Since Prometheus stole the fire of knowledge from right under the noses of the gods on Mount Olympus and bestowed it upon mankind, humans have not stopped fiddling with it and creating striking innovations all throughout their evolution.
Over the course of history, mankind has perfected its industry by not only relying on technical evolution but also by reinventing it as new resources have created new technical means. Therefore, industry has benefited from qualitative advancements which have sometimes been so ingrained in a certain time period and have had such an overwhelming impact that we have dubbed them “revolutions”. Sentryo would like to give you a quick look back in time at these first three industrial revolutions to define the contours of a fourth revolution which is taking shape right before our very eyes.
THE FIRST INDUSTRIAL REVOLUTION – 1765
Following a slow period of proto-industrialization, this first revolution spans from the end of the 18th century to the beginning of the 19th century. It witnessed the emergence of mechanization, a process that replaced agriculture with industry as the foundations of the economic structure of society. Mass extraction of coal along with the invention of the steam engine created a new type of energy that thrusted forward all processes thanks to the development of railroads and the acceleration of economic, human and material exchanges. Other major inventions such as forging and new know-how in metal shaping gradually drew up the blueprints for the first factories and cities as we know them today.
THE SECOND INDUSTRIAL REVOLUTION – 1870
Nearly a century later at the end of the 19th century, new technological advancements initiated the emergence of a new source of energy: electricity, gas and oil. As a result, the development of the combustion engine set out to use these new resources to their full potential. Furthermore, the steel industry began to develop and grow alongside the exponential demands for steel. Chemical synthesis also developed to bring us synthetic fabric, dyes and fertilizer. Methods of communication were also revolutionized with the invention of the telegraph and the telephone and so were transportation methods with the emergence of the automobile and the plane at the beginning of the 20th century. All these inventions were made possible by centralizing research and capital structured around an economic and industrial model based on new “large factories” and the organizational models of production as envisioned by Taylor and Ford.
THE THIRD INDUSTRIAL REVOLUTION – 1969
Nearly a century later, in the second half of the 20th century, a third industrial revolution appeared with the emergence of a new type of energy whose potential surpassed its predecessors: nuclear energy. This revolution witnessed the rise of electronics—with the transistor and microprocessor—but also the rise of telecommunications and computers. This new technology led to the production of miniaturized material which would open doors, most notably to space research and biotechnology. For industry, this revolution gave rise to the era of high-level automation in production thanks to two major inventions: automatons—programmable logic controllers (PLCs)—and robots.
The first industrial revolution used water and steam to mechanize production, the second used electric energy to create mass production and the third used electronics and information technology to automate production. Today a fourth industrial revolution is underway which builds upon the third revolution and the digital revolution that has been taking place since the middle of the last century. This fourth revolution with exponential expansion is characterized by merging technology that blurs the lines between the physical, digital and biological spheres to completely uproot industries all over the world. The extent and depth of these changes are a sign of transformations to entire production, management and governance systems.
We’d need to start with an understanding of what an entrepreneur is. They’re all over the map, which makes the question particularly difficult to navigate.
There’s the 14-year-old girl who hitches a ride to Costco, buys 100 bottles of water for thirty cents each, then sells them at the beach for a dollar a pop. Scale that that every day for a summer and you can pay for college.
Or the 7-time venture-backed software geek who finds a niche, gets some funding, builds it out with a trusted team, sells it for $100 million in stock and then starts again.
Perhaps we’re talking about a non-profit entrepreneur, a woman who builds a useful asset, finds a scalable source of funding and changes the world as she does.
The mistake that’s easy to make is based in language. We say, “she’s an entrepreneur,” when we should be saying, “she’s acting like an entrepreneur.”
Since entrepreneurship is a verb, an action, a posture… then of course, it’s a choice. You might not want to act like one, but if you can model behavior, you can act like one.
And what do people do when they’re acting like entrepreneurs?
1. They make decisions.
2. They invest in activities and assets that aren’t a sure thing.
3. They persuade others to support a mission with a non-guaranteed outcome.
4. This one is the most amorphous, the most difficult to pin down and thus the juiciest: They embrace (instead of run from) the work of doing things that might not work.
As far as I can tell, that’s it. Everything else you can hire.
