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Every academic field has its superstars. But a rare few achieve superstardom not just by demonstrating individual excellence but also by consistently producing future superstars. A notable example of such a legendary doctoral advisor is the Princeton physicist John Archibald Wheeler. A dissertation was once written about his mentorship, and he advised Richard Feynman, Kip Thorne, Hugh Everett (who proposed the “many worlds” theory of quantum mechanics), and a host of others who could collectively staff a top-tier physics department. In ecology, there is Bob Paine, who discovered that certain “keystone species” have an outsize impact on the environment and started a lineage of influential ecologists. And in journalism, there is John McPhee, who has taught generations of accomplished journalists at Princeton since 1975.

Computer science has its own such figure: Manuel Blum, who won the 1995 Turing Award—the Nobel Prize of computer science. Blum’s métier is theoretical computer science, a field that often escapes the general public’s radar. But you certainly have come across one of Blum’s creations: the “Completely Automated Public Turing test to tell Computers and Humans Apart,” better known as the
captcha—a test designed to distinguish humans from bots online.

“I don’t know what his secret has been. But he has been a tremendously successful advisor,” says Michael Sipser, a theoretical computer scientist at MIT who was advised by Blum, referring to the “extraordinary number of PhD students” who have worked with him and then gone on to make an impact in the field. “It is extraordinary in the literal sense of that word—outside the ordinary.”

Three of Blum’s students have also won Turing Awards; many have received other high honors in theoretical computer science, such as the Gödel Prize and the Knuth Prize; and more than 20 hold professorships at top computer science departments. There are five, for example, at MIT and three at Carnegie Mellon University (where there were four until one left to found Duolingo).

Blum is also distinguished by the great plurality of subfields that his students work in. When Mor Harchol-Balter, a professor of computer science at Carnegie Mellon, arrived at the University of California, Berkeley, as a PhD student, she quickly realized that she wanted to work with him. “Manuel was warm, smiling, and just immediately emanated kindness,” Harchol-Balter told me. Her specialty, queueing theory, had little overlap with Blum’s, but he took her on. “Every professor I know, if you start working on what’s way out of their area, they would tell you to go find somebody else,” she said. “Not Manuel.”

A few months ago, as I was reading about some of the most significant yet counterintuitive ideas in modern theoretical computer science, I realized that the vast majority of the researchers responsible for that work had been advised by Blum. I wondered whether there might be some formula to his success. Of course, it’s presumptuous to think such an intimately human process can be distilled into an algorithm. However, conversations with his students gave me a sense of his approach and revealed consistent themes. Many spoke warmly of him: I often heard some version of “I could talk about Manuel all day” or “Manuel is my favorite topic of conversation.” The finer points of mentorship aside, what I learned was at least proof that kindness can beget greatness.

Slow beginning

Manuel Blum is married to Lenore Blum, an accomplished mathematician and computer scientist, who has also been at the forefront of promoting diversity in math and computing (among other things, she founded America’s first computer science department at a women’s college and helped CMU’s computer science department achieve 50-50 gender parity). They are both now emeritus professors at CMU and Manuel Blum is an emeritus professor at UC Berkeley; they split their time between the two coasts.

One day in August, I joined the couple for breakfast at their house in Pittsburgh. Breezy in his manner, Blum, at 85, still has a schoolboy’s smile and frequently erupts into a resonant laugh; he is charismatic in a way typical of people who are utterly oblivious to their charisma. (When he says “WON-derful,” which he frequently does, you can practically hear “WON” in all caps.)

The Blums, who recently celebrated their 62nd anniversary, still shuttlecock research ideas, enthuse over emails from their former students, and complete each other’s memories—some dating from their life in Venezuela, where they met as kids.

Manuel Blum was born in 1938 in Caracas to Jewish parents who had moved from Romania. His first language was German, which his parents spoke at home. But when they moved to the Bronx, his family realized that people did not want to hear German spoken. The year was 1942, and the country was at war. After switching to

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By: Sheon Han
Title: How this Turing Award–winning researcher became a legendary academic advisor
Sourced From: www.technologyreview.com/2023/10/24/1081478/manuel-blum-theoretical-computer-science-turing-award-academic-advisor/
Published Date: Tue, 24 Oct 2023 09:15:00 +0000

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Procurement in the age of AI

amazon business cart

Procurement professionals face challenges more daunting than ever. Recent years’ supply chain disruptions and rising costs, deeply familiar to consumers, have had an outsize impact on business buying. At the same time, procurement teams are under increasing pressure to supply their businesses while also contributing to business growth and profitability.

amazon business cart 1

Deloitte’s 2023 Global Chief Procurement Officer Survey reveals that procurement teams are now being called upon to address a broader range of enterprise priorities. These range from driving operational efficiency (74% of respondents) and enhancing corporate social responsibility (72%) to improving margins via cost reduction (71%).

