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GenAI in Capital Markets- Investment banking, Capital Markets
- 1. B u s i n e s s A n a l y s i s C e n t r e o f E x c e l l e n c e J o u r n a l V o l . 1 2 1 F e b r u a r y 2 0 2 4
- 2. Preethi Shetty Jaydeep Pathare Narayanan Muralidaran Kannagi Mishra Rangaprasad Narayanaswamy Sandeep Mukherjee In this edition, we are thrilled to present the most trending topic 'Generative AI' and its usage in BFSI and ESG sectors. The adoption of Generative AI is a double- edged sword. It offers multiple benefits and opportunities but also poses several risks and challenges. The first article provides insights into how the Banking industry can harness the power of Generative AI in different areas of banking like Investment Banking, Capital Markets, and Risk Management. It also highlights the Benefits and challenges of Generative AI in the Financial sector. The Next article delves into the concept of behavioral Biometrics. It explains when and where this concept first emerged and how it is integrated with Generative AI to mitigate potential risks across industries that include Banking, Medical, and healthcare with greater efficiency. The third article is about Generative AI in BFSI and its significance in areas such as fraud support and wealth management. It also explains in detail about Robo- Advisory and how it helps in offering effective recommendations to customers. Lastly, a widely popular topic that has grabbed everyone's attention in recent times is Environmental, Social and, Governance(ESG). We know how regulators across the world want companies to implement ESG. But what makes our article interesting is how AI integrates with ESG and the significant factors that drive the world towards ESG. We hope you enjoy reading and gaining new perspectives. To add an interactive touch, we've included an engaging quiz at the end of the journal – we encourage you to give it a try. We appreciate your thoughts and feedback on our Journal of 2024. Kindly share it with us at - [email protected] G e n e r a t i v e A I i n t h e F i n a n c e S e c t o r Page 03 G e n e r a t i v e A I a n d i t s A p p l i c a t i o n i n B F S I Page 06 B e h a v i o u r a l B i o m e t r i c s Page 09 A I t o I m p l e m e n t E S G Page 12 V o l u m e 1 1 – C r o s s w o r d A n s w e r Page 18 Editorial Q u i z Page 16 Kishore P Amber Sule Haritha Rath Lov Jain Manish Vishal Uma Shivakedharaman Team
- 3. What is Generative AI? Generative AI refers to a class of artificial intelligence systems that can generate content, such as text, images, or even music etc. These systems employ deep learning techniques to create new data that resembles human-generated content. In this article, we are going to cover Generative AI in the following financial service areas: 1. Investment Banking 2. Capital Markets 3. Risk Management & Compliance Investment banking has seen a rising demand for personalized, data-driven financial strategies where tailored investment advice can be recommended. Leading banks leverage Generative AI for significant cost savings, enhanced decision-making, and increased customer satisfaction, making AI a key competitive force in the industry. Below are a few cases where market players have harnessed the application of Generative AI to add value to their customer experiences. B y S u b h a m S a m a l G e n e r a t i v e A I i n t h e F i n a n c e S e c t o r Betterment: Betterment is an investment platform offering robo-advisory services. It employs Generative AI algorithms to construct and manage diversified investment portfolios based on individual financial goals like home down payment and retirement savings based on an individual’s risk tolerance level. Wealth front: It helps to automate portfolio management, making real-time adjustments based on market conditions and client preferences. BlackRock Aladdin: The world's largest asset management firm, BlackRock, uses its Aladdin platform, again powered by Generative AI, for portfolio construction and risk management. This platform optimizes investment outcomes for institutional clients. Kensho Scribe: Provides AI-driven analytics that transcribes financial and business audio into text with high accuracy. It is a speech-to-text product custom-built for finance that handles complexities including jargon, accents, numbers, and currencies. 3 Capital markets are known for their rapid fluctuations, making real-time insights crucial for traders and investors. Generative AI plays a pivotal role by processing vast volumes of live market data and offering personalized advice to enhance profitability. The following are some key challenges in Capital Markets: Market Volatility: Rapid price changes and trends pose challenges to timely decision- making. Information Overload: Coping with massive data streams is daunting for traders. Sentiment Analysis: Understanding market sentiment from various sources is complex. C a p i t a l M a r k e t s Investment Banking
