AI: The Earth Changing Factor Leading the Fight in 2024 for a Sustainable Future
USC School of Advanced Computing
With GenAI’s arrival, the potential now exists to make the major leap from a data-driven to a knowledge-driven enterprise. Everyone is focusing, however, on the technology’s more obvious automation efficiency benefits and debating its potential to displace jobs. So, we will see the computing power that’s required by enterprises to run large language models accelerate pretty significantly into the near future. We’re still early in this wave but we are seeing signs that it’s coming and that more and more of this is going to be required as the adoption of gen AI increases. But just on the efficiency side, the product managers we talked to today say it’s making an engineer about 30% more efficient today. But the concept of a super engineer being able to produce 5-times or 10-times content likely doesn’t come into view maybe for another year or two, just depending on who you speak to but that gives you a sense of scale.
AI activity, states and regions need to advance systematic and highly focused initiatives to grow differentiated local R&D concentrations at their institutions. Multiple university-based consortia are taking the lead on this, including the Alabama Artificial Intelligence Center of Excellence—a public-private partnership in Auburn, Ala. that involves eight colleges and universities, three cities, and data center AUBix. Some regions are already doing this, aided by federal initiatives such as the BBBRC and Regional Innovation Engines programs. These efforts—like the federal ones—are expanding local research, democratizing computing and data access, promoting cluster development, and investing in talent through bottom-up planning, governance, and execution.
Artificial Intelligence and Data Protection: Delivering Sustainable AI Accountability in Practice
Historically, generative models were limited to the statistical analysis of numerical data. The breakthrough in deep learning technology extended their capabilities to complex data types like images and speech. A milestone in this journey was the advent of variational autoencoders (VAEs) in 2013, the first deep-learning model widely used for generating realistic images and speech.
- Meanwhile, Chinese private investment in AI-related semiconductors was 102 times higher than such investments in the UK and EU.
- Many of the most important debates about access and control of AI systems are downstream of the scale-up vs. scale-down debate, including the debate about open-source vs. closed-source AI.
- Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion.
- Thus, increased ML for incumbents’ operational data may make the creative destruction process not only more creative but also less destructive.
- A number of B2B companies have also been popping up which leverage the growing amount of data available to be sourced or scraped from public sources and package it together to underwrite risk for their customers.
- 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.
Often, this is part of an escalation path where a first cut of the AI will handle 80% of the common use cases, and humans manage the tail. There are exceptions, of course, such as the new behaviors introduced by home voice assistants. But even these underscore how dominant the incumbents are in AI products, given the noticeable lack of widely adopted independents in this space. Generative AI lacks a comprehensive regulatory framework, raising concerns about its potential for both constructive and detrimental impacts on society.
UK digital regulators discuss interagency enforcement, AI governance coordination
Therefore, we all need to get comfortable with the new language of Gen AI, to all appreciate how AI and automation will disrupt how we work, and to ensure we have guardrails to maximize the gains while minimizing the risks. ICTworks™ is the premier resource for international development professionals committed to utilizing new and emerging technologies to magnify the intent of communities to accelerate their social and economic development. He explained the importance of integrating security and engineering teams, advocating for a ‘secure by design’ approach. That way, when tools, capabilities, and developer tooling are developed, and platform engineering is conducted, those elements are secure by design right from the start. For instance, we’ll inform them if a token is already invalid and doesn’t pose a threat. We want to prevent developers from having to spend excessive time determining whether something is a genuine security risk or not,” he explained.
Technology & Markets of the Future McKinsey Global Institute – McKinsey
Technology & Markets of the Future McKinsey Global Institute.
Posted: Tue, 28 Mar 2023 14:59:30 GMT [source]
The artificial intelligence industry—and potentially its sub-category of generative AI—stands out as a prime example of this, given that it encompasses powerfully disruptive digital technologies. For 2-sided marketplaces, an extension to the traditional marketplace business model — incorporating financial services to enable new types of on-platform transactions — is the key to unlocking that higher level of opportunity. In this essay, we’ll explain why this apparently simple change will have enormous implications, and how it has already begun to define the next stage in marketplaces.
The Appendix A also illustrates the impact on the entrepreneur’s behavior when the effectiveness of ML, \(\alpha \), varies. There is recent literature examining how the implementation of AI affects firms’ decisions under uncertainty. Agrawal et al. (2018); Agrawal et al. (2019b) show that better predictions from firms’ implementation of AI will have widespread consequences since predictions are fundamental to decision-making in firms. Gans (2023) proposes a model where the implementation of AI implies a better prediction of demand, allowing firms to match decisions, such as those related to output and employment, with the predicted state. While output might be relatively stable when there is no prediction, the availability of a prediction may cause firms to increase or reduce output accordingly. Nevertheless, some of the efficiency gains come from reducing output, which implies that the external effect of AI adoption on other firms is positive rather than negative.
First, platforms with massive data volumes needed for models that require broad inputs for performance. Second, platforms with limited but especially high-quality data from trustworthy sources, including sources free of toxicity or with particularly ordered and logical structures. Internet businesses – especially those already struggling with monetization – may be able to unlock new revenue streams if they have sufficient high-quality data available.
Contentious areas in the EU AI Act trilogues
Read more about The Economic Potential of Generative Next Frontier For Business Innovation here.
