The A-Z of Generative AI and ChatGPT - Chapter 6

Generative Artificial Intelligence (#GenAI), has the potential to disrupt every job and industry in its path. It's potential is as alarming as it is exciting. How will Generative AI impact you, your business and your industry?

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Generative AI, a subset of AI, has taken the business world by storm since OpenAI released ChatGPT in November 2022. The ability to 'prompt' on any topic and get an answer is as magical as it is mysterious. Businesses want to use this technology to create value - as do the individuals working within those businesses - but they don't yet fully understand generative AI nor it's inherent risks (e.g., bias, IP protection issues, etc.) to realise its full potential just yet.

So why is Generative AI causing such excitement?

Because it is a technology that people are excited to use as it enables them to search, work, learn, and engage in a human-like way that is more productive and creative.

According to a July 2023 report from Goldman Sachs, GenAI could raise productivity by 1.5 percentage points after ten years of broad adoption, which would roughly double the recent pace of US productivity growth.        

We are entering a new era of software and business - Intelligence 2.0 - where generative AI's capabilities and applications are limited only to our imaginations.? And more and more software products are being created to fulfil our every wish.

Generative AI applications can already create many types of written, image, video, audio, and code content. And businesses are accelerating the development of applications that deliver use use cases across everyone of these areas.

Generative AI learns the patterns and structure from its input training data and then generates new data. So, if you can think it and describe then generative AI can (or will) be able to create it. As such, the practical implications and applications of Generative AI are exciting. It has immense potential to revolutionize any field where creativity and innovation are key which is a very different business shift than ever before (i.e., knowledge work).

This is different to previous generations of automation technologies which were focused not on knowledge work but on physical work.

It's precise productivity and economic impact is yet to be worked out but it will be significant.

Now that is exciting. But AI is not without risk e.g., job loss, algorithmic bias, hallucinations, hallucinations, lack of explainability and transparency from opaque inputs, and issues of privacy, confidentiality and copyright. Generative AI is new and potentially choppy waters for businesses who must be cautious - as they should be with any new technology - but inaction is not an option.

AI and Generative AI offer many benefits, but they also come with various risks and challenges.

What are some of the KEY risks associated with AI and Generative AI?

1. Bias and Fairness: AI models, including generative AI, can inherit biases from their training data. This bias can lead to unfair or discriminatory outcomes, impacting marginalized groups and perpetuating existing biases.
2. Privacy Concerns: Generative AI can generate highly convincing fake content, such as text, images, or videos, which can be used for malicious purposes, including malicious deepfakes or spreading disinformation.
3. Security Vulnerabilities: AI models, if not properly secured, can be vulnerable to attacks. Adversarial attacks can manipulate the input data to fool the model or exploit weaknesses in the model's architecture.
4. Legal and Ethical Issues: The use of AI, especially in critical domains like healthcare or finance, can raise legal and ethical questions related to liability, accountability, and regulatory compliance.
5. Data Privacy: The large amounts of data required to train AI models, including generative AI, raise concerns about data privacy, consent, and the potential misuse of personal information.
6. Dependence on AI: Overreliance on AI systems without human oversight can lead to critical errors or decisions that lack empathy or context.
7. Explainability and Interpretability: Generative AI models often lack transparency, making it difficult to understand how they arrive at their outputs. This can be a challenge in critical applications where decision-making transparency is essential.
8. Data Security: AI models can inadvertently reveal sensitive information about their training data, posing data security risks.
9. Job Displacement: Automation driven by AI can lead to job displacement in certain industries, potentially causing economic and societal disruptions.
10. Adverse Effects on Creativity: In creative fields, there's concern that AI-generated content may diminish the value of human creativity and artistic expression.
11. Ethical Usage: The ethical use of generative AI is a concern, as it can be used for purposes like creating fake reviews, misleading content, or deceptive online personas.
12. Regulatory Changes: Rapid advancements in AI technology can outpace regulatory frameworks, leaving a gap in oversight and enforcement.
13. Resource Consumption: Training large AI models consumes significant computational resources and energy, contributing to environmental concerns.
14. Moral Hazard: Overconfidence in AI predictions can lead to complacency in human decision-making, assuming that the AI model will always be correct.
15. Model Degradation: Over time, AI models can degrade in performance due to concept drift (when the real-world data distribution changes) or adversarial actions.

How can businesses overcome risks associated with AI?

