Revolutions often have humble origins, a small group with big ideas gathering to plant seeds of disruption. So, it was in the dog days of summer in 1956, when 10 academics gathered on the campus of Dartmouth College to discuss how to make machines use language and form abstractions and concepts to solve the kinds of problems now reserved for humans. The conference led to the founding of a new field of study, artificial intelligence. Six decades hence, we are in the midst of an AI revolution that is already dramatically changing entire sectors like healthcare, transportation, education, banking, and retail. But AI is not without its critics. Elon Musk famously said that, "With artificial intelligence, we're summoning the demon." While Stephen Hawking believed the development of full artificial intelligence could spell the end of the human race. So, whose job is it to make sure that such a vision never comes to pass? Today on Cold Call, we've invited Professor Tsedal Neeley to discuss her case entitled, "Timnit Gebru: Silenced No More on AI Bias and The Harms of Large Language Models." Tsedal Neeley's work focuses on how leaders can scale their organizations by developing and implementing global and digital strategies.

BRIAN KENNY: Revolutions often have humble origins, a small group with big ideas gathering to plant seeds of disruption. So, it was in the dog days of summer in 1956, when 10 academics gathered on the campus of Dartmouth College to discuss how to make machines use language and form abstractions and concepts to solve the kinds of problems now reserved for humans. The conference led to the founding of a new field of study, artificial intelligence. Six decades hence, we are in the midst of an AI revolution that is already dramatically changing entire sectors like healthcare, transportation, education, banking, and retail. But AI is not without its critics. Elon Musk famously said that, "With artificial intelligence, we're summoning the demon." While Stephen Hawking believed the development of full artificial intelligence could spell the end of the human race. So, whose job is it to make sure that such a vision never comes to pass? Today on Cold Call, we've invited Professor Tsedal Neeley to discuss her case entitled, "Timnit Gebru: Silenced No More on AI Bias and The Harms of Large Language Models." Tsedal Neeley's work focuses on how leaders can scale their organizations by developing and implementing global and digital strategies.

AI is seeping into enterprises from all directions. It's being embedded in applications, software tools, devices and equipment. Yet, some organizations still don't have an AI strategy. "The journey to AI having an impact at a firm is challenging and sometimes long," said Nigel Duffy, global artificial intelligence leader at professional services firm EY. "To have an impact with AI you must solve a lot of problems, many of which have nothing to do directly with AI, [such as] how do you deploy solutions? How do you get them into your infrastructure? How do you get people to use them? What are the workforce and change management considerations? What kind of training is required? AI is too disruptive to ignore. If your company lacks an AI strategy by design, then it has one by default. A default strategy is a Wild, Wild West scenario in which AI is popping up in various places within an organization, without orchestration and alignment. The lack of cohesion and direction can result in several issues including governance and security. "[S]ome of those risks may not be well characterized, so they have not been addressed by the appropriate level of governance and review," said Duffy. "Most AI is going to come through procurement or it's going to come through the backdoor or technology you've deployed." Should an AI strategy be part of a digital transformation strategy? One reason some people think an AI strategy should be part of a digital transformation strategy is because the digital transformation strategy is viewed as the overarching business initiative that's facilitated by modern technologies. "If an organization's digital transformation strategy does not already include AI, then there is a real need to revisit the overall approach to transformation, said David Homa, director of the Digital Initiative at Harvard Business School.

The way we apply for jobs has changed radically over the last 20 years, thanks to the arrival of sprawling online job-posting boards like LinkedIn, Indeed, and ZipRecruiter, and the use by hiring organizations of artificial intelligence (AI) algorithms to screen the tsunami of résumés that now gush forth from such sites into human resources (HR) departments. With video-based online job interviews now harnessing AI to analyze candidates' use of language and their performance in gamified aptitude tests, recruitment is becoming a decidedly algorithmic affair. Yet all is not well in HR's brave new world. After quizzing 8,000 job applicants and 2,250 hiring managers in the U.S., Germany, and Great Britain, researchers at Harvard Business School, working with the consultancy Accenture, discovered that many tens of millions of people are being barred from consideration for employment by résumé screening algorithms that throw out applicants who do not meet an unfeasibly large number of requirements, many of which are utterly irrelevant to the advertised job. For instance, says Joe Fuller, the Harvard professor of management practice who led the algorithmic hiring research, nurses and graphic designers who need merely to use computers have been barred from progressing to job interviews for not having experience, or degrees, in computer programming.

… yielded a lot of thoughts and insights, so many in fact that Datar–a self-described geek–says he is using machine learning to absorb them.

Author Rory McDonald spent several years studying companies pioneering new categories in several fields, most notably software and fintech. He interviewed hundreds of entrepreneurs, corporate innovation chiefs, market analysts, and journalists. Author Keith Krach built and led four category-creating enterprises in industrial robotics, mechanical design automation, B2B ecommerce, and digital signature. Using multiple-case methods, and melding their analysis with personal experience, the authors compared the emerging insights to develop a theory of the category creation process. They also reviewed prior research on category creation and market formation in organization theory and strategy.

… has turned in recent years to such areas as “design thinking and innovative problem solving, as well as machine learning and artificial intelligence”.

Anthony is the co-founder and COO of Dexai Robotics, a startup that automates activities in commercial kitchens using flexible robot arms. Prior to Dexai, Anthony worked as a consultant with the Boston Consulting Group, focusing on growth strategies. Anthony holds a MBA from Harvard Business School, and a B.E. in Mechanical Engineering and a B.S. in Mathematics from the American University of Beirut. Outside of work, Anthony enjoys chasing soccer balls and exploring sunken sea treasures. What is it that attracted you to robotics initially?

Members get 60 days free site access, $6.95/article thereafter. Airbnb is colliding with traditional hotel companies like Marriott International and Hilton. In just over a decade, the online lodging marketplace has assembled an inventory of more than 7 million rooms -- six times as much lodging capacity as Marriott managed to accumulate over 60-plus years. In terms of U.S. consumer spending, Airbnb overtook Hilton in 2018 and is on track to move ahead of Marriott.1 Although Airbnb serves similar consumer needs, it is a completely different kind of company.

The use of artificial intelligence is growing in many areas: hiring, healthcare, travel, household functions. These AI examples rely heavily on deep learning and natural language processing, but its use has sparked a debate about bias and fairness. Human decision making can be shaped by unconscious individual and societal biases. Will AI's decisions be less biased than human ones? How are companies addressing this potential AI bias with regards to hiring, data crunching, and other critical business functions?