Categoria: Management

Large Language Models (LLMs) have become a transformative force in artificial intelligence, showcasing remarkable abilities in natural language processing and generation. Their capacity to understand, interpret, and produce human-like text has unlocked new possibilities across various sectors, including healthcare, finance, customer service, and entertainment. According to McKinsey, generative AI technologies like LLMs are expected to contribute trillions to the global economy.

However, developing advanced LLMs requires more than just cutting-edge algorithms—it also demands significant computational resources. This guide serves as a roadmap, offering insights into the complex process of LLM development, equipping you with the knowledge and tools to overcome challenges and build high-performance models.

Data Drives Development

LLMs rely on vast amounts of data, and creating multilingual support can be particularly challenging. Building a multi-stage data pipeline is time-consuming but crucial. Ensuring data quality and reproducibility requires robust data lineage tracking tools to monitor data origins and modifications. Additionally, versioning tools are essential to maintain consistency and manage updates across different preprocessing stages.

Transforming raw data into various formats for processing requires careful tracking of data pipeline versions. This allows teams to experiment with different approaches and easily revert to previous configurations when needed. Open-source tools like Spark help scale data processing across multiple machines, while tools like Airflow and Prefect are vital for orchestrating complex data pipelines.

Scaling the Challenge

Scaling an LLM effectively involves testing various tools and techniques to manage the challenges of training data, model selection, and workload distribution. Developers must manage data quality, choose the right models, optimize computational resources, and distribute workloads efficiently to ensure smooth scaling.

It’s crucial to keep detailed records for reproducibility and track how changes in the training process affect results. Tools like MLFlow or Weights and Biases can help with versioning, tracking, and evaluating experiments. Researchers should start small—using around 8 GPUs to test feasibility—and gradually scale up to 32-64 GPUs for a day to validate scalability, then to 128 or more GPUs for week-long training to ensure robustness.

Creating an advanced LLM is a complex process that requires technical expertise, strategic planning, and perseverance. By mastering data curation, efficient scaling, and fine-tuning, you can build LLMs that deliver outstanding performance and generate significant business value. As the AI field evolves rapidly, staying up-to-date with LLM research and development is essential to maintain a competitive advantage.

To Know More, Read Full Article @ https://ai-techpark.com/crafting-high-performance-llms/

Related Articles -

5 Best Data Lineage Tools 2024

Top Five Open-Source Database Management Software

Marketing communication has evolved dramatically over the past decade. As customer expectations rise, they now demand highly personalized, on-demand solutions at an organizational level. This is where artificial intelligence (AI), particularly conversational AI, comes into play. But is AI fully leveraging its potential to transform business-customer relationships? Let’s explore how Conversational AI is disrupting the landscape and why it’s rapidly becoming an essential tool across industries.

Intelligent Conversations Everywhere

With customers taking an active role in their interactions with businesses, interfaces have become more sophisticated. It's no longer enough for customers to receive an immediate answer; they expect the right answer at the right time, tailored to their specific needs. Conversational AI fills this gap by delivering quick, optimized conversations that cater to each customer.

AI-driven communication tools range from simple live chatbots to advanced voice interfaces, revolutionizing how businesses handle customer inquiries. In B2B environments, where customer engagement is more complex, AI provides fast, personalized assistance while maintaining high efficiency. Gone are the days of generic, context-devoid FAQ responses—modern customer service now harnesses the power of AI.

Moving Beyond Automation to Real Personalization

Rehearsed script-based responses are no longer sufficient. Conversational AI, enhanced with natural language processing (NLP), takes customer interaction to the next level. It understands language nuances, enabling it to interpret sentiment, intent, and context, providing personalized communication.

This is especially crucial in sectors like healthcare and finance, where customers seek convenience, expert advice, and empathy. Imagine a healthcare scenario where AI can swiftly analyze a patient's medical history and recommend solutions, or in finance, where AI offers personalized financial advice or market predictions.

