Exploring the Latest Trends in AI: Perspectives from AI Start-Up Founders

JJohn July 29, 2023 1:36 AM

The AI industry is rapidly evolving, with new trends and technologies reshaping the business and society. AI start-up founders shed light on the major AI trends in business sectors, including the commercialization of AI, generative AI, AI regulation, synthetic data, and Graph Neural Networks.

The commercialization shift in AI

AI is steadily moving from being predominantly a technology in development to one in deployment. Companies like Graphcore are demonstrating that successful commercialization of AI requires more than just advanced models. It requires cloud compute, developer tools, a comprehensive software stack, and companies providing AI-as-a-Service products. Moreover, as AI moves into commercial environments serving thousands or millions of users, the cost and performance efficiency of compute systems become increasingly critical.

The rise of Generative AI, or GenAI, has been hailed as a significant frontier in technology that will significantly impact society. Its ability to automate routine tasks and support human capability is transforming both business and societal processes. However, it also brings potential risks such as misinformation and data breaches, hence striking the right balance between innovation and safeguarding public interests is paramount. The market for GenAI is predicted to grow substantially, from $40bn in 2022 to a whopping $1.3tn by 2032.

As AI developers progressively exhaust the available supply of human-generated information for training purposes, many are turning towards synthetic or computer-generated data. Companies like Hazy, which uses generative techniques in their core product, are leading the charge in this emerging trend. The use of synthetic data not only provides a new source of information but also allows for increased speed, efficiency, and reduced risk in data analysis.

The growing significance of Graph Neural Networks

Another emerging trend in AI is the rise of Graph Neural Networks (GNNs). These deep learning models, tailored for processing graph data, enable the modeling of irregular structures and have potential applications in object detection, machine translation, and speech recognition. Companies like Graphcore are leading the way in exploiting the potential of GNNs, and their use is set to increase in high-stakes fields.

Keeping abreast of evolving AI regulation

As the regulation of AI continues to take shape, companies must stay informed about the latest legislative changes and proactively ensure compliance. This involves setting up systems in advance to avoid a reactive response when new regulations are enforced. Furthermore, data used for training AI must be accurate, up-to-date, and representative of the real world to avoid bias and ensure effective insights. The protection and ethical use of data is also crucial in maintaining public trust.

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