- 4. Generative AI plays a pivotal role in risk management and compliance within the financial industry by addressing complex and evolving regulatory requirements across different regions, including Basel in Europe and the United States. How Generative AI Helps: • Generative AI can automate Basel III compliance checks by continuously monitoring transactions. • Detailed compliance reports can be generated, reducing administrative burdens and operational costs for European financial institutions. • Generative AI analyzes data to identify complex risk patterns, generates predictive models for early risk detection, automates data analysis and reporting, and enhances overall risk management, helping institutions adhere to regulations like the Dodd- Frank Act. • NLP models can be employed to review and understand written communication, such as emails and chat logs, to detect any language that may indicate compliance violations or potential risks. R i s k M a n a g e m e n t a n d C o m p l i a n c e 4 How Generative AI Helps: • Real-time Insights: Provides immediate recommendations, aiding traders in utilizing opportunities and managing risks. • Personalized Strategies: Customized advice considering trader's preferences, risk tolerance, and market conditions. • Sentiment Analysis: AI gauges market sentiments accurately based on news tracking and social media trends.
- 5. Challenges Privacy and Data Security: Generative AI relies on vast amounts of personal and financial data. Financial institutions need to invest significantly in cybersecurity to protect sensitive information. Ethical Concerns: Transparency is essential to inform customers that they are interacting with AI systems. Financial institutions must allocate resources for ethical AI development and maintain human override mechanisms to ensure compliance with legal standards. Fairness & Bias: Generative AI models can inherit biases present in the training data. Ensuring fairness and reducing bias in generated content is an ongoing challenge, especially when dealing with sensitive topics or underrepresented groups. Challenges and Benefits of Generative AI in the Financial Sector Conclusion Generative AI is transforming finance, offering tailored strategies, real-time insights, and robust risk management. Leading banks have the potential to achieve significant cost savings and improved customer satisfaction. Challenges include privacy and fairness, but the benefits are substantial, including enhanced customer experiences, cost savings, profit enhancements, and improved fraud detection. As financial services embrace Generative AI, it stands to boost performance and revenues. Subham works as a Business Analyst with 5+ years of experience in Financial Services, Capital Markets & IT. He has done his master's from XIM University & has a keen interest in Capital Markets and investment space. 5 Enhanced Customer Experience: Personalized recommendations and quicker response times lead to improved customer satisfaction and retention. A 5% increase in customer retention can boost profits by 25-95%. Cost Saving: As per McKinsey Report, the banking industry is likely to save $200 billion to $340 billion annually through generative AI, contributing 2.8% to 4.7% to its annual revenues. Profit Enhancement: Generative AI can add $2.6 trillion to $4.4 trillion yearly across various applications, enhancing the overall impact of AI by 15% to 40%. Fraud Detection: Generative AI enhances fraud detection by identifying unusual patterns or transactions, safeguarding against financial fraud. Benefits
- 6. B y D e v a k u m a r i R a j e n d r a n B e h a v i o u r a l B i o m e t r i c s An ancient Chinese book of the Qin Dynasty named “The Volume of Crime Scene Investigation” was the first to highlight the usage of first friction ridge impressions as a form of identification during crime investigation. As every human has distinct fingerprints, toe prints, facial, iris, and retinal features, we also have unique patterns and mannerisms while interacting with systems. Behavioural biometrics creates a unique profile for each person based on their interaction with the system. It emphasizes how users carry out certain activities rather than the results of those actions. In behavioral biometrics, AI is used to differentiate between users based on their unique interactions. A user authentication mechanism will be built with a machine-learning algorithm to distinguish between the activities of a genuine user and that of an imposter. D a t a b e h i n d b e h a v i o u r a l b i o m e t r i c s Every contemporary personal electronics product, from cell phones and wearables to household appliances, now contain smart sensors. Our smartphones have sensors that can be setup to collect data passively, accomplished with an accelerometer and gyroscope. When the customer signs in for the first time, the required behavioral biometrics data is collected and stored as a dataset. Individuals’ biometric profiles are evolved using machine learning classifiers, and the behavioral patterns of users are analyzed. Based on these, predictive models are developed and trained. Later, these predictive models compare and match whenever the user uses the application. The created models continuously verify the user profile in the entire session, and the data is used for various purposes like authentication, fraud protection and transactional risk analysis. 6
