses incorporate causal AI alongside generative AI and LLMs. While current machine learning (ML) models remain invaluable, they are expected to undergo upgrades in the near future. Although causal AI has yet to enter the mainstream, experts predict it will enhance decision-making by improving accuracy, especially in scenarios involving complex, conflicting indicators. By understanding cause-and-effect relationships rather than mere correlations, organizations can leverage causal AI to bolster the reliability of generative AI, producing more coherent and relevant outcomes.

Expanding Critical Use Cases Across Industries

As confidence in causal inference grows, its integration with other AI technologies will unlock impactful use cases across various sectors. For example, in healthcare, causal AI can analyze patient history and lifestyle data to predict disease onset, enabling personalized treatment plans and interventions. Financial institutions can use it to develop sophisticated trading algorithms that adapt to market shifts, reducing risks and maximizing returns. Similarly, retailers can optimize pricing, loyalty programs, and promotions with unparalleled precision.

Growth in Community and Open-Source Development

Tech giants like Google, AWS, Uber, Netflix, and IBM are heavily investing in causal AI research, aiming to transition from correlative models to solutions that enable reasoning and real-time cause-and-effect analysis. Mridula highlights the role of open-source initiatives in democratizing access to advanced causal AI frameworks for startups, researchers, and public organizations with limited resources. However, open-source development faces challenges such as scalability, quality control, ethical considerations, and compliance, which require experienced teams and proven technologies for successful implementation.

To Know More, Read Full Article @ https://ai-techpark.com/technological-predictions-causal-ai/

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Large Language Models (LLMs) are transforming the business landscape, particularly in sales. These advanced AI tools harness data to deliver valuable insights, revolutionizing how sales teams interact with customers, generate leads, and develop innovative sales strategies. This article explores how LLMs enhance efficiency, personalization, and strategic depth in sales operations.

"LLMs are just beginning to revolutionize the sales process," said Logan Kelly. "While they currently automate routine tasks, their future potential lies in predicting customer needs, delivering hyper-personalized strategies at scale, and providing real-time insights to help sales teams outperform the competition. The next wave of LLM advancements will redefine customer engagement and enable sales teams to achieve unparalleled success."

Enhanced Personalization at Scale

One of the greatest challenges in sales is scaling personalized outreach. LLMs address this by analyzing vast data sets to create tailored communications, such as emails and conversations, that resonate with individual customers. By examining social media activity, published content, and company news, LLMs provide insights into a prospect’s digital footprint, enhancing engagement and improving conversion rates with personalized messaging.

Streamlined Research and Data Analysis

Market research and data analysis are foundational to the sales process. LLMs streamline these tasks by analyzing and summarizing massive data sets, offering actionable insights on market trends, competitor strategies, and potential leads. This enables sales teams to focus on strategic planning and execution rather than being overwhelmed by time-consuming data analysis.

Automated Lead Qualification

LLMs excel in automating lead qualification, a task traditionally prone to error and inefficiency. By leveraging natural language understanding, LLMs evaluate leads based on online behavior, engagement levels, and pain points. This ensures sales teams can prioritize high-potential leads, optimize resources, and maximize conversion opportunities.

Large Language Models are proving to be transformative tools for sales teams, delivering groundbreaking advancements in personalization, research, lead qualification, coaching, and CRM optimization. These AI-powered tools enable sales professionals to forge deeper customer connections, streamline processes, and achieve unprecedented success.

As sales operations evolve, LLMs are becoming indispensable, offering intelligent, efficient, and personalized solutions. The sales industry is undergoing a paradigm shift, and LLMs are at the forefront, driving innovation and empowering teams to excel in the modern business landscape.

To Know More, Read Full Article @ https://ai-techpark.com/leveraging-large-language-models/

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In a turn of events worthy of a sci-fi thriller, Large Language Models (LLMs) have surged in popularity over the past few years, demonstrating the adaptability of a seasoned performer and the intellectual depth of a subject matter expert.