Buying into an existing business by buying a franchise, to pick one example–there’s very little of any of the four elements of entrepreneurial behavior. Yes, you’re swinging for a bigger win, you’re investing risk capital, you’re going outside the traditional mainstream. But what you’re doing is buying a proven business, not acting like an entrepreneur. The four elements aren’t really there. It’s a process instead. Nothing wrong with that.
All four of these elements are unnatural to most folks. Particularly if you were good at school, you’re not good at this. No right answers, no multiple choice, no findable bounds.
It’s easy to get hung up on the “risk taking” part of it, but if you’re acting like an entrepreneur, you don’t feel like you’re taking a huge risk. Risks are what happens at a casino, where you have little control over the outcome. People acting like entrepreneurs, however, feel as though the four most important elements of their work (see above) are well within their control.
If you’re hoping someone can hand you a Dummies guide, giving you the quick steps, the guaranteed method, the way to turn this process into a job–well, you’ve just announced that you don’t feel like acting like an entrepreneur.
But before you walk away from it, give it a try. Entrepreneurial behavior isn’t about scale, it’s about a desire for a certain kind of journey.
As much as technology continues to advance, even far into the future people will remain a constant key in the success of manufacturing businesses.
Columns Post: 12/1/2017
President & CEO, Core Powered Inc.
Shazam! A few key strokes plus Google and voila! Everything you ever need to know is right at your fingertips. Isn’t technology great?
The speed of change (thanks to the microprocessor) and the millennials’ natural-born talent to use today’s technology have changed the face of how we figure stuff out and who is figuring it out. It’s a good thing we have this knowledge at our fingertips; otherwise, how would we ever fill the knowledge gap that the generation leaving the work force has created?
So, are we done? Is manufacturing’s greatest challenge—the skill shortage—averted? Of course not. The much-discussed skills and knowledge gap in manufacturing today includes other elements. Understanding that there is a difference between knowledge, skill and mastery is an important first step.
Google will help a person gain knowledge. It will even demonstrate necessary skills with fancy “look at what I just did” videos. The magic formula that the boomers in manufacturing had (and we have tried hard to replicate) was that getting good at something required actually doing it, with both victories and failures. I remember in my tool and die maker training days being told, “If you are not making mistakes, you are not trying hard enough.” I also often heard, “If you do not have time to clean up after yourself, you are too slow.” Both are very physical examples of performance and risk that are still with me today.
Okay, so maybe that doesn’t apply today exactly the same way as it did a generation ago. So now what? In Malcolm Gladwell’s Outliers, the 10,000 Hour Rule holds that 10,000 hours of “deliberate practice” are needed to become world-class in any field (well, maybe not any field, considering entrepreneurship and rock and roll, for instance, but certainly in a field with stable structures, such as manufacturing). Tim Ferriss discusses an opposing principle—the 4-Hour Work Week—stating “you can become world class in any skill in 6 months or less.” This idea may or may not be an exaggeration, but what is emphasized here is the quality of practice over the quantity. What’s missing here? Skill, training, mastery, or something else?
All of these points are important to know when dealing with today, or next week, or even next year. In other words, these are addressing the problem that we know about and the one that is with us right now. But there is another important step, and it has nothing to do with now or next year and everything to do with the more distant future.
We need to understand what manufacturing will be like 5 and 10 years from now. This will still be a time when most of us have plans to be making a living in manufacturing, at one level or another. Most businesses have a 5-year plan, but does that plan include a strategy based on the integration of artificial intelligence (AI), artificial consciousness or machine consciousness (AC or MC), additive manufacturing (AM) or any of the other rapidly emerging technologies in which we engage with computers as much as with each other? And what about the bio-revolution, when computers are able to see, understand and modify living things?
Exciting times are ahead; much will change, as has always been the case. The future is also filled with undeniable truths of the greatness we have needed, need today, and will continue to need. Amazon CEO Jeff Bezos suggests a business strategy should be built around things that are known to be stable in time. “When you have something that you know is true, even over the long term, you can afford to put a lot of energy into it,” Mr. Bezos says. In the world of manufacturing trials and tribulations, having a plan to continually adapt to the speed of change and always invest in the people needed to get the job done will not change no matter what the future brings. Technology and people are the future of manufacturing.
