At its best, futures thinking is not about predicting the future; rather, it is about engaging people in thinking deeply about complex issues, imagining new possibilities, connecting signals into larger patterns, connecting the past with the present and the future, and making better…
The average knowledge economy worker spends 28% of his or her time just reading and answering e-mail.
According to a 2012 study from McKinsey Global Institute, the average knowledge economy worker spends 28% of his or her time just reading and answering e-mail.
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A 2010 study by Alex Haslam from the University of Exeter found that allowing employees to choose how many plants and photos they wanted in their office increased productivity by up to 32% compared to employees who had been given no choice.
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this tendency to respond positively to nature and natural settings is called the “biophilia hypothesis,” coined by Edward O. Wilson in his 1984 book, Biophelia.And research bears him out. A 2010 study from Cornell University found that the presence of indoor plants had a beneficial effect on workers’ attention spans. Another study, by researchers from the University of Twente, in the Netherlands in 2008, found that indoor plants reduced stress. And a 2007 study found that windows that looked out on natural settings had more positive health effects than those that looked out on more urban settings. So in addition to making your office more green in terms of sustainability, make it literally green, too.
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A study by University of Illinois researchers found that walking just three times a week for forty minutes at a natural pace helps improve brain connectivity and cognitive function.
Mark Dodgson, Director, Technology and Innovation Management Centre, University of Queensland Business School David Gann, Vice President, Imperial College
Every shop, every enterprise, even outside the times of sharp conflict, of strikes or wage reductions, is the scene of a constant silent war, of a perpetual struggle, of pressure and counter-pressure. Rising and falling under its influence, a certain norm of wages, hours and tempo of labor establishes itself, keeping them just at the limit of what is tolerable and intolerable. Hence the two classes, workers and capitalists, while having to put up with each other in the daily course of work, in deepest essence, by their opposite interests, are implacable fores, living, when not fighting, in a kind of armed peace
Melting water from one of Canada’s largest glaciers used to flow north, to the Bering Sea. Last spring, it reversed course, a case of what scientists call “river piracy.”
Climate change is speeding geological change:
In the blink of a geological eye, climate change has helped reverse the flow of water melting from a glacier in Canada’s Yukon, a hijacking that scientists call “river piracy.”
This engaging term refers to one river capturing and diverting the flow of another. It occurred last spring at the Kaskawulsh Glacier, one of Canada’s largest, with a suddenness that startled scientists.
A process that would ordinarily take thousands of years — or more — happened in just a few months in 2016.
Much of the meltwater from the glacier normally flows to the north into the Bering Sea via the Slims and Yukon Rivers. A rapidly retreating and thinning glacier — accelerated by global warming — caused the water to redirect to the south, and into the Pacific Ocean.
No one really knows how the most advanced algorithms do what they do. That could be a problem.
I disagree with several premises in this paper, most notably that we will need to be able to ‘predict where failures might occur’ in operating AI systems, like driverless cars.
Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence. The car didn’t follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it.
Getting a car to drive this way was an impressive feat. But it’s also a bit unsettling, since it isn’t completely clear how the car makes its decisions. Information from the vehicle’s sensors goes straight into a huge network of artificial neurons that process the data and then deliver the commands required to operate the steering wheel, the brakes, and other systems. The result seems to match the responses you’d expect from a human driver. But what if one day it did something unexpected—crashed into a tree, or sat at a green light? As things stand now, it might be difficult to find out why. The system is so complicated that even the engineers who designed it may struggle to isolate the reason for any single action. And you can’t ask it: there is no obvious way to design such a system so that it could always explain why it did what it did.
The mysterious mind of this vehicle points to a looming issue with artificial intelligence. The car’s underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries.
But this won’t happen—or shouldn’t happen—unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur—and it’s inevitable they will.
On the contrary, considering how many people are killed by human beings operating cars and trucks, my sense is that any solution that offers the combination of lowered mortality rates, increased convenience, and lower cost will be accepted as a gift from the gods.
Consider that most everything that people do -- from something as instinctive as balancing as we walk down the street, to something as cerebral as playing go -- has been learned much like deep learning works in AI. People can’t explain how they balance, and the world’s greatest go players don’t know why one move seems better than another.
The way we have been programming computers to date is the dumb and inflexible way, deterministically. That’s going to be old school very very soon.
Knight interviewed the cognitive scientist/philosopher Daniel Dennett, who offered the very tame observation
If it can’t do better than us at explaining what it’s doing, then don’t trust it.
This is a requirement we don’t use with trust of other people. People are remarkably bad at explaining what they do, or how they go about reasoning, but we manage to live in a world filled with people.
No, we will adapt to a world of opaque AI, doing what it does without us being able to peer into its circuitry or even to parse some logical precepts guiding its reactions. We will have to stick with the basic empirics, like ‘by their fruits shall you know them’.
And, of course, we will monitor what AIs are up to, using other AIs that we also don’t understand. Maybe that will make Knight and Dennett less afraid.