These advanced AI models, powered by immense datasets and cutting-edge algorithms, have transformed basic queries into engaging narratives and mundane reports into compelling insights. Their impact is so significant that, according to a recent McKinsey survey, nearly 65% of organizations now utilize AI in at least one business function, with LLMs playing a pivotal role in this wave of adoption.

But are LLMs truly infallible? This question arose in June when we highlighted in a blog post how LLMs failed at seemingly simple tasks, such as counting the occurrences of a specific letter in a word like strawberry.

So, what’s the real story here? Are LLMs flawed? Is there more beneath the surface? Most importantly, can these vulnerabilities be exploited by malicious actors?

Let’s explore the top five ways in which LLMs can be exploited, shedding light on the risks and their implications.

Data Inference Attacks

Hackers can exploit LLMs by analyzing their outputs in response to specific inputs, potentially revealing sensitive details about the training dataset or the underlying algorithms. These insights can then be used to launch further attacks or exploit weaknesses in the model’s design.

Statistical Analysis: Attackers may use statistical techniques to discern patterns or extract inadvertently leaked information from the model’s responses.

Fine-Tuning Exploits: If attackers gain access to a model’s parameters, they can manipulate its behavior, increasing its vulnerability to revealing sensitive data.

Adversarial Inputs: Carefully crafted inputs can trigger specific outputs, exposing information unintentionally embedded in the model.

Membership Inference: This method involves determining whether a specific data sample was part of the model’s training dataset, which can expose proprietary or sensitive information.

As LLMs continue to transform industries with their capabilities, understanding and addressing their vulnerabilities is essential. While the risks are significant, disciplined practices, regular updates, and a commitment to security can ensure the benefits far outweigh the dangers.

Organizations must remain vigilant and proactive, especially in fields like cybersecurity, where the stakes are particularly high. By doing so, they can harness the full potential of LLMs while mitigating the risks posed by malicious actors.

To Know More, Read Full Article @ https://ai-techpark.com/top-2024-llm-risks/

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As we close out 2024, the pace of data visualization innovation will continue to accelerate. For B2B businesses, the ability to transform complex data into actionable insights is now a necessity rather than a luxury. Central to this evolution is Artificial Intelligence, which is reshaping dashboards and data visualizations to enable organizations to make faster, more impactful decisions. Looking ahead, 2025 is set to be a defining year for large language models (LLMs), real-time analytics, and advanced machine learning algorithms that will elevate AI-driven data visualizations. Here’s a toolkit and strategy guide to make your data dashboards shine while preparing you for the future.

The Evolution of Data Visualization: From Static to Smart

In 2024, data visualization has advanced from static, traditional dashboards to dynamic, AI-driven dashboards capable of generating real-time insights. Organizations are moving beyond basic charts and graphs, leveraging machine learning and AI tools for visualizations that predict needs, offer personalized data views, and integrate seamlessly with business operations.

This shift enables real-time, accurate, and accessible data visualizations. AI allows for automated insights, with natural language generation (NLG) tools simplifying complex data for easier comprehension. Organizations now have interactive, customizable dashboards featuring KPIs, trends, and forecasts that are quick to interpret and act on.

Building Your 2024 AI Data Visualization Toolkit

As 2025 approaches, companies need AI-powered tools to create advanced data dashboards. Here’s what should be in your toolkit for exceptional visualizations:

Real-Time Data Processing and Analysis Tools

Real-time data is now central to decision-making, allowing companies to bypass weekly or monthly reports for immediate insights. In 2024, AI engines process live data streams instantly, enabling faster, more responsive actions. Enhanced platforms like Power BI, Tableau, and Looker now perform real-time analyses on live data, detecting anomalies, identifying key insights, and even suggesting actions. This agility boosts customer experiences, optimizes operations, and supports rapid, insight-driven decisions.

