Adoption of artificial intelligence by businesses rose last year. But if the COVID pandemic worsens, companies may slow their spending as the world’s economy craters.
First up, let’s talk about what happened in 2021. Fifty-seven percent of companies said they were using A.I. in at least one business function, up from 45% in 2020, according to consulting giant McKinsey’s state of A.I. report released in December. Some of the most popular corporate uses of A.I. over the past year included automating calls for customer contact-centers, improving marketing operations, and upgrading existing products with machine learning.
As we enter 2022, the pandemic’s impact on the economy may temper corporate spending somewhat. I don’t expect companies to reduce their A.I. adoption or simply kill machine learning projects that are already in progress. After all, these projects are typically multi-year initiatives, and if companies have already heavily invested in the ventures, they’ll likely want to carry them out to fruition. But they may slow spending on new projects.
Here are my predictions for 2022:
1. This will be a big year for A.I. and security
Last year was seemingly a non-stop series of hackings and discoveries of vulnerabilities like the Log4j open-source software flaw that created pandemonium for corporate security teams.
McKinsey’s state of A.I. report shows that most companies viewed cybersecurity as the top A.I.-related risk to their businesses, besting other concerns like regulatory compliance, the inability to explain how machine learning models make their decisions, and equity and fairness. If companies fail to secure the datasets they use to train their machine-learning models, for instance, hackers may be able to obtain a treasure trove of sensitive information.
This will be big year for A.I.-related cybersecurity acquisitions as enterprise technology giants beef up on machine-learning tools to sell to companies. Google’s cloud computing unit, for example, recently paid a reported $500 million for Israeli security startup Siemplify, which uses machine learning to help companies prioritize security bugs and vulnerabilities.
2. A.I. keeps making strides in healthcare research
Google’s DeepMind A.I. unit published several medical-related papers last year, including one that explored how A.I. can predict the shape of proteins in the human genome and other organisms when they fold. Predicting protein folding could help researchers more quickly develop drugs, raising hopes that A.I. will revolutionize healthcare.
Researchers are only beginning to understand how deep learning could accelerate drug discovery, and the interest is so high that a cottage industry of startups specializing in A.I.-powered drug discovery has emerged. Expect more major A.I. healthcare-related papers this year as well as big partnerships between these A.I. startups and larger pharmaceutical companies, like healthcare giant Sanofi’s recent collaboration with the startup Exscientia.
3. Behold, new machine-learning tools
New startups are emerging that are creating tools specifically for machine learning. Analysts call this burgeoning field “MLOps”, a buzzy term that refers to managing machine-learning operations as part of software development.
These startups are creating products that help companies with several A.I.-related tasks, like training machine-learning models, monitoring the behaviors of those models, and even generating synthetic data to potentially improve the models. As these startups grow, bigger business software companies such as Microsoft and Salesforce will likely eye them as potential acquisitions.
Jonathan Vanian
@JonathanVanian
jonathan.vanian@fortune.com
A.I. IN THE NEWS
Big money for autonomous cars. Wayve, a self-driving automobile company based in the United Kingdom, said it has raised $200 million in funding, but it did not disclose its valuation. Eclipse Ventures was the lead investor in the new funding, with participation from Microsoft,Virgin Group, D1 Capital Partners, and others.
A.I. for commerce. The U.S. Chamber of Commerce has debuted the Artificial Intelligence (AI) Commission on Competition, Inclusion, and Innovation, a group for recommending government policies that “ensure the United States continues to lead in innovation while fostering fairness in deploying this revolutionary technology.” Some of the new commission’s members include Bank of America data science executive Shekar Katuri, IBM vice president and chief privacy officer Christina Montgomery, and Salesforce head of global policy Rachel Gillum.
Healthy A.I. Microsoft has created a new coalition dedicated to A.I. and healthcare called The Artificial Intelligence Industry Innovation Coalition, which is intended to address various A.I.-related healthcare concerns like data privacy, employment, and industry standards. Some of the coalition’s members include the Cleveland Clinic, Novant Health, Duke Health, and the Brookings Institution.
Creating fake voices is getting easier. A National Public Radio report examines how researchers and companies are attempting to use A.I. to create fake but realistic-sounding human voices. From the report: “The global speech and voice recognition industry is worth tens of billions of dollars, and is growing fast. Its uses are evident. The technology has given actor Val Kilmer, who lost his voice owing to throat cancer a few years ago, the chance to reclaim something approaching his former vocal powers.”
EYE ON A.I. TALENT
Landing AI hired David L. Dechow to be the machine learning startup’s vice president of outreach and vision technology. Dechow is an expert in the field of computer vision and was previously the principal vision systems architect of the enterprise technology company Integro Technologies.
GSR chose John MacDonald to be the digital asset trading company’s chief technology officer. MacDonald was previously the managing director and CTO of the Europe division of Citadel Securities.
EYE ON A.I. RESEARCH
Machine learning to predict COVID-19 survival. Researchers from the Charité Berlin academic hospital in Germany, published a paper in PLOS Digital Health about using machine learning to analyze blood samples of COVID-19 patients to deduce whether they will survive or succumb to the disease. The authors claim that their methodology works better than existing prognostic methods.
From the paper: Using a machine learning model which combines the measurements of multiple proteins, we were able to accurately predict survival in critically ill patients with COVID-19 from single blood samples, weeks before the outcome, substantially outperforming established risk predictors.
FORTUNE ON A.I.
BioNTech and London A.I. firm create ‘early warning system’ to spot dangerous new COVID-19 variants before they spread—By Jeremy Kahn
How COVID-19 has impacted corporate and city sustainability efforts—By Jonathan Vanian
Tesla hammered on reports that its futuristic Cybertruck will be delayed to 2023—By Christiaan Hetzner
A new ruling on Meta antitrust accusations could mean selling Instagram and WhatsApp—By Nicole Goodkind
Blockchain, remote work and tax tech—all on the radar of CFOs this week—By Sheryl Estrada
BRAIN FOOD
This A.I. researcher’s big DAIR. Timnit Gebru, the former Google co-lead of its ethical artificial intelligence team who was ousted by the company, is the subject of a Time profile that touches on her new research group, the Distributed AI Research (DAIR) Institute. The institute aims to conduct intensive A.I. research that big technology companies may overlook.
From the article:
At DAIR, Gebru will work with researchers around the world across multiple disciplines to examine the outcomes of AI technology, with a particular focus on the African continent and the African diaspora in the U.S. One of DAIR’s first projects will use AI to analyze satellite imagery of townships in South Africa, to better understand legacies of apartheid.
