When the term “Generative AI” is mentioned, many of us immediately think of ChatGPT. But what exactly is Generative AI? This remarkable technology involves the use of artificial intelligence to create fresh content, spanning text, images, music, audio, and videos. One such remarkable functionality is ChatGPT. In this blog, we’ll delve into the world of Generative AI, its workings, applications, and how organisations can safely and effectively embrace this transformative technology.
Understanding Generative AI:
At its core, Generative AI employs machine learning models to grasp the intricate patterns and relationships present in a dataset of human-generated content. With these learned patterns, the AI then crafts entirely new content, a process that fuels creativity and innovation.
Training a Generative AI Model:
A common approach to training Generative AI involves supervised learning. Here, a model is provided with a set of human-generated content paired with corresponding labels. Through this training, the model learns to generate content akin to the human-generated data, complete with the associated labels.
Applications of Generative AI:
Generative AI has an expansive array of applications that reshape various domains:
1.Enhanced Customer Interactions: Leveraging personalised content delivery, conversational interactions enable tailored recommendations spanning music, videos, and blogs, enriching chat and search experiences.
2.Exploring Unstructured Data: Conversational interfaces and summarizations aid in catalog descriptions, executive summaries, rapid content creation for advertising, social media, websites, emails, and more.
3.Streamlining Repetitive Tasks: From replying to proposals and localizing content to checking contracts and extracting insights from complex documents, Generative AI proves invaluable.
4.Advancements in Music and Video: Generating musical notes, aiding in linear and nonlinear video editing, and more – the possibilities are vast.
The Generative AI Landscape:
Tech giants like Microsoft, Google, Meta, and OpenAI are heavily investing in this space. Forums, podcasts, exhibitions, and discussions are consistently featuring Generative AI. Industry leaders such as Gartner and McKinsey predict significant growth. A new breed of companies is emerging to develop applications atop Generative AI, while others specialise in custom training generative models, backed by substantial venture capital funding.
Embracing Generative AI Safely and Effectively:
As a decision-maker, adopting Generative AI involves careful oversight:
1.Transparency: Generative AI models, including ChatGPT, can be unpredictable. Understanding their inner workings is a challenge even for the companies behind them.
2.Accuracy: Despite their capabilities, Generative AI systems may produce inaccurate or fabricated outputs. Always verify the accuracy, appropriateness, and usefulness of generated content.
3.Bias: Policies must be in place to detect and address biased outputs, aligning with company policies and legal requirements.
4.Intellectual Property (IP) and Copyright: Confidential enterprise information lacks verifiable data governance assurances. Assume entered data becomes public and implement controls to safeguard IP.
5.Cybersecurity and Fraud: Guard against malicious actors exploiting Generative AI for cyber and fraud attacks. Consult cyber-insurance providers to assess coverage for AI-related breaches.
6.Sustainability: Generative AI consumes significant electricity. Opt for vendors prioritising power efficiency and renewable energy to minimize environmental impact.
hmmm… Then How do i address this
Gartner’s AI TRiSM Framework:
The concept of AI TRiSM, introduced by Gartner, outlines a framework supporting AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and privacy. This encompasses solutions for interpretability, explainability, privacy, operations, and adversarial attack resistance
Practical Steps for Embracing Generative AI:
To adopt Generative AI effectively, Gartner suggests:
1.Internal Testing: Prioritize testing with internal stakeholders before deploying Generative AI for external interactions.
2.Transparent Communication: Clearly label conversations as AI interactions, maintaining transparency with staff, customers, and citizens.
3.Quality Control: Establish processes to identify biases and trustworthiness issues. Regularly validate results and monitor the model’s performance.
4.Privacy and Security: Ensure sensitive data isn’t used for training or input. Confirm with model providers that data won’t be used beyond your organization.
5.Gradual Implementation: Keep initial functionality in beta to manage expectations and fine-tune results over time.
Generative AI is a dynamic force shaping industries and paving the way for innovative content creation. As organizations embrace this transformative technology, careful consideration of the associated risks and proactive measures for oversight are paramount. By adhering to frameworks like AI TRiSM and following Gartner’s recommendations, organisations can harness Generative AI’s potential while mitigating potential pitfalls.