Irving Wladawsky-Berger

The Impressive Scope of the Linux Foundation in the 21st Century Digital Economy

Last week, the Linux Foundation held its North America Open Source Summit in Austin. The week-long summit included a large number of breakout sessions as well as several keynotes. Open Source Summit Europe will take place in Dublin in September and Open Source Summit Japan in Yokohama in December.

I’ve been closely involved with open, collaborative innovation and open source communities since the 1990s. In particular, I was asked to lead a new Linux initiative that IBM launched in January of 2000 to embrace Linux across all the company’s products and services.

At the time, Linux had already been embraced by the research, internet, and supercomputing communities, but many in the commercial marketplace were perplexed by IBM’s decision. Over the next few years, we spent quite a bit of effort explaining to the business community why we were supporting Linux, which included a number of Linux commercials like this one with Muhammad Ali that ran in the 2006 Super Bowl. IBM also had to fight off a multi-billion dollar lawsuit for alleged intellectual property violations in its contributions to the development of Linux. Nevertheless, by the late 2000s, Linux had crossed the chasm to mainstream adoption, having been embraced by a large number of companies around the world.

In 2000, IBM, along with HP, Intel, and several other companies formed a consortium to support the continued development of Linux, and founded a new non-profit organization, the Open Source Development Labs (OSDL). In 2007, OSDL merged with the Free Standards Group (FSG) and became the Linux Foundation (LF). In 2011, the LF marked the 20th anniversary of Linux at its annual LinuxCon North America conference. I had the privilege of giving one of the keynotes at the conference in Vancouver, where I recounted my personal involvement with Linux and open source.

How to Build Organizational Resilience to Cyberattacks

I became quite interested in cybersecurity around a year ago, given the growing threats of cyberattacks by criminal groups and adversarial governments. I then joined CAMS, MIT’s interdisciplinary cybersecurity consortium and started attending its online weekly seminars. A few weeks ago I attended a CAMS seminar on cyber resilience by Manuel Hepfer, a research affiliate at Oxford University and a research analyst at ISTARI, a cyber risk management company.

What is cyber resilience? While cybersecurity is the practice of protecting critical systems and sensitive information from digital attacks, cyber resilience is the ability to prepare for, withstand, and recover rapidly from any major disruption, whether an intentional cyberattack or a natural disaster.

The seminar was based on a study by Hepfer and colleagues of the 2017 NotPetya ransomware attacks, a series of powerful cyberattacks which caused over $10 billion in global economic damage across a number of industries. Their study was primarily focused on three global companies that were the subject of NotPetya attacks: a logistics company with over 60,000 employees, a manufacturing one with over 20,000 employees, and a third in professional services with over 5,000 employees.

The study conducted around 50 interviews with senior executives across the three companies, including CEOs, CFOs, CIOs, and CISOs. In addition, it reviewed internal documents, presentations, press releases, newspaper coverage, and audio and video files related to events before, during, and after the attack. Their study was published in two articles in the MIT Sloan Management Review, the first in 2020, the second in 2022.

Can We Trust Our Impressive AI Language Models?

One of the key findings of the 2022 AI Index Report was that large language models (LLMs) are setting records on technical benchmarks thanks to advances in deep neural networks and computational power that allows them to be trained using huge amounts of data. LLMs are now surpassing human baselines in a number of complex language tasks, including English language understanding, text summarization, natural language inference, and machine translation.

A.I. Is Mastering Language. Should We Trust What It Says?, a recent NY Times Magazine article by science writer Steven Johnson, took a close look at one such LLM, the Generative Pre-Trained Transformer 3, generally referred to as GPT-3. GPT-3 was created by the AI research company OpenAI. It’s been trained with over 700 gigabytes of data from across the web, along with a large collection of text from digitized books. “Since GPT-3’s release, the internet has been awash with examples of the software’s eerie facility with language - along with its blind spots and foibles and other more sinister tendencies,” said Johnson.

“So far, the experiments with large language models have been mostly that: experiments probing the model for signs of true intelligence, exploring its creative uses, exposing its biases. But the ultimate commercial potential is enormous. If the existing trajectory continues, software like GPT-3 could revolutionize how we search for information in the next few years.” Instead of typing a few keywords into Google and getting back a long list of links that might have the answer, you’d ask GPT-3 what you’re looking for in English and get back a well-written, accurate answer. “Customer service could be utterly transformed: Any company with a product that currently requires a human tech-support team might be able to train an L.L.M. to replace them.”