The PMPA Board of Directors recently approved the creation of a new forward-looking Future of Manufacturing Committee whose mission is to help our members adapt and thrive, by facilitating a conversation on emerging changes that will affect our businesses. The committee will provide inferences on the 5-10 year evolution in technology, culture and economics in order to provide PMPA member companies with a clearer vision of the future and insights for creating competitive advantage.
The committee is comprised of the following PMPA members who are excited about the future and its impact on our industry and want to share it with you:
An all-new PMPA listserve has also been created – PMPA_FutureofMFG@lists.pmpa.org for all members to engage in the conversation. Along with presentations at the 2018 National Tech Conference, listserve members will be presented with information and have an opportunity to contribute to this exciting and relevant dialogue.
Subscribe to the new Future of Manufacturing listserve today! Access the MY PROFILE page of the PMPA website and click on Edit My List Subscriptions. Scroll down to the Future of Manufacturing heading and click on Subscribed in the box on the right. Remember, you must be a member of the list in order to post messages and receive replies from the list.
We thank Charles Ruecker for his ambitious efforts to get this committee off the ground, for the benefit of PMPA members and the entire industry.
An interesting concept of what could lay ahead. .a bit mind blowing really…..
In a recent interview the MD of Daimler Benz (Mercedes Benz) said their competitors are no longer other car companies but Tesla (obvious), Google, Apple, Amazon ‘et al’ are…… There have always been the 3 constants … Death, Taxes and CHANGE!
Software will disrupt most traditional industries in the next 5-10 years.
Uber is just a software tool, they don’t own any cars, and are now the biggest taxi company in the world.
Airbnb is now the biggest hotel company in the world, although they don’t own any properties.
Artificial Intelligence: Computers become exponentially better in understanding the world. This year, a computer beat the best Go player in the world, 10 years earlier than expected.
In the US, young lawyers already don’t get jobs. Because of IBM Watson, you can get legal advice (so far for more or less basic stuff) within seconds, with 90% accuracy compared with 70% accuracy when done by humans.
So if you study law, stop immediately. There will be 90% less lawyers in the future, only specialists will remain.
Watson already helps nurses diagnosing cancer, 4 times more accurate than human nurses. Facebook now has a pattern recognition software that can recognize faces better than humans. In 2030, computers will become more intelligent than humans.
Autonomous cars: In 2018 the first self driving cars will appear for the public. Around 2020, the complete industry will start to be disrupted. You don’t want to own a car anymore. You will call a car with your phone, it will show up at your location and drive you to your destination. You will not need to park it, you only pay for the driven distance and can be productive while driving. Our kids will never get a driver’s licence and will never own a car.
It will change the cities, because we will need 90-95% less cars for that. We can transform former parking spaces into parks. 1.2 million people die each year in car accidents worldwide. We now have one accident every 60,000 miles (100,000 km), with autonomous driving that will drop to one accident in 6 million miles (10 million km). That will save a million lives each year.
Most car companies will probably become bankrupt. Traditional car companies try the evolutionary approach and just build a better car, while tech companies (Tesla, Apple, Google) will do the revolutionary approach and build a computer on wheels.
Many engineers from Volkswagen and Audi; are completely terrified of Tesla.
Insurance companies will have massive trouble because without accidents, the insurance will become 100x cheaper. Their car insurance business model will disappear.
Real estate will change. Because if you can work while you commute, people will move further away to live in a more beautiful neighborhood.
Electric cars will become mainstream about 2020. Cities will be less noisy because all new cars will run on electricity. Electricity will become incredibly cheap and clean: Solar production has been on an exponential curve for 30 years, but you can now see the burgeoning impact.
Last year, more solar energy was installed worldwide than fossil. Energy companies are desperately trying to limit access to the grid to prevent competition from home solar installations, but that can’t last. Technology will take care of that strategy.
With cheap electricity comes cheap and abundant water. Desalination of salt water now only needs 2kWh per cubic meter (@ 0.25 cents). We don’t have scarce water in most places, we only have scarce drinking water. Imagine what will be possible if anyone can have as much clean water as he wants, for nearly no cost.
Health: The Tricorder X price will be announced this year. There are companies who will build a medical device (called the “Tricorder” from Star Trek) that works with your phone, which takes your retina scan, your blood sample and you breath into it.
It then analyses 54 biomarkers that will identify nearly any disease. It will be cheap, so in a few years everyone on this planet will have access to world class medical analysis, nearly for free. Goodbye, medical establishment.
