What Is Generative AI? Generative AI Explained

Generative AI is a cutting-edge technology that leverages existing artifacts to seamlessly produce new and realistic content on a large scale, capturing the essence of the training data without duplicating it. This sophisticated system is capable of generating diverse forms of creative output, ranging from images, videos, and music to speech, text, software code, and product designs.

The foundation of generative AI technology lies in advanced techniques, notably the use of Gartner Artificial Intelligence (AI) foundation models. These models undergo training on a broad set of unlabeled data, enabling their application across various tasks through additional fine-tuning. The development of these models demands complex mathematical algorithms and substantial computing power, essentially rendering them as advanced prediction algorithms.

Presently, generative AI is most commonly employed to generate content in response to natural language requests, eliminating the need for explicit knowledge of or inputting code. However, its applications extend far beyond this, with enterprise use cases encompassing groundbreaking innovations in fields such as drug and chip design, as well as material science development.

What Fuels the Recent Surge in Enthusiasm for Generative AI?

Generative AI has been a focal point on Gartner’s Hype Cycle™ for Artificial Intelligence since 2020, earning recognition as one of the Top Strategic Technology Trends for 2022. Progressing from the Innovation Trigger phase to the Peak of Inflated Expectations, it took a significant leap into mainstream consciousness in late 2022, propelled by the launch of ChatGPT—an OpenAI-developed chatbot renowned for its remarkably human-like interactions.

The debut of ChatGPT marked a watershed moment, capturing widespread attention and sparking a surge in popularity virtually overnight. Notably, OpenAI’s DALL·E 2 tool, which generates images from text, contributed to this transformative landscape in generative AI.

Gartner envisions generative AI evolving into a general-purpose technology, wielding an impact akin to historical milestones such as the steam engine, electricity, and the internet. As the initial hype gradually subsides, the true implications of implementation will come to the forefront. Yet, the enduring influence of generative AI is expected to grow steadily as individuals and enterprises uncover increasingly innovative applications, integrating this transformative technology into the fabric of daily work and life.

Generative AI Applications

Foundation models, exemplified by generative pretrained transformers such as the driving force behind ChatGPT, represent a paradigm shift in AI architecture. These innovations empower automation, augmentation of both human and machine capabilities, and the autonomous execution of business and IT processes.

Generative AI offers manifold advantages, ranging from expediting product development to elevating customer experience and bolstering employee productivity. However, the tangible benefits are contingent upon the specific use case. End users must temper expectations, especially when utilizing a service in its current state, as it may harbor significant limitations. Notably, generative AI can produce artifacts that are inaccurate or biased, underscoring the imperative role of human validation and potentially mitigating the time-saving potential for workers. Gartner underscores the importance of aligning use cases with key performance indicators (KPIs) to ensure that projects either enhance operational efficiency or generate new revenue and improved experiences.

What Are the Risks of Generative AI?

The risks associated with generative AI examples are dynamic and substantial, with threat actors exploiting the technology to craft “deep fakes” and deceptive artifacts for sophisticated scams. Tools like ChatGPT, trained on extensive publicly available data, lack compliance with regulations such as the General Data Protection Regulation (GDPR) and copyright laws. Vigilance is crucial in managing enterprise usage of these platforms.

Key oversight risks include:

• Lack of Transparency: Generative AI and ChatGPT models are unpredictable, with even the creators having incomplete understanding. Addressing this requires close monitoring.
• Accuracy: Generative AI systems may produce inaccurate or fabricated outputs, necessitating thorough assessment before reliance or public distribution.
• Bias: Policies and controls are essential to detect and handle biased outputs in accordance with company policy and legal requirements.
• Intellectual Property (IP) and Copyright: Lack of verifiable data governance assurances necessitates controls to prevent inadvertent exposure of confidential enterprise information.
• Cybersecurity and Fraud: Enterprises must prepare for malicious use of generative AI in cyber and fraud attacks, implementing mitigating controls and verifying coverage with cyber-insurance providers.
• Sustainability: Generative AI’s electricity consumption poses sustainability challenges; choosing vendors with reduced power usage and reliance on renewable energy aligns with sustainability goals.

Gartner recommends addressing critical questions:

• Responsible Use: Define responsible use, ensuring compliance, consequences for irresponsible use, and adaptation to evolving cultural norms and social engineering approaches.
• Action in Event of Issues: Establish procedures for individuals to take action in case of problems.
• Consent: Clarify opt-in or opt-out mechanisms for user consent, learning from ongoing privacy debates.
• Trust Impact: Evaluate whether generative AI use enhances or diminishes trust in the organization and institutions overall.
• IP Control and Compensation: Safeguard content creators’ control of IP and fair compensation, exploring new economic models.
• Lifecycle Oversight: Determine responsibility for ensuring proper functioning throughout the AI life cycle, potentially appointing an AI ethics lead at the board level.

