In the rapidly evolving world of Artificial Intelligence, terms like “assistants,” “agents,” and “chatbots” often used interchangeably. Yet, as similar as these technologies may seem, they have distinct roles and functionalities that set them apart. This article discusses about these terms in detail and the differences to help one understand which tool might be best suite for their needs

Chatbot is a software application designed to have conversations with human users via text or text-to-speech. Chatbots are often rule-based or use simple natural language processing (NLP) techniques to understand and respond to user queries. They operate on a set of predefined pathways and responses, making them great for handling FAQs or providing standard customer service on websites.

Scripted responses: Chatbots follow predefined scripts and are limited to the scenarios they were programmed to handle.Limited context understanding: They typically handle short exchanges and struggle with understanding context or past interactions unless specifically designed to do so.Automation: Ideal for automating standard tasks without needing human intervention.

Key characteristics of Chatbots:

Agent is an autonomous AI system that can perceive its environment, reason about it, and take actions to achieve specific goals. Agents are often associated with more complex AI systems that can learn and adapt their behavior based on interactions and feedback

Autonomy: Agents operate independently and make decisions without human intervention.Adaptive: They can adjust their behavior based on changes in their environment or input.Goal-oriented: Agents work towards achieving specific goals, which can involve complex problem-solving.

Key characteristics of Agents

Assistants, often called virtual or digital assistants, combine the functionalities of both chatbots and agents but are typically more sophisticated. They use advanced AI, processing natural language to understand user requests and learning from interactions to improve over time.

Natural Language Understanding: Assistants can interpret and respond to spoken or written language in a way that mimics human interaction.Contextual awareness: They remember past interactions and can handle follow-up questions and requests accordingly.Proactivity: Assistants can anticipate needs based on user preferences and habits.

Key Characteristics of Assistants

Understanding the differences between these AI tools is important for deploying the right solutions to enhance your operations or improve your user experience. If you’re looking to answer specific queries efficiently, a chatbot might be your go-to. For more dynamic decision-making tasks, an agent could be more suitable. Meanwhile, if you need a more interactive and adaptive solution, a digital assistant would be ideal.

Understand the difference between AI terms 

In the rapidly evolving world of Artificial Intelligence, terms like “assistants,” “agents,” and “chatbots” often used interchangeably. Yet, as similar as these technologies may seem, they have…

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Decoding AI Interactions: Chatbots, Agents, and Assistants Explained

As Artificial Intelligence (AI) technology continues to evolve, need for understanding the role of specialised hardware that power AI applications is important. From training machine learning models to processing big data, the choice of processor impacts the efficiency and performance of AI tasks. Whether you are a developer, data scientist, or tech enthusiast, this guide will help you to understand the most common types of AI hardware: CPUs, GPUs, TPUs, and LPUs.

AI Hardware

Central Processing Unit (CPU) is the traditional heart of a computer, capable of handling a variety of tasks from basic calculations to complex software operations

Examples : Intel Core processors and Apple M1/M2 chipsBest for: General-purpose computing, smaller datasets, or tasks requiring complex logic where data processing needs are sequential.Application: Running data preprocessing or light machine learning models like linear/logistic regression on a dataset with less number of data poin

More About CPU

Graphics Processing Units (GPUs) are known for their ability to handle multiple operations simultaneously in parallel, making them ideal for graphics rendering in Games and AI computations

Examples: Nvidia Tesla and Nvidia Drive PXBest for: Large-scale machine learning tasks that requires handling of massive datasets with millions of data points or running complex deep learning models.Application: Training a convolutional neural network on a dataset with millions of images. A task that could take days on a CPU might only take hours on a GPU due to its parallel processing capabilities.

More About GPU

Tensor Processing Units (TPUs) are Application Specific Integrated Circuits (ASIC), developed by Google, designed to accelerate TensorFlow, a popular machine learning framework.

Examples: Apple neural engine and Intel’s Gaudi AI processorBest for: TPUs are built to enhance the speed and efficiency of TensorFlow applications, making them particularly suitable for tasks that can leverage this specific framework.Application: TPUs are commonly used in advanced Google services like Google Search and Google Photos, where rapid data processing and model improvements are continously required.

