The generative world order: AI, geopolitics, and power

It embodies a shift from narrow, task-specific intelligence to a more flexible, creative, and adaptable form of AI. The progression in generative AI capabilities, particularly in creativity, problem-solving, and social interaction, underscores a significant move towards AGI’s broader, more nuanced intelligence characteristic. This evolution in the AI landscape indicates a gradual but steady advancement toward systems that mimic human intelligence’s diverse and comprehensive abilities. This set the stage for the current landscape of generative AI, with models such as GPT-4, BERT, and DALL-E 2 showcasing the potential of using broad, unlabeled datasets for various tasks with minimal fine-tuning. In summary, the categorization of AI into Weak (ANI) and Strong AI (comprising AGI and ASI) helps in understanding the current capabilities and future aspirations of AI technologies.

And taking advantage of code generation capabilities and the ability to detect implicit data structure, we could see gen AI being applied to create data transformation pipelines. Following in the footsteps of automatic code generation or guidance, gen AI could also help database designers streamline development and deployment of databases. Of course, this will continue to require humans in the loop – we shouldn’t let a smart bot loose on designing a database without intervention. But the ability for language models to scan, summarize and highlight a corpus of data could make it a major productivity tool in database development. Generative AI, on the other hand, automates natural language processing and content creation—tasks the human brain has spent far less time evolving toward (arguably less than 100,000 years). Generative AI can already perform many of these tasks orders-of-magnitude cheaper, faster, and, in some cases, better than humans.

The connectionist approach

While the EU’s leadership potential on AI is limited because it has fewer, and smaller, AI companies than the US or China, the EU AI Act has significant commercial and geopolitical implications. The EU is home to 450 million inhabitants, and it is the West’s single-largest demographic bloc and a leading global commercial player. The AI Act’s regulatory framework could shape AI’s development and adoption elsewhere, as the proposed text includes extraterritorial applications on providers and users.

The era of building global supply chains entirely on the basis of efficiency and comparative advantage has clearly come to a close. The potential of technological capabilities in a lab does not necessarily mean they can be immediately integrated into a solution that automates a specific work activity—developing such solutions takes time. Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor. Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy. Hence, our adoption scenarios, which consider these factors together with the technical automation potential, provide a sense of the pace and scale at which workers’ activities could shift over time. One of the reasons why generative AI has seen limited adoption so far is because the fundamental infrastructure behind the technology is expensive—making scaled deployments difficult so far.

Frontiers of Computing

Supported by a vast ecosystem of knowledge, the mechanic doesn’t need to be the company’s top expert to diagnose and fix the problem. Through the GenAI ecosystem, they can leverage the collective experience captured over time from all mechanics across the enterprise. This material represents an assessment of the market environment at a specific point in time and is not intended to be a forecast of future events, or a guarantee of future results. This information is not intended to be individual or personalized research or investment advice and should not be used for trading purposes. Lastly, we also believe the rise in AI services could boost data management apps and services.

Going forward, business intelligence platforms will leverage the advent of mainstream generative AI to drive an increasing volume of structured data in every geography and sector. What this will do is generate data that can be read, analysed and assessed by algorithms not just in English, but in multiple non-English languages that include several Indic languages too. Even the government is working on it—the Centre’s India Datasets Platform, announced earlier this year by Union Minister of State for IT, Rajeev Chandrasekhar, will play a key role in harnessing this data. Voice&Data takes a look at 24 pivotal technology trends that will have the highest impact on the world, next year. While some may eventually not play out, the following gives a fairly robust take across all industries and geographies in terms of the kind of solutions one can expect largely everywhere. At the same time, drifting through an AI-driven world without getting engaged with this technology is a recipe for disaster, as a fool with a tool is still a fool.

Frontier Technology

The emergence and growth of low-code and no-code AI platforms are pivotal in propelling the progress of Artificial General Intelligence. In a landscape where the demand for skilled AI engineers far outstrips supply, these platforms offer a transformative solution. They democratize AI development, enabling individuals and organizations to create sophisticated AI systems without the need for deep technical expertise. Moreover, GPT-4’s advanced language models serve as a foundation for developing AI systems that can learn, reason, and make decisions in a way that mirrors human cognitive processes.

While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”). Through all of these developments, one key theme that has risen to the forefront is sustainability. No longer a corporate afterthought, sustainability and the need to develop solutions for sustainable tech deployment have risen as a top business avenue around the world.

With the enhanced sophistication of GPT-4, machines are getting ever closer to interacting and engaging with humans in ways that are indistinguishable from real human-to-human communication. This level of interaction is crucial for AGI, as it demands an AI system that can understand, interpret, and respond to a vast array of human languages, emotions, and nuances in a natural and intuitive manner. The recent advancements in NLP, especially with the advent of OpenAI’s GPT-4, underscore the remarkable progress in this field.

Read more about The Economic Potential of Generative Next Frontier For Business Innovation here.