What follows is a set of recent reports that assess the ‘state of AI’ for discussion purposes. The past five years have delivered impressive advances and, as captured in the about section, AI is no longer a frontier technology, though it still qualifies as emerging tech… with all of what that entails in the fluid world of innovation, as pressing challenges happen to be commonplace.
“Findings from the 2021 survey indicated that AI adoption is continuing its steady rise (…) up from 50 percent in 2020.”
“The business functions where AI adoption is most common are service operations, product and service development, and marketing and sales, though the most popular use cases span a range of functions.”
“The top three use cases are service-operations optimization, AI-based enhancement of products, and contact-center automation, with the biggest percentage-point increase in the use of AI being in companies’ marketing-budget allocation and spending effectiveness.”
“The companies seeing the biggest bottom-line impact from AI adoption are more likely to follow both core and advanced AI best practices, including MLOps; move their AI work to the cloud; and spend on AI more efficiently and effectively than their peers.”
Chui, Michael et all. The State of AI in 2021. McKinsey. December 8, 2021. Accessed February 1, 2022.
“AI is stepping up in more concrete ways, including being applied to mission critical infrastructure like national electric grids and automated supermarket warehousing optimization during pandemics.”
“Investors have taken notice, with record funding this year into AI startups, and two first ever IPOs for AI-first drug discovery companies, as well as blockbuster IPOs for data infrastructure and cybersecurity companies that help enterprises retool for the AI-first era.”
Benaich, Nathan and Hogarth, Ian. The State of AI Report. October 12, 2021. Accessed February 1, 2022.
“Organizations that document and enforce MLOps processes are twice as likely to achieve their AI goals. They’re also nearly twice as likely to report being extremely prepared for risks associated with AI.”
“Becoming an AI-fueled organization is to understand that the transformation process is never complete, but rather a journey of continuous learning and improvement.”
“The risks associated with AI remain top of mind for executives. We found that high-achieving organizations report being more prepared to manage risks associated with AI and confident that they can deploy AI initiatives in a trustworthy way.”
State of AI in the Enterprise Fourth Edition. Deloitte. October 231, 2021. Accessed February 1, 2022.
“Generative everything: AI systems can now compose text, audio, and images to a sufficiently high standard that humans have a hard time telling the difference between synthetic and non-synthetic outputs for some constrained applications of the technology.”
“The industry shift continues. In 2019, 65% of graduating North American PhDs in AI went into industry—up from 44.4% in 2010, highlighting the greater role industry has begun to play in AI development. AI has a diversity challenge: 45% new U.S. resident AI PhD graduates were white—by comparison, 2.4% were African American and 3.2% were Hispanic.
“Though a number of groups are producing a range of qualitative or normative outputs in the AI ethics domain, the field generally lacks benchmarks that can be used to measure or assess the relationship between broader societal discussions about technology development and the development of the technology itself.”
2021 AI Index Report. HAI Stanford University. Accessed February 2, 2022.
“An overarching and inspiring challenge (…) is to build machines that can cooperate and collaborate seamlessly with humans and can make decisions that are aligned with fluid and complex human values and preferences.”
“AI approaches that augment human capabilities can be very valuable (…). An Ai system might be better at synthesizing available data and make decisions in well characterized parts of a problem, while a human may be better at understanding the implications of the data (…). It is increasingly clear that all stakeholders need to be involved in the design of AI assistants to produce a human-AI team that outperforms either alone.”
“The core technology behind most of the most visible advances is machine learning, especially deep learning (including generative adversarial networks or GANs) and reinforcement learning powered by large-scale data and computing resources. GANs are a major breakthrough, endowing deep networks with the ability to produce artificial content.”
“With the explosion of information available to us, recommender systems that automatically prioritize what we see when we are online have become absolutely essential. Such systems have always drawn heavily on AI, and now they have a dramatic influence on people’s consumption of products, services and content.”
“A major area of opportunity for augmentation is for AI-based methods to assist with decision-making (…) ongoing research seeks to determine how to determine how to divide up tasks between the human user and the AI system, as well as how to manage the interaction between the human and the AI software (…) to produce a human-AI team that outperforms either alone.”
“At the end of the day, the success of the field will be measured by how it has empowered all people, not by how efficiently machines devaluate the very people we are trying to help.”
Littman, Michael et all. Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report. Stanford University. September 2021. Accessed: February 7, 2022.
Human Factors . AI 