When it comes to A.I., worry about ‘job churn’ instead of ‘job loss’
Many people are concerned about artificial intelligence eliminating jobs, but they may be overlooking its likelier and more subtle impact: job churn.
As companies increasingly adopt machine learning, they’ll need fewer full-time employees for certain tasks, resulting in more part-time jobs or temporary work—all without health and retirement benefits.
“In an economy where benefits are tied to full-time employment, any increase in job churn would create instabilities and insecurities in people’s ability to maintain their income and their health and retirement benefits,” said Brookings Institution director of governance studies Darrell West last week during a U.S. House Budget Committee hearing about A.I. and the workforce.
It’s unclear how the U.S. would deal with laid off or under-employed workers who are unable to find new jobs because of their lack of technical training. Countries like South Korea and Germany that have experienced technological upheaval were able to create new high-tech jobs when some older ones disappeared, explained Massachusetts Institute of Technology economics professor Daron Acemoglu. That hasn’t been the case in the U.S., he said.
He drew a parallel between the impact of A.I. on jobs and the U.S. auto industry’s shift to more automated manufacturing. Production jobs disappeared in cities like Flint, M.I, and nothing replaced them.
Speakers during the hearing floated several ideas about helping workers in the machine learning era. West said the U.S. should improve Internet access for people living outside of metropolitan areas for better access to online job training and education services as well as to help them work remotely, as more companies shift their operations in light of COVID-19.
Participants also expressed the need for schools to focus more on areas like computer science and math, described as a key to workers succeeding in A.I. West noted the crucial role community colleges play in providing workers with skills more cheaply than four-year universities. He also advocated for the creation of so-called life-long learning accounts for workers, akin to a retirement fund but focused on education so people can receive training throughout their careers.
Indeed, there may be no way to stop the rise of machine learning, but there’s still time to prepare.
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A.I. IN THE NEWS
That’s a lot of cash for some chips. Nvidia said it would buy computer chip design company Arm Limited for a whopping $40 billion. Nvidia CEO Jensen Huang described the deal as important for his company’s A.I. plans, saying in a statement that “our combination will create a company fabulously positioned for the age of AI.” This week’s Brain Food section has more analysis on the deal’s potential impact to the A.I. market.
The TikTok drama continues. Fortune’s Eamon Barrett and Grady McGregor reported on the ongoing saga involving the potential sale of Chinese tech giant ByteDance’s TikTok to a U.S. company. TikTok has chosen Oracle to be the company’s “trusted tech partner” over Microsoft, which the Fortune scribes describe as “a move TikTok hopes will avoid an outright sale of its hugely popular app.” But as Fortune’s David Meyer reports, it’s unclear what this means for TikTok’s core A.I. systems that power its ability to recommend videos to users, because the Chinese government recently debuted tough A.I. export laws.
Big bucks for A.I.-powered drug discovery. Recursion Pharmaceuticals, which specializes in using A.I. for digital drug discover, has scored $239 million in funding and a partnership with Bayer, Forbes reported. The report said that the startup "employs both scientists and software engineers to keep their entire pipeline in-house, from creating massive datasets to taking their drugs through clinical development."
How A.I. could help combat COVID-19. Google said it would donate about $8.5 million to 31 organizations that specialize in COVID-19 relief and research, with a focus on groups using machine learning and data analytics. Among the groups receiving funding include Keio University, which is “investigating the reliability of large-scale surveys in helping model the spread of COVID-19” and Tel Aviv University, which is “developing simulation models using synthetic data to investigate the spread of COVID-19 in Israel.”
EYE ON A.I. TALENT
Expert System hired Colin Matthews to be the machine learning and natural language processing startup’s chief revenue officer, and Keith Lincoln to be its chief marketing officer. Matthews was previously a senior vice president of sales at Avoka, and Lincoln worked at companies like InsightSquared and XebiaLabs.
Oxford University said it appointed John Tasioulas to be the inaugural director for its Institute for Ethics in AI. Tasioulas was a philosophy professor at King’s College London. Some prominent A.I. experts expressed disappointment with the appointment, hoping that the university would have chosen a woman or a person of color for the position.
Shadow has hired Jean-Baptiste Kempf to be the cloud gaming company’s chief technology officer. Kempf was the founder and lead developer of the popular video-player software VLC media player.