Predictive Analytics and Forecasting Algorithms

Predictive analytics, powered by machine learning and AI, enhances an organization’s ability to anticipate trends. By analyzing historical patterns, AI-enabled dashboards forecast behavior, sales fluctuations, and potential market shifts. As 2025 approaches, forecasting with AI is becoming critical. Tools like Google Analytics, IBM Watson, and Microsoft Azure employ powerful algorithms to deliver data-driven predictions, such as forecasting customer demand or predicting churn. AI-driven forecasting enables organizations to stay ahead of changes rather than reacting to them.

To Know More, Read Full Article @ https://ai-techpark.com/2025-ai-data-visualization-toolkit-for-b2b/

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Dev, can you start by sharing the journey that led you to establish QueryPal and what inspired you to focus on transforming customer support through AI-powered ticket automation?

The journey to QueryPal began with my experiences at Google and PayPal, where I saw firsthand the challenges of scaling customer support. I realized that while AI was transforming many industries, customer support remained largely unchanged. The inspiration came from seeing how Large Language Models (LLMs) could understand and generate human-like text. I knew we could leverage this technology to revolutionize customer support, making it more efficient and effective. QueryPal was born from the vision of creating an AI system that could understand customer inquiries at a deep level and provide accurate, helpful responses at scale.

How has AI enhanced the accuracy of customer support responses at QueryPal, and what role does it play in improving response times and customer satisfaction?

AI has dramatically enhanced the accuracy of customer support responses at QueryPal. Our advanced natural language understanding allows us to comprehend the nuances of customer inquiries, including context and intent. This leads to more precise and relevant responses. Moreover, our AI can access and synthesize information from vast knowledge bases in seconds, providing comprehensive answers faster than any human could. This improvement in both accuracy and speed has led to significant increases in customer satisfaction scores for our clients. We’re also in the early stages of researching Causal AI, which could enable our system to understand cause-and-effect relationships in customer issues, potentially allowing it to reason about novel situations it hasn’t explicitly seen in training data.

Personalized customer support is a significant advancement in customer service. Can you explain how AI-powered systems at QueryPal tailor responses to individual customer inquiries?

Personalization in QueryPal’s AI system operates on multiple levels. First, it considers the customer’s context, including channel metadata. Second, it analyzes the specific language and tone of the current inquiry. Finally, it takes into account how past responses for similar questions have satisfied customers. By combining these factors, our AI can tailor responses that not only answer the specific question but also address potential underlying concerns, use appropriate language and tone, and even anticipate follow-up questions. Personalization in QueryPal’s AI system is already advanced, but we’re excited about the potential of Agentic AI. We’re in the process of integrating this technology, which could allow our system to handle complex, multi-step tasks with minimal human specification. In the future, it might be able to understand the broader context of a customer’s journey, anticipate needs, and even take proactive steps to resolve issues before they escalate.

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

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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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The recent ransomware attack on Change Healthcare, a subsidiary of UnitedHealth Group, has highlighted critical vulnerabilities within the healthcare sector. This incident disrupted the processing of insurance claims, causing significant distress for patients and providers alike. Pharmacies struggled to process prescriptions, and patients were forced to pay out-of-pocket for essential medications, underscoring the urgent need for robust cybersecurity measures in healthcare.

The urgency of strengthening cybersecurity is not limited to the United States. In India, the scale of cyber threats faced by healthcare institutions is even more pronounced. In 2023 alone, India witnessed an average of 2,138 cyber attacks per week on each organization, a 15% increase from the previous year, positioning it as the second most targeted nation in the Asia Pacific region. A notable incident that year involved a massive data breach at the Indian Council of Medical Research (ICMR), which exposed sensitive information of over 81.5 crore Indians, thereby highlighting the global nature of these threats.