Businesses can take several steps to overcome risks associated with AI (Artificial Intelligence). Here are some strategies to mitigate those risks:

1. Understand the Risks: Start by understanding the potential risks and challenges specific to your AI application. These may include bias, data privacy concerns, security threats, and legal/regulatory compliance issues.
2. Data Quality and Management: Ensure that the data used to train and test your AI models is of high quality, representative, and diverse. Implement data governance practices to maintain data integrity and protect sensitive information.
3. Bias Mitigation: Address bias in AI systems by carefully selecting training data, using diverse datasets, and applying bias-detection and correction algorithms. Regularly audit and update your models to reduce bias.
4. Transparency and Explainability: Use interpretable AI models whenever possible, and develop systems to provide explanations for AI decisions. This enhances transparency and helps build trust with stakeholders.
5. Security Measures: Implement robust security measures to protect AI systems from cyber threats. Regularly update software, apply security patches, and conduct security audits and penetration testing.
6. Compliance with Regulations: Stay informed about relevant regulations and standards related to AI, such as GDPR, HIPAA, or industry-specific rules. Ensure that your AI systems are compliant with these regulations.
7. Ethical AI Principles: Establish ethical guidelines for AI development and usage within your organization. These guidelines should align with your company's values and prioritize fairness, transparency, and responsible AI.
8. Regular Audits and Monitoring: Continuously monitor AI systems for performance, bias, and security vulnerabilities. Conduct regular audits to identify and rectify issues.
9. Human Oversight: Maintain human oversight over AI systems. Ensure that humans are involved in critical decision-making processes and can override AI decisions when necessary.
10. Education and Training: Train your staff in AI ethics, best practices, and cybersecurity. Make sure they understand the potential risks and how to mitigate them.
11. Collaboration and External Audits: Collaborate with external experts or organizations to conduct third-party audits of your AI systems. This can provide an independent assessment of AI system safety and fairness.
12. Insurance Coverage: Consider obtaining AI-specific insurance coverage to protect against unforeseen risks and liabilities associated with AI technologies.
13. Fail-Safe Mechanisms: Implement fail-safe mechanisms in AI systems that can detect and respond to unexpected situations or errors to prevent catastrophic outcomes.
14. Continuous Improvement: Embrace a culture of continuous improvement and adaptation. As AI technologies evolve and new risks emerge, your organization should stay agile and responsive.
15. Stakeholder Engagement: Engage with stakeholders, including customers, employees, and regulators, to gather feedback and address concerns related to AI. This can help build trust and ensure that AI systems meet user expectations.

By taking a proactive approach to managing AI risks and implementing these strategies, businesses can harness the potential of AI while minimizing potential negative consequences.

NOTE: Remember that risk management is an ongoing process that should evolve alongside your AI initiatives and the changing landscape of AI technology and regulations.

The rise and democratization of GenAI is already augmenting global citizens with capabilities they never dreamt of before. Individuals with zero corporate experience are now able to draft emails with the finesse of a seasoned staff assistant, while freshmen in B-Schools are able to create vivid and captivating presentations for their audiences without having to go through the learning curve. This new paradigm is set to make humans more independent and more capable than ever before.

Rahul Zende Principal Data Science at Navy Federal Credit Union

Let's take, for example, a mechanical technician. The essence of innovation lies in the primary function of a tool, which should be to enable them to perform tasks more efficiently and accurately than manual methods do. The excitement surrounding the emergence of boundless potential in the Generative AI 'as a tool,' especially when integrated with the capabilities of Conversational AI, represents an innovation worth exploring to enhance our lives across various industries and roles. Innovation is often accompanied by a degree of risk, and we should not be afraid of it. On the contrary, we should delve deeper into it and take full advantage of the opportunities it presents.

Denny Morais, Partner Pre-Sales Enablement Director & Intelligent Automation Evangelist

Companies that build a strong foundation of AI by adopting and scaling it now, where the technology is mature and delivers clear value, will be better positioned to reinvent, compete and achieve new levels of performance.

Julie Sweet, chair and CEO, Accenture.

Need help understanding Generative AI and how it applies to your business ?

Then book a?FREE 30 minute introductory call?so we can discuss your specific business Intelligent Automation, Robotic Process Automation, Data Analytics, Artificial Intelligence, Generative AI needs -?click here.

Generative AI has changed the very nature of how businesses operate and deliver value. It can 'generate' text, speech, images, music, video, and computer code in response to a command.

When that capability is joined securely with a business's own proprietary information then how businesses make money dramatically changes (i.e., public data and a private LLM with company proprietary data securely protected).

Generative AI will drive efficiency and innovation across all industries at scale. What ChatGPT can enable will be greater than any existing software produced to date.

For example, today we have to spend inordinate amounts of time manually searching through applications for the most basic of data. Now, our conversational queries can be recognized and answered in milliseconds freeing up knowledge workers by up to 40% - that is time that be reinvested much more productively than before.