Riiid Labs, a startup specializing in machine learning and education technology, picked Jim Larimore to be the company’s chief officer for equity in learning. Larimore was previously the chief officer, Center for Equity in Learning for education non-profit ACT, which develops standardized tests for students.
EYE ON A.I. RESEARCH
Healthcare and “ambient intelligence.” Researchers from Stanford University, including deep-learning pioneer Fei Fei Li, published a paper in Nature about the rise and potential for A.I. in healthcare, specifically around a concept known as “ambient intelligence.”
The basic idea is that locations like hospital spaces or even home living spaces will have an increasing amount of devices like video cameras that contain a multitude of different sensors—radio sensors, for instance, could be used to detect motion in a certain place, while acoustic sensors in microphones could detect when people are speaking.
The Stanford team reviewed multiple academic studies exploring this idea of ambient computing, and how the data gleaned from the sensors could help healthcare workers more accurately monitor patients as they deal with various ailments.
From the paper:
In one of the first studies of its kind, researchers installed a depth and thermal sensor inside the bedroom of an older individual and observed 1,690 activities during 1 month, including 231 instances of caregiver assistance. A convolutional neural network was 86% accurate at detecting assistance. In a different study, researchers collected ten days of video from six individuals in an elderly home and achieved similar results. Although visual sensors are promising, they raise privacy concerns in some environments, such as bathrooms, which is where grooming, bathing and toileting activities occur, all of which are strongly indicative of cognitive function. This led researchers to explore acoustic and radar sensors. One study used microphones to detect showering and toileting activities with accuracies of 93% and 91%, respectively.
Still, the use of these surveillance technologies raise potential privacy questions as well as numerous ethical issues. Realizing that, the authors included a section highlighting some of those risks.
Trustworthiness of ambient intelligence systems is critical to achieve the potential of this technology. Although there is an increasing body of literature on trustworthy artificial intelligence, we consider four separate dimensions of trustworthiness: privacy, fairness, transparency and research ethics. Developing the technology while addressing all four factors requires close collaborations between experts from medicine, computer science, law, ethics and public policy.
FORTUNE ON A.I.
Amazon’s A.I. voice project gets help from Facebook, Dolby, and Garmin—By Jonathan Vanian
What Nvidia’s blockbuster purchase of Arm means for A.I. First things first: Nvidia’s planned purchase of semiconductor design company Arm is not good news for rival Intel. As Fortune’s Adam Lashinsky explained, “Intel’s era is over now, thanks to the dominating run of its crosstown rival, Nvidia.”
“The younger company had already surged past Intel, thanks to its attention to chips powering the graphics of video games, technology that came in handy for all sorts of artificial intelligence-fueled applications,” he added.
Now, regarding A.I.
Indeed, the proposed deal will help solidify Nvidia’s transformation into the most dominant semiconductor company today. Nvidia’s graphics processing units, or GPUs, have become the leading way firms are training their powerful deep-learning systems. By owning Arm, Nvidia gets a new way to target various Internet-connected devices like smartphones, which could be beneficial as the company seeks to expand out of corporate data centers or personal computers.
From Fortune and Eye on A.I. essayist Jeremy Kahn’s report:
Huang cited Arm's experience in finding ways to make hardware that can run sophisticated A.I. programs while consuming far less energy as a key asset Nvidia was hoping to exploit across its portfolio of A.I.-dedicated chips in the future. He also said Arm would allow Nvidia to reach a whole new set of customers beyond its traditional datacenter and gaming markets.
"We would never have been able to reach the broadness and vastness of the ecosystem that Arm has created with this incredibly energy-efficient architecture they’ve created," he said. "Energy efficiency is the single most important thing in computing going forward."
Still, former Fortune editor and Internet-of-things expert Stacey Higginbotham believes that this deal is all about Nvidia gaining a stronger foothold in corporate data centers as opposed to web-connected devices:
But this deal feels like it’s about the data center, specifically how to help Nvidia eliminate or seriously constrain Intel’s headway in the chips inside the servers that host the internet, our mobile apps, and pretty much everything else out there. Nvidia has long been eyeing the space. As far back as 2008, it was looking at a combination of GPU and ARM-based processor for handling some of the graphics-heavy components of corporate computing and also pushing its GPUs as accelerators inside supercomputers.