3D printing: The price of the cheapest 3D printer came down from $18,000 to $400 within 10 years. In the same time, it became 100 times faster. All major shoe companies have already started 3D printing shoes.
Some spare airplane parts are already 3D printed in remote airports. The space station now has a printer that eliminates the need for the large amount of spare parts they used to have in the past.
At the end of this year, new smart phones will have 3D scanning possibilities. You can then 3D scan your feet and print your perfect shoe at home.
In China, they already 3D printed and built a complete 6-storey office building. By 2027, 10% of everything that’s being produced will be 3D printed.
Business opportunities: If you think of a niche you want to go in, ask yourself: “in the future, do you think we will have that?” and if the answer is yes, how can you make that happen sooner?
If it doesn’t work with your phone, forget the idea. And any idea designed for success in the 20th century is doomed to failure in the 21st century.
Work: 70-80% of jobs will disappear in the next 20 years. There will be a lot of new jobs, but it is not clear if there will be enough new jobs in such a small time.
Agriculture: There will be a $100 agricultural robot in the future. Farmers in 3rd world countries can then become managers of their field instead of working all day on their fields.
Aeroponics will need much less water. The first Petri dish produced veal, is now available and will be cheaper than cow produced veal in 2018. Right now, 30% of all agricultural surfaces is used for cows. Imagine if we don’t need that space anymore. There are several startups who will bring insect protein to the market shortly. It contains more protein than meat. It will be labelled as “alternative protein source” (because most people still reject the idea of eating insects).
There is an app called “moodies” which can already tell in which mood you’re in. By 2020 there will be apps that can tell by your facial expressions, if you are lying. Imagine a political debate where it’s being displayed when they’re telling the truth and when they’re not.
Bitcoin may even become the default reserve currency … Of the world!
Longevity: Right now, the average life span increases by 3 months per year. Four years ago, the life span used to be 79 years, now it’s 80 years. The increase itself is increasing and by 2036, there will be more than one year increase per year. So we all might live for a long long time, probably way more than 100.
Education: The cheapest smart phones are already at $10 in Africa and Asia. By 2020, 70% of all humans will own a smart phone. That means, everyone has the same access to world class education.
Every child can use Khan academy for everything a child needs to learn at school in First World countries. There have already been releases of software in Indonesia and soon there will be releases in Arabic, Suaheli and Chinese this summer. I can see enormous potential if we give the English app for free, so that children in Africa and everywhere else can become fluent in English and that could happen within half a year.
Automakers with ambitious plans to roll out more than a hundred new battery-powered models in the next five years appear to be forgetting one little thing: Drivers aren’t yet buzzed about the new technology.
Electric cars — which today comprise only 1 per cent of auto sales worldwide, and even less in the U.S. — will account for just 2.4 per cent of U.S. demand and less than 10 per cent globally by 2025, according to researcher LMC Automotive. But while consumer appetite slogs along, carmakers are still planning a tidal wave of battery-powered models that may find interested buyers few and far between.
“When you hear people talk about the tipping point, it’s really that they’re counting the number of product offerings,” Hau Thai-Tang, Ford’s global head of product development and purchasing, said of electric cars. “Nobody can cite what the actual demand will be.”
With battery costs declining rapidly and Tesla’s stock price on a tear, automakers are rushing to get in the game with their own all-electric models. General Motors has announced plans to roll out 20 models by 2023, while Ford and Volkswagen are among those planning new electric lineups in China. Toyota this week promised more than 10 electric models by early next decade.
In total, 127 battery-electric models will be introduced worldwide in the next five years, Thai-Tang said, with LMC predicting pure electric offerings will increase by more than five-fold to 75 models in the U.S. alone.
“There’s certainly more hype than real growth in sales volume,” Jeff Schuster, senior vice-president of forecasting for LMC, said in an interview. “How long have we been talking about EVs? We’re now finally seeing them in numbers, but the sales numbers are not taking over the industry by any means.”
It’s a mix of panic and promise that’s driving automakers to set ambitious goals to catch up to perceived market leaders such as Tesla and GM, which each are enjoying a run-up in their stock prices this year. GM chief executive officer Mary Barra said her company will sell more than one million electric vehicles per year — profitably — by 2026. Tesla CEO Elon Musk had been planning to build half a million electric cars in 2018, although that timeline could be jeopardized by missed production targets for the $35,000 Model 3 sedan.