Will this time save impact knowledge workers? Well just under two thirds of you seem to think so.

What are the long term implications of Generative AI? No one can be sure.

Why? The long-term implications of this technology are yet to reveal themselves but they will be significant. For example, McKinsey has predicted that AI could add up to $4.4 trillion to the global economy. Though time will tell if that number is big enough as Goldman Sachs believe it to be $7 trillion.

To date AI permeated our lives buy only incrementally and unconsciously e.g., think Netflix movie recommendations or Amazon product recommendations.

But make no mistake, we have entered an exciting and tumultuous era of exponential AI technology innovation. Where we work, and how we work, will change because the work we do will is changing with AI accelerating that change at a pace we have never witnessed before.

Where consumers were somewhat conscious of AI in driverless vehicles proportionally few of us went on to own such vehicles. Now we are much more are aware of ChatGPT, AWS CodeWhisper, GitHub Copilot and Stable Diffusion as we can all use AI using natural language for free from our smartphones or desktops. AI has finally been democratised at an insignificant marginal cost.

We have reached a major junction in time. Governments, industries, businesses, and workers that rapidly adapt to this brave new AI world will thrive - those that don't, simple wont.

Humanity needs to change. Why?

Knowledge work has seen minimal automation to date. But now, for one of the first times, higher paid knowledge workers are in the direct firing line of an AI / automaton technology. Though arguably personal computers and the automated ecommerce platforms impacted knowledge workers roles before it but not in the same way.

Yet where PCs and ecommerce websites enabled workers to be more productive they have not had the same impact on the anatomy of work that Generative AI will.

For example, knowledge workers spend a tremendous amount of time searching for information and emailing others. Generative AI can significantly reduce the time taken for both because it is so good at interpreting and summarizing wide varieties of information accurately and quickly. In addition, it can learn from the past experiences and perspectives in the same way that people do. Improving the efficiency of both these tasks can have a significant impact on productivity.

As a result, McKinsey argue that generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees' time today. And if knowledge workers are negatively impacted then the financial and tax food basket of most western economies will disappear. Knowledge workers are usually the highest earning and the highest taxed. Remove or severely diminish that spend, investment money, trickle down income and tax income and whole economies will be hurt.

How can we change?

We need to urgently develop and enhance our people skills in ways that machines cannot replicate - empathy, intuition, critical thinking, adaptability, emotional and social intelligence - and learn to use AI technology to catalyse them to remain employable. AI won't take your job, but someone who knows how to use AI surely will.

Keen to learn more? Then keep reading.

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1. Feature Engineering in Generative AI ??

Feature Engineering in Generative AI involves selecting and creating relevant data attributes to improve the AI model's performance. Like assembling the right ingredients to craft a delectable recipe, feature engineering enables AI models to focus on essential data elements, leading to more accurate and meaningful content generation.

For example, an online travel agency might employ Feature Engineering in Generative AI to generate personalized travel itineraries. By selecting relevant user preferences, such as preferred destinations and travel dates, the AI model creates tailored vacation plans.

Example prompt: "Use feature engineering to generate personalized workout routines for our fitness app users. Consider factors like fitness goals, available equipment, and preferred exercise types to create customized plans."

2. Feedback Loop in Generative AI ??

The Feedback Loop in Generative AI involves continuously gathering and utilizing user feedback to improve AI-generated content. Like a musician refining a performance based on audience reactions, the feedback loop helps AI models adapt and enhance their content generation process.

For example, a social media platform could use the Feedback Loop in Generative AI to improve content moderation. The AI model learns from user reports and feedback to detect and filter inappropriate or harmful posts more effectively.

Example prompt: "Incorporate a feedback loop to generate relevant news summaries. Learn from user preferences and interactions to fine-tune the content selection and presentation."

3. Fine-Tuning in Generative AI ??

Fine-Tuning in Generative AI involves updating a pre-trained AI model using a smaller, domain-specific dataset. Like customizing a pre-made template to fit unique requirements, fine-tuning allows businesses to tailor AI models to their specific needs, resulting in more accurate and contextually relevant content generation.

For example, a legal firm might perform Fine-Tuning in Generative AI to draft legal documents. By training the AI model on specific legal jargon and case precedents, the system generates legally accurate contracts.

Example prompt: "Fine-tune your language generation abilities for our marketing department. Train the AI model using our marketing materials to ensure the content aligns with our brand voice."

4. Frameworks for Generative AI ??

Frameworks for Generative AI are pre-built software tools that streamline the development and deployment of generative models. Like a construction kit, these frameworks provide ready-made building blocks, making it easier for developers to create powerful and sophisticated generative AI applications.

