Navigating Generative AI: Understanding, Governance, and Applications
Updated: Oct 8
As the world delves deeper into the realm of artificial intelligence (AI), one particular branch, generative AI (GenAI), has been making waves with its innovative approach.
Generative AI: The Basics
Generative AI, a recent advancement in AI technology, diverges from traditional predictive models by focusing on creating new data rather than making specific predictions based on existing data. This distinction is often blurred, as the same algorithms can be applied to both types of tasks.
Although the excitement surrounding recent releases like ChatGPT may suggest otherwise, GenAI is not entirely new. It draws on research and computational progress spanning over 50 years. Early examples, such as Markov chains that predict an event based only on the previous event, were used for tasks like text prediction, albeit with limited complexity and scale.
Unlike simpler models, ChatGPT and similar systems leverage massive datasets and complex algorithms to generate more sophisticated outputs. These models, with billions of parameters, are trained on vast amounts of text data from the internet. By recognizing patterns and dependencies within this data, they can propose plausible continuations of text sequences.
Businesses Investing in Generative AI
The GenAI landscape is evolving rapidly, yet most organizations have not achieved sustainable growth beyond productivity gains. According to KPMG LLP’s (a U.S. audit and advisory firm) inaugural AI & Digital Innovation quarterly pulse survey, executives and business leaders in the United States are looking to accelerate their GenAI strategies. However, they acknowledge the need for substantial investments in data security, governance frameworks, and workforce readiness to realize long-term business value.
Steve Chase, KPMG’s Vice Chair of AI & Digital Innovation, notes that organizations are transitioning from pilot projects to transformational programs in the realm of GenAI. Early experiments have demonstrated its potential, prompting a readiness for increased investments that promise enterprise-wide productivity gains, business model reshaping, and new revenue streams. KPMG’s experience highlights the critical role of workforce readiness in determining the projects’ success, particularly as organizations enter this next phase.
KPMG’s survey showed the following plans that executives and business leaders have regarding AI:
Investment priorities:
o Ninety-seven percent of leaders plan to invest in GenAI in the next year, with 43 percent planning to allocate between $100 to $499 million, and 6 percent planning investments exceeding $500 million.
o Key considerations shaping their investment strategies include the readiness of risk management processes and governance (77 percent) and data security (76 percent).
o Current top investment priorities include enhancing customer experience (45 percent), workforce training and capability building (35 percent), purchasing GenAI technology and solutions (30 percent), and establishing responsible GenAI and governance programs (30 percent).
· CEOs’ role in AI readiness:
o CEOs or Chief AI Officers are crucial in driving AI transformation across enterprises, facilitating integration at various levels, implementing new processes, and ensuring workforce preparation.
o Forty-five percent of leaders have hired or are planning to hire new leadership to oversee AI investment and strategy, with 34 percent stating that CEOs are currently leading GenAI strategies.
· Measuring ROI (return on investment) during AI implementation:
o Half of the leaders gauge their return on GenAI investments through enhanced productivity (51 percent), followed by employee satisfaction and revenue generated (48 percent and 47 percent respectively).
o Increased productivity from GenAI investments may also lead to reduced employee stress and burnout, as anticipated by 90 percent of leaders.
Governance and Generative AI
Beth Simone Noveck, New Jersey’s first chief AI strategist, views the rapid rise of artificial intelligence not as a cause for alarm, but as an opportunity. She emphasizes the importance of open dialogue and proactive engagement with AI technologies, encouraging government workers to explore and discuss their implications.
Noveck’s initiative in releasing New Jersey’s first AI policy last November aims to kickstart this conversation. Nationally, government IT leaders are also crafting policies to promote the exploration of emerging AI capabilities, particularly in GenAI, while remaining vigilant about potential risks.
Experts stress the need for government oversight as GenAI applications become more widely available and adopted. Without sufficient guardrails, the rapid adoption of these tools could have unforeseen outcomes. Mehtab Khan, a fellow at the Harvard Berkman Klein Center, highlights the importance of internal policies and principles to mitigate risks associated with AI adoption. Mehtab Khan concludes, “These tools are already publicly available and are being adopted or used privately.” He argues that to have more control over the tools, we need “internal policies and principles.”
California’s Emerging AI Policy
In California, Liana Bailey-Crimmins, the state’s Chief Information Officer (CIO) and Director of the Department of Technology, outlines an AI policy providing initial administrative guidance. Shaped by California Governor Gavin Newsom’s executive order, the policy aims to understand the risks and benefits of GenAI, fostering a safe and ethical innovation environment within state government. It draws from federal guidance like the National Institute of Standards and Technology’s (NIST) AI Risk Management Framework and the White House’s directives. California intends to collaborate with industry and academia to refine its policies.
New Jersey’s AI Task Force
Meanwhile, New Jersey’s Governor Phil Murphy has established an AI task force, co-chaired by Beth Simone Noveck, aiming to leverage AI for enhanced government services. The task force issued its inaugural policy announcement, emphasizing inclusion, transparency, and responsible experimentation. Specific examples and guidance accompany the policy to encourage responsible AI use for the benefit of residents.
International Applications of Generative AI
On April 2nd, the World Health Organization (WHO) announced the launch of S.A.R.A.H (Smart AI Resource Assistant for Health), a prototype digital health promoter powered by GenAI. S.A.R.A.H. engages users in eight languages across various health topics, providing 24-hour support on any device.
It aims to enhance people’s understanding of health issues, including leading causes of death like cancer and heart disease, offering guidance on healthy habits and mental well-being. WHO Director-General Dr Tedros Adhanom Ghebreyesus emphasizes the importance of digital health, highlighting S.A.R.A.H.’s potential to improve access to health information and address inequities.
Powered by GenAI, S.A.R.A.H. provides accurate, real-time responses, engages in personalized conversations, and offers empathetic support, reflecting human interactions. WHO stresses the need for ongoing research to maximize AI’s benefits while addressing ethical concerns like equitable access, privacy, and bias.
Continuous evaluation and refinement underscore the WHO’s commitment to delivering reliable, ethical, and accessible health information through AI. The S.A.R.A.H. project aims for responsible and inclusive AI development to benefit all individuals.
Conclusion
The discussion surrounding GenAI reflects its transformative potential across various sectors, from healthcare to governance and business. As organizations invest in GenAI, they navigate challenges and opportunities, recognizing the importance of governance frameworks and workforce readiness.
The emergence of S.A.R.A.H. by WHO exemplifies the international application of GenAI in enhancing access to health information and promoting well-being. This prototype underscores the need for ongoing research to address ethical concerns while maximizing the technology’s benefits.
In government, initiatives like New Jersey’s AI task force and California’s emerging AI policy highlight efforts to harness AI’s potential responsibly. Through collaboration with industry and academia, policymakers strive to refine policies and ensure equitable access to AI-driven solutions.
As the GenAI landscape continues to evolve, it is essential to prioritize ethical considerations, transparency, and inclusivity. By fostering open dialogue, proactive engagement, and continuous evaluation, stakeholders can leverage GenAI to drive innovation while upholding ethical standards and benefiting society.
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