What is Generative AI?

Generative AI (GenAI) is a branch of Artificial Intelligence that focuses on creating new content (text, images, audio, video, code, or even designs) instead of just analyzing or classifying existing data.

Unlike traditional AI, which is mainly about recognizing patterns and making predictions, Generative AI learns from huge amounts of data and then generates something entirely new that resembles the training data.

Objective 

Tagline Ideas:

• “Where Quality Meets Innovation with Generative AI”

• “Shaping Smarter Processes through Generative AI and Quality Training”

• “AI-Powered Excellence for a Resilient Future”

AI opportunities and risk

1. Risk related to increased capacity for technology.  Even if there are a lot of known GPT danger regions, there will surely be more because GPT-4 was just released. Intentional or not, technology misuse is unavoidable. It is essential to have proactive planning, governance, risk management, and ongoing research.

2. Stereotypes and biases can be reinforced by language models.  There is still a lot of focus on computational aspects (such represented data and fairness), but systemic and human biases as well as societal concerns are not addressed. In many cases, inputs are already skewed because the data that users provide to generative AI technologies will be utilized to affect future outputs..

3. Laws have long lagged behind the rapid advancements in technology. The proliferation of generative AI has given rise to a number of intellectual property-related concerns and highlights the necessity of both effective privacy laws (particularly in the US) and oversight.

4. Risks associated with automated systems include processing errors, inadequate design, implementation, and operation, as well as inadequate oversight. Both human alternatives and clear, succinct notifications to users that offer generally readable plain language documentation about the general operation of the system and the role automation performs are essential. Additionally, it is the duty of businesses to establish explicit policies on the use of technology in the workplace.

5. Numerous vendor solutions that claim to address any enterprise problem have historically been spawned by the mismatch between the supply and demand for tech talent. GPT-4’s cybersecurity utility is currently somewhat restricted. Social engineering mitigation will be strained by GPT-4’s anticipated increase in phishing email plausibility, necessitating improvements to cybersecurity education and awareness training.

6. FUD about AI displacing human occupations is not new, but the present focus appears to be on augmenting human capital, which may not always be the case. Importantly, people continue to be essential for communication, creativity, and contextualization because AI is only as good as the data it is trained on. Some jobs could be replaced by AI, which makes policymaking more important both internationally and domestically. Instead of displacing workers, it is more likely that the emergence of technologies like GPT-4 would lead to a reimagining of work and a redistribution of some work tasks in IT-related industries.

Learning outcomes: 

• Generative AI Concepts and Challenges—Gain an understanding of the fundamental principles and hurdles encountered in Generative AI.
• Analyze Generative AI and Formulate AI Strategy—Acquire the skills to identify and dissect the role of Generative AI within broader AI strategies.
• Evaluate Generative AI Risks Across the AI Lifecycle—Develop proficiency in interpreting, classifying, and evaluating risks associated with Generative AI throughout its lifecycle.
• Develop Auditing Methodology and Guidelines—Enhance your understanding by comprehending and crafting methodologies and guidelines specific to auditing Generative AI.
• Address Generative AI Auditing Challenges—Recognize and overcome unique auditing challenges related to Generative AI to mitigate auditing concernStereotypes and biases can be reinforced by language models.