Hundreds of scientists around the world are working together to understand one of the most powerful emerging technologies before it’s too late.by 

• Karen Haoarchive page

May 20, 2021ARIEL DAVIS

On May 18, Google CEO Sundar Pichai announced an impressive new tool: an AI system called LaMDA that can chat to users about any subject.

To start, Google plans to integrate LaMDA into its main search portal, its voice assistant, and Workplace, its collection of cloud-based work software that includes Gmail, Docs, and Drive. But the eventual goal, said Pichai, is to create a conversational interface that allows people to retrieve any kind of information—text, visual, audio—across all Google’s products just by asking.

LaMDA’s rollout signals yet another way in which language technologies are becoming enmeshed in our day-to-day lives. But Google’s flashy presentation belied the ethical debate that now surrounds such cutting-edge systems. LaMDA is what’s known as a large language model (LLM)—a deep-learning algorithm trained on enormous amounts of text data.

Studies have already shown how racist, sexist, and abusive ideas are embedded in these models. They associate categories like doctors with men and nurses with women; good words with white people and bad ones with Black people. Probe them with the right prompts, and they also begin to encourage things like genocide, self-harm, and child sexual abuse. Because of their size, they have a shockingly high carbon footprint. Because of their fluency, they easily confuse people into thinking a human wrote their outputs, which experts warn could enable the mass production of misinformation.

In December, Google ousted its ethical AI co-lead Timnit Gebru after she refused to retract a paper that made many of these points. A few months later, after wide-scale denunciation of what an open letter from Google employees called the company’s “unprecedented research censorship,” it fired Gebru’s coauthor and co-lead Margaret Mitchell as well.

It’s not just Google that is deploying this technology. The highest-profile language models so far have been OpenAI’s GPT-2 and GPT-3, which spew remarkably convincing passages of text and can even be repurposed to finish off music compositions and computer code. Microsoft now exclusively licenses GPT-3 to incorporate into yet-unannounced products. Facebook has developed its own LLMs for translation and content moderation. And startups are creating dozens of products and services based on the tech giants’ models. Soon enough, all of our digital interactions—when we email, search, or post on social media—will be filtered through LLMs.

Unfortunately, very little research is being done to understand how the flaws of this technology could affect people in real-world applications, or to figure out how to design better LLMs that mitigate these challenges. As Google underscored in its treatment of Gebru and Mitchell, the few companies rich enough to train and maintain LLMs have a heavy financial interest in declining to examine them carefully. In other words, LLMs are increasingly being integrated into the linguistic infrastructure of the internet atop shaky scientific foundations.

More than 500 researchers around the world are now racing to learn more about the capabilities and limitations of these models. Working together under the BigScience project led by Huggingface, a startup that takes an “open science” approach to understanding natural-language processing (NLP), they seek to build an open-source LLM that will serve as a shared resource for the scientific community. The goal is to generate as much scholarship as possible within a single focused year. Their central question: How and when should LLMs be developed and deployed to reap their benefits without their harmful consequences?

“We can’t really stop this craziness around large language models, where everybody wants to train them,” says Thomas Wolf, the chief science officer at Huggingface, who is co-leading the initiative. “But what we can do is try to nudge this in a direction that is in the end more beneficial.”

Stochastic parrots

In the same month that BigScience kicked off its activities, a startup named Cohere quietly came out of stealth. Started by former Google researchers, it promises to bring LLMs to any business that wants one—with a single line of code. It has developed a technique to train and host its own model with the idle scraps of computational resources in a data center, which holds down the costs of renting out the necessary cloud space for upkeep and deployment.

Among its early clients is the startup Ada Support, a platform for building no-code customer support chatbots, which itself has clients like Facebook and Zoom. And Cohere’s investor list includes some of the biggest names in the field: computer vision pioneer Fei-Fei Li, Turing Award winner Geoffrey Hinton, and Apple’s head of AI, Ian Goodfellow.

Cohere is one of several startups and initiatives now seeking to bring LLMs to various industries. There’s also Aleph Alpha, a startup based in Germany that seeks to build a German GPT-3; an unnamed venture started by several former OpenAI researchers; and the open-source initiative Eleuther, which recently launched GPT-Neo, a free (and somewhat less powerful) reproduction of GPT-3.

But it’s the gap between what LLMs are and what they aspire to be that has concerned a growing number of researchers. LLMs are effectively the world’s most powerful autocomplete technologies. By ingesting millions of sentences, paragraphs, and even samples of dialogue, they learn the statistical patterns that govern how each of these elements should be assembled in a sensible order. This means LLMs can enhance certain activities: for example, they are good for creating more interactive and conversationally fluid chatbots that follow a well-established script. But they do not actually understand what they’re reading or saying. Many of the most advanced capabilities of LLMs today are also available only in English.

Related Story

We read the paper that forced Timnit Gebru out of Google. Here’s what it says.

The company’s star ethics researcher highlighted the risks of large language models, which are key to Google’s business.

Among other things, this is what Gebru, Mitchell, and five other scientists warned about in their paper, which calls LLMs “stochastic parrots.” “Language technology can be very, very useful when it is appropriately scoped and situated and framed,” says Emily Bender, a professor of linguistics at the University of Washington and one of the coauthors of the paper. But the general-purpose nature of LLMs—and the persuasiveness of their mimicry—entices companies to use them in areas they aren’t necessarily equipped for.

In a recent keynote at one of the largest AI conferences, Gebru tied this hasty deployment of LLMs to consequences she’d experienced in her own life. Gebru was born and raised in Ethiopia, where an escalating war has ravaged the northernmost Tigray region. Ethiopia is also a country where 86 languages are spoken, nearly all of them unaccounted for in mainstream language technologies.

Despite LLMs having these linguistic deficiencies, Facebook relies heavily on them to automate its content moderation globally. When the war in Tigray first broke out in November, Gebru saw the platform flounder to get a handle on the flurry of misinformation. This is emblematic of a persistent pattern that researchers have observed in content moderation. Communities that speak languages not prioritized by Silicon Valley suffer the most hostile digital environments.

Gebru noted that this isn’t where the harm ends, either. When fake news, hate speech, and even death threats aren’t moderated out, they are then scraped as training data to build the next generation of LLMs. And those models, parroting back what they’re trained on, end up regurgitating these toxic linguistic patterns on the internet.

In many cases, researchers haven’t investigated thoroughly enough to know how this toxicity might manifest in downstream applications. But some scholarship does exist. In her 2018 book Algorithms of Oppression, Safiya Noble, an associate professor of information and African-American studies at the University of California, Los Angeles, documented how biases embedded in Google search perpetuate racism and, in extreme cases, perhaps even motivate racial violence.

“The consequences are pretty severe and significant,” she says. Google isn’t just the primary knowledge portal for average citizens. It also provides the information infrastructure for institutions, universities, and state and federal governments.

Google already uses an LLM to optimize some of its search results. With its latest announcement of LaMDA and a recent proposal it published in a preprint paper, the company has made clear it will only increase its reliance on the technology. Noble worries this could make the problems she uncovered even worse: “The fact that Google’s ethical AI team was fired for raising very important questions about the racist and sexist patterns of discrimination embedded in large language models should have been a wake-up call.”

BigScience

The BigScience project began in direct response to the growing need for scientific scrutiny of LLMs. In observing the technology’s rapid proliferation and Google’s attempted censorship of Gebru and Mitchell, Wolf and several colleagues realized it was time for the research community to take matters into its own hands.

Inspired by open scientific collaborations like CERN in particle physics, they conceived of an idea for an open-source LLM that could be used to conduct critical research independent of any company. In April of this year, the group received a grant to build it using the French government’s supercomputer.

At tech companies, LLMs are often built by only half a dozen people who have primarily technical expertise. BigScience wanted to bring in hundreds of researchers from a broad range of countries and disciplines to participate in a truly collaborative model-construction process. Wolf, who is French, first approached the French NLP community. From there, the initiative snowballed into a global operation encompassing more than 500 people.

The collaborative is now loosely organized into a dozen working groups and counting, each tackling different aspects of model development and investigation. One group will measure the model’s environmental impact, including the carbon footprint of training and running the LLM and factoring in the life-cycle costs of the supercomputer. Another will focus on developing responsible ways of sourcing the training data—seeking alternatives to simply scraping data from the web, such as transcribing historical radio archives or podcasts. The goal here is to avoid toxic language and nonconsensual collection of private information.

Related Story

Why GPT-3 is the best and worst of AI right now

Open AI’s language AI wowed the public with its apparent mastery of English – but is it all an illusion?

Other working groups are dedicated to developing and evaluating the model’s “multilinguality.” To start, BigScience has selected eight languages or language families, including English, Chinese, Arabic, Indic (including Hindi and Urdu), and Bantu (including Swahili). The plan is to work closely with every language community to map out as many of its regional dialects as possible and ensure that its distinct data privacy norms are respected. “We want people to have a say in how their data is used,” says Yacine Jernite, a Huggingface researcher.

The point is not to build a commercially viable LLM to compete with the likes of GPT-3 or LaMDA. The model will be too big and too slow to be useful to companies, says Karën Fort, an associate professor at the Sorbonne. Instead, the resource is being designed purely for research. Every data point and every modeling decision is being carefully and publicly documented, so it’s easier to analyze how all the pieces affect the model’s outcomes. “It’s not just about delivering the final product,” says Angela Fan, a Facebook researcher. “We envision every single piece of it as a delivery point, as an artifact.”