A Human-Compatible Safe, Controllable AI

“Even in its present state, the technology of artificial intelligence raises many concerns as it transitions from research into widespread use,” wrote UC Berkeley professor Stuart Russell in a recent essay, If We Succeed. “These concerns include potential misuses such as cybercrime, surveillance, disinformation, and political manipulation; the exacerbation of inequality and of many forms of bias in society; the creation and deployment of lethal autonomous weapons; and the usurpation of human roles in the economy and in social relationships.”

“My concern here, however, is with the potential consequences of success in creating general-purpose AI: that is, systems capable of quickly learning to perform at a high level in any task environment where humans (or collections of humans) can perform well.” Decades ago, Alan Turing expressed a similar concern. While answering a question during a lecture in 1951, Turing said: “It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers. … At some stage therefore we should have to expect the machines to take control.” Over the past decade, a number of public figures like Stephen Hawking and Nick Bostrom have argued that a superintelligent AI could become difficult or impossible for humans to control, and might even pose an existential threat to humanity.

Until recently, we didn’t have to worry about the consequences of such superhuman AIs because AI applications weren’t all that smart and were mostly confined to research labs. But, powerful AI systems have now matched or surpassed human levels of performance in a number of application- specific tasks such as image and speech recognition, skin cancer classification, breast cancer detection, and complex games like Jeopardy and Go. While these application-specific breakthroughs, - generally referred to as soft, or narrow AI, - were once viewed as the exclusive domain of humans, AI still lacks the deeper, general-purpose human intelligence that’s long been measured in IQ tests.

The MIT Digital Insider Podcast with Sinan Aral

Earlier this week, MIT’s Initiative on the Digital Economy launched a new podcast, The Digital Insider with Sinan Aral. Aral is Professor in the MIT Sloan School of Management and Director of the Initiative on the Digital Economy, where I’ve been a Fellow since it was founded in 2012. He’s the author of the 2020 book The Hype Machine.

I like to take a walk every day, weather permitting, and one of the reasons I enjoy my walks is the opportunity to listen to a podcast. I just listened to the first episode of The Digital Insider. It was truly excellent.

In the episode, Sinan interviewed Maria Ressa, a Filipino-American journalist and author, and co-founder and CEO of Rappler, a Philippine online new website. Maria has been a prominent critic of the human right abuses of Philippine President Rodrigo Duterte, for which she’s been arrested and convicted. Along with Russian journalist Dmitry Muratov, Maria was awarded the 2021 Nobel Peace Prize for “their efforts to safeguard freedom of expression, which is a precondition for democracy and lasting peace.”

The Digital Insider podcast aims to explore the hard science behind the digital economy as well as how the digital economy actually works. “From AI bias to NFTs; fake news to two-sided markets; cryptocurrencies to quantum computing, Sinan and guests make sense of the lightning-fast changes taking place in the tech driven world.”

I highly recommend it.

The Digital Divide in How We Use the Internet

A few weeks ago I attended an MIT seminar by Esteban Moro, professor at Universidad Carlos III in Madrid and visiting professor at MIT. Moro’s talk discussed the socio-economic digital divide based on how we use the internet, focused on two key questions: how profound is the digital usage gap in our society?; and can we use mobile phone traffic to infer socioeconomic status? His seminar was based on a recently published article in the Journal of the Royal Society Interface co-authored with four collaborators, - News or social media? Socio-economic divide of mobile service consumption.

“Inequality is a central societal problem, especially within rapidly expanding urban areas,” wrote the authors. “While it is a crucial driver for economic growth, the progressive clusterization of workers, industries, companies and services in cities has a tremendous cost in terms of segregation and discrimination. This cost is not only economic: in the same city, different areas can have a 10- to 15-year imbalance in life expectancy and highly divergent education levels, with little chances of social mobility.”

“The design and successful implementation of policies to alleviate these problems require fine-grained, frequently updated information about income, education or inequality across metropolitan areas,” they added. “However, most data sources employed today, such as population censuses or surveys, suffer from sparsity in population coverage or infrequent updating, hence they do not allow the swift evolution that urban societies experience nowadays to be followed. Thus, the traditional ways of understanding cities tend to explain what happened 5 years earlier rather than nowcasting or even predicting urban transformations.”

In his seminar, Moro referred to the socio-economic divide based on how we use the internet as the second digital divide, in contrast to the original digital divide which was based on access to the internet.