This challenge is not one that funding alone can solve. It requires a comprehensive approach that fights fire with fire—or, in modern times, staves off AI attacks with AI security. Anything short of this leaves private institutions, and ultimately their patients, at risk of losing personal information, limiting access to healthcare, and destabilising the flow of necessary medication. Attackers have shown us that the healthcare sector must be considered critical infrastructure.

The Healthcare Sector: A Prime Target for Cyberattacks

Due to the sensitive nature of the data it handles, the healthcare industry has become a primary target for cybercriminals. Personal health information (PHI) is precious on the black market, making healthcare providers attractive targets for ransomware attacks—regardless of any moral ground they may claim to stand on regarding healthcare.

In 2020, at the beginning of the pandemic, hospitals were overrun with patients, and healthcare systems seemed to be in danger of collapsing under the strain. It was believed that healthcare would be a bridge too far at the time. Hacking groups DoppelPaymer and Maze stated they “[D]on’t target healthcare companies, local governments, or 911 services.” If those organisations accidentally became infected, the ransomware groups’ operators would supply a free decryptor.

Since AI technology has advanced and medical device security lags, the ease of attack and the potential reward for doing so have made healthcare institutions too tempting to ignore. The Office of Civil Rights (OCR) at Health and Human Services (HHS) is investigating the Change Healthcare attack to understand how it happened. The investigation will address whether Change Healthcare followed HIPAA rules. However, in past healthcare breaches, HIPAA compliance was often a non-factor. Breaches by both Chinese nationals and various ransomware gangs show that attackers are indifferent to HIPAA compliance.

To Know More, Read Full Article @ https://ai-techpark.com/cybersecurity-urgency-in-healthcare/

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Large Language Models (LLMs) are considered to be an AI revolution, altering how users interact with technology and the world around us. Especially with deep learning algorithms in the picture data, professionals can now train huge datasets that will be able to recognize, summarize, translate, predict, and generate text and other types of content.

As LLMs become an increasingly important part of our digital lives, advancements in natural language processing (NLP) applications such as translation, chatbots, and AI assistants are revolutionizing the healthcare, software development, and financial industries.

However, despite LLMs’ impressive capabilities, the technology has a few limitations that often lead to generating misinformation and ethical concerns.

Therefore, to get a closer view of the challenges, we will discuss the four limitations of LLMs devise a decision to eliminate those limitations, and focus on the benefits of LLMs.

Limitations of LLMs in the Digital World

We know that LLMs are impressive technology, but they are not without flaws. Users often face issues such as contextual understanding, generating misinformation, ethical concerns, and bias. These limitations not only challenge the fundamentals of natural language processing and machine learning but also recall the broader concerns in the field of AI. Therefore, addressing these constraints is critical for the secure and efficient use of LLMs.

Let’s look at some of the limitations:

Contextual Understanding

LLMs are conditioned on vast amounts of data and can generate human-like text, but they sometimes struggle to understand the context. While humans can link with previous sentences or read between the lines, these models battle to differentiate between any two similar word meanings to truly understand a context like that. For instance, the word “bark” has two different meanings; one “bark” refers to the sound a dog makes, whereas the other “bark” refers to the outer covering of a tree. If the model isn’t trained properly, it will provide incorrect or absurd responses, creating misinformation.

Misinformation

Even though LLM’s primary objective is to create phrases that feel genuine to humans; however, at times these phrases are not necessarily to be truthful. LLMs generate responses based on their training data, which can sometimes create incorrect or misleading information. It was discovered that LLMs such as ChatGPT or Gemini often “hallucinate” and provide convincing text that contains false information, and the problematic part is that these models point their responses with full confidence, making it hard for users to distinguish between fact and fiction.

To Know More, Read Full Article @ https://ai-techpark.com/limitations-of-large-language-models/

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In a shocking revelation, a massive data leak has exposed sensitive personal information of over 1.6 million individuals, including Indian military personnel, police officers, teachers, and railway workers. This breach, discovered by cybersecurity researcher Jeremiah Fowler, included biometric data, birth certificates, and employment records and was linked to the Hyderabad-based companies ThoughtGreen Technologies and Timing Technologies.