The project is undoubtedly ambitious—more globally expansive and collaborative than any the AI community has seen before. The logistics of coordinating so many researchers is itself a challenge. (In fact, there’s a working group for that, too.) What’s more, every single researcher is contributing on a volunteer basis. The grant from the French government covers only computational, not human, resources.

But researchers say the shared need that brought the community together has galvanized an impressive level of energy and momentum. Many are optimistic that by the end of the project, which will run until May of next year, they will have produced not only deeper scholarship on the limitations of LLMs but also better tools and practices for building and deploying them responsibly.

The organizers hope this will inspire more people within industry to incorporate those practices into their own LLM strategy, though they are the first to admit they are being idealistic. If anything, the sheer number of researchers involved, including many from tech giants, will help establish new norms within the NLP community.

In some ways the norms have already shifted. In response to conversations around the firing of Gebru and Mitchell, Cohere heard from several of its clients that they were worried about the technology’s safety. On its site it includes a page on its website featuring a pledge to continuously invest in technical and non-technical research to mitigate the possible harms of its model. It says it will also assemble an advisory council made up of external experts to help it create policies on the permissible use of its technologies.

“NLP is at a very important turning point,” says Fort. That’s why BigScience is exciting. It allows the community to push the research forward and provide a hopeful alternative to the status quo within industry: “It says, ‘Let’s take another pass. Let’s take it together—to figure out all the ways and all the things we can do to help society.’”

“I want NLP to help people,” she says, “not to put them down.”

Update: Cohere’s responsibility initiatives have been clarified.

Article link: The race to understand the thrilling, dangerous world of language AI | MIT Technology Review

The way we search online hasn’t changed in decades. A new idea from Google researchers could make it more like talking to a human expert

In 1998 a couple of Stanford graduate students published a paper describing a new kind of search engine: “In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.”

The key innovation was an algorithm called PageRank, which ranked search results by calculating how relevant they were to a user’s query on the basis of their links to other pages on the web. On the back of PageRank, Google became the gateway to the internet, and Sergey Brin and Larry Page built one of the biggest companies in the world.

Now a team of Google researchers has published a proposal for a radical redesign that throws out the ranking approach and replaces it with a single large AI language model—a future version of BERT or GPT-3. The idea is that instead of searching for information in a vast list of web pages, users would ask questions and have a language model trained on those pages answer them directly. The approach could change not only how search engines work, but how we interact with them.

Many issues with existing language models will need to be fixed first. For a start, these AIs can sometimes generate biased and toxic responses to queries—a problem that researchers at Google and elsewhere have pointed out.

Rethinking PageRank

Search engines have become faster and more accurate, even as the web has exploded in size. AI is now used to rank results, and Google uses BERT to understand search queries better. Yet beneath these tweaks, all mainstream search engines still work the same way they did 20 years ago: web pages are indexed by crawlers (software that reads the web nonstop and maintains a list of everything it finds), results that match a user’s query are gathered from this index, and the results are ranked.

“This index-retrieve-then-rank blueprint has withstood the test of time and has rarely been challenged or seriously rethought,” Donald Metzler and his colleagues at Google Research write. (Metzler declined a request to comment.)

The problem is that even the best search engines today still respond with a list of documents that include the information asked for, not with the information itself. Search engines are also not good at responding to queries that require answers drawn from multiple sources. It’s as if you asked your doctor for advice and received a list of articles to read instead of a straight answer.

Metzler and his colleagues are interested in a search engine that behaves like a human expert. It should produce answers in natural language, synthesized from more than one document, and back up its answers with references to supporting evidence, as Wikipedia articles aim to do.  

Large language models get us part of the way there. Trained on most of the web and hundreds of books, GPT-3 draws information from multiple sources to answer questions in natural language. The problem is that it does not keep track of those sources and cannot provide evidence for its answers. There’s no way to tell if GPT-3 is parroting trustworthy information or disinformation—or simply spewing nonsense of its own making.

Metzler and his colleagues call language models dilettantes—“They are perceived to know a lot but their knowledge is skin deep.” The solution, they claim, is to build and train future BERTs and GPT-3s to retain records of where their words come from. No such models are yet able to do this, but it is possible in principle, and there is early work in that direction.

There have been decades of progress on different areas of search, from answering queries to summarizing documents to structuring information, says Ziqi Zhang at the University of Sheffield, UK, who studies information retrieval on the web. But none of these technologies overhauled search because they each address specific problems and are not generalizable. The exciting premise of this paper is that large language models are able to do all these things at the same time, he says.

Yet Zhang notes that language models do not perform well with technical or specialist subjects because there are fewer examples in the text they are trained on. “There are probably hundreds of times more data on e-commerce on the web than data about quantum mechanics,” he says. Language models today are also skewed toward English, which would leave non-English parts of the web underserved.  

Hanna Hajishirzi, who studies natural language processing at the University of Washington, welcomes the idea but warns that their would be problems in practice. “I believe large language models are very important and potentially the future of search engines, but they require large memory and computational resources,” she says. “I don’t think they would replace indexing.”

Still, Zhang is excited by the possibilities. “This has not been possible in the past, because large language models only took off recently,” he says. “If it works, it would transform our search experience.”

Update: we have changed the text to more clearly represent the problems with existing large language models.

Article link: Language models like GPT-3 could herald a new type of search engine | MIT Technology Review

How to create more value by connecting experts from inside and outside the organization by 

• Tiziana Casciaro,
• Amy C. Edmondson,
• Sujin Jang

From the Magazine (May–June 2019)

About the Art: In 2010, Christopher Payne discovered an old (but still functioning) yarn mill in Maine and was inspired to explore, through his photography, how the iconic American textile industry has changed and what its future may hold.  Christopher Payne/Esto

Today the most promising innovation and business opportunities require collaboration among functions, offices, and organizations. To realize them, companies must break down silos and get people working together across boundaries. But that’s a challenge for many leaders. Employees naturally default to focusing on vertical relationships, and formal restructuring is costly, confusing, and slow. What, then, is the solution? Engaging in four activities that promote horizontal teamwork: (1) developing cultural brokers, or employees who excel at connecting across divides; (2) encouraging people to ask questions in an open-ended, unbiased way that genuinely explores others’ thinking; (3) getting people to actively take other points of view; and (4) broadening employees’ vision to include more-distant networks.

By supporting these activities, leaders can help employees connect with new pools of expertise and learn from and relate to people who think very differently from them. And when that happens, interface collaboration will become second nature.

Idea in Brief

The Challenge

Innovation initiatives, globalization, and digitalization increasingly require people to collaborate across functional and national boundaries. But breaking down silos remains frustratingly difficult.

The Cause

Employees don’t know how to identify expertise outside their own work domains and struggle to understand the perspectives of colleagues who think very differently from them.

The Solution

Leaders can help employees connect with and relate to people across organizational divides by doing four things: developing and deploying “cultural brokers,” who help groups overcome differences; encouraging and training workers to ask the right questions; getting people to see things through others’ eyes; and broadening everyone’s vision of networks of expertise inside and outside the company.

Though most executives recognize the importance of breaking down silos to help people collaborate across boundaries, they struggle to make it happen. That’s understandable: It is devilishly difficult. Think about your own relationships at work—the people you report to and those who report to you, for starters. Now consider the people in other functions, units, or geographies whose work touches yours in some way. Which relationships get prioritized in your day-to-day job?

We’ve posed that question to managers, engineers, salespeople, and consultants in companies around the world. The response we get is almost always the same: vertical relationships.

But when we ask, “Which relationships are most important for creating value for customers?” the answers flip. Today the vast majority of innovation and business-development opportunities lie in the interfaces between functions, offices, or organizations. In short, the integrated solutions that most customers want—but companies wrestle with developing—require horizontal collaboration.

The value of horizontal teamwork is widely recognized. Employees who can reach outside their silos to find colleagues with complementary expertise learn more, sell more, and gain skills faster. Harvard’s Heidi Gardner has found that firms with more cross-boundary collaboration achieve greater customer loyalty and higher margins. As innovation hinges more and more on interdisciplinary cooperation, digitalization transforms business at a breakneck pace, and globalization increasingly requires people to work across national borders, the demand for executives who can lead projects at interfaces keeps rising.

Our research and consulting work with hundreds of executives and managers in dozens of organizations confirms both the need for and the challenge of horizontal collaboration. “There’s no doubt. We should focus on big projects that call for integration across practices,” a partner in a global accounting firm told us. “That’s where our greatest distinctive value is developed. But most of us confine ourselves to the smaller projects that we can handle within our practice areas. It’s frustrating.” A senior partner in a leading consulting firm put it slightly differently: “You know you should swim farther to catch a bigger fish, but it is a lot easier to swim in your own pond and catch a bunch of small fish.”

One way to break down silos is to redesign the formal organizational structure. But that approach has limits: It’s costly, confusing, and slow. Worse, every new structure solves some problems but creates others. That’s why we’ve focused on identifying activities that facilitate boundary crossing. We’ve found that people can be trained to see and connect with pools of expertise throughout their organizations and to work better with colleagues who think very differently from them. The core challenges of operating effectively at interfaces are simple: learning about people on the other side and relating to them. But simple does not mean easy; human beings have always struggled to understand and relate to those who are different.

Leaders need to help people develop the capacity to overcome these challenges on both individual and organizational levels. That means providing training in and support for four practices that enable effective interface work.

1. Develop and Deploy Cultural Brokers

Fortunately, in most companies there are people who already excel at interface collaboration. They usually have experiences and relationships that span multiple sectors, functions, or domains and informally serve as links between them. We call these people cultural brokers. In studies involving more than 2,000 global teams, one of us—Sujin—found that diverse teams containing a cultural broker significantly outperformed diverse teams without one. (See “The Most Creative Teams Have a Specific Type of Cultural Diversity,” HBR.org, July 24, 2018.) Companies should identify these individuals and help them increase their impact.