More About TPU

Language Processing Units (LPUs) are specialised processors designed to excel in natural language tasks such as translation and sentiment analysis.

Groq, a new player, in the AI hardware industry, is pioneering the development of LPUsBest for: LPUs are suitable for applications that require processing complex language data efficiently.Example: LPUs power advanced chatbots, enabling them to understand and respond to human queries quickly and accurately, improving user interactions in customer service settings.

More About LPU

CPUs are suitable for general-purpose computing and less complex AI tasks.GPUs are the go-to for large-scale data processing and deep learningTPUs offer the best performance for TensorFlow-based applications.LPUs excel in handling optical data for imaging and real-time analysis.

Selecting the right AI hardware

As Artificial Intelligence (AI) technology continues to evolve, need for understanding the role of specialised hardware that power AI applications is important. From training machine learning models…

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A Beginner’s guide to AI Processors: CPUs, GPUs, TPUs and LPUs

Large Language Models (LLMs) like ChatGPT and Gemini are advanced AI technologies designed to understand and generate human-like text based on the data they are trained on. These models are part of a broader category of AI called natural language processing (NLP), which helps computers interpret human language.

ChatGPT, developed by OpenAI, is one of the most well-known LLMs. It excels in engaging in conversational dialogue and can perform a variety of language-based tasks, such as answering questions, composing emails, and even writing creative fiction. It learns from a vast corpus of text data, enabling it to generate responses that are not only accurate but also contextually relevant.

Gemini, another example of an LLM, is designed to handle more specialized tasks. It can understand and generate text in specific fields such as law, medicine, or customer support, making it particularly useful for businesses that need precise and knowledgeable assistance in these areas

One of the significant benefits of LLMs is their versatility. They can be used in various applications, from chatbots and customer service aids to tools that help writers and researchers generate ideas and content. Their ability to understand context and generate coherent and contextually appropriate responses makes them valuable in many industries.

However, LLMs also have limitations. They can generate incorrect or biased information if the data they were trained on is biased. They also do not truly "understand" language or concepts in the way humans do; they are just very good at predicting text patterns.

As technology continues to develop, LLMs are expected to become more sophisticated and reliable. For now, they represent a remarkable step forward in artificial intelligence, offering a glimpse into how machines might help us manage and interpret the vast amounts of information we generate every day.

Large Language Models for beginners

With the launch of Open AI’s ChatGPT a year ago, the popularity of ‘AI’ and to be more specific “Generative AI” has gained a lot of momentum in users. A lot of new tools and AI integrations are developed on top of large models that would assist employees at work to improve productivity and automate tasks. Then, employees began using these tools secretly at their work without informing the organisation known as ‘Shadow AI’ but at the same time, companies also found value with these tools that eventually encouraged them to rethink their AI strategies and consider adopting Bring Your Own AI

Shadow AI

Bring Your Own AI is the practice of employees utilising personal AI tools at their work without explicit IT approval. A study by Forrester Research says that approximately 60% of employees are expected to employ personal AI tools for their job duties by 2025. BYOAI is a double edged sword as it has benefits and challenges which needs to be addressed with policies.

Increased productivity: Employees can use AI tools they are familiar with which can save time and make more efficient. Code co-pilots increase developer’s productivityEnhanced innovation: Employees can use AI tools to implement their ideas without much tech expertise. AI prototyping tools can help people without specialised AI expertise to create their own AI-powered application Faster time to market: Companies can quickly adopt new AI tools and start using them to improve products and services. With automated AI tools used in testing, a company can release products at a much faster pace.

Benefits of BYOAI

Security concerns: Personal AI tools may not be as secure as company-approved tools, which could put sensitive data at risk.Compliance issues: Personal AI tools may not be in compliance with data privacy regulations, which could lead to heavy fines and other problems.Inconsistent quality: Personal AI tools may vary in quality, which could make it difficult to ensure that employees are using reliable tools.

Challenges of BYOAI

<ul><li>Establish clear policies on what tools to use and what not to. </li><li>Have a centralised portal to register the tools and approve / disapprove based on set policies </li><li>Provide trainings on risks and potential of approved tools </li><li>Monitor the usage of tools and check for any violations </li></ul>

How to overcome Challenges ?

