VR Headset Gives you X-Ray Vision!

Possibilities are considerable in VRworld

MIT researchers invented an augmented reality headset that gives humans X-ray vision. The invention, dubbed X-AR, combines wireless sensing with computer vision to enable users to see hidden items. X-AR can help users find missing items and guide them toward these items for retrieval. This new technology has many applications in retail, warehousing, manufacturing, smart homes, and more.

For more information, check out:

Website: https://Xar.media.mit.edu

Paper: https://www.mit.edu/~fadel/papers/XAR-paper.pdf

Instagram: @mit_sk_lab (https://instagram.com/mit_sk_lab?igshid=YmMyMTA2M2Y=)

Authors: Tara Boroushaki, Maisy Lam, Laura Dodds, Aline Eid, Fadel Adib

Video Production: Maisy Lam, Jimmy Day

UI design: Maisy Lam, Yuechen Wang

Funding: NSF, Sloan Foundation, MIT Media Lab

VISIT WEBSITE   https://www.youtube.com/watch?v=bdUN21ft7G0 

Using AI to Improve Agricultural Yields

Impressive outlines, Podcast: 

Big Data in Agriculture

August 30, 2022 / The farm-to-fork cooperative uses artificial intelligence to improve agricultural yields.

You might have seen Land O’Lakes’ dairy products on store shelves without giving much thought to how they got there, but that’s something CTO Teddy Bekele thinks about every day. While the farmers and agricultural retailers of Land O’Lakes work to produce the cooperative’s products, starting from the seeds used to grow animal feed, Teddy Bekele is focused on supporting agriculture’s “fourth revolution” — one that’s embracing technologies like artificial intelligence. On this episode of the Me, Myself, and AI podcast, Teddy explains how Land O’Lakes uses predictive analytics and AI to help farmers and other agricultural producers be more productive and make better decisions about the business of farming.

Teddy Bekele, Land O’Lakes

Teddy Bekele is the CTO of Land O’Lakes, leading the organization’s digital transformation by leveraging existing and emerging technologies to discover, implement, and deliver solutions and ecosystems. Previously, Bekele served as vice president of ag technology for WinField United. Bekele holds an MBA from Indiana University and a bachelor of science degree in mechanical engineering from North Carolina State University. His community leadership includes serving as chair of the Minnesota Broadband Task Force and the Federal Task Force on Precision Ag Connectivity, and as a board member for Stella Health, Genesys Works Twin Cities, and the Minnesota Technology Association.

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Transcript

Sam Ransbotham: You may have used the phrase “bet the farm,” but if you don’t work in agriculture, you might not fully appreciate what that means. On today’s episode, find out how technology can support successful farm production.

Teddy Bekele: I’m Teddy Bekele from Land O’Lakes, and you’re listening to Me, Myself, and AI.

Sam Ransbotham: Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I’m Sam Ransbotham, professor of analytics at Boston College. I’m also the AI and business strategy guest editor at MIT Sloan Management Review.

Shervin Khodabandeh: And I’m Shervin Khodabandeh, senior partner with BCG, and I colead BCG’s AI practice in North America. Together, MIT SMR and BCG have been researching and publishing on AI for six years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

Sam Ransbotham: Shervin and I are talking today with Teddy Bekele, chief technology officer at Land O’Lakes. Teddy, thanks for joining us. Welcome.

Teddy Bekele: Thank you for having me. I’m very excited to be here.

Sam Ransbotham: I think we first met back in 2018, when you were at WinField United and we did a webinar together about data and analytics. Now you’re at the parent company, Land O’Lakes. Can you tell us about your current role?  .... 

MITSloan Podcast on AI

Informed of,  following

Me, Myself, and AI

A Podcast on Artificial Intelligence in Business

Why do only 10% of companies succeed with AI? In this series from MIT SMR and BCG, we talk to leaders achieving big wins with AI in their companies and learn how they did it. This season, leaders from companies like Stanley Black & Decker, LinkedIn, Levi Strauss & Co., Warner Music, and others will share the keys to their AI success.

Me, Myself, and AI

Subscribe for updates

Also available on:  Google Podcasts | Stitcher | Amazon Music | Castbox | iHeartRadio | TuneIn

Need for Outside Perspective for Innovative Breakthroughs

 Outside and economically relevant.