- 7. Banking Knowledge-based behavioral biometrics and preference-based behavioral biometrics play a crucial role in identifying fraud within the banking and financial sector. The user's knowledge level is assessed through their typical behavior, and advanced AI algorithms, employing cutting-edge technologies, meticulously analyze user interactions with banking platforms or applications. Data, including the user's preferred words, letters, language, regular bank transactions, card details, specific payment tools, etc., is collected to construct a comprehensive user profile. Subsequently, this profile is cross-referenced with the user's actual behavior to facilitate a more secure and minimally invasive verification process. This encompasses various factors such as biometric sketches, text authorship, typing biometrics, typing speed, keystroke dynamics, and the user's browsing speeds. 7 B e h a v i o u r a l b i o m e t r i c s a n d G e n A I s o m e u s e c a s e s Medical and healthcare The skill-based behavioral mechanism proves highly effective in the medical industry and the development of fitness trackers. It hinges on the user's instinctive and stable muscle actions, detected through sensors. These sensors capture behavioral signals, enabling the creation of in-depth behavioral profiles. This encompasses kinesthetics, such as a person's bodily motion, voice patterns, and mouse movements. Additionally, gait analysis, a specific aspect of kinesthetics, pertains to how an individual walks, holds objects, and interacts with devices. Market insights and Strategic planning AI stores the unique strategies users adopt when interacting with an application or platform, serving as the foundation for user verification. For instance, the habitual sequence of actions a user takes within a banking application—navigating to the dashboard and proceeding to the loan or deposit pages—is meticulously stored. This data plays a crucial role in designing and automating customer journeys using generative AI. ❖ Major applications of behavioural biometrics include online banking on different digital channels, e-commerce, and high-security authentication businesses. ❖ Behavioural biometrics can be used for both one-time and continuous authentication since static authorization is unable to maintain security in the face of the growing threat of cybercrimes. Hence, Security experts recommend behavioural biometrics to mitigate potential risks such as session hijacking and man-in-the- middle attacks. ❖ In the context of third-party identity verification, credit bureau scoring, and history verification, business users ranked behavioral biometrics higher than risk engines. ❖ Behavioral biometrics plays a vital role in legal and license management due to the difficulty of emulating or copying behavioral traits. Moreover, behavioral data can be collected seamlessly without interfering with existing customer services. ❖ Greater operational efficiency, improved customer services, and enhanced security can be achieved with a behavioral biometrics database. ❖ Behavioral biometrics can be gathered using existing hardware and only require software for analyzing the collected data. A d v a n t a g e a n d u s a g e
- 8. Deva is a Senior Consultant at Capco with 18 years of experience in Business analysis and project management. She successfully delivered transformation, implementation, and migration projects for international banks. Deva is currently part of the HSBC account. She loves spending time with family and reading. S h o r t c o m i n g s ❖ Implementing a new, robust, organization-wide framework is costly, as it needs to be seamlessly integrated with existing security systems. ❖ Effectively profiling a user's "typical behavior" through the integration of behavioral biometric authentication requires the acquisition of substantial amounts of personal data. ❖ Behavioral biometrics are typically obtained without user input; hence, it may also encounter several challenges related to privacy and ethical issues. ❖ Changes in human behavior can occur due to factors such as aging, tiredness, accidents, and environmental elements like weather. Therefore, behavioral biometric authentication models need to be consistently re-trained to stay current with changes in human behavior. F u t u r e o f b e h a v i o u r a l b i o m e t r i c s Given the rise in cybercrimes and identity theft related to passwords, relying solely on physical biometrics is insufficient for ensuring secure transactions. It is imperative to incorporate multiple layers of security. behavioral biometrics can complement traditional authentication methods, thereby fortifying overall security measures. By scrutinizing a user's digital and cognitive abilities, behavioral biometrics emerges as one of the most secure authentication methods against fraud detection. Many experts predict its increasing prevalence in the coming years. Behavioural biometrics can help financial services organizations comply with regulatory requirements like PSD2 and GDPR. Behavioural biometrics is the future of digital banking mainly because it provides a high level of security with a seamless and non-intrusive way of authentication and data protection. 8