BYOAI can be a powerful tool for companies, but it is important to manage it carefully to avoid the risks. By following the above practices, companies can harness the benefits of BYOAI while keeping their data safe and compliant.

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Workplace AI (Bring Your Own Culture)

In today’s digital age, social media has disrupted the way we connect, communicate, and consume content. It is also the first place where human-to-robot interaction is rapidly evolving where millions of people are following and engaging with virtually created online personalities powered by Artificial Intelligence, often referred as AI influencers.

AI influencers are virtual personas created using complex algorithms and natural language processing capabilities to engage with audiences on social media platforms offering product endorsements and lifestyle insights.

What are AI Influencers ?

Top AI influencer Lu from Brazil shares lifestyle content, including unboxings and product reviews has about 6.8 M followers

Lil Miquela, a music-loving CGI teenager with over 2.6 million followers, collaborates with major brands like Prada.

AI influencers offer innovative tools for brand engagement and storytelling, capable of reaching audiences with precision and scale.AI influencers have the potential to become powerful advocates for social causes and cultural change by raising awareness about pressing issues.Critics say AI influencers promote unrealistic beauty and turn relationships into products for sale leading to debates

Impact and Implications

Data privacy: Concerns about data privacy arise as AI influencers collect and analyse user data to personalise content and advertisements.Algorithmic bias: The algorithms powering AI influencers may have biases, leading to discrimination.Misuse and manipulation: The potential for misuse, such as deepfake technology creating AI influencers without consent or spreading misinformation demands the need for robust ethical guidelines.

Ethical Considerations

<ul><li>Identify your audience </li><li>Study trends </li><li>Build Persona </li><li>Create avatars using AI </li><li>Create social media profiles </li><li>Develop content strategy </li><li>Engage and Grow </li><li>Monetise the content </li><li>Analyse performance </li><li>Ensure ethical standards </li></ul>

Guide to create AI influencers

The rise of AI influencers seems to be an interesting intersection of technology and marketing. While they may have ethical concerns, their impact on society cannot be ignored. As we navigate this new world of virtual personalities, it is essential to approach them with critical thinking, empathy, and an awareness about them. Only then we can utilise their potential for positive change and ensure that they contribute to a more inclusive, ethical, and human-centered future of marketing.

Conclusion

In today’s digital age, social media has disrupted the way we connect, communicate, and consume content. It is also the first place where human-to-robot interaction is rapidly evolving where millions…

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Redefining Engagement: The role of AI Influencers in modern Marketing

As the United States gears up for another election cycle, the use of Artificial Intelligence (AI) in political campaigns has sparked both fascination and concern. AI is promising to change how campaigns are run, how voters are engaged, and how messages are communicated. It’s applications ranges from creating personalised messages to deploying AI-powered avatars for engaging with voters.

Introduction

Recent news about AI in election campaigns

Generative AI, with its ability to create content that mimics human-like writing, images, and even videos, offers a powerful tool for political campaigns.Personalised messaging at scale:By analysing vast datasets on voter preferences, concerns, and behaviours, AI can create messages that are highly personalised and likely to resonate with voters on an individual level.Real-time adaptation and strategy:As public opinion shifts or new issues emerge, AI can help campaigns pivot, adjusting messaging and tactics to stay relevant and effective.

Transforming communication & strategy

​​Jason Palmer, who secured a victory in American Samoa against President Biden, says that AI has played an important role for his campaign. His campaign consisted of Palmer AI, a talking avatar of Jason Palmer that engaged voters

Democrat Shamaine Daniels leverages AI volunteer Ashley, the first political phone banker that uses Generative AI, to support her campaign. Ashley stands out from other typical robocallers as none of her responses are pre-recorded.

AI-generated deepfakes have been used to create images that show public figures, such as former President Donald Trump, engaging with black voters. These deepfakes aim to manipulate perceptions and spread misinformation, demonstrating the darker applications of AI in political contexts.

Generative AI’s ability to create convincing, yet false, content poses a challenge to the integrity of electoral processes.Deepfakes, or AI-generated videos that can make it appear as though individuals are saying or doing things they never did, represent a particular threat.Campaigns and regulatory bodies must address these risks, implementing measures to ensure transparency and the authenticity of AI-generated content.

