In the five years since we released the first AI100 report, much has been written about the state of artificial intelligence and its influences on society. Nonetheless, AI100 remains unique in its combination of two key features. First, it is written by a Study Panel of core multi-disciplinary researchers in the field—experts who create artificial intelligence algorithms or study their influence on society as their main professional activity, and who have been doing so for many years. The authors are firmly rooted within the field of AI and provide an “insider’s” perspective. Second, it is a longitudinal study, with reports by such Study Panels planned once every five years, for at least one hundred years.

… Like the first report, the second report aspires to address four audiences. For the general public, it aims to provide an accessible, scientifically and technologically accurate portrayal of the current state of AI and its potential. For industry, the report describes relevant technologies and legal and ethical challenges, and may help guide resource allocation. The report is also directed to local, national, and international governments to help them better plan for AI in governance. Finally, the report can help AI researchers, as well as their institutions and funders, to set priorities and consider the economic, ethical, and legal issues raised by AI research and its applications. Read More

It’s been a hot, hot year in the world of data, machine learning and AI. 

Just when you thought it couldn’t grow any more explosively, the data/AI landscape just did: rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc.  

It has also been a year of multiple threads and stories intertwining.

One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings.  Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, Datarobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index – both indexes will be updated soon).

But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups.  Whether they were founded a few years or a few months ago, many experienced a growth spurt in the last year or so.  As we will discuss, part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. Read More

Digital innovation spurred by Covid-19 has put AI and analytics at the center of business operations. AI and analytics are boosting productivity, delivering new products and services, accentuating corporate values, addressing supply chain issues, and fueling new startups. In this article, we address lessons learned from the pandemic and how they can be applied to spurring new economic opportunity. Read More

PWC Study

Our ten-year plan to make Britain a global AI superpower

Over the next ten years, the impact of AI on businesses across the UK and the wider world will be profound – and UK universities and startups are already leading the world in building the tools for the new economy. New discoveries and methods for harnessing the capacity of machines to learn, aid and assist us in new ways emerge every day from our universities and businesses. Read More

The volume of data keeps growing. Statista believe that 59 Zettabytes were produced in 2020 and that 74 Zettabytes will be produced in 2021. A Zettabyte is a trillion gigabytes! And AI is driven by data. Read More

#strategy

In a new interview, AI expert Kai-Fu Lee — who worked as an executive at Google (GOOG, GOOGL), Apple (AAPL), and Microsoft (MSFT) — explained the top four dangers of burgeoning AI technology: externalities, personal data risks, inability to explain consequential choices, and warfare.

“The single largest danger is autonomous weapons,” he says. Read More

Which cities will drive the artificial intelligence revolution?

The Bay Area and 13 early adopter metro areas dominate the nation’s emerging AI economy

#strategy

By 2025, artificial intelligence (AI) will significantly improve our daily life by handling some of today’s complex tasks with great efficiency.

The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great.

This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. Read More

Several years into what many people expected to be an AI revolution, there is a nagging sense that we are at a crossroads. Artificial intelligence is an evolutionary step forward for business optimization strategies — and rightly so — but the companies that saw AI as the path to the promised land could be forgiven for thinking that the hype has outweighed successful implementation.

Granted, there are numerous organizations that have integrated AI into their business processes, and it is already a routine part of software development, cybersecurity, natural language processing and robotic process automation (RPA).

And yes, making AI a priority in terms of scalability and an accelerated time to market has shown a modicum of success. Two years ago, for example, AI adoption rates reportedly grew by 270% from 2015 to 2018, according to Gartner, and some observers enthusiastically predicted that a brave new world was already here.

But adoption doesn’t equal success. Read More