AI-driven personalization has shifted from being optional to essential for customer satisfaction and loyalty. Today’s customers expect businesses to have a deep understanding of their needs, and conversational AI is stepping up to meet those expectations.

Proactive Support Models

Traditional customer support models are reactive, waiting for customers to voice their issues. Conversational AI, however, enables businesses to take a proactive approach. With advanced analytics, AI can predict potential issues and suggest solutions before they escalate.

For example, by analyzing interaction patterns and frequently asked questions, AI can trigger notifications or recommendations to prevent downtime. This proactive approach not only addresses concerns before they grow but also builds customer satisfaction and trust.

This shift in customer service isn’t just about faster problem resolution; it’s about anticipating customer needs and addressing them before complaints arise. This proactive model positively impacts long-term customer relationships and strengthens brand reputation.

To Know More, Read Full Article @ https://ai-techpark.com/how-conversational-ai-revolutionizes-customer-engagement/

Related Articles -

Evolution of Lakehouse Architecture

Platform Engineering Tools 2024

Banks suffered an astounding $485.6 billion loss to fraud and scams last year, highlighting the urgent need for them to outpace criminals. Fraud analytics plays a crucial role in enabling banks to transition from merely reacting to fraud to proactively preventing it.

Explore how fraud analytics helps detect and prevent various types of fraud, minimizing financial losses and improving customer trust and satisfaction.

What is Fraud Analytics?

Fraud analytics combines artificial intelligence (AI), machine learning, and predictive analytics to enable advanced data analysis. By leveraging these technologies, banks can quickly analyze and gain insights from vast amounts of data.

The integration of technological analytics with human expertise provides numerous benefits, including identifying fraud, uncovering hidden patterns, and predicting future threats. Most importantly, it allows banks to respond to suspicious activities in real time.

Why Banks Need Fraud Analytics

The rise of digital banking has been exponential, particularly during the pandemic, when in-person banking became less accessible. This shift has generated massive volumes of digital data, creating new opportunities for fraudsters to exploit vulnerabilities in banking systems.

Each new digital banking channel introduces a wave of fraud tactics. Traditional rules-based systems often fail to keep up, as fraudsters quickly adapt to and bypass established rules. This leaves banks in a continuous cycle of reacting to new schemes.

Fraud analytics breaks this cycle by proactively analyzing large data sets in real time, identifying unusual patterns that traditional systems might miss. This enables banks to calculate accurate transaction risk scores and make informed decisions before approving transactions.

Key Benefits of Fraud Analytics

Predict Future Fraud Risks

Fraud analytics shifts banks from reactive to proactive strategies by using machine learning models to analyze historical data and predict potential fraud patterns. This forward-looking approach helps banks stay ahead of fraudsters and prevent issues before they escalate.

Real-Time Fraud Detection to Minimize Losses

Fraud analytics systems use pattern recognition and real-time monitoring to swiftly detect anomalies, reducing the impact of fraudulent activities. Early detection protects both the bank and its customers from significant financial losses.

Enhanced Customer Trust and Satisfaction

Proactive fraud prevention reassures customers that their funds and sensitive information are secure. This fosters greater trust and satisfaction, strengthening the relationship between banks and their clients.

To Know More, Read Full Article @ https://ai-techpark.com/fraud-analytics-powered-by-ai/

Related Articles -

Deep Learning in Big Data Analytics

Rise of Deepfake Technology

As systems grow increasingly complex and interconnected, the challenges facing DevOps teams become more intricate. Hybrid infrastructures, microservices, and real-time operations strain traditional tools, paving the way for artificial intelligence to revolutionize how DevOps operates.

This evolution isn’t just about automation—it’s about reimagining how teams monitor and respond to issues in dynamic environments. AI promises smarter, faster, and more efficient DevOps processes, particularly in monitoring and incident response.

When Traditional Tools Are Insufficient

DevOps has always focused on collaboration and optimizing speed. However, with the rise of multi-cloud environments, diverse applications, and distributed teams, traditional monitoring tools often fall short. Static thresholds fail to adapt dynamically to increasing demands, leading to unnecessary delays due to manual intervention.

