Exploring the Evolution of Artificial Intelligence
An Overview of Trends Driving Growth of AI
27-Feb-2023
Corporate investment in artificial intelligence (AI) plays a crucial role in the development of AI research. In 2021, private investment was the largest contributor to AI funding, accounting for around $93.5 billion, followed by mergers and acquisitions at $72 billion, public offerings at $9.5 billion, and minority stake at $1.3 billion.
Private investment in AI has more than doubled since 2020, with $93.5 billion in 2021. This marks the most significant year-over-year increase since 2014. Among companies that disclosed funding amounts, the number of AI funding rounds ranging from $100 million to $500 million more than doubled in 2021 compared to 2020.
In 2020, only four funding rounds were worth $500 million or more; in 2021, this number grew to 15. The average private investment deal size in 2021 was 81.1% higher than in 2020, resulting in significantly higher investment for companies.
Collaborations between academics, researchers, industry experts, and others across borders are important for modern STEM development. They help to spread new ideas and accelerate growth of research teams.
The United States and China had the most cross-country collaborations in AI publications from 2010 to 2021, increasing five times since 2010. The collaboration between the two countries produced 2.7 times more publications than between the United Kingdom and China—the second highest on the list.
AI systems have been deployed in the real world in recent years, and their real-world harms are being recognized by researchers and practitioners. These models have been found to reflect and amplify human social biases, discriminate based on protected attributes, and generate false information about the world. These findings have increased interest within the academic community in studying AI ethics, fairness, and bias which has prompted industry practitioners to direct resources toward remediating these issues, and attracted attention from the media, governments, and the people who use and are affected by these systems.
Research on fairness and transparency in AI has exploded since 2014, with a fivefold increase in related publications at ethics-related conferences. Algorithmic fairness and bias have shifted from being primarily an academic pursuit to becoming firmly embedded as a mainstream research topic with wide-ranging implications. Researchers with industry affiliations contributed 71% more publications year over year at ethics-focused conferences in recent years.
The cost to train an image classification system has decreased by 63.6% since 2018, while training times have improved by 94.4%. This trend of lower training cost but faster training time appears across other MLPerf task categories such as recommendation, object detection, and language processing. This trend favors the more widespread commercial adoption of AI technologies. In the past decade, image recognition systems have made tremendous advances in technical capacity, especially as researchers have embraced more machine learning techniques. Additionally, image recognition has become more affordable, widely applicable, and accessible than ever before due to progress in algorithmic, hardware, and data technologies.
Source: Stanford University
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