Cultural brokers promote cross-boundary work in one of two ways: by acting as a bridge or as an adhesive.

A bridge offers himself as a go-between, allowing people in different functions or geographies to collaborate with minimal disruption to their day-to-day routine. Bridges are most effective when they have considerable knowledge of both sides and can figure out what each one needs. This is why the champagne and spirits distributor Moët Hennessy España hired two enologists, or wine experts, to help coordinate the work of its marketing and sales groups, which had a history of miscommunication and conflict. The enologists could relate to both groups equally: They could speak to marketers about the emotional content (the ephemeral “bouquet”) of brands, while also providing pragmatic salespeople with details on the distinctive features of products they needed to win over retailers. Understanding both worlds, the enologists were able to communicate the rationale for each group’s modus operandi to the other, allowing marketing and sales to work more synergistically even without directly interacting. This kind of cultural brokerage is efficient because it lets disparate parties work around differences without investing in learning the other side’s perspective or changing how they work. It’s especially valuable for one-off collaborations or when the company is under intense time pressure to deliver results.

Employees who can reach outside their silos learn more and sell more.

Adhesives, in contrast, bring people together and help build mutual understanding and lasting relationships. Take one manager we spoke with at National Instruments, a global producer of automated test equipment. He frequently connects colleagues from different regions and functions. “I think of it as building up the relationships between them,” he told us. “If a colleague needs to work with someone in another office or function, I would tell them, ‘OK, here’s the person to call.’ Then I’d take the time to sit down and say, ‘Well, let me tell you a little bit about how these guys work.’” Adhesives facilitate collaboration by vouching for people and helping them decipher one another’s language. Unlike bridges, adhesives develop others’ capacity to work across a boundary in the future without their assistance.

Company leaders can build both bridging and adhesive capabilities in their organizations by hiring people with multifunctional or multicultural backgrounds who have the strong interpersonal skills needed to build rapport with multiple parties. Because it takes resilience to work with people across cultural divides, firms should also look for a growth mindset—the desire to learn and to take on challenges and “stretch” opportunities.

In addition, leaders can develop more brokers by giving people at all levels the chance to move into roles that expose them to multiple parts of the company. This, by the way, is good training for general managers and is what many rotational leadership-development programs aim to accomplish. Claudine Wolfe, the head of talent and development at the global insurer Chubb, maintains that the company’s capacity to serve customers around the world rests on giving top performers opportunities to work in different geographies and cultivate an international mindset. “We give people their critical development experiences steeped in the job, in the region,” she says. “They get coaching in the cultural norms and the language, but then they live it and internalize it. They go to the local bodega, take notice of the products on the shelves, have conversations with the merchant, and learn what it really means to live in that environment.”

Matrix organizational structures, in which people report to two (or more) groups, can also help develop cultural brokers. Despite their inherent challenges (they can be infuriatingly hard to navigate without strong leadership and accountability), matrices get people used to operating at interfaces.

We’re not saying that everyone in your organization needs to be a full-fledged cultural broker. But consciously expanding the ranks of brokers and deploying them to grease the wheels of collaboration can go a long way.

2. Encourage People to Ask the Right Questions

It’s nearly impossible to work across boundaries without asking a lot of questions. Inquiry is critical because what we see and take for granted on one side of an interface is not the same as what people experience on the other side.

Indeed, a study of more than 1,000 middle managers at a large bank that Tiziana conducted with Bill McEvily and Evelyn Zhang of the University of Toronto and Francesca Gino of Harvard Business School highlights the value of inquisitiveness in boundary-crossing work. It showed that managers with high levels of curiosity were more likely to build networks that spanned disconnected parts of the company.

But all of us are vulnerable to forgetting the crucial practice of asking questions as we move up the ladder. High-achieving people in particular frequently fail to wonder what others are seeing. Worse, when we do recognize that we don’t know something, we may avoid asking a question out of (misguided) fear that it will make us look incompetent or weak. “Not asking questions is a big mistake many professionals make,” Norma Kraay, the managing partner of talent for Deloitte Canada, told us. “Expert advisers want to offer a solution. That’s what they’re trained to do.”

Leaders can encourage inquiry in two important ways—and in the process help create an organization where it’s psychologically safe to ask questions.

Be a role model.

When leaders show interest in what others are seeing and thinking by asking questions, it has a stunning effect: It prompts people in their organizations to do the same.

Asking questions also conveys the humility that more and more business leaders and researchers are pointing to as vital to success. According to Laszlo Bock, Google’s former senior vice president of people operations, humble people are better at bringing others together to solve tough problems. In a fast-changing business environment, humility—not to be confused with false modesty—is simply a strength. Its power comes from realism (as in It really is a complex, challenging world out there; if we don’t work together, we don’t stand a chance).

Gino says one way a leader can make employees feel comfortable asking questions is by openly acknowledging when he or she doesn’t know the answer. Another, she says, is by having days in which employees are explicitly encouraged to ask “Why?” “What if…?” and “How might we…?” (See “The Business Case for Curiosity,” HBR, September–October 2018.)

Teach employees the art of inquiry.

Training can help expand the range and frequency of questions employees ask and, according to Hal Gregersen of the MIT Leadership Center, can reinvigorate their sense of curiosity. But some questions are better than others. And if you simply tell people to raise more questions, you might unleash interrogation tactics that inhibit rather than encourage the development of new perspectives. As MIT’s Edgar Schein explains in his book Humble Inquiry, questions are the secret to productive work relationships—but they must be driven by genuine interest in understanding another’s view.

How to Ask Good Questions

COMMON PITFALLS	EFFECTIVE INQUIRY
Start with yes-or-no questions.	Start with open-ended questions that minimize preconceptions. (“How are things going on your end?” “What does your group see as the key opportunity in this space?”)
Continue asking overly general questions (“What’s on your mind?”) that may invite long off-point responses.	As collaborations develop, ask questions that focus on specific issues but allow people plenty of room to elaborate. (“What do you know about x?” “Can you explain how that works?”)
Assume that you’ve grasped what speakers intended.	Check your understanding by summarizing what you’re hearing and asking explicitly for corrections or missing elements. (“Does that sound right—am I missing anything?” “Can you help me fill in the gaps?”)
Assume the collaboration process will take care of itself.	Periodically take time to inquire into others’ experiences of the process or relationship. (“How do you think the project is going?” “What could we do to work together more effectively?”)

Show more

It’s also important to learn how to request information in the least biased way possible. This means asking open-ended questions that minimize preconceptions, rather than yes-or-no questions. For instance, “What do you see as the key opportunity in this space?” will generate a richer dialogue than “Do you think this is the right opportunity to pursue?”

As collaborations move forward, it’s helpful for team leaders or project managers to raise queries that encourage others to dive more deeply into specific issues and express related ideas or experiences. “What do you know about x?” and “Can you explain how that works?” are two examples. These questions are focused but neither limit responses nor invite long discourses that stray too far from the issue at hand.

How you process the answers also matters. It’s natural, as conversations unfold, to assume you understand what’s being said. But what people hear is biased by their expertise and experiences. So it’s important to train people to check whether they’re truly getting their colleagues’ meaning, by using language like “This is what I’m hearing—did I miss anything?” or “Can you help me fill in the gaps?” or “I think what you said means the project is on track. Is that correct?”

Finally, periodic temperature taking is needed to examine the collaborative process itself. The only way to find out how others are experiencing a project or relationship is by asking questions such as “How do you think the project is going?” and “What could we do to work together more effectively?”

3. Get People to See the World Through Others’ Eyes

Leaders shouldn’t just encourage employees to be curious about different groups and ask questions about their thinking and practices; they should also urge their people to actively consider others’ points of view. People from different organizational groups don’t see things the same way. Studies (including research on barriers to successful product innovation that the management professor Deborah Dougherty conducted at Wharton) consistently reveal that this leads to misunderstandings in interface work. It’s vital, therefore, to help people learn how to take the perspectives of others. One of us, Amy, has done research showing that ambitious cross-industry innovation projects succeed when diverse participants discover how to do this. New Songdo, a project to build a city from scratch in South Korea that launched a decade ago, provides an instructive example. Early in the effort, project leaders brought together architects, engineers, planners, and environmental experts and helped them integrate their expertise in a carefully crafted learning process designed to break down barriers between disciplines. Today, in striking contrast to other “smart” city projects, New Songdo is 50% complete and has 30,000 residents, 33,000 jobs, and emissions that are 70% lower than those of other developments its size.

In a study of jazz bands and Broadway productions, Brian Uzzi of Northwestern University found that leaders of successful teams had an unusual ability to assume other people’s viewpoints. These leaders could speak the multiple “languages” of their teammates. Other research has shown that when members of a diverse team proactively take the perspectives of others, it enhances the positive effect of information sharing and increases the team’s creativity.

Creating a culture that fosters this kind of behavior is a senior leadership responsibility. Psychological research suggests that while most people are capable of taking others’ perspectives, they are rarely motivated to do so. Leaders can provide some motivation by emphasizing to their teams how much the integration of diverse expertise enhances new value creation. But a couple of other tactics will help:

Organize cross-silo dialogues.

Instead of holding one-way information sessions, leaders should set up cross-silo discussions that help employees see the world through the eyes of customers or colleagues in other parts of the company. The goal is to get everyone to share knowledge and work on synthesizing that diverse input into new solutions. This happens best in face-to-face meetings that are carefully structured to allow people time to listen to one another’s thinking. Sometimes the process includes customers; one consulting firm we know started to replace traditional meetings, at which the firm conveyed information to clients, with a workshop format designed to explore questions and develop solutions in collaboration with them. The new format gives both the clients and the consultants a chance to learn from each other.