- 9. Can you imagine a machine that thinks like humans? Yes! John McCarthy, the father of Artificial Intelligence (AI), dreamt of achieving this by describing human thinking as the mechanical manipulation of symbols way back in the 1950s. Further invention of programmable digital computers performing sequences of arithmetic or logical operations inspired researchers to think of building an electronic brain. The progress between the 1970s and 1990s was quite sluggish, majorly due to the hype generated by high expectations of end users, media promotions, and promises from developers However, renewed interest from 2012 led to an increase in investment and funding within the Artificial Intelligence space. A well-known quote from Microsoft founder Bill Gates emphasizes the importance of digitalization in banking: "Banking is necessary, banks are not." In parallel to the journey of Artificial Intelligence, the banking sector began its digitization in the 1960s. Subsequently, the advent of the internet in the 1990s saw the rise of online banking, gradually becoming the norm. Throughout this transformative period in banking, economic forces and technology played significant roles, ultimately signaling the end of the universal bank model. B y S i d d h a r a j M a s a l i Generative AI and it’s Application in BFSI Waves: Digitalization in Banking Especially Generative AI has shown vast potential in industry advancement as it can generate new data and patterns resembling human-created content in contrast to traditional AI that focuses on responding to specific data sets. Generative AI’s diverse abilities persuaded industries and households to perform experiments and derive use cases to make human life easier and more advanced. While other technologies have shown hype cycles, Generative AI exhibits clear use cases leading to the creation of robust solutions and is developing swiftly. This technology is poised to be transformative across every sector. Organizations are implementing AI in multiple ways to optimize cost, prevent human error, and alleviate repetitive tasks among many others. Generative AI has played a key role or a rather king-pin role in transforming the overall banking sector. 9
- 10. Credit Decisioning Models created via AI Algorithm offer more accurate default probability and loss severity resulting in better credit forecasting. AI works well with structured and unstructured data later translated into more accurate and informed credit decisions. In Credit risk management, AI is applied to improve credit approval, risk determination and portfolio management. Wealth Management Generative AI can provide accurate specific guidance on various scenarios to Wealth Managers by incorporating client needs and market events data in models. Virtual assistants are powered via generative AI to answer complex client questions on behalf of Investment advisors. AI led chatbot can interpret and understand human language as it is written, allowing it to operate on its own providing banking service 24 hours a day 7 days a week skipping numerous steps like hiring, training and shift arrangements for workforce. AI Powered Chatbots can detect emotions, interpret the context, respond to customer queries appropriately and even learn from previous conversations, allowing them to adapt to new patterns and even predict clients’ buying patterns. Fraud Support Banking systems took a major hit during the Pandemic. There was a hard push to transform the banking system from the traditional model to Digital (make complete banking on fingertips via smartphones). Generative AI data can improve fraud detection accuracy by reducing false positive and negative risks and data could be used to create algorithms that can differentiate legitimate from suspicious activities. Transaction fraud detection and AI led solutions have reduced daily operational cost by $7.3 billion globally. Robo-Advisory Robo-Advisory is an automated investment management service that understands customer’s financial position and history based on a few basic information such as goals, risk tolerance, and the length of time the customer wants to stay invested and suggests an optimal portfolio. In addition to recommendations, it will create, manage and rebalance the portfolio to ensure that the asset mix is on track to meet the investment goals. Investors need not worry about tracking the markets, just sit and forget about it. Money will work hard automatically, with other perks of lower fees compared to traditional advisory services with the flexibility of leveraging the service with a lower initial deposit as well. U S E C A S E S 10 AI implementation in Banking is expected to surge from USD 5.13B in 2021 to USD 64.03B by 2030 and have an impressive CAGR of 32.36%. As per PWC analysis, 45.% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. Potential contribution to global economy is expected to be $15.7tr, up to 26% boost in GDP for local economies.