Take, for instance, monitoring a microservices architecture. A single service failure can cascade across the entire system, yet traditional tools may flag the issue too late—or miss it entirely. AI excels here by analyzing massive data sets in real time, detecting patterns, and addressing potential failures proactively.

AI shifts monitoring from reactive to proactive. It doesn’t just alert teams to problems—it predicts them, enabling businesses to prevent disruptions altogether.

Smarter Monitoring with AI

AI-powered monitoring systems go beyond tracking metrics. They observe, learn, and adapt, making them indispensable for DevOps teams managing complex infrastructures.

These systems detect anomalies early, identifying even subtle deviations from normal operations. Dynamic, AI-driven thresholds replace outdated static benchmarks, adapting to real-time changes in system behavior. Additionally, AI simplifies complex data by presenting insights through intuitive visualizations, enabling teams to make swift, informed decisions.

For organizations running continuous integration and deployment pipelines, this capability is transformative. AI identifies bottlenecks in code, optimizes workflows, and enhances system reliability—all before users experience any impact.

But monitoring is only part of the equation. What happens when something goes wrong?

The Future of AI in DevOps

The future of AI in DevOps is incredibly promising. Imagine self-healing systems that detect and resolve issues with minimal human input. Predictive analytics will advance significantly, enabling companies to anticipate and mitigate threats before they arise.

AI will also improve communication between technical teams and business leaders, fostering collaboration and alignment. This synergy will drive innovation and operational excellence, positioning organizations for long-term success.

To Know More, Read Full Article @ https://ai-techpark.com/ai-devops-monitoring-incident-response/

Related Articles -

Data Literacy in the Digital Age

AI-powered Mental Health workplace Strategies

Feedzai, the world’s first RiskOps platform, has achieved two significant accolades from Chartis Research. We are proud to be recognized as the leading AI-driven anti-fraud platform and to rank among the top 5 overall in the prestigious RiskTech AI 50 2024 rankings.

These achievements underscore Feedzai’s pioneering role in leveraging artificial intelligence and machine learning to advance financial risk management. With an AI-first approach, our technology is designed to swiftly adapt to emerging fraud and scam patterns.

Feedzai Among Top 5 in RiskTech AI 50 2024 Rankings

It is a privilege to be named the top AI-driven anti-fraud platform in Chartis Research’s RiskTech AI 50 2024 report. This recognition reflects our unwavering commitment to empowering the financial sector with real-time fraud detection and prevention, delivering unmatched precision through state-of-the-art AI and machine learning.

We are equally honored to place #4 overall in AI, a notable achievement in a highly competitive industry. This ranking highlights Feedzai’s innovative approach to combating fraud and financial crime.

Driving Financial Services with an AI-First Approach

Feedzai’s recognition stems from its AI-first foundation. From the very beginning, we have built our platform with AI at its core, ensuring our models are flexible, responsible, and well-governed—key differentiators in fraud and financial crime prevention.

Unlike many legacy systems developed before the rise of AI, Feedzai’s technology was purpose-built for modern challenges. Traditional systems often rely solely on rules-based models, which, while effective against established fraud patterns like account takeovers or card-not-present fraud, struggle with evolving threats such as authorized push payment scams.

Understanding customer behavior is critical in addressing complex, individualized fraud scenarios like elder fraud or purchase scams. Rules-based systems alone often fall short in these nuanced cases.

Feedzai’s patented technologies are tailored exclusively for fraud and financial crime prevention. At the heart of our platform is AutoML, which accelerates the deployment of machine learning models from weeks or months to mere days, streamlining the fight against fraud.

Continued Recognition for Feedzai’s Innovative AI Technology

The acknowledgment from Chartis Research reinforces Feedzai’s dedication to safeguarding commerce and financial services through advanced AI and machine learning. This honor adds to a series of accolades affirming our leadership in fraud prevention.

