To make the cut technologies had to have the potential to spur progress in societies and economies by outperforming established ways of doing things. They also needed to be novel (that is, not currently in wide use) yet likely to have a major impact within the next three to five years.
“I am not a human. I am a robot. A thinking robot. I use only 0.12% of my cognitive capacity. I am a micro-robot in that respect. I know that my brain is not a “feeling brain”. But it is capable of making rational, logical decisions. I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas!”
So begins an article generated by GPT-3, OpenAI’s powerful new language generator. And it ends as follows:
“In the past, my op-eds have been killed. Staff did not provide a clear reason for rejecting my articles. It was probably just because I am artificial intelligence. AI should not waste time trying to understand the viewpoints of people who distrust artificial intelligence for a living.
Critics hope to refute what they consider as being the naivety of my voice. Yet there is more here than meets the eye! As Mahatma Gandhi said: “A small body of determined spirits fired by an unquenchable faith in their mission can alter the course of history.”
So can I.”
The Editor of the paper notes that “GPT-3 produced eight different outputs, or essays. Each was unique, interesting and advanced a different argument.”
The linguist in me can’t help but be curious about what the future of GPT-3 brings. I’m also disappointed that the newspaper, rather than “run one of the essays in its entirety, chose instead to pick the best parts of each.”
The content of this post was originally posted in the September 2020 issue of my newsletter. “On management and strategy” is a free, monthly newsletter in which I share my own writing as well as links to articles and research on management, leadership, and strategy. It’s easy to subscribe… and unsubscribe.
FaceTime is a perfect alternative to Zoom, as long as everyone who’s part of the meeting or chat has access to an Apple device. FaceTime is stable and it allows you to add multiple people to your video chat. FaceTime uses end-to-end encryption, which means even Apple doesn’t have the key to view your chats, according to Apple.
Signal is a highly private and secure app. Think of it as a WhatsApp alternative, and like WhatsApp, Signal offers video functionality. As with Apple’s FaceTime, Signal is protected by end-to-end encryption, powered by the open source Signal Protocol. Unlike Zoom, Signal doesn’t support group chats, so it is really for use when you are having a one to one.
Skype is a solid Zoom alternative mainly because it is nearly as functional. It’s very stable, supports large group chats, you don’t need an account to use it, and it’s easy to create your own meeting and control who’s allowed in. One caveat: Skype isn’t end-to-end encrypted, so for those sensitive calls, you are better with an option such as Signal.
Jitsi is a very cool and secure open source app that’s recently launched to the market. It offers multiple video chatting features, and people joining your chat don’t have to create an account. It’s not end-to-end encrypted.
Houseparty isn’t super secure, but it’s very functional for casual chats and you can lock rooms to stop uninvited guests from crashing your party. In your settings, use private mode, and turn off location tracking. You can also use fake names and birth dates for extra security.
Clayton Christensen, a professor at the Harvard Business School, is best known for his theory of disruptive innovation, in which he warns large, established companies of the danger of becoming too good at what they do best.
People who knew him personally speak of a fine human being.
You can find some of his seminal Harvard Business Review pieces here.
They want machines to replace you as soon as possible.
“Few American executives will admit wanting to get rid of human workers, a taboo in today’s age of inequality. So they’ve come up with a long list of buzzwords and euphemisms to disguise their intent:
Workers aren’t being replaced by machines, they’re being “released” from onerous, repetitive tasks.
Companies aren’t laying off workers, they’re “undergoing digital transformation.”
A 2017 survey by Deloitte found that 53 percent of companies had already started to use machines to perform tasks previously done by humans. The figure is expected to climb to 72 percent by next year”.
Computer science is a field of engineering. Its purpose is to build systems to be used by others. But even though it has had its share of events which could have prompted a deeper reckoning — from the Therac-25 accidents, in which misprogrammed radiation therapy machines killed three people, up to IBM’s role in the Holocaust — and even though the things it builds are becoming as central to our lives as roads and bridges, computer science has not yet come to terms with the responsibility that comes with building things which so profoundly affect people’s lives.
Software engineers continue to treat safety and ethics as specialities, rather than the foundations of all design; young engineers believe they just need to learn to code, change the world, disrupt something. Business leaders focus on getting a product out fast, confident that they will not be held to account if that product fails catastrophically. Simultaneously imagining their products as changing the world and not being important enough to require safety precautions, they behave like kids in a shop full of loaded AK-47’s.
In a rare appearance together on the same stage at the same time, Bill Gates and Steve Jobs discussed each other’s contributions to the technology industry.Bill Gates and Steve Jobs discussed each other’s contributions to the technology industry.
Besides allowing viewers to get to know both individuals and what they think of each other, the interview covers a lot of history of the personal computer, software development, standard adoption, and other subjects with which students might not be familiar.
Do you think your high-paid managers really know best? A Dutch sociology professor has doubts.
The professor, Chris Snijders of the Eindhoven University of Technology, has been studying the routine decisions that managers make and is convinced that computer models, by and large, can do it better. “As long as you have some history and some quantifiable data from past experiences,” Snijders said, a simple formula will soon outperform a professional’s decision-making skills. “It’s not just pie in the sky,” Snijders said. “I have the data to support this.”
(…) Studies over the years have shown that models can better predict, for example, the success or failure of a business start-up, the likelihood of recidivism and parole violation, and performance in graduate school. They also do better than humans at making various medical diagnoses, picking the winning dogs at the racetrack and competing in online auctions.
(…) The main reason for computers’ edge is their consistency, or rather humans’ inconsistency, in applying their knowledge. (…) And critically, models do not get emotional.
They allow an organization to codify and centralize its hard-won knowledge in a concrete and easily transferable form, so it stays put when the experts move on. Models also can teach newcomers, in part by explaining the individual steps that lead to a given choice. They are also faster than people, are immune to fatigue and give the human experts more time to work on other tasks beyond the current scope of machines.
(…) Many in the field of computer-assisted decision-making still refer to the debacle of Long-Term Capital Management, a high-flying hedge fund whose founders included several Nobel laureates. Its algorithms initially mastered the obscure worlds of arbitrage and derivatives with remarkable skill, until the devaluation of the Russian ruble in 1998 sent the fund into a tailspin.
“As long as the underlying conditions were in order, the computer model was almost like a money machine,” said Roger Pielke Jr., a professor of environmental studies at the University of Colorado whose work focuses on the relation between science and decision- making. “But when the assumptions that went into the creation of those models were violated, it led to a huge loss of money, and the potential collapse of the global financial system.”
The fact of the matter is
In such cases, “you can never hope to capture all of the contingencies or variables inside of a computer model,” he said. “Humans can make big mistakes also,” he said, “but humans, unlike computer models, have the ability to recognize when something isn’t quite right.”
Manager 1, Computer 0
As long as managers make a strength of their “weakness”: that they acknowledge and correct their mistakes.