What do you think? Please participate in this short survey, Trusting AIs, (three questions), to see what members of the workfutures.io community think.
The average age of a Japanese farmer is now 67, and in all developed nations the average age is 60. Agriculture ministers from the G7 last year were worried about how this high age could lead to issues over food security. And as the world population is still increasing, the need for food also increases.
The Japanese government is increasing its support for more robots to be developed to help with farming. Japanese farms tend to be small and intensely farmed–rice paddies, often on terraced slopes, and greenhouses for vegetables. They are looking at very small robotic tractors to mechanize formerly manual processes in rice paddies and wearable devices, exoskeletons of sorts, to help elderly people, now that their strength is waning, continue to do the same lifting tasks with fruits and vegetables that they have done for a lifetime.
In the US farms tend to be larger, and for things like wheat farming a lot of large farm equipment is already roboticized. Production versions of many large pieces of farm equipment, such as those made by John Deere (see this story from the Washington Post for an example) have been capable of level 3 autonomous driving (see my blog post for a definition) for many years, and can even be used at level 4 with no one in the cab (see this 2013 YouTube video for an example).
There is now robotics research around the world for robots to help with fruits and vegetables. At robotics conferences one can see prototype machines for weeding, for precision application of herbicides and insecticides, and for picking fruits and vegetables. All these parts of farming currently require lots of labor. In the US and Europe only immigrants are willing to do this labor, and with backlashes against immigration it leaves the land owners with no choice but to look for robotic workers, despite the political rhetoric that immigrants are taking jobs that citizens want–it is just not true.
Tied into this is are completely new ways to do food production. We are starting to see more and more computer controlled indoor farming systems both in research labs in Universities and in companies, and as turn key solutions from small suppliers such as Indoor Farms of America and Cubic Farms, to name just two. The key idea is to put computation in the loop, carefully monitoring and controlling temperature, humidity, lighting, water delivery, and nutrient delivery. These solutions use tiny amounts of water compared to conventional outdoor farming. More advanced research solutions use computer vision to monitor crop growth and put that information into the controlling algorithms. So far we have not seen plays in this space from large established companies, but I have seen research experiments in the labs of major IT suppliers in both Taiwan and mainland China. We now have enough computation in the cloud to monitor every single plant that will eventually be consumed by humans. Farming still requires clouds, jut entirely different ones than historically. Indoor farms promise much more reliable sources of food than those that rely on outside weather cooperating.
Once food is grown it requires processing, and that too is labor intensive, especially for meat or fish of any sort. We are still a few years away from bionically grown meat that is practical, so in the meantime, again driven by lack of immigrants and a shortage of young workers, food processing is turning more and more to automation and robots. This includes both red meat cutting and poultry processing. These jobs are hard and unpleasant, and lead to many repetitive stress injuries. There are now many industrial robots in both the US and Australia being used to do some of these tasks. Reliance on robots will continue to grow as the population ages.
The firm is using an algorithm designed for the CERN laboratory
A GLIMPSE into the future of retailing is available in a smallish office in Hamburg. From there, Otto, a German e-commerce merchant, is using artificial intelligence (AI) to improve its activities. The firm is already deploying the technology to make decisions at a scale, speed and accuracy that surpass the capabilities of its human employees.
Big data and “machine learning” have been used in retailing for years, notably by Amazon, an e-commerce giant. The idea is to collect and analyse quantities of information to understand consumer tastes, recommend products to people and personalise websites for customers. Otto’s work stands out because it is already automating business decisions that go beyond customer management. The most important is trying to lower returns of products, which cost the firm millions of euros a year.
Its conventional data analysis showed that customers were less likely to return merchandise if it arrived within two days. Anything longer spelled trouble: a customer might spot the product in a shop for one euro less and buy it, forcing Otto to forgo the sale and eat the shipping costs.
But customers also dislike multiple shipments; they prefer to receive everything at once. Since Otto sells merchandise from other brands, and does not stock those goods itself, it is hard to avoid one of the two evils: shipping delays until all the orders are ready for fulfilment, or lots of boxes arriving at different times.
The typical solution would be slightly better forecasting by humans of what customers are going to buy so that a few goods could be ordered ahead of time. Otto went further and created a system using the technology of Blue Yonder, a startup in which it holds a stake. A deep-learning algorithm, which was originally designed for particle-physics experiments at the CERN laboratory in Geneva, does the heavy lifting. It analyses around 3bn past transactions and 200 variables (such as past sales, searches on Otto’s site and weather information) to predict what customers will buy a week before they order.
The AI system has proved so reliable—it predicts with 90% accuracy what will be sold within 30 days—that Otto allows it automatically to purchase around 200,000 items a month from third-party brands with no human intervention. It would be impossible for a person to scrutinise the variety of products, colours and sizes that the machine orders. Online retailing is a natural place for machine-learning technology, notes Nathan Benaich, an investor in AI.