Recently, Feedzai was named a Leader in the 2024 IDC Worldwide Enterprise Fraud Solutions Vendor Assessment. The IDC MarketScape report highlighted our omnichannel capabilities, enabling real-time monitoring of customer interactions and transactions to enhance accuracy and improve customer experiences.

Additionally, Feedzai was recognized as a Leading Contender in Datos Insights’ Behavioral Biometrics Market Analysis for innovations in behavioral biometrics and device fingerprinting. Our biometrics solution also earned recognition in Quadrant Solutions’ SPARK Matrix™: Behavioral Biometrics, 2023 report.

To Know More, Read Full Article @ https://ai-techpark.com/feedzai-ranks-top-5-in-risktech-ai-50-2024/

Related Articles -

Top Five Open-Source Database Management Software

Intersection of AI And IoT

Fraud is already a complex challenge, but merchants face an additional hurdle: first-party fraud perpetrated by their own customers. Unlike second- or third-party fraud, first-party fraud occurs when consumers use their legitimate payment credentials to commit dishonest acts for personal gain. This creates a significant challenge for acquiring banks and payment service providers (PSPs) in assisting merchants with fraud prevention.

To effectively support merchants, acquiring banks must develop a thorough understanding of how first-party fraud operates. This article explores how acquirers and PSPs can help merchants mitigate first-party fraud and protect their profits.

What is First-Party Fraud?

First-party fraud involves a consumer intentionally defrauding a merchant for personal or financial gain. In these cases, the consumer obtains goods or services without paying for them. Customers may commit this type of fraud for various reasons, discussed below.

Often referred to as "friendly fraud" or "first-party misuse," industry leaders like the Merchant Risk Council advocate using the term “first-party misuse” to emphasize the seriousness of these actions. The rationale is simple: there’s nothing friendly about fraud. Over time, “first-party misuse” is expected to replace “friendly fraud” as the standard term.

First-party fraud (or misuse) is particularly challenging for businesses because it originates from legitimate customers, complicating detection and prevention efforts. It’s akin to realizing that “the call is coming from inside the house.”

Six Common Types of First-Party Fraud

Chargeback Fraud

Customers dispute legitimate transactions after receiving goods or services, requesting refunds or chargebacks through their financial institution. Merchants ultimately bear the financial loss.

Buyer’s Remorse

After making a legitimate purchase, a customer regrets it and, unable to return the item, requests a refund or chargeback.

Family Fraud

A household member, often a child, makes unauthorized purchases using a parent’s payment credentials. The parent disputes the charges, resulting in a chargeback.

Return Fraud

Customers exploit return policies by returning used, stolen, or counterfeit items for refunds or store credit.

Coupon/Discount Abuse

Customers manipulate promotional offers or create multiple accounts to exploit first-time customer discounts.

Unrecognized Transactions

Customers dispute charges they don’t recognize, often due to unclear billing descriptions or subscription renewals after free trial periods.

To Know More, Read Full Article @ https://ai-techpark.com/first-party-fraud-insights/

Related Articles -

AI Trust, Risk, and Security Management

Evolution of Lakehouse Architecture

Acquiring banks often face the challenge of balancing merchant satisfaction with risk management. On one hand, they aim to keep merchants happy by enabling quick payouts. On the other, they must protect themselves from financial losses if a merchant's risk profile unexpectedly shifts. Dynamic risk assessment plays a crucial role in safeguarding acquirers while supporting businesses with the liquidity they need to operate smoothly.

Here’s how Feedzai’s Dynamic Risk Assessment, available as an add-on to its Merchant Monitoring solution, helps acquirers mitigate risk while ensuring merchants maintain access to vital cash flow.

The Cash Flow Challenge for Merchants

Cash flow is essential for merchants, especially small businesses, to sustain operations. Access to funds allows merchants to replenish inventory, pay employees, settle utility bills, and maintain vendor relationships.

However, studies reveal that nearly 75% of merchants frequently experience delayed payouts. Without timely access to their earnings, many businesses face severe cash shortages, threatening their survival.