For example, an e-commerce platform could leverage Frameworks for Generative AI to create personalized product recommendations. Using a pre-built recommendation engine, the platform suggests relevant products to users based on their browsing history and preferences.

Example prompt: "Employ a framework for generative AI to generate dynamic pricing suggestions for our retail website. Utilize the framework to integrate historical sales data and competitor pricing information."

5. Fusion of Generative AI and Other Technologies ??

The Fusion of Generative AI and Other Technologies involves integrating generative models with other AI techniques to enhance performance. Like combining various musical instruments to create a harmonious symphony, fusion techniques leverage the strengths of different AI technologies to achieve superior results.

For example, a healthcare research institute could combine Generative AI with image recognition technology to diagnose medical conditions from X-rays more accurately. The fusion of these technologies provides more precise and early disease detection.

Example prompt: "Demonstrate the fusion of generative AI and natural language processing to create an AI-powered virtual assistant for customer support. Train the AI model to understand customer queries and generate contextually relevant responses."

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6. Fairness in Generative AI ??

Fairness in Generative AI addresses the issue of bias and discrimination in AI-generated content. Like ensuring equal opportunities for everyone, fairness in AI ensures that content generated by AI models does not favour any particular group and remains unbiased.

For example, a recruiting platform might prioritize Fairness in Generative AI to generate job advertisements. The AI model avoids using gender-specific language, promoting gender-neutral content to encourage diversity in job applications.

Example prompt: "Adhere to fairness principles while generating content for our hiring process. Ensure that the AI-generated job descriptions avoid any discriminatory language or bias."

7. Flexibility and Adaptability in Generative AI ??

Flexibility and Adaptability in Generative AI allow AI models to adjust and respond to changing conditions and preferences. Like a versatile actor adapting to different roles, flexible and adaptable AI models can generate diverse content to meet evolving business requirements.

For example, a digital marketing agency could embrace Flexibility and Adaptability in Generative AI to create dynamic ad campaigns. The AI model generates multiple ad variants, adapting to different target audience segments.

Example prompt: "Demonstrate flexibility and adaptability by generating content for our social media campaigns. Create adaptable messages that cater to different demographics and market segments."

8. Federated Learning in Generative AI ??

Federated Learning in Generative AI enables AI models to train collaboratively across multiple devices or locations while preserving data privacy. Like a team of distributed researchers sharing insights without revealing sensitive information, federated learning allows AI models to learn from diverse data sources securely.

For example, a mobile app might use Federated Learning in Generative AI to improve language translation. The AI models on users' devices share translation improvements without sharing individual text data.

Example prompt: "Utilize federated learning to enhance your multilingual capabilities. Collaborate with multiple devices to improve your understanding and translation of various languages."

9. Feature Learning in Generative AI ??

Feature Learning in Generative AI involves training AI models to automatically discover relevant patterns and features from data. Like a skilled detective uncovering hidden clues, feature learning enables AI models to recognize and represent essential characteristics, resulting in more accurate and creative content generation.

For example, a music streaming service could employs Feature Learning in Generative AI to generate personalized playlists. The AI model learns user preferences, such as favourite genres and artists, to create tailored playlists.

Example prompt: "Demonstrate feature learning in your responses to customer inquiries. Recognize and represent different aspects of customer queries to provide more contextually relevant answers."

10. Future Prediction with Generative AI ??

Future Prediction with Generative AI involves using generative models to forecast future trends or scenarios. Like a fortune teller envisioning future events, generative AI can analyse historical data to make predictions about future outcomes.

For example, a financial firm could employ Future Prediction with Generative AI to predict stock market trends. The AI model uses past financial data to generate projections for potential future market movements.

Example prompt: "Demonstrate future prediction capabilities by forecasting customer demand for our products. Use historical sales data and market trends to generate predictions for upcoming sales periods."

11. Fitness with Generative AI ??

Prompts don't have to be work related. If you want to get into shape you can always prompt ChatGPT to give you a fitness program.

Example of a Fitness prompt.

Understanding the A-Z of Generative AI opens up a rich world of possibilities for business leaders. These concepts provide valuable insights into how AI can create new content, solve problems, and drive innovation across various industries. Embracing Generative AI can lead to enhanced creativity, improved decision-making and a competitive edge in the rapidly evolving data infused digital landscape.

A foundation model (also called a base model) is a large machine learning (ML) model trained on a vast quantity of raw data at scale (often by self-supervised learning or semi-supervised learning) such that it can be adapted to a wide range of downstream tasks e.g., writing, translating text, creating music, analysing medical images.