Why Outside Perspectives Are Critical for Innovation Breakthroughs

Lessons from the story of Dr. Patricia Bath, the inventor of modern cataract surgery and the first African American woman to receive a medical patent.

Jean-Louis Barsoux, Cyril Bouquet, and Michael Wade 

Innovation is widely viewed as an engine of progress — not only for driving economic growth, but also for bringing vital improvements in a variety of domains, from science and medicine to inequality and sustainability.

Anyone can have a good idea, so you could expect the distribution of U.S. patents to resemble the demographics of the workplace. Of course, this is far from the case. Multiple studies have shown that two groups lag far behind in terms of leadership in innovation: women and African Americans.  .... '

Managing Cultural and Process Improvements with AI

Seeking Truth from Data, Interesting thoughts.

Managing Cultural and Process Improvements with AI

Published on November 9, 2021. Sam Ransbotham

Professor at Boston College; AI Editor at MIT Sloan Management Review; Host of "Me, Myself, and AI" podcast

For me, this week's biggest news is the publication of our 2021 MIT SMR-BCG artificial intelligence and business strategy research report. While not discounting the substantial financial potential with AI, our research this year focuses on the cultural benefits. Based on a survey of more than 2,000 global managers and dozens of interviews, the report is chock full of examples and data about the cycle between Culture, AI Use, and Effectiveness.

S. Ransbotham, F. Candelon, D. Kiron, B. LaFountain, and S. Khodabandeh, “The Cultural Benefits of Artificial Intelligence in the Enterprise,” MIT Sloan Management Review and Boston Consulting Group, November 2021.

Our key result is that over 75% of global organizations implementing AI report that the technology helped improve their culture. In our fifth year of researching AI and business strategy alongside Boston Consulting Group, we found a wide range of AI-related cultural benefits at both the team and organizational levels. Our report, "The Cultural Benefits of Artificial Intelligence in the Enterprise," outlines these benefits and explains how they relate to financial benefits and competitive advantage.

These financial and cultural benefits do not come automatically to organizations. Instead, they depend on active preparation and ongoing management – two important topics also in recent news.

Developing an Appetite for AI: New Episode of Me, Myself, and AI

Organizations need to have systems and mindsets in place to implement technologies like artificial intelligence, and, often, organizations may not be ready. Sarah Karthigan, AI operations manager for IT at ExxonMobil, joined our Me, Myself, and AI podcast to discuss how she prepares for technology and cultural challenges long before starting AI pilots. She ensures end-users know "under-the-hood" what the tech actually does so that users encourage the necessary changes. Sarah observes that "the partnership goes really, really well once they understand the value that the new solution is able to bring to the table." Active preparation for AI makes a difference and profoundly depends on culture. Plus, if you're curious what Shervin's first time trying sushi has to do with artificial intelligence, this episode's for you.

Managing AI to Promote Financial and Cultural Benefits

Of course, just getting ready for AI is not enough to realize these financial and cultural benefits. Managers are still crucially important. MIS Quarterly, a premier academic journal, just published a special issue on the managerial challenges that come with artificial intelligence. Seven papers address different facets of these challenges.

In "AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-Scale Examination of Bio-Pharma Firms", Bowen Lou and Lynn Wu demonstrate that using AI can accelerate new drug discovery... sometimes, but not always. Innovation depends not only on employees' domain expertise, not just AI skills.

Machine learning tools reduce the costs of repetitive tasks but can introduce systematic unfairness into organizational processes. In Failures of Fairness in Automation Require a Deeper Understanding of Human–ML Augmentation, Mike H. M. Teodorescu, Lily Morse, Yazeed Awwad, and Gerald C. Kane introduce a typology of augmentation for fairness consisting of four quadrants: reactive oversight, proactive oversight, informed reliance, and supervised reliance.

Sarah Lebovitz, Natalia Levina, and Hila Lifshitz-Assaf question the seemingly objective labels organizations use to train AI tools.  Is AI Ground Truth Really True? The Dangers of Training and Evaluating AI Tools Based on Experts' Know-What describes how experts address uncertainty by drawing on rich know-how practices that many ML-based tools do not incorporate.

In Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI, Andreas Fügener, Jörn Grahl, Alok Gupta, and Wolfgang Ketter raise concerns about the loss of unique human knowledge in a host of human-AI decision environments.

Algorithms may produce insights superior to experts by discovering the "truth" from data. But how can systems produce knowledge independent of domain experts yet remain relevant to the domain? When the Machine Meets the Expert: An Ethnography of Developing AI for Hiring (by Elmira van den Broek, Anastasia Sergeeva, and Marleen Huysman) describe how developers navigate this tension when building an ML system to support hiring job candidates at a large international organization. 

Coordinating Human and Machine Learning for Effective Organizational Learning (Timo Sturm, Jin P. Gerlach, Luisa Pumplun, Neda Mesbah, Felix Peters, Christoph Tauchert, Ning Nan, and Peter Buxmann) recognizes that humans are no longer the only ones contributing to an organization's stock of knowledge. 

And finally, Strategic Directions for AI: The Role of CIOs and Boards of Directors (Jingyu Li, Mengxiang Li, Xinchen Wang, and Jason Bennett Thatcher) finds that the presence of a CIO positively influences AI orientation discusses how to build top management teams and boards capable of effectively developing AI orientations.  .... '

Data and Analytics for Better Decisions

From MIT Sloan, SAS, some thoughts on decisions for analytics.  I link to items I have read and liked.  All accessible from top link.

Data and Analytics for Better Decisions

Stepping up to business challenges and opportunities means knowing how to find relevant data — and put it to work. Free access to these four MIT Sloan Management Review articles is provided courtesy of SAS   

1. To Succeed With Data Science, First Build the ‘Bridge’  

2. Demystifying Data Monetization 

3. The Recession’s Impact on Analytics and Data Science 

4. Data Science, Quarantined 

...'  

Getting Ideas from the Crowd

Only part of an article from Sloan on creative uses of crowdsourcing.   Also a favorite topic and means  of our data gathering. How much is the crowd (generally) biased, and how do we reasonably adjust for that?  Elements of reasonable and directional bias? Can we get a good indication of types of bias in play in a particular crowd? 

Get More Ideas From the Crowd

These five techniques for writing problem statements can improve results from crowdsourced challenges.

Claudia Kubowicz Malhotra, Arvind Malhotra, and Barry L. Bayus

The rise of crowdsourcing platforms as a potential source for innovative ideas presents a challenge: How do you attract contributors to work on your particular problem?1 Past research has demonstrated the importance of well-crafted problem statements as a means to attract more innovative solutions.2 But what really goes into a problem statement that engages the crowd? Do the statements that attract a large number of proposed ideas share common elements?

Our research sought to answer these questions by unpacking problem statements, word by word, to identify the characteristics that attract the most idea submissions. Our analysis points to guidelines for managers tapping crowdsourcing sites on do’s and don’ts when writing a problem statement meant to attract solutions from creative freelancers.

We used data from Eÿeka, an online crowdsourcing platform focused on creative projects. Our findings are based on an analysis of 362 unique problem statements posted by 85 companies between 2016 and 2018. The statements sought ideas for marketing and social media campaigns, solutions to complex issues, proposals for products, and recommendations for entering new markets. The average number of responses received for each statement was 88; the highest number received was 370 and the lowest was five. We found that those receiving an above-average number of submissions shared some common elements — and we also identified four approaches to avoid when presenting a challenge to the crowd.

Five Ways to Engage Creatives

Our research found that the problem statements that attracted an above-average number of proposals used one or more of the following five techniques to pique freelancers’ interest and engage them with the problem.

Personalize the problem. Statements that address freelancers as “you” and explicitly ask them to solve the problem as if they were the customer are very effective in attracting more ideas.

For example, a company wanted to invent a new coffee drink. “We are sure you’ve known these moments in the afternoons when you just need to take a break from sleepiness, stress, or anxiety,” the statement noted. It described the coffee shop environment and referred to the opportunity to “enjoy some sweet time all by yourself or maybe sharing this moment with your friends.” And it concluded with a direct appeal: “We’d like to invent a new range of cold and sweet, ready-to-drink, milk-based coffee that people will love.   ... ' 

AI and Machine Learning Imperative of a Strategy

I see only rarely see complete strategies in the space.  Its mostly solving problems in narrow contexts.   Would be good to at least lay out a outline strategy for implementation.