One of the more thoughtful uses of cross-silo dialogue is the “focused event analysis” (FEA) at Children’s Minnesota. In an FEA people from the health system’s different clinical and operational groups come together after a failure, such as the administration of the wrong medication to a patient. One at a time participants offer their take on what happened; the goal is to carefully document multiple perspectives before trying to identify a cause. Often participants are surprised to learn how people from other groups saw the incident. The assumption underlying the FEA is that most failures have not one root cause but many. Once the folks involved have a multifunctional picture of the contributing factors, they can alter procedures and systems to prevent similar failures.

Hire for curiosity and empathy.

You can boost your company’s capacity to see the world from different perspectives by bringing on board people who relate to and sympathize with the feelings, thoughts, and attitudes of others. Southwest Airlines, which hires fewer than 2% of all applicants, selects people with empathy and enthusiasm for customer service, evaluating them through behavioral interviews (“Tell me about a time when…”) and team interviews in which candidates are observed interacting.

4. Broaden Your Employees’ Vision

You can’t lead at the interfaces if you don’t know where they are. Yet many organizations unwittingly encourage employees to never look beyond their own immediate environment, such as their function or business unit, and as a result miss out on potential insights employees could get if they scanned more-distant networks. Here are some ways that leaders can create opportunities for employees to widen their horizons, both within the company and beyond it:

Bring employees from diverse groups together on initiatives.

As a rule, cross-functional teams give people across silos a chance to identify various kinds of expertise within their organization, map how they’re connected or disconnected, and see how the internal knowledge network can be linked to enable valuable collaboration.

At one global consulting firm, the leader of the digital health-care practice used to have its consultants speak just to clients’ CIOs and CTOs. But she realized that that “unnecessarily limited the practice’s ability to identify opportunities to serve clients beyond IT,” she says. So she began to set up sessions with the entire C-suite at clients and brought in consultants from across all her firm’s health-care practices—including systems redesign, operations excellence, strategy, and financing—to provide a more integrated look at the firm’s health-care innovation expertise.

Those meetings allowed the consultants to discover the connections among the practices in the health-care division, identify the people best positioned to bridge the different practices, and see novel ways to combine the firm’s various kinds of expertise to meet clients’ needs. That helped the consultants spot value-generating opportunities for services at the interfaces between the practices. The new approach was so effective that, in short order, the leader was asked to head up a new practice that served as an interface across all the practices in the IT division so that she could replicate her success in other parts of the firm.

Urge employees to explore distant networks.

Employees also need to be pushed to tap into expertise outside the company and even outside the industry. The domains of human knowledge span science, technology, business, geography, politics, history, the arts, the humanities, and beyond, and any interface between them could hold new business opportunities. Consider the work of the innovation consultancy IDEO. By bringing design techniques from technology, science, and the arts to business, it has been able to create revolutionary products, like the first Apple mouse (which it developed from a Xerox PARC prototype into a commercial offering), and help companies in many industries embrace design thinking as an innovation strategy.

The tricky part is finding the domains most relevant to key business goals. Although many innovations have stemmed from what Abraham Flexner, the founding director of the Institute for Advanced Study, called “the usefulness of useless knowledge,” businesses can ill afford to rely on open-ended exploratory search alone. To avoid this fate, leaders can take one of two approaches:

A top-down approach works when the knowledge domains with high potential for value creation have already been identified. For example, a partner in an accounting firm who sees machine learning as key to the profession’s future might have an interested consultant or analyst in her practice take online courses or attend industry conferences about the technology and ask that person to come back with ideas about its implications. The partner might organize workshops in which the junior employee shares takeaways from the learning experiences and brainstorms, with experienced colleagues, potential applications in the firm.

You can’t lead at the interfaces if you don’t know where they are.

A bottom-up approach is better when leaders have trouble determining which outside domains the organization should connect with—a growing challenge given the speed at which new knowledge is being created. Increasingly, leaders must rely on employees to identify and forge connections with far-flung domains. One approach is to crowdsource ideas for promising interfaces—for example, by inviting employees to propose conferences in other industries they’d like to attend, courses on new skill sets they’d like to take, or domain experts they’d like to bring in for workshops. It’s also critical to give employees the time and resources to scan external domains and build connections to them.

Breaking Down Silos

In today’s economy everyone knows that finding new ways to combine an organization’s diverse knowledge is a winning strategy for creating lasting value. But it doesn’t happen unless employees have the opportunities and tools to work together productively across silos. To unleash the potential of horizontal collaboration, leaders must equip people to learn and to relate to one another across cultural and logistical divides. The four practices we’ve just described can help.

Not only is each one useful on its own in tackling the distinct challenges of interface work, but together these practices are mutually enhancing: Engaging in one promotes competency in another. Deploying cultural brokers who build connections across groups gets people to ask questions and learn what employees in other groups are thinking. When people start asking better questions, they’re immediately better positioned to understand others’ perspectives and challenges. Seeing things from someone else’s perspective—walking in his or her moccasins—in turn makes it easier to detect more pockets of knowledge. And network scanning illuminates interfaces where cultural brokers might be able to help groups collaborate effectively.

Over time these practices—none of which require advanced degrees or deep technical smarts—dissolve the barriers that make boundary-crossing work so difficult. When leaders create conditions that encourage and support these practices, collaboration across the interface will ultimately become second nature. A version of this article appeared in the May–June 2019 issue (pp.130–139) of Harvard Business Review.

Article link: What Cross-Silo Leadership Looks Like (hbr.org)

by 

• Tom Davenport,
• Theodoros Evgeniou,
• Thomas C. Redman

June 24, 2021

Summary.   

Data is more important than ever, but most organizations still struggle with a few common issues: They focus more on data infrastructure than data products; data is often created with the needs of a particular department in mind, but little thought for the end use; they lack a common “data language” with each department coding and classifying with their own system; and they’re increasingly focused on outside data, but have few quality control systems in place. By focusing on “data supply chain” management, companies can address these and other issues. Similar to physical supply chains, companies should think systematically, focus on end products, define standards and measurements, introduce quality controls, and constantly refine their approach across all phases of data gathering and analysis.

Data management has bedeviled large companies for decades. Almost all firms spend a lot on it but find the results unsatisfactory. While the issue does not appear to be growing worse, resolving it is increasingly urgent as managers and companies strive to become more data driven, leverage advanced analytics and artificial intelligence, and compete with data. In this article we’ll explore a powerful approach to data management through the lens of “data products” and “data supply chains.”

Most companies struggle with a few common but significant data management issues.

First, companies have concentrated on the technical capabilities of data management, which are controlled by the IT function and are needed to acquire, store, and move data. This is no mean feat — building technical “pipes” is a challenging job. But in so doing they have focused more on infrastructure and much less on the outputs: the data products that are used to make decisions, differentiate products and services, and satisfy customers.

Second, data is created in different parts of the organization to meet the needs of various departments, not for later use by others in data products, business decisions, or processes. Contrast that with a physical product, such as a car, where components such as the chassis and the starter are designed with the end product in mind.

Third, most organizations lack a common data language. Data is subtle and nuanced and has different meanings for different people in different contexts. Exacerbating this, some departments, taking ownership for “their data,” may be reluctant to share. Or while willing to share, they will not make time to explain these nuances so others can use it effectively. This leads other departments to set up their own “near-redundant” databases, adding to the overall confusion.

Finally, companies are increasingly interested in what happens outside their walls, tapping external data to answer a variety of questions. But external data is largely unmanaged, with little supplier qualification or data quality assessment.

Data supply chain management, with data products as the end result of the process, can help to address each of these issues. It puts equal emphasis on all phases of data management — from collection to organization to consumption of data products. It’s a means of balancing the benefits of common data with those of unique and tailored data in products, and it’s equally suited to internal and external data. Relatively few companies employ data supply chain management, but those that do tend to report better results.

Process and Supplier Management for Data Products

Companies have always produced data products in the form of financial statements, reports to regulators, and so forth. Still, the range and importance of such products is growing. For many, the goal is to embed analytics and AI-derived models into products that serve both internal and external customers. Morgan Stanley’s Next Best Action, LinkedIn’s People You May Know, Google’s many search offerings, and MasterCard’s SpendingPulse and Business Locator are good examples. With the issues cited above in full display, “wrangling” the data takes far longer than building the model and still doesn’t solve all the issues.

Fortunately, there is a better way to source high-quality data. It builds on the process and supplier management techniques used by manufacturers of physical products. In particular, manufacturers extend deep into their supply chains to clarify their requirements, qualify suppliers, insist that suppliers measure quality, and make needed improvements at the source(s) of problem(s). This enables them to assemble components into finished products with minimal “physical product wrangling,” improving quality and lowering costs.

One organization employing supplier quality management in its data supply chain is Altria, the U.S.-based provider of tobacco and smoke-free products. Altria depends on point-of-sale data from more than 100,000 convenience stores daily to complete its market reports and analysis. A team reporting to Kirby Forlin, VP Advanced Analytics, manages this base. Data requirements are spelled out in contracts, and the team aims to help stores meet them. To begin, Altria concentrated on its most basic requirements. Quality was poor, with only 58% of daily submissions meeting them. But the Altria team worked patiently, improving quality to 98% in three years. As the score for basic quality improved, the Altria team added its more advanced requirements to the mix. As Forlin noted, “This is a work in progress. The evidence that we can increasingly trust the data saves us a lot of work in our analytics practice and builds trust into our work.”