- 11. Siddharaj Masali joined Capco in May 2022 as BA/PM. He has 13 years of total experience into banking operations, I.T business analysis, product and project management with end-to-end implementation exposure, digital transformation in fintech space into product and service-based organizations.” Conclusion: AI at present is doing an excellent job in transforming various industries and revolutionizing the way humans interact with technology. Chat GPT and Stable Diffusion have everyone talking about AI. It can assist in the production of movies and create realistic virtual actors in the gaming industry. In healthcare, AI can facilitate new drug compound synthesis, assist in medical imaging analysis and generate personalized treatment plans. Needless to say, AI has grown leaps and bounds in many sectors and has proved to be essential in maintaining a competitive edge. We encounter AI every day without noticing, it is now a household term. With so many growth prospects, we are still at the beginning of the AI journey to uncover immense potential reach and capabilities. There has been continuous research to assess the impact of the new AI era. Currently, models and capabilities are enhanced with more data which will lead to even stronger possibilities and tools. In the coming years, AI will be capable of carrying an ever-growing number of tasks and augmenting our skills in all ways and new opportunities will continue to appear. 11
- 12. B y N a r e n d r a S i n g h a l A I t o I m p l e m e n t E S G ESG stands for Environmental Social and Governance. ESG has evolved from being a corporate social responsibility (CSR) department with siloed initiatives to inclusive ESG frameworks and risk management. It is about how an organization impacts the Environment and Communities; the way an organisation gets impacted by climate change, socio-economic factors, etc. Regulators across the world today want companies to report on ESG. To state in financial terms, companies increasingly need to identify, quantify, and mitigate ESG risks. With the adverse effects of climate change manifesting around us in the form of flash floods and droughts, finding ways for Sustainable Development (SD) has become inevitable. The figure covers some of the most important ESG metrics. Following are some of the significant factors driving the world working towards ESG: • Investors and stakeholders (Customers, employees) increasing awareness and concerns about ESG risks, and the ways companies are mitigating them. For example, BlackRock considers ESG as the most crucial factor in investing. • Regulators becoming increasingly stringent and bringing in stricter regulations and ESG disclosure requirements across the globe. • Accelerating Global warming is putting a question on the very survival of humans on earth. ESG has been acknowledged as one of the most critical non-financial risks by organizations worldwide. However, there are several challenges in measuring the ESG performance of a company and baselining investment decisions on the same. The challenges can be broadly categorised as: • Governance and Strategy • Absence of Standardized frameworks & metrics • Data definition, Collection & measurement • Complexity of Upstream & Downstream impact Data The Governance and Strategy area reflects the challenges in integrating ESG into the organisation’s culture, existing risk management framework and business strategy. The other two areas: Data and Upstream & downstream measurement are more related to the data strategy of the organisation and the way organisation is defining and measuring the ESG data across the value chain. C h a l l e n g e s i n E S G I m p l e m e n t a t i o n & I n v e s t i n g 12