To meet these rising expectations, many procurement teams are turning to advanced analytics, AI, and machine learning (ML) to transform the way they make smart business buying decisions and create value for the organization.

New procurement capabilities unlocked by AI

AI and ML tools have long helped procurement teams automate mundane and manual procurement processes, allowing them to focus on more strategic initiatives. But recent advances in natural language processing (NLP), pattern recognition, cognitive analytics, and large language models (LLMs) are “opening up opportunities to make procurement more efficient and effective,” says Julie Scully, director of software development at Amazon Business.

The good news is procurement teams are already well-positioned to capitalize on these technological advances. Their access to rich data sources, ranging from contracts to invoices, enables AI/ML solutions that can illuminate the insights contained within this data. Acting on these insights unlocks new capabilities that can enhance decision-making and improve spending patterns across the organization.

Predicting supply chain disruptions. In an era of constant supply chain disruptions, procurement teams are often faced with inconsistent item availability, which can negatively impact employee and customer experience. Indeed, the Deloitte 2023 Global Chief Procurement Officer survey finds that only 25% of firms are able to identify supply disruptions promptly “to a large extent.”

AI tools can help address this issue by recognizing patterns that indicate an emerging supply shortage and automatically recommending two or three product alternatives to business buyers, thereby preventing supply disruptions. These predictive capabilities also empower procurement teams to establish buying policies that proactively account for items that are more likely to go out of stock.

Answering pressing questions quickly. Sifting through data to understand the cause of a supply chain disruption, product defect, or other risk is time-consuming for a procurement professional. LLM-powered chatbots can streamline these processes by understanding complex queries about orders and “putting together a nuanced answer,” says Scully. “AI can query a wide variety of sources to fully answer a question quickly and in a way that feels natural and understandable.” In addition to providing fast and accurate answers to pressing questions, AI promises to reduce the need to explain procurement issues eventually. Instead, it will proactively analyze orders, buying patterns, and the current situation to provide instant support.

Offering customized recommendations. As business buyers increasingly demand personalized experiences, procurement officers seek ways to customize their interactions with business procurement systems. Scully provides the example of an employee tasked with hosting a holiday party for 150 employees who needs help deciding what to order. An AI-based procurement tool posed that scenario, she says, could generate a proposed shopping cart, sifting through “millions and billions of data points to recommend and suggest items that the employee may not have even thought of.”

Better yet, she adds, “as we get into really large language models, AI/ML can help answer questions or help buy items you didn’t even know you needed by understanding your particular situation in a much more detailed way.”

Influencing compliance spend. Procurement professionals aim to balance employees’ freedom to purchase the items they need with minimal intervention. However, self-sufficiency should not come at the cost of proper spend

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By: MIT Technology Review Insights
Title: Procurement in the age of AI
Sourced From: www.technologyreview.com/2023/11/28/1083628/procurement-in-the-age-of-ai/
Published Date: Tue, 28 Nov 2023 16:00:00 +0000

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The Download: COP28 controversy and the future of families

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This is today’s edition of The Download our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Why the UN climate talks are a moment of reckoning for oil and gas companies

The United Arab Emirates is one of the world’s largest oil producers. It’s also the site of this year’s UN COP28 climate summit, which kicks off later this week in Dubai.

It’s a controversial host, but the truth is that there’s massive potential for oil and gas companies to help address climate change, both by cleaning up their operations and by investing their considerable wealth and expertise into new technologies.

The problem is that these companies also have a vested interest in preserving the status quo. If they want to be part of a net-zero future, something will need to change—and soon. Read the full story.

—Casey Crownhart

How reproductive technology can reverse population decline

Birth rates have been plummeting in wealthy countries, well below the “replacement” rate. Even in China, a dramatic downturn in the number of babies has officials scrambling, as its population growth turns negative.

So, what’s behind the baby bust and can new reproductive technology reverse the trend? MIT Technology Review is hosting a subscriber-only Roundtables discussion on how innovations from the lab could affect the future of families at 11am ET this morning, featuring Antonio Regalado, our biotechnology editor, and entrepreneur Martín Varsavsky, founder of fertility clinic Prelude Fertility. Don’t miss out—make sure you register now.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Instagram recommends sexual content to adults that follow kids

Test accounts were served risqué posts and disturbing videos. (WSJ $)
Meta was aware it had millions of underage users, a complaint alleges. (NYT $)

2  The first transatlantic flight powered by alternative fuels has taken off
Waste fats and corn leftovers are fueling the flight between London and New York. (BBC)
Here are the key phrases you need to know to understand climate change. (Vox)
Everything you need to know about the wild world of alternative jet fuels. (MIT Technology Review)