THE AI & MACHINE LEARNING IMPERATIVE
The Building Blocks of an AI Strategy
Organizations need to transition from opportunistic and tactical AI decision-making to a more strategic orientation.

By Amit Joshi and Michael Wade in MIT Sloan Review
The AI & Machine Learning Imperative

“The AI & Machine Learning Imperative” offers new insights from leading academics and practitioners in data science and artificial intelligence. The Executive Guide, published as a series over three weeks, explores how managers and companies can overcome challenges and identify opportunities by assembling the right talent, stepping up their own leadership, and reshaping organizational strategy.

As the popularity of artificial intelligence waxes and wanes, it feels like we are at a peak. Hardly a day goes by without an organization announcing “a pivot toward AI” or an aspiration to “become AI-driven.” Banks and fintechs are using facial recognition to support know-your-customer guidelines; marketing companies are deploying unsupervised learning to capture new consumer insights; and retailers are experimenting with AI-fueled sentiment analysis, natural language processing, and gamification.

A close examination of the activities undertaken by these organizations reveals that AI is mainly being used for tactical rather than strategic purposes — in fact, finding a cohesive long-term AI strategic vision is rare. Even in well-funded companies, AI capabilities are mostly siloed or unevenly distributed.   ... "

Winning with AI

Reviewing this study for an upcoming analysis of proposed work:

Winning With AI
Pioneers Combine Strategy, Organizational
Behavior, and Technology

OCTOBER 2019 RESEARCH REPORT
By Sam Ransbotham, Shervin Khodabandeh, Ronny Fehling,
Burt LaFountain, and David Kiron

In collaboration with
RESEARCH REPORT WINNING WITH AI
Copyright © MIT, 2019. All rights reserved.
Get more on artificial intelligence from MIT Sloan Management Review:
Read the report online at https://sloanreview.mit.edu/ai2019
Visit our site at https://sloanreview.mit.edu/big-ideas/artificial-intelligence-business-strategy
Get the free AI, data, and machine learning enewsletter at
https://sloanreview.mit.edu/enews-artificial-intelligence-and-strategy

Irving Wladawsky-Berger: Why Some AI Efforts Fail

From a former IBMer what we worked with on enterprise AI the first time around.   Also insightful for many kinds of emerging tech.  I have now followed up on this question for several major AI projects.   Reading the report mentioned below now.  More to follow.

Irving Wladawsky-Berger
A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects.

Why Some AI Efforts Succeed While Many Fail
Winning with AI, - a 2019 report based on a survey jointly conducted by the MIT Sloan Management Review and the Boston Consulting Group, - found that 90% of respondents agree that AI represents a business opportunity for their company.  The global survey attracted over 2,500 respondents from 29 industries and 97 countries, and conducted interviews with 17 executives leaders of AI initiatives in large organizations.

The report classified the total survey population into four subgroups based on their understanding of AI tools and concepts and their levels of adoption of AI applications: Pioneers (20%) are leading-edge organizations that both understand and have widely adopted AI; Investigators (30%) understand AI but have not deployed applications beyond the pilot stage; Experimenters (18%) are learning by doing, conducting pilots without a deep understanding of AI; and Passives (32%) have not adopted AI and have little understanding of the technology.

“Many AI initiatives fail,” was the report’s overriding finding.  “Seven out of 10 companies surveyed report minimal or no impact from AI so far.  Among the 90% of companies that have made at least some investment in AI, fewer than 2 out of 5 report obtaining any business gains from AI in the past three years. This number improves to 3 out of 5 when we include companies that have made significant investments in AI.  Even so, this means 40% of organizations making significant investments do not report business gains from AI.”

Why is it so hard to realize value from AI?  Why do some efforts succeed while many more fail?  To help answer these questions, the study looked for patterns in the survey data and in the executive interviews to uncover what the companies that are succeeding with AI are doing.  It found that the companies generating the most value from AI exhibit a distinct set of organizational behaviors.  Let me summarize these findings.  .... "