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Additionally, we connected these advancements to the growing tech landscape in the Asia-Pacific (APAC) region, as observed by Sharryn Napier, VP of APAC at GitHub. Not surprisingly, the database sector responded last year by adding the ability to store vector embeddings. For existing operational databases, it was pretty much a no-brainer, as vectors comprise just another data type to add to the mix. AWS, DataStax Inc., Microsoft, MongoDB Inc., Snowflake Inc. and various PostgreSQL variants hopped the bandwagon.
Going forward, 2024 is the year when such adoptions are likely to scale up after 2023 showcased that drone adoption and drone services in India can indeed be scaled—and it is no longer a small-scale sector. The coming year is when local drone manufacturing and development may also attract increasing component localisation, thereby adding more revenue to India itself. While it is understandably too early to say autonomous driving will go mainstream, 2024 is the year when semi-autonomous vehicles start becoming mainstream. Many mainstream cars in India last year saw technologies such as 360-degree cameras, remote connectivity, lane warnings, adaptive braking and hands-free parking. Expect advanced technologies to start coming to mainstream car brands as well, instead of just being limited to luxury brands or super-niche vehicles. For consumers, 5G will be the single biggest factor to upgrade their gadgets in 2024—specifically, smartphones.
Automation potential has accelerated, but adoption to lag
For its part, the NSF should keep building out its important National Artificial Intelligence Research Institutes program and related initiatives for strengthening AI activities in new geographies. This reflects the fact that some of the most compelling new AI use cases will likely arise out of existing industry clusters building out new applications in unheralded sectors such as agriculture, transportation, and logistics. Decentralized research investments could rapidly accelerate serious AI development in new places and industry spaces. Already the agency is on track to launch seven more institutes in 2023, and it recently announced the ExpandAI program—a specially targeted awards opportunity focused on capacity-building and partnerships at minority serving institutions (MSIs). As an innovation matter, the nation’s hyper-concentrated tech geography may be narrowing the range of possible tech advancements and creating harmful imbalances among firms, local ecosystems, and the resources they command. And as an economic development issue, such imbalances tend to create large pools of high-skill workers in some areas while other areas suffer a “brain drain” that leaves lower-skill workers behind.
Examples include adapting LLMs to help with other productivity applications, such as spreadsheets and email, and pairing LLMs with robotic systems to improve and expand the operation of these systems. If these various applications are implemented effectively across the economy, a large and extended surge in productivity and other measures of economic performance seems almost certain to follow. Many large employment sectors, including government, health care, traditional retail, hospitality, and construction, have critical shortages of workers.
William Blair Thinking Presents—Generative AI: The New Frontier of Automation
Beyond that, there is no doubt higher education systems need more (and more flexible) resources in new formats to deliver new and relevant technology training in AI workforce development. In addition, a National AI Research Resource (NAIRR) Task Force began working in 2021 on a major roadmap for “democratizing” access to AI development resources (including computation resources), with special attention to regional and demographic disparities. Relatedly, most of these programs highlight the provision of STEM education and workforce development as key supports of AI talent development.
This frees up human agents to work on more complex customer queries, resulting in a higher level of service. In our survey, 50% of senior leaders said they were already using generative AI to pull data from customer conversations to address customer needs. Others have warned of the risks of misuse by bad actors with various motivations, as well as unconstrained military applications of AI in the absence of international regulations. But it is wrong to assume that simply limiting the misuse and harmful side effects of AI will ensure that its economic dividends will be delivered in a broadly inclusive way. Active policies and regulations aimed at unleashing those benefits will play a major role in determining whether AI realizes its full economic potential. For many industries, generative AI, trust, data security and improved digital experiences are key priorities to improve the overall customer experience.
Debates around AI safety, content moderation, and even the effectiveness of export controls on semiconductors are all heavily impacted by this question. Today’s branches of generative AI represent a novel approach, but they did not develop overnight. The foundations of machine learning were laid more than 70 years ago, and the mathematician Alan Turing introduced his “imitation game,” the Turing Test, in 1950. The most significant scientific breakthrough behind LLMs came in a Google research paper published in 2017.
Read more about The Economic Potential of Generative Next Frontier For Business Innovation here.