- 13. 13 Generative Artificial Intelligence (Gen AI) is a type of AI technology that can take huge volumes of data as input and produce various outputs such as Content including text, imagery, audio, and synthetic data. The recent hype around Gen AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics, and videos in a matter of seconds. AI can help orchestrate the ESG efforts of an organization. AI can help in ESG in two different ways: Macro environmental Metrics: First is a direct way where AI and machine learning can help measure, monitor, and track environment metrics such as weather patterns, Global warming, the extent of pollution and so on. These measures help us take timely actions to mitigate risks. ESG Data measurement analysis & Reporting: The indirect way is by utilizing AI-driven predictive models, companies can identify and mitigate risks associated with ESG factors. Algorithms can analyze volumes of data from sources, such as distributed internal systems, supply chain data, regulatory changes and social media to assess potential risks to businesses. U s i n g A I t o f a c i l i t a t e E S G R e p o r t i n g & I n v e s t i n g One of the most important aspects of ESG investing and reporting is identifying the right metrics and measuring its ESG impact across the value chain. While AI will not be of much help in identifying what to measure, it facilitates accurate and real-time tracking of ESG performance metrics. Some of the areas where AI can help in ESG reporting are as follows: Processing huge volumes of data from multiple sources to provide a single view of ESG metrics such as emissions: Companies can use AI to collect, process, and analyze data from various sources across the value chain including suppliers, distributors, and customers. This will provide a single view of metrics such as Scope 1 2, and 3 emissions and overall sustainability efforts across the value chain and geographies. Large Language Models (LLM) can help with Sectoral Analysis: The context and the material ESG metrics in different industry sectors will vary. LLMs can be trained on metrics applicable to a particular sector to analyze large datasets and arrive at ESG performance scores for companies to help make investment decisions. Sentiment Analysis: AI algorithms trained to find out the tone in content can analyze all information available for a company (which is huge) to identify tone based on keywords related to ESG. This will help in predicting how a company will perform on ESG risks in the future. H o w c a n A I h e l p i n E S G I m p l e m e n t a t i o n ? U s i n g A I f o r E n v i r o n m e n t C o n s e r v a t i o n AI in agriculture & Biodiversity: AI can help farmers with monitoring of crops in real-time in detecting insects and diseases, providing insights to take corrective action and enhance productivity. AI-based methods can identify changes in the distribution, abundance, and traits of species over space and time. More accurate weather forecasts: IBM has already applied the use of AI for optimizing their weather forecasting, resulting in a 30% improvement in their predictions. Better Conservation of Natural Resources: Technologies such as Smart Grids can help us conserve electricity. Satellite imagery data can help in measuring land use, vegetation, forest cover, and the fallout of natural disasters.