Generative AI Use Cases

Generative AI by Mobiz is poised for rapid advancement in scientific discovery and technology commercialization, with swiftly emerging use cases in creative content, content enhancement, synthetic data, generative engineering, and generative design.

Current high-impact examples of generative AI encompass:

• Written Content Augmentation and Creation: Generating draft outputs of text in desired styles and lengths.
• Question Answering and Discovery: Facilitating users in locating answers based on input, leveraging data and prompt information.
• Tone Manipulation: Text adjustment to soften language or professionalize content.
• Summarization: Providing condensed versions of conversations, articles, emails, and webpages.
• Simplification: Breaking down titles, creating outlines, and extracting key content.
• Content Classification: Sorting content by sentiment, topic, and more.
• Chatbot Performance Improvement: Enhancing “sentity” extraction, sentiment classification throughout conversations, and generating journey flows from general descriptions.
• Software Coding: Code generation, translation, explanation, and verification.

The best generative AI use cases with enduring impacts include:

• Medical Image Prediction: Creating images depicting the future development of diseases.
• Synthetic Data Augmentation: Assisting in mitigating bias, preserving data privacy, and simulating future scenarios with scarce data.
• Proactive Application Suggestions: Offering additional actions and providing users with relevant information.
• Legacy Code Modernization: Updating and improving outdated code structures.

The generative AI model promises transformative applications across diverse domains, shaping the future of scientific exploration and technological innovation.

How Generative AI Works: How Will Generative AI Contribute Business Value?

Generative AI emerges as a transformative force, presenting novel and disruptive opportunities for revenue growth, cost reduction, enhanced productivity, and more effective risk management. In the near future, it is poised to evolve into a crucial competitive advantage and differentiator in the business landscape.

Gartner divides these opportunities into three distinct categories:

Revenue Opportunities

Product Development

Generative AI facilitates the rapid creation of innovative products, spanning from new drugs and eco-friendly household cleaners to unique flavors, fragrances, alloys, and improved diagnostic tools.

New Revenue Channels

Enterprises with advanced AI maturity levels stand to gain substantial revenue benefits, according to Gartner’s research.

Cost and Productivity Opportunities

Worker Augmentation

Generative AI enhances workers’ capabilities in drafting, editing text, images, and media. It excels in summarizing, simplifying, and classifying content, as well as generating, translating, and verifying software code. Technology exhibits high proficiency in creating diverse artifacts quickly and at scale.

Long-term Talent Optimization

A symbiotic relationship between employees and AI fosters differentiation based on the ability to conceive, execute, and refine ideas, projects, processes, services, and relationships. This collaboration accelerates proficiency and significantly broadens the competency of workers across various domains.

Process Improvement

Generative AI extracts contextual value from vast content stores, transforming workflows and leveraging previously untapped resources.

Risk Opportunities

Risk Mitigation

Generative AI’s analytical prowess provides comprehensive visibility into data, such as customer transactions and potentially flawed software code, enhancing pattern recognition and expediting the identification of potential risks to enterprises.

Sustainability

Generative AI plays a pivotal role in helping enterprises comply with sustainability regulations, mitigating the risk of stranded assets, and integrating sustainability into decision-making, product design, and processes.

Generative AI stands at the forefront of business innovation, poised to reshape industries by unlocking unprecedented possibilities across revenue generation, operational efficiency, and risk management.

The Bottom Line

Generative AI is a revolutionary technology using existing data to create diverse content. Fueled by advanced AI models like ChatGPT, it has surged in popularity, transitioning from hype to mainstream recognition. While offering advantages such as product development acceleration, it poses risks like biases and lack of transparency. Practical uses span content creation, question answering, and medical image prediction. As it advances, Generative AI promises significant business value, creating revenue opportunities, augmenting worker capabilities, improving processes, mitigating risks, and promoting sustainability. Positioned as a crucial competitive advantage, it stands to reshape industries through innovation in revenue generation, operational efficiency, and risk management.

Frequently Asked Questions

How Does a Generative AI Model Work?

A generative model learns patterns from data to create new, similar instances. It captures underlying structures and can generate novel content, such as images, text, or other forms.

Which Technique Is Commonly Used in Generative AI?

Generative AI often employs techniques like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) to create new data instances based on learned patterns.

Difference Between ChatGPT and Generative AI?

ChatGPT is a specific instance of generative AI. While generative AI encompasses various models creating diverse content, ChatGPT is tailored for conversational text generation, designed by OpenAI.

Disadvantages of Generative AI?

Disadvantages include potential biases in generated content, difficulty in control over output, and challenges in ensuring ethical use. Training large models also demands substantial computing resources.

Biggest Benefits of Generative AI?

Generative AI’s key benefits include creative content generation, data augmentation, and potential applications in diverse fields like art, language processing, and even aiding in problem-solving through simulation and scenario generation.