Steps Toward a Data Supply Chain

The data supply chain can be established within a company by following some of the same steps used in process and quality management for physical supply chains:

1. Establish management responsibilities. As step 1a, the chief data officer or product manager should name a “data supply chain manager” from their staff to coordinate the effort and recruit “responsible parties” from each department (including external data sources) across the supply chain. Step 1b is to put issues associated with data sharing and ownership front and center. We find that most issues melt away, as few managers wish to take a hard stance against data sharing in front of their peers.
2. Identify and document the data and associated cost, time, and quality requirements needed to create and maintain data products.
3. Describe the supply chain. Develop a flowchart that describes points of data creation/original sources of data and the steps taken to move, enrich, and analyze data for use in data products.
4. Define and establish measurements. Generally, the idea is to implement measurements that indicate whether requirements are met. Start with data accuracy and the elapsed time from data creation to incorporation into a data product. Measures will vary for each data product’s supply chain.
5. Establish process control and assess conformance to requirements. Use the measurements of step four to put the process in control and determine how well the requirements of step two are met and to identify gaps.
6. Investigate the supply chain to identify needed improvements — overall and for particular data products. Determine where gaps uncovered in step five originate in the flowchart of step three.
7. Make improvements and continuously monitor. Identify and eliminate root causes of gaps identified in step six, and return to previous steps if necessary. Continuously monitor both the input data and the data products, looking to improve products and for the new data and better sources needed to do so.
8. “Qualify” data sources. Companies will continue to employ increasing numbers of external data suppliers and it is helpful to identify those that consistently provide high-quality data. Audits of their data quality programs provide the means “qualify” those that do and identify areas of weakness in those that do not.

Key Bank, a top 20 U.S. bank in asset size, uses a broad data supply chain concept to structure its data management initiatives. It breaks its process into the areas of “capture/organize/consume” and attempts to improve efficiency and effectiveness in each area. It recently shifted much of its data storage and analytics to the cloud, and found major improvements in flexibility and speed across the supply chain. Its consumption activities were historically focused on classic business intelligence capabilities, but now it also has a strong data science function.

That necessitated a change in the supply chain toward greater data virtualization and the ability to construct views of data that cut across different data marts and that incorporate external data as well. The bank has been able to use its data supply chain to rapidly develop new banking products that rely heavily on data. For example, it was one of the largest lenders of Payroll Protection Plan loans in the U.S., and also recently introduced a national digital bank for doctors. Mike Onders, the bank’s chief data officer, is effectively the data supply chain manager. He and his staff have evaluated the ability of the bank’s data supply chain to supply a variety of needed data products.

We urge all companies to aggressively manage their most important data supply chains. Data is as important an asset to businesses as any other type, and data products are increasingly as important as physical ones. The same thinking that has improved physical supply chains for decades is proving equally valuable for data.

Article link: Your Data Supply Chains Are Probably a Mess. Here’s How to Fix Them. (hbr.org)

Deep learning has a terrible carbon footprint.by 

• Karen Haoarchive page

June 6, 2019

The artificial-intelligence industry is often compared to the oil industry: once mined and refined, data, like oil, can be a highly lucrative commodity. Now it seems the metaphor may extend even further. Like its fossil-fuel counterpart, the process of deep learning has an outsize environmental impact.

In a new paper, researchers at the University of Massachusetts, Amherst, performed a life cycle assessment for training several common large AI models. They found that the process can emit more than 626,000 pounds of carbon dioxide equivalent—nearly five times the lifetime emissions of the average American car (and that includes manufacture of the car itself).

It’s a jarring quantification of something AI researchers have suspected for a long time. “While probably many of us have thought of this in an abstract, vague level, the figures really show the magnitude of the problem,” says Carlos Gómez-Rodríguez, a computer scientist at the University of A Coruña in Spain, who was not involved in the research. “Neither I nor other researchers I’ve discussed them with thought the environmental impact was that substantial.”

The carbon footprint of natural-language processing

The paper specifically examines the model training process for natural-language processing (NLP), the subfield of AI that focuses on teaching machines to handle human language. In the last two years, the NLP community has reached several noteworthy performance milestones in machine translation, sentence completion, and other standard benchmarking tasks. OpenAI’s infamous GPT-2 model, as one example, excelled at writing convincing fake news articles.

But such advances have required training ever larger models on sprawling data sets of sentences scraped from the internet. The approach is computationally expensive—and highly energy intensive.

The researchers looked at four models in the field that have been responsible for the biggest leaps in performance: the Transformer, ELMo, BERT, and GPT-2. They trained each on a single GPU for up to a day to measure its power draw. They then used the number of training hours listed in the model’s original papers to calculate the total energy consumed over the complete training process. That number was converted into pounds of carbon dioxide equivalent based on the average energy mix in the US, which closely matches the energy mix used by Amazon’s AWS, the largest cloud services provider.

They found that the computational and environmental costs of training grew proportionally to model size and then exploded when additional tuning steps were used to increase the model’s final accuracy. In particular, they found that a tuning process known as neural architecture search, which tries to optimize a model by incrementally tweaking a neural network’s design through exhaustive trial and error, had extraordinarily high associated costs for little performance benefit. Without it, the most costly model, BERT, had a carbon footprint of roughly 1,400 pounds of carbon dioxide equivalent, close to a round-trip trans-America flight for one person.

What’s more, the researchers note that the figures should only be considered as baselines. “Training a single model is the minimum amount of work you can do,” says Emma Strubell, a PhD candidate at the University of Massachusetts, Amherst, and the lead author of the paper. In practice, it’s much more likely that AI researchers would develop a new model from scratch or adapt an existing model to a new data set, either of which can require many more rounds of training and tuning.

To get a better handle on what the full development pipeline might look like in terms of carbon footprint, Strubell and her colleagues used a model they’d produced in a previous paper as a case study. They found that the process of building and testing a final paper-worthy model required training 4,789 models over a six-month period. Converted to CO2 equivalent, it emitted more than 78,000 pounds and is likely representative of typical work in the field.

The significance of those figures is colossal—especially when considering the current trends in AI research. “In general, much of the latest research in AI neglects efficiency, as very large neural networks have been found to be useful for a variety of tasks, and companies and institutions that have abundant access to computational resources can leverage this to obtain a competitive advantage,” Gómez-Rodríguez says. “This kind of analysis needed to be done to raise awareness about the resources being spent […] and will spark a debate.”

“What probably many of us did not comprehend is the scale of it until we saw these comparisons,” echoed Siva Reddy, a postdoc at Stanford University who was not involved in the research.

The privatization of AI research

The results underscore another growing problem in AI, too: the sheer intensity of resources now required to produce paper-worthy results has made it increasingly challenging for people working in academia to continue contributing to research.

“This trend toward training huge models on tons of data is not feasible for academics—grad students especially, because we don’t have the computational resources,” says Strubell. “So there’s an issue of equitable access between researchers in academia versus researchers in industry.”

Strubell and her coauthors hope that their colleagues will heed the paper’s findings and help level the playing field by investing in developing more efficient hardware and algorithms.

Reddy agrees. “Human brains can do amazing things with little power consumption,” he says. “The bigger question is how can we build such machines.”

Article link: https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/?

Article meta

Share

• facebooklink opens in a new window
• twitterlink opens in a new window
• redditlink opens in a new window
• linkedinlink opens in a new window
• whatsapplink opens in a new window
• emaillink opens in a new window
• Link

Author

Karen Hao

• https://d0df1b1c6e010ca2fc94c4254c6863a8.safeframe.googlesyndication.com/safeframe/1-0-38/html/container.html

After spending a few years cutting through Big Tech’s B.S. about AI ethics, one of our AI writers created a glossary to help you decode what all of their favorite terms actually mean.

tech #technews #mit #mittechnologyreview #technologyreview #techreview #bigtech #artificialintelligence #siliconvalley #aiethics

AI researchers often say good machine learning is really more art than science. The same could be said for effective public relations. Selecting the right words to strike a positive tone or reframe the conversation about AI is a delicate task: done well, it can strengthen one’s brand image, but done poorly, it can trigger an even greater backlash.

The tech giants would know. Over the last few years, they’ve had to learn this art quickly as they’ve faced increasing public distrust of their actions and intensifying criticism about their AI research and technologies.