Ethical and societal considerations

While offering the promise of more efficient and personalised campaigns, AI also challenges us to confront the ethical implications of this technology. As the technology advances, so too must our vigilance in ensuring that the electoral process remains fair, transparent, and true to the principles upon which democracy is built.

Final words

Explore role of AI in elections with focus on US

In recent years, the landscape of election campaigns has transformed dramatically with the advent of digital technology, social media, and data analytics. As the United States gears up for another…

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The Role of Artificial Intelligence in Election Campaigns 

6% of e-commerce visits that include engagement with AI-powered recommendations drive 37% of revenue — SalesforceOne day per working week (19.8% of work time) is wasted by employees searching for information to do their job effectively — Interact

AI algorithms are at the heart of personalised search experiences. These algorithms analyse a user’s past behavior, including search queries, clicks, and even time spent on various pages. By understanding what each user finds relevant and engaging, AI can tailor search results to match personal preferences. Netflix uses sophisticated AI algorithms to analyse a vast array of user data, including the genres you watch most frequently, the titles you’ve rated highly, how much of a show or movie you watch before stopping, and even the time of day you prefer to watch certain types of content.

AI Algorithms presents results based on user preferences

Beyond just analysing keywords, AI-powered search engines understand the context of queries. This involves interpreting the intent behind a search and considering factors such as the user’s location, device used, and even the time of day.If you search for “pizza places” on your phone at lunchtime, an AI-powered search engine considers your location, the time, and the fact you’re using a mobile device. It then shows nearby pizza restaurants open for lunch, rather than just any pizza-related information.

Contextual understanding gives accurate results for queries

Conversational search leverages natural language processing (NLP) to allow users to interact with search engines in a more natural, human-like manner. Instead of relying on specific keywords, users can ask questions or make requests in full sentences, just as they would when talking to another person.Perplexity AI’s search engine uses natural language processing to personalise search results by understanding user queries’ context and summarising information with citations. It updates with newer sources, allows follow-up queries, and offers various search modes for free.

Conversational search enables natural interactions

Visual search enables users to use images or videos as their search queries. This is particularly useful in scenarios where describing an item in words is difficult. By analysing visual content, AI can identify objects, text, and even context within the image or video, returning relevant informationImagine you see a stylish lamp in a magazine but don’t know where to buy it. By taking a photo and using a visual search app, like Google Lens, the app analyzes the image, identifies the lamp, and shows you where you can purchase it online

Visual search allows image based queries

The technical complexity of developing and maintaining complex AI algorithms demands high-level expertise.Ethical considerations around data privacy and compliance with stringent regulations are critical.Integrating AI seamlessly with existing digital infrastructures presents its own set of challenges, that demands a balanced approach to innovation and user-centric design.

Challenges

In an era where information is both a commodity and a currency, the way we search for and interact with data online is going through a significant transformation with Artificial Intelligence (AI)…

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The future of search using AI

In an era where artificial intelligence (AI) is rapidly being integrated into various aspects of our lives, ensuring trust, managing risks and maintaining security in AI systems is really important. Enter AI TRiSM (Artificial Intelligence Trust, Risk, and Security Management), a framework developed by Gartner to address the challenges associated with AI Governance.

Organizations that incorporate AI TRiSM framework into business operations of AI models can see a 50% improvement in adoption rates due to the model’s accuracy. Organizations leveraging AI TRiSM can expect significant benefits, including the removal of up to 80% of inaccurate or fake data by 2026.

AI Trust: Building confidence in AI systemsIt ensures ethical and fair practices are followed throughout the development and deployment processAI Risk: Mitigating potential risks and pitfallsProactive risk management strategies are implemented to minimize negative impacts on stakeholders and society as a whole.AI Security Management: Safeguarding AI systems and dataIt focuses on protecting AI systems and data from cyber threats and vulnerabilities. Data privacy and compliance measures are enforced, and security is a priority for AI models, algorithms, and infrastructure.

Understanding AI TRiSM

AI TRiSM is a framework that relies on 4 key areas to achieve its goals - Explainability, ModelOps, App Security and Data Privacy

AI TRiSM enables organizations to understand how AI models make decisions. It is important for ensuring transparency and identifying potential biases which in turn promotes trust and accountability in AI systems.

