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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Productivity

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Technology & The Future

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Learning & Education

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Startups

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Artificial Intelligence

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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Science Fiction

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Marketing & Sales

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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Strategy

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Politics

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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Cryptocurrency

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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Cybersecurity

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Software Engineering

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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Movies & Shows

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Computer Science

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…

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Health

Recent AI advancements in healthcare

Deepfakes are hyper-realistic digital forgeries of human created using Generative AI.They use the deep learning principles in the backend to learn from human expressions and voice to later imitate them to do what they have never done.

What are Deep Fakes ?

<ul><li class="ql-align-justify">In entertainment industry, deep fakes can be used to create amazing visual effects and also bring back legendary actors who passed away.</li><li class="ql-align-justify">Artists and creators leverage deep fakes to try new content to play around with famous people in past to create an impact in art, education </li><li class="ql-align-justify">Personalised marketing is done by engaging consumers by featuring familiar faces in advertising content.</li></ul>

Positive Impacts

A short film explores deep fake technology with James Bond 007 Deepfake generated videos of historians in museum keep visitor engaged and excited

<ul><li class="ql-align-justify">Political figures deep fakes are used to spread fake news and misinformation which is a serious threat to democracy </li><li class="ql-align-justify">Deep fakes created without people‘s concern lead to privacy violations </li><li class="ql-align-justify">They actively contribute to fraud by impersonating people in video calls and getting funds. </li></ul>

Negative impact

Trump supporters targeted black voters with faked AI images. Italy‘s PM images are used to created compromising deep fake videos of her

Manual detection: one can detect visual deep fakes easily by observing features - inconsistent lighting or unnatural blinkingUsing technology: AI can be used to detect deep fakes and there are good amount of companies in this space. Blockchain can also be used to digital authenticateAwareness campaigns can empower individuals to critically evaluate the digital content they consume.

How to fight deepfakes ?

The dual nature of deepfake technology poses a significant challenge: it can either benefit society or fuel nefarious agendas. Effective management requires responsible use, strong legal frameworks, and advanced detection method

Explore about deepfakes - a double edged sword

The dual face of deepfakes - Innovation and Illusion

Artificial Intelligence (AI) has become an integral part of our daily lives, influencing decisions from personalised recommendations to critical healthcare diagnostics. Now with the ChatGPT, LLMs and other Generative AI technologies being emerging at a jet speed, there are some critical grey areas which demand our attention, with ethical considerations taking center stage. This article discusses about six key pillars of AI ethics

Accountability means making sure someone is responsible for the actions and outcomes of AI systems. It’s about being ready to fix mistakes and improve the system when needed.Example - Self-driving vehicles: For AI-driven cars, if there’s an accident, figuring out if the car's programming, the manufacturer, or something else is at fault is key to accountability.

Accountability

Fairness in AI ensures that AI systems treat everyone equally, without bias against any gender, race, or ethnicity. It aims to prevent discrimination and promote equality.Example - Application Tracking Systems (ATS): If an ATS used in hiring favors certain demographics because of its training data, it's crucial to address this bias. Ensuring fairness in such AI models helps create a more inclusive society.

Fairness:

Explainability is about making AI's decision-making transparent, so users can understand and trust how it reaches its conclusions.Example - AI in Finance: For an AI deciding on loan approvals, explainability means it can show which factors influenced its decision, helping applicants understand or contest the outcome.

Transparency means openly sharing how AI systems work, from data use to decision-making processes, fostering trust and informed user interaction.Example - AI in Social Media: When social media platforms explain how their algorithms tailor user feeds, they promote transparency, empowering users to better control their online experience.

Transparency

Robustness is about making AI reliable in unforeseen conditions, ensuring it performs well even when faced with challenges like data changes or attacks.Example - Facial Recognition: A robust facial recognition system accurately identifies users under various conditions, like changes in lighting or expression, minimising errors and boosting dependability.