Now they’ve developed a new vocabulary to use when they want to assure the public that they care deeply about developing AI responsibly—but want to make sure they don’t invite too much scrutiny. Here’s an insider’s guide to decoding their language and challenging the assumptions and values baked in.

accountability (n) – The act of holding someone else responsible for the consequences when your AI system fails.

accuracy (n) – Technical correctness. The most important measure of success in evaluating an AI model’s performance. See validation.

adversary (n) – A lone engineer capable of disrupting your powerful revenue-generating AI system. See robustness, security.

alignment (n) – The challenge of designing AI systems that do what we tell them to and value what we value. Purposely abstract. Avoid using real examples of harmful unintended consequences. See safety.

artificial general intelligence (phrase) – A hypothetical AI god that’s probably far off in the future but also maybe imminent. Can be really good or really bad whichever is more rhetorically useful. Obviously you’re building the good one. Which is expensive. Therefore, you need more money. See long-term risks.

audit (n) – A review that you pay someone else to do of your company or AI system so that you appear more transparent without needing to change anything. See impact assessment.

augment (v) – To increase the productivity of white-collar workers. Side effect: automating away blue-collar jobs. Sad but inevitable.

beneficial (adj) – A blanket descriptorfor what you are trying to build. Conveniently ill-defined. See value.

by design (ph) – As in “fairness by design” or “accountability by design.” A phrase to signal that you are thinking hard about important things from the beginning.

compliance (n) – The act of following the law. Anything that isn’t illegal goes.

data labelers (ph) – The people who allegedly exist behind Amazon’s Mechanical Turk interface to do data cleaning work for cheap. Unsure who they are. Never met them.

democratize (v) – To scale a technology at all costs. A justification for concentrating resources. See scale.

diversity, equity, and inclusion (ph) – The act of hiring engineers and researchers from marginalized groups so you can parade them around to the public. If they challenge the status quo, fire them.

efficiency (n) – The use of less data, memory, staff, or energy to build an AI system.

ethics board (ph) – A group of advisors without real power, convened to create the appearance that your company is actively listening. Examples: Google’s AI ethics board (canceled), Facebook’s Oversight Board (still standing).

ethics principles (ph) – A set of truisms used to signal your good intentions. Keep it high-level. The vaguer the language, the better. See responsible AI.

explainable (adj) – For describing an AI system that you, the developer, and the user can understand. Much harder to achieve for the people it’s used on. Probably not worth the effort. See interpretable.

fairness (n) – A complicated notion of impartiality used to describe unbiased algorithms. Can be defined in dozens of ways based on your preference.

for good (ph) – As in “AI for good” or “data for good.” An initiative completely tangential to your core business that helps you generate good publicity.

foresight (n) – The ability to peer into the future. Basically impossible: thus, a perfectly reasonable explanation for why you can’t rid your AI system of unintended consequences.

framework (n) – A set of guidelines for making decisions. A good way to appear thoughtful and measured while delaying actual decision-making.

generalizable (adj) – The sign of a good AI model. One that continues to work under changing conditions. See real world.

governance (n) – Bureaucracy.

human-centered design (ph) – A process that involves using “personas” to imagine what an average user might want from your AI system. May involve soliciting feedback from actual users. Only if there’s time. See stakeholders.

human in the loop (ph) – Any person that is part of an AI system. Responsibilities range from faking the system’s capabilities to warding off accusations of automation.

impact assessment (ph) – A review that you do yourself of your company or AI system to show your willingness to consider its downsides without changing anything. See audit.

interpretable (adj) – Description of an AI system whose computation you, the developer, can follow step by step to understand how it arrived at its answer. Actually probably just linear regression. AI sounds better.

integrity (n) – Issues that undermine the technical performance of your model or your company’s ability to scale. Not to be confused with issues that are bad for society. Not to be confused with honesty.

interdisciplinary (adj) – Term used of any team or project involving people who do not code: user researchers, product managers, moral philosophers. Especially moral philosophers.

long-term risks (n) – Bad things that could have catastrophic effects in the far-off future. Probably will never happen, but more important to study and avoid than the immediate harms of existing AI systems.

partners (n) – Other elite groups who share your worldview and can work with you to maintain the status quo. See stakeholders.

privacy trade-off (ph) – The noble sacrifice of individual control over personal information for group benefits like AI-driven health-care advancements, which also happen to be highly profitable.

progress (n) – Scientific and technological advancement. An inherent good.

real world (ph) – The opposite of the simulated world. A dynamic physical environment filled with unexpected surprises that AI models are trained to survive. Not to be confused with humans and society.

regulation (n) – What you call for to shift the responsibility for mitigating harmful AI onto policymakers. Not to be confused with policies that would hinder your growth.

responsible AI (n)- A moniker for any work at your company that could be construed by the public as a sincere effort to mitigate the harms of your AI systems.

robustness (n) – The ability of an AI model to function consistently and accurately under nefarious attempts to feed it corrupted data.

safety (n)- The challenge of building AI systems that don’t go rogue from the designer’s intentions. Not to be confused with building AI systems that don’t fail. See alignment.

scale (n)- The de facto end state that any good AI system should strive to achieve.

security (n) – The act of protecting valuable or sensitive data and AI models from being breached by bad actors. See adversary.

stakeholders (n) – Shareholders, regulators, users. The people in power you want to keep happy.

transparency (n) – Revealing your data and code. Bad for proprietary and sensitive information. Thus really hard; quite frankly, even impossible. Not to be confused with clear communication about how your system actually works.

trustworthy (adj) – An assessment of an AI system that can be manufactured with enough coordinated publicity.

universal basic income (ph) – The idea that paying everyone a fixed salary will solve the massive economic upheaval caused when automation leads to widespread job loss. Popularized by 2020 presidential candidate Andrew Yang. See wealth redistribution.

validation (n) – The process of testing an AI model on data other than the data it was trained on, to check that it is still accurate.

value (n) – An intangible benefit rendered to your users that makes you a lot of money.

values (n) – You have them. Remind people.

wealth redistribution (ph) – A useful idea to dangle around when people scrutinize you for using way too many resources and making way too much money. How would wealth redistribution work? Universal basic income, of course. Also not something you could figure out yourself. Would require regulation. See regulation.

withhold publication (ph) – The benevolent act of choosing not to open-source your code because it could fall into the hands of a bad actor. Better to limit access to partners who can afford it.2 free stories remaining Sign in Subscribe now

Article link:
https://www.technologyreview.com/2021/04/13/1022568/big-tech-ai-ethics-guide/

When Xiaomi entered the fiercely competitive smartphone market in 2010, it did so without even offering a real phone. The company only offered a free Android-based operating system (OS). Yet, within seven years, Xiaomi became one of the world’s largest smartphone makers, reaching $15 billion in revenue. Accelerating its growth rate, Xiaomi transformed into the world’s largest consumer IoT (Internet of Things) firm by 2020, with its revenue surpassing $37 billion and more than 210 million IoT devices (excluding smartphones and laptops) sold across more than 90 countries. How was Xiaomi able to grow so explosively and what lessons can other companies learn from Xiaomi’s rise?

We sought answers via an in-depth, multi-year study of the firm, including extensive interviews with 12 top executives (including cofounders, chairman, CEO, president, senior VPs, and executives leading R&D, distribution, and marketing), as well as the founder and CEO of Smartmi, Xiaomi’s largest ecosystem partner. Our research also involved analyzing more than 100 hours of conversations and reviewing more than 5,000 Xiaomi documents (from 2010–2020) as well as 470 external reports and data sets.

We learned that the secret to Xiaomi’s growth lies in what we term as “strategic coalescence.” The word “coalesce” originates from the Latin words co (“together”) and alescere (“to grow”). Strategic coalescence thus refers to a process through which a firm intimately connects with demand and supply-side stakeholders, bolsters tangible benefits for all, and triggers exponential market growth. Let’s first understand the key aspects of strategic coalescence at Xiaomi.

Coalescence with consumers

Xiaomi entered its first market — China — by offering a smartphone OS, called MIUI, for free. At the time, there were several strong domestic (e.g., Huawei, Lenovo) and international players (e.g., Apple, Samsung) battling over every tier of the market, from economical to premium. Most Chinese manufacturers simply smacked the Chinese version of Android on their smartphones, with little customization.

Instead of competing head on, Xiaomi courted tech-savvy smartphone users by offering them free software and building a fully-fledged online community to engage with them and understand which features they craved and which they disliked. This segment of consumers loved the unprecedented attention from a tech firm and were highly motivated to interact and contribute suggestions.

Xiaomi released a new OS version for download every Friday afternoon, as its tech-savvy consumers were heading home for the weekend. Its engineers followed up on user suggestions as soon as they were received, often corresponding with users to resolve issues together. This co-development process enhanced Xiaomi’s brand awareness and likability and prepared a segment of potential consumers for the entry of Xiaomi’s phones, without spending money on traditional advertising.

When it introduced its first phone in August 2011, Xiaomi positioned itself as offering “quality technology at an affordable price.” It sold directly to consumers, through its own website, at a margin of below 5% — the thinnest margin in the industry. Because of its direct engagement with tech-savvy consumers, Xiaomi was able to trim out all intermediaries — the many tiers of national, regional and local wholesalers and retailers, each of which charged a markup. Its direct-to-consumer approach created a significant cost advantage — the phone’s feature to price ratio was far more favorable than anything else on the market — and increased the speed at which Xiaomi could reach its consumers. Target consumers responded: Demand outpaced production so much that the firm could only open its e-commerce site one day per week and stocks sometimes sold out within minutes. The constant and instant sell-outs led to social media storms, spreading the brand to an ever-wider audience, stimulating further demand.

Coalescing operations around the core value proposition

After gaining a foothold in the tech-savvy, value-conscious segment in the top cities, Xiaomi began to expand into other segments — consumers who were less tech savvy, as well as those residing in smaller cities. Many of these consumers preferred an offline shopping experience, wanting to discuss their needs with a staff member or get a demonstration.

To serve these new customers, Xiaomi built an offline retail infrastructure, setting up hundreds of stores spanning major metros and small cities. Unlike other smartphone makers, who co-located their stores at the “telecom street” (an area dedicated to telecom stores), Xiaomi set up its stores in locations with high foot traffic, like malls, where its new target consumers were likely to shop. Importantly, Xiaomi chose malls where existing “high value at a reasonable price” anchor stores could help reinforce its own positioning. It also started offering different sub-brands (Redmi as an economical product line and Mi MIX for more advanced tech seekers), always ensuring that the features-to-price ratio of each new phone was more appealing than competing products.

In sum, during its initial phase, Xiaomi focused on quickly building a large smartphone consumer base across value-seeking consumer segments along with an appropriate on- and offline distribution infrastructure, always keeping its promised low margin on hardware. This enabled it to achieve massive volumes. Xiaomi expanded the share of wallet of this huge and growing customer base, with higher margin post-purchase services (commissions on music, videos, or game purchases), to help attain profitability. These laid the foundation for Xiaomi’s subsequent IoT endeavors.