- 14. Narendra Singhal is a Senior Consultant at Capco with over 12 years of experience as a Business Analyst in Financial Services. He has experience across domains such as Cards, Billing, and Investment Banking. He is a sports-enthusiast who loves traveling and poetry-reading 14 Availability of Robust and Reliable Data For AI to provide accurate insights, robust and reliable data is indispensable. However, in the ESG investing area, data quality and availability remain significant challenges. Inconsistent reporting standards, lack of standardized data formats, and limited access to relevant ESG data pose hurdles. The absence of standardized frameworks and metrics For evaluating ESG performance it is essential to create universal AI models, metrics and comparisons across industries and regions. The development of consistent standards and methodologies are still in a nascent stage and would require them to accelerate AI adoption for ESG. AI bias and safety issues If the AI models are trained on biased data, results would also be biased and will not reflect the real picture. Also, general biases as a society and civilization can skew the outputs from AI. AI, the double-edged sword The counter effect on the environment, for example, is the electricity consumption by AI for processing large volumes of data leads to increased emissions. When employing AI, we need to be mindful that AI itself is sustainable and consumes less or green energy for processing. World Economic Forum (WEF) feels that the world will not be able to meet ESG goals and address climate change without extensive use of AI. The alarming rate at which environmental degradation is happening makes it imperative for humanity to accelerate the Net-zero emission objectives. As AI continues to evolve, its potential to enhance ESG investing for retail investors will only grow, offering new opportunities for responsible and impactful investment strategies, including using predictive modeling to identify trends and patterns for investment opportunities. On a final note, it is important to strike a balance between reliance on AI and human oversight. ESG implementation and investing sometimes require thoughtful consideration of complex social and environmental factors that may not be fully captured by algorithms alone. It has enormous potential in helping us accelerate ESG goals, it should always be used with human oversight in any context. C o n c l u s i o n ESG investing is making ESG performance of a company as the main criteria. Many large Fund Managers around the world such as BlackRock have made ESG their primary criteria for investing in any company or fund. C h a l l e n g e s i n u s i n g A I t o a d v a n c e E S G i m p l e m e n t a t i o n & i n v e s t i n g
- 15. T H E M E : G e n e r a t i v e A I Q u i z 1. What are some of the potential benefits of Generative AI? A. Generative AI can be used to create new and innovative products and services. B. Generative AI can be used to improve the quality of life for people with disabilities. C. Generative AI can be used to solve complex problems that are currently beyond the reach of human intelligence. D. All the above. 2. What is the purpose of a language model in Generative AI? A. To generate new text that is indistinguishable from human-created text. B. To automate tasks that are currently done by humans, such as writing emails or generating reports. C. To learn from a large dataset of text and use that data to generate new examples. D. To classify existing text into one of a set of categories. 3. The performance of large language models (LLMs) generally improves as more data and parameters are added. A. True B. False 4. How does generative AI work? A. It uses a neural network to learn from a large dataset. B. It uses a generic algorithm to evolve a population of models until it finds one that can generate the desired output. C. It uses the internet to repeat answers for common questions. D. It uses a rule-based system to generate output based on a set of predefined rules. 5. What action does Google recommend organizations to take to ensure that AI is used responsibly? A. Use a checklist to evaluate responsible AI. B. Follow a top-down approach to increase AI adoption. C. Focus on being efficient. D. Seek participation from a diverse range of people. 6. What is the most common type of Generative AI? A. Neural networks B. Genetic algorithms C. Decision trees D. Rule-based systems B y E d i t o r i a l T e a m 15
- 16. 7. What are the foundation models in Generative AI? A. They are a type of Generative AI that uses two neural networks that compete against each other. B. They are a type of Generative AI that uses a single neural network to encode and decode data. C. They are a type of Generative AI that is used to create new text that is indistinguishable from human-created text. D. They are a type of Generative AI that is used to create new images that are indistinguishable from human-created images. 8. Which of the following is a type of Generative AI that is used to create new text that is indistinguishable from human-created text? A. GANs B. VAEs C. Decision trees D. Rule-based systems 9. What is the difference between Generative AI and Discriminative AI? A. Generative AI creates new content, while discriminative AI classifies existing content. B. Generative AI is more accurate than discriminative AI. C. Generative AI is more efficient than discriminative AI. D. All the above. 10. What are some of the challenges of Generative AI? A. It can be difficult to train Generative AI models. B. Generative AI models can be biased. C. Generative AI models can be used to create harmful content. D. All the above. 16
- 17. 17 1 D. All the above 2 C. To learn from a large dataset of text and use that data to generate new examples. 3 A. True 4 A. It uses a neural network to learn from a large dataset. 5 D. Seek participation from a diverse range of people. 6 A. Neural networks 7 B. They are a type of Generative AI that uses a single neural network to encode and decode data. 8 A. GANs 9 A. Generative AI creates new content, while discriminative AI classifies existing content. 10 D. All the above. Q u i z A n s w e r s
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