Robustness

Privacy focuses on protecting personal data from misuse or unauthorized access, emphasizing the responsible management of sensitive information.Example - Fitbit watches: Privacy in devices like Fitbit ensures users' health data is securely stored and accessed responsibly, fostering trust in how their information is handled.

Privacy

Adhering to the six pillars of AI ethics — Accountability, Fairness, Explainability, Transparency, Robustness, and Privacy — ensures AI systems are developed responsibly. These principles guide organizations in using AI while safeguarding users' rights and safety. Committing to these values fosters a trustworthy, ethical AI environment for all.

Explore pillars of Trustworthy and Responsible AI

Artificial Intelligence (AI) has become an integral part of our daily lives, influencing decisions from personalised recommendations to critical healthcare diagnostics. Now with the ChatGPT, LLMs and…

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Human Resources

Ethical foundations of AI

The world of AI is evolving rapidly, and one of the hottest trends in 2024 is Edge AI. This technology that revolutionises the way we interact with machines and make decisions using the power of on-device intelligence.Imagine a world where your smart speaker understands your voice instantly, your self-driving car reacts to obstacles in real-time, and your medical device makes critical decisions without waiting for cloud approval. This is the power of Edge AI.

What is Edge AI?

By 2025, over half of all data analysis performed by deep learning algorithms will occur directly on edge devices, a significant jump from just 10% in 2021.

Edge AI performs calculations directly on the device itself in real-time where the data is created which creates <ul><li class="ql-align-justify">Faster response times: No more waiting for data to travel back and forth, reducing the ‘cloud lag’ leading to immediate and accurate decisions. This is really important for applications like autonomous vehicles and critical healthcare devices.</li><li class="ql-align-justify">Enhanced privacy and security: Sensitive data is localised that reduces the risk of external threats. Edge AI can be beneficial for protecting sensitive medical information or financial transactions.</li></ul>

Advantages:

This rapid growth is driven by several factors:<ul><li class="ql-align-justify">Advancements in chip technology: More powerful and energy-efficient processors are making it possible to run complex AI algorithms on smaller devices.</li><li class="ql-align-justify">Growth of the Internet of Things (IoT): The connected devices, from smart homes to industrial sensors, generates vast amounts of data that can be analysed locally.</li><li class="ql-align-justify">Increasing demand for real-time decision-making: Applications like autonomous vehicles and smart factories require instant responsiveness</li></ul>

Drivers of Edge AI

Healthcare: Wearable devices can analyse health data in real-time, enabling early detection of diseases and personalised treatment plans.Manufacturing: Smart factories can optimize production processes, predict equipment failures, and improve overall efficiency.Retail: Personalised recommendations and targeted advertising based on real-time customer behavior can be delivered in stores.Smart homes: Devices like smart speakers and thermostats can learn individual preferences and adjust automatically, creating a more comfortable and energy-efficient living environment.

The future of Edge AI and applications

<ul><li class="ql-align-center">Securing edge devices and protecting sensitive data processed on them is important.</li><li class="ql-align-center">Balancing the benefits of personalised experiences with user privacy requires careful consideration.</li><li class="ql-align-center">Not all devices have the processing power to run complex AI models. Finding efficient algorithms and hardware solutions will be key.</li></ul>

Edge AI is not just a trend; it’s a paradigm shift in how we interact with intelligent systems. As technology continues to advance, Edge AI promises to bring faster, more responsive, and secure AI capabilities to every aspect of our lives, shaping a future where intelligence is truly distributed and embedded in every fabric of our world.

Learn about Edge AI for faster, more responsive decisions

The world of AI is evolving rapidly, and one of the hottest trends in 2024 is Edge AI. This technology that revolutionises the way we interact with machines and make decisions using the power of…

Workplace AI (Bring Your Own Culture)

Redefining Engagement: The role of AI Influencers in modern Marketing

The Role of Artificial Intelligence in Election Campaigns

The future of search using AI

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

Raja Sekhar Thota