Some examples of foundation models include:

• GPT-3 (Generative Pretrained Transformer 3) by OpenAI1
• BERT (Bidirectional Encoder Representations from Transformers) by Google
• DALL-E 2 by OpenAI
• GANs (Generative Adversarial Networks)
• LLMs (Large Language Models)
• VAEs (Variational Autoencoders)

Foundation models underpin generative AI capabilities.

Some of the best Generative AI articles from some of the best sources on the internet.

• Generative AI will reshape industries, spark new creativity and permanently revolutionise work - Kieran Gilmurray
• ERP Today?Generative AI for the enterprise The AI Guru View?-?Pascal BORNET?and?Kieran Gilmurray
• Exploring Generative AI's Impact on Public Education - Kieran Gilmurray and Denny Morais
• Lunch Time Learning - Generative AI in 15 Minutes or Less - Kieran Gilmurray

Every single role you can think of will be impacted by Generative AI. In fact understanding how to interact with ChatGPT will soon be an essential key skill.?

Below are 5 roles that begin with the letter F with an example prompt each massively boosting role productivity.

Financial Analyst. Financial analysts assess financial data, analyze investment opportunities, and provide recommendations to help individuals or organizations make informed financial decisions.

• Prompt: Perform a detailed financial analysis of the company's budget and expenses to identify areas for cost-saving measures.
• Prompt: Create a comprehensive report comparing the financial performance of the company with its competitors and provide insights for improvement.

Forensic Accountant. Forensic accountants investigate financial crimes, analyze financial data, and provide expert testimony in legal proceedings.

• Prompt: Conduct a thorough audit of financial records to detect any fraudulent activities within a company.
• Prompt: Prepare a detailed financial report and expert analysis to present as evidence in a legal case involving financial fraud.

Fundraising Manager. Fundraising managers oversee fundraising campaigns and initiatives for non-profit organizations, aiming to secure donations and financial support.

• Prompt: Develop a targeted fundraising strategy for a new fundraising campaign to attract more donors.
• Prompt: Analyze past fundraising campaigns to identify successful tactics and areas for improvement.

Facilities Manager. Facilities managers are responsible for overseeing the operations and maintenance of buildings and facilities.

• Prompt: Implement a preventive maintenance schedule to minimize downtime and increase the longevity of equipment.
• Prompt: Create a plan to optimize the workspace layout for improved workflow and employee productivity.

Financial Planner. Financial planners work closely with clients to understand their financial goals and provide personalized financial advice and investment strategies.

• Prompt: Develop a financial planning template to streamline the process of creating tailored financial plans for clients.
• Prompt: Research and recommend new investment opportunities aligned with clients' risk tolerance and financial objectives.

Need help understanding Generative AI and how it applies to your business ?

Then book a?FREE 30 minute introductory call?so we can discuss your specific Data Analytics, Artificial Intelligence and Generative AI needs today -?click here.

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Who am I?

I am a senior executive with 28+ years of experience leading digital programs?and the author of the book “The A-Z of Organizational Digital Transformation.”?I have been a director, board member, research fellow, and advisor to multiple international companies.

Please find me?on social LinkedIn?|?Kieran Gilmurray?|?Twitter?|?YouTube

I am regularly ranked as one of the top global experts in Artificial Intelligence, Intelligent Automation, Data Analytics, Brand Influence, and Business Technology Innovation and have won multiple international awards, including:

??Top 14 people to follow in data in 2023

??Top 20 Data Pros you NEED to follow?

??World's Top 200 Business and Technology Innovators??

??Global Automation Award?Winner

??Top 50 Intelligent Automation Influencers??

??Top 50 Brand Ambassadors??

I am an hugely experienced data science leader who has lead teams of PHDs, data analysts, data engineers, and database administrators for many years, creating one of the few genuine Decision Intelligence companies to date along the way.

But don't just take my word for it.

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'Kieran is an exceptional technologist, automation expert, and skilled at AI, Data Analytics, and Decision Insight. His business and technical knowledge are second to none. If you or your business want to achieve your goals, then connect with Kieran'

Pascal Bornet.?Top Voice in Tech, Best Selling Author, AI & Automation Expert and Forbes Technology Council Member

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To stay on top of the latest news on Generative AI, Data Analytics, or emerging tech trends, make sure to subscribe to?visit my website, follow me on?Twitter,?LinkedIn, and?YouTube, and check out my best-selling book ‘The A-Z of Organizational Digital Transformation’ or?book a free 30 call?to chat on your business, AI, Generative AI, Intelligent Automation or Data Analytics needs.

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