Developers of generative AI applications attracted $1.37 billion of investments On January 7, 2023 data on the volume of investments into the companies which are engaged in generative artificial intelligence (AI) are published. It is reported that in 2022 such projects raised $1.37 billion, almost the same amount as in the previous five years combined. Generative AI involves the use of technologies that allow the formation of text, images, sounds and other media in response to user requests. It is estimated that more than 450 startups were working on such solutions by the end of 2022. In particular, OpenAI’s ChatGPT chatbot, which, according to The Wall Street Journal, is now worth $29 billion, made a lot of noise.
During 2022, according to According to PitchBook, a total of 78 deals were made to invest in developers of generative AI applications. As the most striking examples, The New York Times names the project of the startup Jasper, founded in 2021: in October 2022, it raised $125 million, and its market value reached $1.5 billion. Stability AI, founded in 2020, develops solutions for image creation, raised $101 million in the same month, with a $1 billion valuation. Smaller generative AI projects, including Character.AI, Replika, and You.com, have also attracted investor interest.
Sequoia Capital experts say that generative AI "could create trillions of dollars of economic value." And Lonne Jaffe, an investor at Insight Partners, compared the development of the generative AI industry to the early days of the Internet. However, some experts fear that the hype around generative AI has outstripped reality. The technology has raised thorny ethical questions about how such applications can affect copyrights and whether companies need to get permission to use the data that trains their algorithms. McKinsey named 2 main trends in the field of artificial intelligence On August 25, 2022, a McKinsey study was published, according to which applied artificial intelligence and the introduction of machine learning are the two most significant technological trends in the AI market. Implementation of applied AI Applied AI, which according to McKinsey is based on proven and mature technologies, has viable applications in more industries, and is closer to mainstream adoption than other trends. In the 2021 McKinsey Global State of AI Survey, 56% of respondents said their organizations had implemented AI, up from 50% in the 2020 survey.
According to the 2022 report, technology industries are leading the adoption of AI, and product development and service operations are the business functions that have benefited the most from the application of AI. Roger Roberts, a McKinsey partner and one of the report's co-authors, had the following to say about applied AI, which the report defines "quite broadly" as follows: " We're seeing a shift from advanced analytics to.. applying machine learning to big data to solve complex problems in a new way. » This trend is reflected in the explosive growth of AI publications, not only because AI scientists are publishing more papers, but also because people in a wide variety of fields are using AI in their research and pushing the application of AI into the future, he explained. .
" Indeed, there is a shift from science to technical development and scaling, he said. We're seeing developments in AI move along this path pretty quickly, and I'm very excited to see more things moving from science to scale. » However, the McKinsey report also highlights a number of key uncertainties that could affect the future of applied AI, including talent and funding availability, cybersecurity concerns, and stakeholder questions about the responsible and trustworthy use of AI. Implementing Machine Learning According to a McKinsey report, adopting machine learning (ML) "implies the creation of an interoperable stack of technical tools to automate ML and expand its use so that organizations can realize its full potential." The report notes that McKinsey expects ML adoption to spread as more companies look to use AI for a growing number of applications. More broadly, ML includes the concept of a technology stack that promotes scalability, which can go as far as innovating at the level of redesigning microprocessors, Roberts said. You see a lot of new features in silicon that support the acceleration of certain types of AI tasks, and these innovations will be used more widely, enabling faster and more efficient scaling in terms of both compute resources and compute stability.
» The trend also includes integrated hardware and heterogeneous computing used in ML workflows. Roberts added that he believes big tech organizations like Google, Meta and Microsoft are leading the way in industrial ML adoption "by a wide margin." However, he suggested that this trend would soon go far beyond these companies: " We will begin to see more and more venture capital activity and corporate investment as we build the tools for this new class of software and new class products in the form of custom services,” he explained. .