Explainability

Just like traditional software systems, AI models also require management throughout their lifecycle. AI TRiSM offers tools and processes for automating and monitoring the development, deployment, and maintenance of AI models, known as ModelOps. This ensures models remain effective and up-to-date in dynamic environments, mitigating risks associated with outdated models.

Model Ops

As AI applications become more prevelant, they require robust security measures. AI TRiSM addresses this by implementing measures to protect against unauthorized access, data breaches, and malicious attacks, safeguarding both the applications and the sensitive data they handle.

AI App security

With the increasing use of AI comes the need to protect sensitive data and ensure compliance with privacy regulations. AI TRiSM assists organizations in implementing robust data privacy measures, including data encryption, access controls, and anonymization techniques, to protect individuals’ privacy rights and maintain regulatory compliance.

Data Privacy

<ul><li class="ql-align-justify">Form an AI taskforce</li><li class="ql-align-justify">Prioritise explainability:</li><li class="ql-align-justify">Implement continuous monitoring</li><li class="ql-align-justify">Regular training and education</li><li class="ql-align-justify">Stay informed about regulations</li><li class="ql-align-justify">Collaborate with external experts</li></ul>

Best practices for organisations

The Danish Business Authority’s ethical AI protocol shows AI TRiSM in action. By monitoring models and testing for fairness, they managed billions in financial transactions. This example highlights how AI TRiSM helps entrepreneurs succeed in the AI market by ensuring accuracy, navigating regulations, and building trust.

AI TRiSM offers a structured approach to address these risks, providing organizations with the confidence to develop and deploy AI solutions responsibly. By prioritizing trust, risk management, and security, organizations can unlock the full benefits of AI while safeguarding against potential pitfalls.

Learn about AI TRiSM framework of Gartner

In an era where artificial intelligence (AI) is rapidly being integrated into various aspects of our lives, ensuring trust, managing risks and maintaining security in AI systems is really important…

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AI TRiSM: A framework for Trust, Risk and Security Management

Fintech industry has been quickly adopting Artificial Intelligence, from automating basic tasks to advanced operations like fraud detection, providing personalised customer service, and delivering strategic financial advice. The adoption of enhanced data analytics, machine learning algorithms and Generative AI has opened up new opportunities, fundamentally improving the operational dynamics of financial services.

In trading, Credit scoring, Fraud detection, customer service chatbots, Robo advisors, Compliance and AML, User authentication

AI-driven algorithmic trading platforms analyse large datasets to predict market movements and optimise trading strategies. These platforms use historical and real-time market data, applying complex algorithms to identify profitable trading opportunities.

In trading

In lending and credit scoring, AI improves decision-making processes. Traditional credit scoring methods often overlook potential creditworthy candidates as it works on general rule based approach. AI models can build more accurate models for risk assessment by considering non-traditional data points like rental payment histories and social media behaviour. This allows lenders to give loans to wider audience and manage risk effectively.

Credit scoring

ID Finance, a fintech firm based in Spain, is an online consumer lending platform that has credit scoring and risk management system built using sophisticated ML techniques.

AI systems excel in identifying and preventing fraudulent activities in real-time by learning from transaction patterns. They can dynamically adapt to new fraudulent techniques, unlike static rule-based systems.AI can analyse customer behavior, location data, and transaction history to flag unusual activities, significantly reducing false positives and improving fraud detection accuracy.

Fraud detection

JPMorgan Chase uses anomaly detection algorithms to flag unusual transactions or activities that do not fit the customer’s profile. It also uses Natural Language Processing techniques to analyze customer interactions and identify potential fraud.

AI-powered chatbots offer personalised customer service experiences, available 24/7, without the need for human intervention. These chatbots can handle a range of inquiries, from account balance requests to transaction disputes, learning from each interaction to improve future responses. The cost savings for financial institutions are substantial, as chatbots can scale to handle increasing volumes of queries without additional human resources.

Customer service chatbots

Klarna, a Swedish fintech company, introduced an AI assistant developed by OpenAI, which managed 2.3 million customer service chats in 35 languages within a month, effectively handling work of 700 full-time agents and simplifying the process of refunds and returns for users through its app.