Leveraging coalescing synergies 

Xiaomi’s expansion into the IoT sphere was further empowered by four coalescing synergies.

In-Home IoT Synergy

Xiaomi leveraged its smartphones as an “omni-remote control” and began launching products that could be linked to and controlled by its phones (such as, TVs, air conditioners, air purifiers, smart lamps). In addition to developing its own products, Xiaomi sought partners who could help the firm quickly expand the range of its IoT offerings. The products from the partners were easily integrated into Xiaomi’s in-home system as they were built on its IoT protocol. This meant that once consumers acquired their first Xiaomi IoT product, they were more likely to seek out other products from Xiaomi. In other words, it became progressively harder for competitors to lure customers away in an IoT category.

Design Aesthetics Synergy

To further strengthen the bond between Xiaomi and its customers, the firm ensured that all Xiaomi-branded IoT products, including those manufactured by ecosystem partners, followed similar design aesthetics. So, if a consumer purchased another Xiaomi product, that item would be more aesthetically congruent with the Xiaomi products they already owned, creating synergy through design congruency and visual gestalt.

Product Portfolio Synergy

A key challenge associated with offline distribution is the high and ever-increasing square footage cost, especially at prime locations. Non-smartphone products (including those by partner firms) could yield much higher margins than smartphones, making the opening and running of offline stores more financially viable. Also, selling a variety of products in the stores attracted consumers who were not specifically looking for smartphones, creating opportunities to promote Xiaomi smartphones and, more generally, to cross-sell its entire portfolio. Furthermore, a broader product portfolio encompassing items with shorter replacement cycles (such as fitness bands and smart light bulbs) created higher foot traffic, leading to additional unplanned purchases and cross-selling opportunities in store.

Multi-Channel Synergy

To maximize returns on its brick-and-mortar stores, Xiaomi leveraged online sales data, using analytics to inform which products to sell offline and how to optimize the product mix at the store level. Offline stores were leveraged to offer potential consumers demonstrations for more experiential products (such as vacuum cleaner robots or AI speakers), moving those potential customers along the decision process whereby the demonstration could either seal an immediate purchase or nudge the consumer towards a later online purchase. The latter provided an additional multichannel synergy, from offline to online.

These four synergies coalesced together, amplifying each other’s effect. Consequently, Xiaomi was able to attract a growing number of potential customers to visit its stores (as opposed to smartphone competitors’ stores) and enhance the likelihood that they make purchases within Xiaomi’s ecosystem. This propelled rapid adoption of Xiaomi’s IoT products, with customers frequently visiting Xiaomi stores and purchasing multiple items.

Coalescence with partners

To effectively expand into categories outside Xiaomi’s expertise and bolster the four synergies, Xiaomi implemented a unique process for identifying and developing partnerships. These yielded some advantages:

1. Partners were hand-picked by Xiaomi cofounders and top executives through their personal networks. Because of the close personal connections, executives at Xiaomi had in-depth knowledge about each partner. They understood the capabilities and values of the management team, enabling Xiaomi to better assess the likelihood of collaboration success.
2. Leveraging personal networks meant that Xiaomi executives were also well connected to the social network of each partner. If some partners performed poorly or violated the partnership agreement, there would be an immediate and direct reputational cost to them, making it harder for them to leverage their social network for further business endeavors, a crucial success factor, particularly in the Chinese business context. This social cost complemented the economic incentive for partnering, bolstering the likelihood of successful collaboration. Certainly, this cherry-picking approach also had drawbacks — it limited the number of potential partners from which Xiaomi could select. However, in the case of Xiaomi’s IoT transformation, its executives believed the pros outweighed the cons.
3. Xiaomi invested in the partner firms but did not acquire controlling shares. While the investment incurred a risk, it created significant benefits. The “co-owner” relationship facilitated communication and increased trust. Xiaomi was able to gain access to information about each partner’s cost structure and operations, as well as participate in their business decisions. Because partners retained majority shares, they were motivated to develop and sell successful products. As a shareholder, Xiaomi benefited from the growth of its partner firms and the profits they made. Simply put, this form of co-ownership created a win-win outcome for both Xiaomi and its partners.
4. Xiaomi purposefully selected firms that were small or startups, so that partnering with Xiaomi offered significant value to them. These firms typically focused on a single category of products, and this specialization meant higher likelihood of producing great products. One important benefit Xiaomi offered to its partners was “incubation”: It assisted them with R&D by sending in teams of its own engineers, and it helped its partners identify key suppliers and negotiate contracts. Xiaomi’s investment and operational involvement brought brand awareness and prestige, making suppliers more willing to offer favorable terms to partner firms (compared to a “nobody” startup). Importantly, by ensuring that partner firms had access to solid designs and used quality inputs at reasonable cost, Xiaomi safeguarded the quality and price attractiveness of their final products.

These approaches enabled Xiaomi to effectively manage the partner network and offer an ever-growing portfolio of products consistent with the Xiaomi brand in design, aesthetics, quality, and technology/price ratio. Xiaomi’s coalescence with partners laid another foundation for the firm to become a global IoT giant.    

Xiaomi’s growth path differs from conventional strategic thinking. While we are often taught that a firm’s strategy should be based on either cost leadership or differentiation and must serve either a few needs of a broad segment or broad needs of a narrow segment, Xiaomi is clearly an outlier. It differentiated on multiple frontiers and at the same time attained cost leadership. It achieved these through strategic coalescence — by coalescing with consumers and partners, which erected and continuously fortified barriers to entry on both the demand and supply sides. This  resulting sustainable competitive advantage catapulted Xiaomi forward at warp speed.

Haiyang Yang is an associate professor at the Johns Hopkins Carey Business School, Johns Hopkins University. His research focuses on decision-making. His work has appeared in premier journals such as the Journal of Marketing Research, Journal of Consumer Research, Journal of Consumer Psychology, and Psychological Science.

Jingjing Ma is an assistant professor at the National School of Development, Peking University. Her research focuses on marketing. Her work has appeared in premier journals such as the Journal of Marketing Research, Journal of Consumer Research, and Journal of Consumer Psychology.

Article link: How Xiaomi Became an Internet-of-Things Powerhouse (ampproject.org)

Amitava Chattopadhyay is the GlaxoSmithKline Chaired Professor of Corporate Innovation at INSEAD. He is co-author of The New Emerging Market Multinationals: Four Strategies for Disrupting Markets and Building Brands and has published extensively in premier journals such as the Journal of Marketing, Journal of Marketing Research, Journal of Consumer Research, Marketing Science, and Management Science. You can follow him on Twitter @AmitavaChats.  

SIERRA & LENNYby 

• by the Editors

February 24, 2021

This list marks 20 years since we began compiling an annual selection of the year’s most important technologies. Some, such as mRNA vaccines, are already changing our lives, while others are still a few years off. Below, you’ll find a brief description along with a link to a feature article that probes each technology in detail. We hope you’ll enjoy and explore—taken together, we believe this list represents a glimpse into our collective future.

---

Messenger RNA vaccines

We got very lucky. The two most effective vaccines against the coronavirus are based on messenger RNA, a technology that has been in the works for 20 years. When the covid-19 pandemic began last January, scientists at several biotech companies were quick to turn to mRNA as a way to create potential vaccines; in late December 2020, at a time when more than 1.5 million had died from covid-19 worldwide, the vaccines were approved in the US, marking the beginning of the end of the pandemic.

The new covid vaccines are based on a technology never before used in therapeutics, and it could transform medicine, leading to vaccines against various infectious diseases, including malaria. And if this coronavirus keeps mutating, mRNA vaccines can be easily and quickly modified. Messenger RNA also holds great promise as the basis for cheap gene fixes to sickle-cell disease and HIV. Also in the works: using mRNA to help the body fight off cancers. Antonio Regalado explains the history and medical potential of the exciting new science of messenger RNA.2021

10 Breakthrough Technologies

Messenger RNA VaccinesGPT-3Data trustsLithium-metal batteriesDigital contact tracingHyper-accurate positioningRemote everythingMulti-skilled AITikTok recommendation algorithmsGreen hydrogenAdvertisementhttps://49416f223e9128323cd6b38c4493b8e8.safeframe.googlesyndication.com/safeframe/1-0-37/html/container.html

GPT-3

Large natural-language computer models that learn to write and speak are a big step toward AI that can better understand and interact with the world. GPT-3 is by far the largest—and most literate—to date. Trained on the text of thousands of books and most of the internet, GPT-3 can mimic human-written text with uncanny—and at times bizarre—realism, making it the most impressive language model yet produced using machine learning.

But GPT-3 doesn’t understand what it’s writing, so sometimes the results are garbled and nonsensical. It takes an enormous amount of computation power, data, and money to train, creating a large carbon footprint and restricting the development of similar models to those labs with extraordinary resources. And since it is trained on text from the internet, which is filled with misinformation and prejudice, it often produces similarly biased passages. Will Douglas Heaven shows off a sample of GPT-3’s clever writing and explains why some are ambivalent about its achievements.

---

TikTok recommendation algorithms

Since its launch in China in 2016, TikTok has become one of the world’s fastest-growing social networks. It’s been downloaded billions of times and attracted hundreds of millions of users. Why? Because the algorithms that power TikTok’s “For You” feed have changed the way people become famous online.

While other platforms are geared more toward highlighting content with mass appeal, TikTok’s algorithms seem just as likely to pluck a new creator out of obscurity as they are to feature a known star. And they’re particularly adept at feeding relevant content to niche communities of users who share a particular interest or identity.

