Tag: Large Language Models

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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The intersection of artificial intelligence (AI) and digital advertising to create truly engaging experiences across global audiences and cultures is reaching an inflection point. Companies everywhere are leveraging powerful trends in AI, machine learning and apps for performance marketing.

Today’s AI and machine learning technologies are allowing apps to understand speech, images, and user behavior more naturally. As a result, apps with AI capabilities are smarter and more helpful, and companies are using these technologies to create tailored experiences for customers, regardless of language or background. AI is leveling the playing field by making advanced data tools accessible to anyone, not just data scientists.

Kochava has incorporated AI and machine learning across our diverse solutions portfolio for years, such as within our advanced attribution and fraud prevention products. We have also adopted advanced technologies, like large language models (LLMs) to develop new tools.

Many organizations are instituting internal restructuring with a focus on enhancing the developer experience. The aim is to leverage the full potential of AI for smart applications, providing universal access to advanced tech tools, while adapting to changes in app store policies. Engineering teams are spearheading the development of self-service platforms managed by product teams. The primary objective is to optimize developers’ workflows, speeding up the delivery of business value, and reducing stress. These changes improve the developer experience which can help companies retain top talent.

From an overall organizational structure perspective, in pursuit of a more efficient and effective approach, Kochava is focused on enhancing developer experiences, leveraging AI for intelligent applications, democratizing access to advanced technologies, and adapting to regulatory changes in app marketplaces.

Reimagining the Future

The software and applications industry is one that evolves particularly quickly. The app market now represents a multibillion-dollar sector exhibiting no signs of slowing. This rapid growth and constant change presents abundant opportunities for developers to build innovative new applications while pursuing their passions. For app developers, monitoring trends provides inspiration for maintaining engaging, innovative user experiences.

As AI integration increases, standards will develop to ensure AI can automatically interface between applications. It will utilize transactional and external data to provide insights. Applications will shift from set features to AI-driven predictions and recommendations tailored for each user. This advances data-driven decision making and transforms the experience for customers, users, teams, and developers.

To Know More, Read Full Article @ https://ai-techpark.com/generative-ai-marketing-trends/ 

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As we have stepped into the realm of 2024, the artificial intelligence and data landscape is growing up for further transformation, which will drive technological advancements and marketing trends and understand enterprises’ needs. The introduction of ChatGPT in 2022 has produced different types of primary and secondary effects on semantic technology, which is helping IT organizations understand the language and its underlying structure.

For instance, the semantic web and natural language processing (NLP) are both forms of semantic technology, as each has different supportive rules in the data management process.

In this article, we will focus on the top four trends of 2024 that will change the IT landscape in the coming years.

Reshaping Customer Engagement With Large Language Models

The interest in large language models (LLMs) technology came to light after the release of ChatGPT in 2022. The current stage of LLMs is marked by the ability to understand and generate human-like text across different subjects and applications. The models are built by using advanced deep-learning (DL) techniques and a vast amount of trained data to provide better customer engagement, operational efficiency, and resource management.

However, it is important to acknowledge that while these LLM models have a lot of unprecedented potential, ethical considerations such as data privacy and data bias must be addressed proactively.

Importance of Knowledge Graphs for Complex Data

The introduction of knowledge graphs (KGs) has become increasingly essential for managing complex data sets as they understand the relationship between different types of information and segregate it accordingly. The merging of LLMs and KGs will improve the abilities and understanding of artificial intelligence (AI) systems. This combination will help in preparing structured presentations that can be used to build more context-aware AI systems, eventually revolutionizing the way we interact with computers and access important information.

As KGs become increasingly digital, IT professionals must address the issues of security and compliance by implementing global data protection regulations and robust security strategies to eliminate the concerns.  

Large language models (LLMs) and semantic technologies are turbocharging the world of AI. Take ChatGPT for example, it's revolutionized communication and made significant strides in language translation.

But this is just the beginning. As AI advances, LLMs will become even more powerful, and knowledge graphs will emerge as the go-to platform for data experts. Imagine search engines and research fueled by these innovations, all while Web3 ushers in a new era for the internet.

To Know More, Read Full Article @ https://ai-techpark.com/top-four-semantic-technology-trends-of-2024/ 

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Shaun, could you please introduce yourself and elaborate your role as a CEO of NINJIO?