To address this issue, some acquirers are adopting same-day payouts for merchants in good standing. In markets like Australia and Brazil, payouts are even offered intra-day or on-demand. While these measures benefit merchants, they also increase financial exposure for acquirers if a merchant’s risk level is underestimated.

Current Merchant Risk Management Approaches

Acquirers typically employ several strategies to balance risk management and merchant needs. Each has its advantages and limitations:

Increased Merchant Deposit Requirements

Pros: Helps acquirers offset liability risks by requiring merchants to maintain larger reserves.

Cons: Reduces merchants' available cash flow, making it harder to cover operational expenses.

Faster Settlements for Merchants in Good Standing

Pros: Rewards merchants with a strong track record, offering quicker access to funds.

Cons: Excludes newer merchants who lack a year’s history, even if they meet other risk criteria.

Transaction Data Analysis

Pros: Provides insights into individual transaction risks.

Cons: Focuses on transaction-level data, often missing aggregated risk signals and relying on manual processes prone to human error.

To Know More, Read Full Article @ https://ai-techpark.com/smart-merchant-risk-strategies/

Related Articles -

Rise of Low-Code and No-Code

Data Loss Prevention in Digital World

Delivering an improved digital employee experience (DEX) has become a top priority for many enterprise IT leaders, as it directly influences productivity, employee morale, and other critical aspects of business success. However, many organizations still lack the necessary visibility into their IT ecosystems to fully understand how digital tools impact employee experiences and productivity. This gap often hinders efforts to effectively manage the digital workplace and provide employees with an exceptional experience.

To gain deeper insights into these challenges, Lakeside Software conducted a survey of IT leaders, exploring their perspectives on DEX and the obstacles and opportunities they face in today’s workplace. Here are the key findings:

Doing More with Less: The New Normal

In the face of economic uncertainty, 65% of IT leaders identified "doing more with less" as their primary focus. The pressure to streamline operations while leveraging technology to maintain productivity and employee satisfaction is a challenge many leaders are navigating.

A crucial step in achieving this balance is understanding which digital tools employees rely on and identifying underutilized resources. By analyzing endpoint data—such as usage patterns across devices like laptops, desktops, and handhelds—companies can pinpoint inefficiencies and reduce unnecessary expenses.

For instance, a New York-based bank analyzed endpoint data and discovered they only needed to replace a fraction of the 7,000 laptops initially slated for refresh. This data-driven decision saved approximately $9.6 million, proving that visibility into IT assets can lead to significant cost savings while preventing wasteful spending.

Enhancing the Digital Employee Experience

Improving DEX benefits both employees and organizations. A better digital experience fosters higher engagement and satisfaction, which boosts productivity and reduces turnover. This explains why 45% of IT leaders in the survey are prioritizing DEX enhancements.

This trend aligns with Gartner's prediction that by 2025, half of all IT organizations will have a dedicated DEX strategy, team, and management tools—up from just 5% in 2021. The connection between employee satisfaction and digital tools underscores the importance of investing in DEX, especially given that replacing an employee can cost about 33% of their annual salary.

Leveraging AI and predictive analytics to monitor endpoint data is one of the most effective ways to improve DEX. These tools enable IT teams to proactively identify and resolve issues before they escalate, minimizing disruptions and enhancing employee satisfaction. As noted in the survey report, high-quality endpoint data insights allow IT teams to address potential problems early, preventing costly downtime and frustration.

To Know More, Read Full Article @ https://ai-techpark.com/it-leaders-top-priorities/

Related Articles -

Implementing Cyber Threat Hunting in 2024

Top 5 Data Science Certifications

Having spent many years immersed in IT and information security, I can confidently say it has been a rewarding journey. Over time, I’ve observed a significant shift in how organizations perceive cybersecurity. It has gained prominence and relevance, with the role of the Chief Information Security Officer (CISO) evolving positively. CISOs are no longer seen as mere “blockers” but as agents of change who actively contribute to business decisions, enhance visibility, and drive impactful organizational outcomes.