Overall, the surplus stock that Otto must hold has declined by a fifth. The new AI system has reduced product returns by more than 2m items a year. Customers get their items sooner, which improves retention over time, and the technology also benefits the environment, because fewer packages get dispatched to begin with, or sent back.
“Hey Siri, how’s the weather today?” “OK Google, remind me to pay the power bill.” “Alexa, tell me a joke!” Bots are eating the world. Whether you are an enterprising app developer building the ess…
Jensen Harris offers us ‘headhless AI’: AI without the chatter.
Headless AI is the application of artificial intelligence to vastly improve internal business processes.
It is fully transforming the crucial machinery of business — processes like hiring, lead generation, financial modeling, and information security. Legacy software has become a commodity in all of these areas, and purpose-built AI solutions will get a larger and larger wallet share of these huge enterprise cost centers.
Headless AI combines machine intelligence and learning loops to constantly evolve. Because these solutions plug into the data lifeblood of a company, they become incredibly valuable as the algorithms adapt to the patterns that work.
I call this form of AI “headless” because, unlike bots, the value is mostly not about the personality. Headless AI works with humans and augments their strengths. It doesn’t try to replace people; it gives them superpowers.
While being able to talk to your CRM is cool, having a sales platform that accurately predicts the 100 opportunities you can close this quarter is worth breaking the bank for. Having a cute avatar answer your customer support chats seems nice enough, but predicting ahead of time what areas of your product will get support requests so that you can fix them before customers suffer is pure gold.
Closing a factory is just one way to undermine a local community. Competition from superstores and shopping malls also devastated many small-city downtowns; now many small-town malls are failing too. And we shouldn’t minimize the extent to which the long decline of small newspapers has eroded the sense of local identity.
A different, less creditable reason mining and manufacturing have become political footballs, while services haven’t, involves the need for villains. Demagogues can tell coal miners that liberals took away their jobs with environmental regulations. They can tell industrial workers that their jobs were taken away by nasty foreigners. And they can promise to bring the jobs back by making America polluted again, by getting tough on trade, and so on. These are false promises, but they play well with some audiences.
By contrast, it’s really hard to blame either liberals or foreigners for, say, the decline of Sears. (The chain’s asset-stripping, Ayn Rand-loving owner is another story, but one that probably doesn’t resonate in the heartland.)
Finally, it’s hard to escape the sense that manufacturing and especially mining get special consideration because, as Slate’s Jamelle Bouie points out, their workers are a lot more likely to be male and significantly whiter than the work force as a whole.
Anyway, whatever the reasons that political narratives tend to privilege some jobs and some industries over others, it’s a tendency we should fight. Laid-off retail workers and local reporters are just as much victims of economic change as laid-off coal miners.
Microsoft and Facebook have rolled out competing tools. But Slack’s chief executive says small tech companies with new ideas have a history of defeating the big guys.
The battle between Slack and its competitors is essentially a fight over who will make the next piece of workplace software that no one can live without.
People from nutrition geeks to philosophers are using a metaphor from software to describe the world.
The stack metaphor is today’s ‘ghost in the machine’:
“Stack,” in technological terms, can mean a few different things, but the most relevant usage grew from the start-up world: A stack is a collection of different pieces of software that are being used together to accomplish a task. A smartphone’s software stack, for instance, could be described as a layered structure: There’s the low-level code that controls the device’s hardware, and then, higher up, its basic operating system, and then, even higher, the software you use to message a friend or play a game. An individual application’s stack might include the programming languages used to build it, the services used to connect it to other apps or the service that hosts it online; a “full stack” developer would be someone proficient at working with each layer of that system, from bottom to top.
The stack isn’t just a handy concept for visualizing how technology works. For many companies, the organizing logic of the software stack becomes inseparable from the logic of the business itself. The system that powers Snapchat, for instance, sits on top of App Engine, a service owned by Google; to the extent that Snapchat even exists as a service, it is as a stack of different elements. “What you end up with is entire companies being built on a set of software tools and services,” says Yonas Beshawred, whose own company, StackShare, lets tech professionals publish their companies’ stacks and see what others are using, comparing technology the way hobbyists might compare gear. “You can think of them as Lego blocks.” A healthy stack, or a clever one, is tantamount (the thinking goes) to a well-structured company.
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As theory, the stack remains mostly a speculative exercise: What if we imagined the whole world as software? And as a popular term, it risks becoming an empty buzzword, used to refer to any collection, pile or system of different things. (What’s your dental care stack? Your spiritual stack?) But if tech start-ups continue to broaden their ambitions and challenge new industries — if, as the venture-capital firm Andreessen-Horowitz likes to say, “software is eating the world” — then the logic of the stack can’t be trailing far behind, ready to remake more and more of our economy and our culture in its image. It will also, of course, be subject to the warning with which Daugman ended his 1990 essay. “We should remember,” he wrote, “that the enthusiastically embraced metaphors of each ‘new era’ can become, like their predecessors, as much the prison house of thought as they first appeared to represent its liberation.”
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