While this occurrence is painful, it is far from shocking.

The database, containing 496.4 GB of unprotected data, was reportedly found to be available on a dark web-related Telegram group. The exposed information included facial scans, fingerprints, identifying marks such as tattoos or scars, and personal identification documents, underscoring a growing concern about the security protocols of private contractors who manage sensitive government data.

The impact of such breaches goes far beyond what was capable years ago. In the past, stolen identity would have led to the opening of fake credit cards or other relatively containable incidents. Today, a stolen identity that includes biometric data or an image with personal information is enough for threat actors to create a deep fake and sow confusion amongst personal and professional colleagues. This allows unauthorised personnel to gain access to classified information from private businesses and government agencies, posing a significant risk to national security.

Deepfakes even spread fear throughout southeast Asia, specifically during India’s recent Lok Sabha, during which 75% of potential voters reported being exposed to the deceitful tool.

The Risks of Outsourcing Cybersecurity

Governments increasingly rely on private contractors to manage and store vast amounts of sensitive data. However, this reliance comes with significant risks. Private firms often lack the robust cybersecurity measures that government systems can implement.

However, with India continuing to grow as a digital and cybersecurity powerhouse, the hope was that outsourcing the work would save taxpayers money while providing the most advanced technology possible.

However, a breach risks infecting popular software or other malicious actions such as those seen in other supply chain attacks, which are a stark reminder of the need for stringent security measures and regular audits of third-party vendors.

To Know More, Read Full Article @ https://ai-techpark.com/ai-secures-global-data/

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Leslie, can you please introduce yourself and share your experience as a CEO and Founder at TurinTech?

As you say, I’m the CEO and co-founder at TurinTech AI. Before TurinTech came into being, I worked for a range of financial institutions, including Credit Suisse and Bank of America. I met the other co-founders of TurinTech while completing my Ph.D. in Computer Science at University College London. I have a special interest in graph theory, quantitative research, and efficient similarity search techniques.

While in our respective financial jobs, we became frustrated with the manual machine learning development and code optimization processes in place. There was a real gap in the market for something better. So, in 2018, we founded TurinTech to develop our very own AI code optimization platform.

When I became CEO, I had to carry out a lot of non-technical and non-research-based work alongside the scientific work I’m accustomed to. Much of the job comes down to managing people and expectations, meaning I have to take on a variety of different areas. For instance, as well as overseeing the research side of things, I also have to understand the different management roles, know the financials, and be across all of our clients and stakeholders.

One thing I have learned in particular as a CEO is to run the company as horizontally as possible. This means creating an environment where people feel comfortable coming to me with any concerns or recommendations they have. This is really valuable for helping to guide my decisions, as I can use all the intel I am receiving from the ground up.

To set the stage, could you provide a brief overview of what code optimization means in the context of AI and its significance in modern businesses?

Code optimization refers to the process of refining and improving the underlying source code to make AI and software systems run more efficiently and effectively. It’s a critical aspect of enhancing code performance for scalability, profitability, and sustainability.

The significance of code optimization in modern businesses cannot be overstated. As businesses increasingly rely on AI, and more recently, on compute-intensive Generative AI, for various applications — ranging from data analysis to customer service — the performance of these AI systems becomes paramount.

Code optimization directly contributes to this performance by speeding up execution time and minimizing compute costs, which are crucial for business competitiveness and innovation.

For example, recent TurinTech research found that code optimization can lead to substantial improvements in execution times for machine learning codebases — up to around 20% in some cases. This not only boosts the efficiency of AI operations but also brings considerable cost savings. In the research, optimized code in an Azure-based cloud environment resulted in about a 30% cost reduction per hour for the utilized virtual machine size.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-leslie-kanthan/ 

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