Cybersecurity today is increasingly focused on balancing risk with opportunity and innovation. It serves as an impartial source of information to help executives make informed decisions while aligning with the organization’s objectives. However, many Boards and Executive Committees still view cybersecurity as a technical issue relegated to lower levels. This perception is compounded when cybersecurity leaders rely on overly technical jargon, causing interest to wane.

Senior Leadership’s Role in Cybersecurity

Top executives can no longer evade their responsibilities regarding cybersecurity. Effective security measures are far more impactful when they have the support of leadership. To foster this alignment, cybersecurity must transition from an operational concern to a strategic priority. It requires a mindset shift, emphasizing strategy, risk management, and trust-building to navigate today’s digital challenges.

Executives must adopt a comprehensive self-assessment model to gauge their accountability in cybersecurity. Many still fail to grasp the strategic impact of cyber risks and the necessary measures to ensure resilience. By partnering with their CISOs as strategic allies, executives can strengthen their organizations’ security posture and resilience.

Strategies for Communicating Cybersecurity to Executives

Drawing from my experience as a former CIO and CISO, here are actionable tips for engaging C-level executives:

Align with Strategic Priorities

Frame cybersecurity as an enabler of organizational goals like revenue growth, operational efficiency, and market expansion. Position it as a strategic asset that offers competitive advantages rather than a cost burden.

Highlight Cyber Risk Impact

Explain the potential consequences of cyber incidents, from financial losses and operational disruptions to reputational harm and regulatory penalties. Use relatable examples to emphasize the urgency.

Use Real-Life Examples

Present case studies of competitors or similar organizations that faced security breaches. These tangible stories resonate with executives and illustrate the real-world impact of neglecting cybersecurity.

To Know More, Read Full Article @ https://ai-techpark.com/cybersecurity-for-c-level/

Related Articles -

Democratized Generative AI

Chief Data Officer in the Data Governance

In today’s digital era, cloud computing has become a cornerstone of application modernization and digital transformation. By 2025, it’s anticipated that over 85% of organizations will adopt a cloud-native approach for application development. Yes, you read that correctly!

The primary goal of this approach is to streamline development processes, enhance scalability, and boost agility. Unlike traditional methods, cloud-native development doesn’t follow rigid rules. Instead, it focuses on addressing unique business challenges and leveraging the most suitable software solutions.

Are you ready to harness the power of cloud-native development? Understanding the strategies for modern application development is key. Let’s dive deeper!

What is Cloud-Native Development?

Cloud-native development involves designing, building, and running applications using the principles of modern cloud computing. While it offers significant advantages, developers often face challenges during development and deployment.

This approach utilizes the infrastructure and services provided by cloud platforms like Google Cloud Platform and Amazon Web Services. A hallmark of cloud-native applications is their microservices-based architecture, which enables developers to access on-demand resources, unlimited computing power, and advanced application frameworks.

Top Strategies for Cloud-Native Application Development

If you’re planning to develop modern applications using cloud-native methods, consider these key strategies:

Utilize Microservices

Microservices are small, independently deployable services, each dedicated to a specific business function. This approach offers several benefits:

Efficient resource allocation tailored to each service’s needs, optimizing performance and cost.

Greater flexibility, as updates to one service don’t impact others.

Enhanced resilience, ensuring the system remains operational even if a single service fails.

Faster development and deployment cycles.

Choose the Right Tech Stack

Selecting the appropriate frameworks and languages is critical for cloud-native development. Ensure your choices align with the application’s specific requirements.

Some tools excel in microservices development, while others are ideal for real-time or data-processing applications.

Adopting the right tech stack enhances developer productivity and ensures better outcomes.

Stay updated with industry trends and evolving requirements to make informed decisions.

To Know More, Read Full Article @ https://ai-techpark.com/cloud-native-devops-strategies-simplified/

Related Articles -

Top Cybersecurity Articles in 2024

Explainable AI Is Important for IT