The ability of new creators to get a lot of views very quickly—and the ease with which users can discover so many kinds of content—have contributed to the app’s stunning growth. Other social media companies are now scrambling to reproduce these features on their own apps. Abby Ohlheiser profiles a TikTok creator who was surprised by her own success on the platform.   

---

Lithium-metal batteries

Electric vehicles come with a tough sales pitch; they’re relatively expensive, and you can drive them only a few hundred miles before they need to recharge—which takes far longer than stopping for gas. All these drawbacks have to do with the limitations of lithium-ion batteries. A well-funded Silicon Valley startup now says it has a battery that will make electric vehicles far more palatable for the mass consumer.

It’s called a lithium-metal battery and is being developed by QuantumScape. According to early test results, the battery could boost the range of an EV by 80% and can be rapidly recharged. The startup has a deal with VW, which says it will be selling EVs with the new type of battery by 2025.

The battery is still just a prototype that’s much smaller than one needed for a car. But if QuantumScape and others working on lithium-metal batteries succeed, it could finally make EVs attractive to millions of consumers. James Temple describes how a lithium-metal battery works, and why scientists are so excited by recent results.Advertisement

---

Data trusts

Technology companies have proven to be poor stewards of our personal data. Our information has been leaked, hacked, and sold and resold more times than most of us can count. Maybe the problem isn’t with us, but with the model of privacy to which we’ve long adhered—one in which we, as individuals, are primarily responsible for managing and protecting our own privacy.

Data trusts offer one alternative approach that some governments are starting to explore. A data trust is a legal entity that collects and manages people’s personal data on their behalf. Though the structure and function of these trusts are still being defined, and many questions remain, data trusts are notable for offering a potential solution to long-standing problems in privacy and security. Anouk Ruhaak describes the powerful potential of this model and a few early examples that show its promise.

---

Green hydrogen

Related Story

How falling solar costs have renewed clean hydrogen hopesAs nations do the hard math on how to meet their climate goals, green hydrogen increasingly appears essential.

Hydrogen has always been an intriguing possible replacement for fossil fuels. It burns cleanly, emitting no carbon dioxide; it’s energy dense, so it’s a good way to store power from on-and-off renewable sources; and you can make liquid synthetic fuels that are drop-in replacements for gasoline or diesel. But most hydrogen up to now has been made from natural gas; the process is dirty and energy intensive.

The rapidly dropping cost of solar and wind power means green hydrogen is now cheap enough to be practical. Simply zap water with electricity, and presto, you’ve got hydrogen. Europe is leading the way, beginning to build the needed infrastructure. Peter Fairley argues that such projects are just a first step to an envisioned global network of electrolysis plants that run on solar and wind power, churning out clean hydrogen.2021

10 Breakthrough Technologies

Messenger RNA VaccinesGPT-3Data trustsLithium-metal batteriesDigital contact tracingHyper-accurate positioningRemote everythingMulti-skilled AITikTok recommendation algorithmsGreen hydrogen

Digital contact tracing

As the coronavirus began to spread around the world, it felt at first as if digital contact tracing might help us. Smartphone apps could use GPS or Bluetooth to create a log of people who had recently crossed paths. If one of them later tested positive for covid, that person could enter the result into the app, and it would alert others who might have been exposed.

But digital contact tracing largely failed to make much impact on the virus’s spread. Apple and Google quickly pushed out features like exposure notifications to many smartphones, but public health officials struggled to persuade residents to use them. The lessons we learn from this pandemic could not only help us prepare for the next pandemic but also carry over to other areas of health care. Lindsay Muscato explores why digital contact tracing failed to slow covid-19 and offers ways we can do better next time.

Sign up for The Coronavirus Tech Report

 – A weekly newsletter about covid-19 and how it’s changing our world.Sign up

Stay updated on MIT Technology Review initiatives and events?

YesNo

---

Hyper-accurate positioning

We all use GPS every day; it has transformed our lives and many of our businesses. But while today’s GPS is accurate to within 5 to 10 meters, new hyper-accurate positioning technologies have accuracies within a few centimeters or millimeters. That’s opening up new possibilities, from landslide warnings to delivery robots and self-driving cars that can safely navigate streets.

China’s BeiDou (Big Dipper) global navigation system was completed in June 2020 and is part of what’s making all this possible. It provides positioning accuracy of 1.5 to two meters to anyone in the world. Using ground-based augmentation, it can get down to millimeter-level accuracy. Meanwhile, GPS, which has been around since the early 1990s, is getting an upgrade: four new satellites for GPS III launched in November and more are expected in orbit by 2023. Ling Xin reports on how the greatly increased accuracy of these systems is already proving useful.

---

Remote everything

The covid pandemic forced the world to go remote. Getting that shift right has been especially critical in health care and education. Some places around the world have done a particularly good job at getting remote services in these two areas to work well for people.

Snapask, an online tutoring company, has more than 3.5 million users in nine Asian countries, and Byju’s, a learning app based in India, has seen the number of its users soar to nearly 70 million. Unfortunately, students in many other countries are still floundering with their online classes.

Meanwhile, telehealth efforts in Uganda and several other African countries have extended health care to millions during the pandemic. In a part of the world with a chronic lack of doctors, remote health care has been a life saver. Sandy Ong reports on the remarkable success of online learning in Asia and the spread of telemedicine in Africa.

---

Multi-skilled AI

Despite the immense progress in artificial intelligence in recent years, AI and robots are still dumb in many ways, especially when it comes to solving new problems or navigating unfamiliar environments. They lack the human ability, found even in young children, to learn how the world works and apply that general knowledge to new situations.

One promising approach to improving the skills of AI is to expand its senses; currently AI with computer vision or audio recognition can sense things but cannot “talk” about what it sees and hears using natural-language algorithms. But what if you combined these abilities in a single AI system? Might these systems begin to gain human-like intelligence? Might a robot that can see, feel, hear, and communicate be a more productive human assistant? Karen Hao explains how AIs with multiple senses will gain a greater understanding of the world around them, achieving a much more flexible intelligence.

---

For a look at what technologies made our 10 Breakthrough Technologies lists in previous years, check out this page, which starts with 2020’s list.

Article link: 10 Breakthrough Technologies 2021 | MIT Technology Review

In an effort to help improve healthcare organizations’ resilience against ransomware, MITRE this week unveiled its new Ransomware Resource Center, offering an array of tools and strategies for IT and infosec professionals to better guard against the growing epidemic of costly malware.

WHY IT MATTERS
The Ransomware Resource Center tailors its many offerings around the role of the healthcare professional who might be accessing them – whether business manager, technical manager or IT or cybersecurity practitioner – and also around the five stages of the National Institute of Standards and Technology (NIST) Cybersecurity Framework: Identify, protect, detect, respond and recover.

It also offers a well-stocked resource library that’s searchable and can be filtered for the materials that might be the most useful.

The tools are drawn from MITRE’s own expertise, from government sources and from provider best practices. The goal is to convene a variety of resources in a single accessible and intuitive location, say MITRE officials, and to help “network defenders, IT administrators and business managers better prepare for, respond to, and recover from ransomware attacks.”

In a Q&A on the MITRE website, Joanne Fitzpatrick, lead cybersecurity engineer in MITRE’s Cyber Solutions Innovation Center, said particular attention was paid to small and/or underfunded IT and security staff.

“There are two key considerations,” she explained.

“First, such organizations typically have smaller IT and security departments, with a handful of talented people wearing many hats, and each responsible for several major operational IT areas. Staff tend to be experienced in the operations of their own organization, but often have little access to growth/training/professional development on cybersecurity issues, such as threats and attacks. Lack of time or budget is usually the reason.”

Moreover, at under-resourced organizations, there’s often “little-to-no extra staff available to dedicate to specialty cyber topics, such as threat modeling or attack surface assessments,” said Fitzpatrick.

“Second, we recognize that both small and large healthcare organizations may be targets for adversaries. Size does not matter. We’ve witnessed successful attacks at all types of health organizations. Adversaries may even exploit a smaller hospital as part of their attack navigation to exploit a larger, partnering organization.”

THE LARGER TREND
MITRE points to a recent report that showed 560 healthcare facilities suffered a successful ransomware attack in 2020 – and another that saw a 45% increase in exploitation attempts just in the past four months.

“We are currently fighting not only the COVID-19 pandemic, but also an epidemic that is spreading through cyberspace: ransomware,” said newly appointed Homeland Security Director Alejandro Mayorkas recently. “In addition to disrupting city governments, schools and companies, ransomware has also been disrupting hospitals and health care facilities, who are already strained, going above and beyond the call of duty during this ongoing crisis.

“Last October, CISA, together with other government agencies, warned of the growing threat of ransomware targeting the healthcare and public health sector,” he added. “Previous ransomware attacks illustrate the risk to COVID-19 vaccine deployment efforts that depend on key production and logistics facilities.”

Healthcare IT News recently put together a primer for how healthcare organizations should respond to such an attack.

ON THE RECORD
“We hope the Ransomware Resource Center will make two key contributions,” said Fitzpatrick. “It will inform hospitals and healthcare organizations about how to prepare, respond to and recover from such an attack. It also will share freely with the broader community the unbiased guidance and best practices that MITRE cybersecurity and cyber resiliency professionals have provided for years to our many federal government sponsors.”

Twitter: @MikeMiliardHITN
Email the writer: mike.miliard@himssmedia.com
Healthcare IT News is a publication of HIMSS Media

Article link: MITRE launches ransomware support hub for hospitals and health systems | Healthcare IT News