Leveraging AI, robo-advisors provide personalised investment advice at a fraction of the cost of human advisors. These platforms analyse individual financial goals, risk tolerance, and investment preferences to create customized investment strategies. AI-driven personal finance apps also offer budgeting and savings advice, helping users manage their finances more effectively.

Robo advisors

Betterment, a pioneer robo-advisor, has expanded its offerings over a decade, adding cryptocurrency investing to its sophisticated platform, earning Forbes Advisor’s title as the best robo-advisor.

AI can automate the monitoring and reporting processes that are required for compliance, efficiently scanning transactions for suspicious activities and ensuring adherence to regulatory standards. This automation reduces the risk of non-compliance and lowers operational costs associated with manual compliance tasks.

Compliance and Anti-Money Laundering

In 2019, HSBC introduced an AI system for AML, using machine learning to spot suspicious transactions, cutting review time, enhancing accuracy, and saving $400,000 annually.

Advanced AI technologies, including biometric and behavioural analysis, provide secure and user-friendly methods for customer authentication. Facial recognition, fingerprint scanning, and voice recognition enhance security while offering a seamless authentication experience. Behavioural biometrics, analysing patterns in device interaction, further improves security by detecting anomalies that may indicate unauthorised access.

User authentication

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AI in Finance

In healthcare, AI’s impact has been very noticeable and deals with how diseases are detected, diagnosed, and treated. One of the first applications of AI in healthcare is IBM’s Watson, which got a lot of recognition for its ability to analyse large amounts of medical data to assist in diagnosing diseases.

Initial impact in healthcare

Machine Learning algorithms analyse medical imaging data such as X-rays and MRIs with high accuracy which helps in the early identification of diseases such as cancer. For example, Google’s AI system demonstrated the ability to detect breast cancer from mammograms with greater accuracy than human radiologists.

Detection

AI tool Mia, piloted by NHS, identified subtle signs of breast cancer in 11 women missed by human doctors out of 10,000 mammograms analyzed. With its early detection capabilities, Mia has the potential to save countless lives, highlighting the critical role of AI in improving breast cancer survival rates.

Google scientists developed HeAR, a machine-learning tool that can detect and monitor health conditions by analysing sounds like cough and breathing. Trained on millions of human audio clips, HeAR has potential for diagnosing diseases such as COVID-19 and tuberculosis, and assessing lung function. Unique to HeAR is its vast dataset and ability to perform various tasks.

AI-powered diagnostic tools analyze patient data to identify patterns and anomalies, helping clinicians in making more informed decisions. IBM’s Watson, for instance, assists oncologists in diagnosing and treating cancer by analyzing extensive patient data, leading to more personalized and effective treatment plans.

Diagnosis

AI is reshaping treatment methods with personalized medicine, where therapies are recommended to individual patients based on genetic and molecular profiles. AI algorithms predict optimal treatment for cancer patients, maximizing efficacy and minimizing side effects.

Treatment

AI-powered chatbots provide personalized healthcare recommendations, answer patient queries, and streamline administrative tasks. Predictive analytics algorithms have been used to forecast disease outbreaks, optimize hospital resource allocation, and improve patient care management.AI-driven robotic systems in the future will enhance surgical procedures, offering greater precision, efficiency, and safety.

Other areas

As AI continues to evolve, it will continue to drive innovation across the healthcare ecosystem in the way how diseases are detected, diagnosed, and treated. By leveraging the power of AI-driven technologies and collaboration among stakeholders, we can unlock new opportunities to enhance patient care, improve healthcare outcomes, and shape a healthier future for all.

In healthcare, AI’s impact has been very noticeable that deals with how diseases are detected, diagnosed, and treated. One of the first applications of AI in healthcare is IBM’s Watson, which got a…

Decoding AI Interactions: Chatbots, Agents, and Assistants Explained

A Beginner’s guide to AI Processors: CPUs, GPUs, TPUs and LPUs

Large Language Models for beginners

Workplace AI (Bring Your Own Culture)

Redefining Engagement: The role of AI Influencers in modern Marketing

Raja Sekhar Thota