I’m Shaun McAlmont, CEO of NINJIO Cybersecurity Awareness Training. I came to NINJIO after decades leading organizations in higher education and workforce development, so my specialty is in building solutions that get people to truly learn.

Our vision at NINJIO is to make everyone unhackable, and I lead an inspiring team that approaches cybersecurity awareness training as a real opportunity to reduce organizations’ human-based cyber risk through technology and educational methodologies that really change behavior.

Can you share insights into the most underestimated or lesser-known cyber threats that organisations should be aware of?

The generative AI boom we’re experiencing now is a watershed moment for the threat landscape. I think IT leaders have a grasp of the technology but aren’t fully considering how that technology will be used by hackers to get better at manipulating people in social engineering attacks. Despite the safeguards the owners of large language models are implementing, bad actors can now write more convincing phishing emails at a massive scale. They can deepfake audio messages to bypass existing security protocols. Or they can feed a few pages of publicly available information from a company’s website and a few LinkedIn profiles into an LLM and create an extremely effective spearphishing campaign.

These aren’t necessarily new or lesser-known attack vectors in cybersecurity. But they are completely unprecedented in how well hackers can pull them off now that they’re empowered with generative AI.

With the rise of ransomware attacks, what steps can organisations take to better prepare for and mitigate the risks associated with these threats?

The first and biggest step to mitigating that risk is making sure that everyone in an organization is aware of it and can spot an attack when they see one. It took a ten-minute phone call for a hacking collective to breach MGM in a ransomware attack that the company estimates will cost it over $100 million in lost profits. Every person at an organization with access to a computer needs to be well trained to spot potential threats and be diligent at confirming the validity of their interactions, especially if they don’t personally know the individual with whom they’re supposedly speaking. The organizational cybersecurity culture needs to extend from top to bottom.

Building that overarching cultural change requires constant vigilance, a highly engaging program, and an end-to-end methodological approach that meets learners where they are and connects the theoretical to the real world.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-dr-shaun-mcalmont-ceo-at-ninjio/ 

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To start, Daniel, could you please provide a brief introduction to yourself and your work at Kognic?

 I’m an experienced machine-learning expert and passionate about making AI useful for safety critical applications. As CEO and Co-Founder of Kognic, I lead a team of data scientists, developers and industry experts. The Kognic Platform empowers industries from autonomous vehicles to robotics – Embodied AI as it is called – to accelerate their AI product development and ensure AI systems are trusted and safe.

Prior to founding Kognic, I worked as a Team Lead for Collection & Analysis at Recorded Future, gaining extensive experience in delivering machine learning solutions at a global scale and I’m also a visiting scholar at both MIT and UC Berkeley.

Could you share any real-world examples or scenarios where AI alignment played a critical role in decision-making or Embodied AI system behaviour?

One great example within the automotive industry and the development of autonomous vehicles, starts with a simple question: ‘what is a road?’

The answer can actually vary significantly, depending on where you are in the world, the topography of the area you are in and what kind of driving habits you lean towards. For these factors and much more, aligning and agreeing on what is a road is far easier said than done.

So then, how can an AI product or autonomous vehicle make not only the correct decision but one that aligns with human expectations? To solve this, our platform allows for human feedback to be efficiently captured and used to train the dataset used by the AI model.

Doing so is no easy task, there’s huge amounts of complex data an autonomous vehicle is dealing with, from multi-sensor inputs from a camera, LiDAR, and radar data in large-scale sequences, highlighting not only the importance of alignment but the challenge it poses when dealing with data.

Teaching machines to align with human values and intentions is known to be a complex task. What are some of the key techniques or methodologies you employ at Kognic to tackle this challenge?

Two key areas of focus for us are machine accelerated human feedback and the refinement and fine-tuning of data sets.

First, without human feedback we cannot align AI systems, our dataset management platform and its core annotation engine make it easy and fast for users to express opinions about this data while also enabling easy definition of expectations.

The second key challenge is making sense of the vast swathes of data we require to train AI systems. Our dataset refinement tools help AI product teams to surface both frequent and rare things in their datasets. The best way to make rapid progress in steering an AI product is to focus on that which impacts model performance. In fact, most teams find tons of frames in their dataset that they hadn’t expected with objects they don’t need to worry about – blurry images at distances that do not impact the model. Fine-tuning is essential to gaining leverage on model performance.  

To Know More, Read Full Article @ https://ai-techpark.com/aitech-interview-with-daniel-langkilde/ 

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