3 The United Arab Emirates planned to strike oil deals during COP28 
Which doesn’t seem terribly climate-friendly. (BBC)
AUAE AI firm is believed to have covertly worked with Chinese companies. (NYT $)
China’s own carbon emissions are on course to peak soon. (Economist $)

4 Starlink can only operate in Gaza with Israel’s approval
That’s according to Elon Musk, who is visiting Israel currently. (FT $) 

5 Foxconn is struggling to build iPhones in India
So the manufacturer started shipping over skilled workers from China. (Rest of World)

6 The world’s banana supply is in serious trouble
🍌
A deadly fungus is sweeping through crops—and there’s no known cure. (Bloomberg $)

7 Digital car keys don’t always work the way they’re supposed to
Which is a major problem if you can’t guarantee your vehicle is secure. (The Verge)

8 It’s not just you—dating is tough
But these tips can help to make it a less harrowing experience. (WP $)
Here’s how the net’s newest matchmakers help you find love. (MIT Technology Review)

9 Big dogs don’t live that long
🐶
But biotech company Loyal is hoping to change that with an experimental drug. (Wired $)
These scientists are working to extend the life span of pet dogs—and their owners. (MIT Technology Review)

10 The quiet bliss of living in an internet-free home
And how you can achieve it, too. (The Atlantic $)
How to log off. (MIT Technology Review)

Quote of the day

“He ignored me royally, which is his privilege. And he lost almost all the money that he had invested.”

—Christine Lagarde, president of the European Central Bank, explains to students in Frankfurt how one of her sons lost his money on crypto, despite her repeated warnings, Reuters reports.

The big story

Are you ready to be a techno-optimist again?

February 2021

Back in 2001, MIT Technology Review picked 10 emerging areas of innovation that we promised would “change the world.” It was a time of peak techno-optimism.

We eschewed

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By: Rhiannon Williams
Title: The Download: COP28 controversy and the future of families
Sourced From: www.technologyreview.com/2023/11/28/1083923/the-download-cop28-controversy-and-the-future-of-families/
Published Date: Tue, 28 Nov 2023 13:10:00 +0000

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Finding value in generative AI for financial services

UBS report cover

With tools such as ChatGPT, DALLE-2, and CodeStarter, generative AI has captured the public imagination in 2023. Unlike past technologies that have come and gone—think metaverse—this latest one looks set to stay. OpenAI’s chatbot, ChatGPT, is perhaps the best-known generative AI tool. It reached 100 million monthly active users in just two months after launch, surpassing even TikTok and Instagram in adoption speed, becoming the fastest-growing consumer application in history.

UBS report cover 1

According to a McKinsey report, generative AI could add $2.6 trillion to $4.4 trillion annually in value to the global economy. The banking industry was highlighted as among sectors that could see the biggest impact (as a percentage of their revenues) from generative AI. The technology “could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented,” says the report.

For businesses from every sector, the current challenge is to separate the hype that accompanies any new technology from the real and lasting value it may bring. This is a pressing issue for firms in financial services. The industry’s already extensive—and growing—use of digital tools makes it particularly likely to be affected by technology advances. This MIT Technology Review Insights report examines the early impact of generative AI within the financial sector, where it is starting to be applied, and the barriers that need to be overcome in the long run for its successful deployment.

DOWNLOAD THE REPORT

The main findings of this report are as follows:

Corporate deployment of generative AI in financial services is still largely nascent. The most active use cases revolve around cutting costs by freeing employees from low-value, repetitive work. Companies have begun deploying generative AI tools to automate time-consuming, tedious jobs, which previously required humans to assess unstructured information.
UBS web ready 5
There is extensive experimentation on potentially more disruptive tools, but signs of commercial deployment remain rare. Academics and banks are examining how generative AI could help in impactful areas including asset selection, improved simulations, and better understanding of asset correlation and tail risk—the probability that the asset performs far below or far above its average past performance. So far, however, a range of practical and regulatory challenges are impeding their commercial use.Legacy technology and talent shortages may slow adoption of generative AI tools, but only temporarily. Many financial services companies, especially large banks and insurers, still have substantial, aging information technology and data structures, potentially unfit for the use of modern applications. In recent years, however, the problem has eased with widespread digitalization and may continue to do so. As is the case with any new technology, talent with expertise specifically in generative AI is in short supply across the economy. For now, financial services companies appear to be training staff rather than bidding to recruit from a sparse specialist pool. That said, the difficulty in finding AI talent is already starting to ebb, a process that would mirror those seen with the rise of cloud and other new technologies.
UBS web ready 2
More difficult to overcome may be weaknesses in the technology

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By: MIT Technology Review Insights
Title: Finding value in generative AI for financial services
Sourced From: www.technologyreview.com/2023/11/26/1083841/finding-value-in-generative-ai-for-financial-services/
Published Date: Mon, 27 Nov 2023 01:00:00 +0000

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