Wall Street continues to reward Tesla and values the Silicon Valley electric-car maker as worth more than Ford, even as the Detroit automaker dwarfs Musk’s company in nearly all metrics, from output to revenue. Tesla shares are up about 60 per cent this year, while Ford has gained closer to 5 per cent.
“Tesla has a cult following and that helps build the hype,” Schuster said. “Other companies say, ‘How do we capture some of this buzz Tesla has? Can we do it by electrifying our lineup, too?’ ”
There’s a growing optimism that the electric market is ready for liftoff, based in part on improvements in battery chemistry and costs and in part on the Field of Dreams adage: If you build it, they will come. Still, the rush to electrify in the face of uncertain demand has left auto suppliers on edge. They have to build factories and invest to develop components of battery-powered propulsion systems to support the automakers’ aggressive ambitions.
Magna International Inc., for example, the largest auto supplier in North America, is having vigorous debates over whether to add capacity to tool up for electric cars when its executives don’t see much demand for them over the next eight years. The company predicts EVs will only grow to between 3 per cent and 6 per cent of global auto sales by 2025, said Jim Tobin, chief marketing officer at the Canadian company.
Industry executives convinced drivers will abruptly exit their internal combustion engine vehicles in favour of electrics may find themselves too overzealous, with LMC forecasting gasoline-powered engines will still make up about 85 per cent of U.S. new car sales in 2025. But that shift could accelerate as electrified vehicles reach price parity with gasoline-powered cars, which Bloomberg New Energy Finance predicts will happen by 2029 or sooner for most models.
Rick Haas, former chief engineer of the Tesla Model S who now runs the North American operations of Indian automaker Mahindra & Mahindra, counts himself in the optimistic camp. Although today’s drivers aren’t too excited about battery cars, tougher regulations in places like China and the power-thirsty needs of driverless features could help speed the transition along.
“Things move about 10 times the speed that they moved 25 years ago,” Haas said. “As soon as the ball crests the hill and everyone thinks, ‘I’m comfortable with this,’ then the whole industry will flip.”
And no automaker wants to be left behind to sell the 21st-century version of the buggy whip: a car that runs on fossil fuel.
“There will be a lot of winners and losers,” said Haas. “Companies will die because of this.”
Ford does not want to be one of the casualties. Thai-Tang said his engineers and suppliers are working hard on developing a cost-efficient battery that is better and cheaper than today’s lithium-ion versions. Toyota is working on energy-dense solid-state batteries, seen as the next frontier in electric power, with Panasonic.
Yet the greatest challenge may not be technological. It could be marketing, as more than 10 dozen models fight over a sliver of market share.
“The question we’ve been asking ourselves is, ‘OK, if you’re going to launch in that clutter of 120 competitive products, what’s going to allow somebody to want to even consider your product?’ ” Thai-Tang said, noting that the “provocative” design for the small electric SUV Ford’s planning may help differentiate it in a crowded field. “But not in a weird science-fair kind of provocative” way, he added.
While the math doesn’t yet add up for the glut of models chasing the tiny market for EVs, no automaker wants to be caught short when the switch gets flipped to battery power.
“Our ambition for electrification is not modest,” Jim Farley, Ford’s executive vice-president of global markets, said in an interview. “We’re going for it.”
Faced with an automated future, what moral framework should guide us?
Image: Matthew Wiebe
21 Oct 2016
Optimizing logistics, detecting fraud, composing art, conducting research, providing translations: intelligent machine systems are transforming our lives for the better. As these systems become more capable, our world becomes more efficient and consequently richer.
Tech giants such as Alphabet, Amazon, Facebook, IBM and Microsoft – as well as individuals like Stephen Hawking and Elon Musk – believe that now is the right time to talk about the nearly boundless landscape of artificial intelligence. In many ways, this is just as much a new frontier for ethics and risk assessment as it is for emerging technology. So which issues and conversations keep AI experts up at night?
Unemployment. What happens after the end of jobs?
The hierarchy of labour is concerned primarily with automation. As we’ve invented ways to automate jobs, we could create room for people to assume more complex roles, moving from the physical work that dominated the pre-industrial globe to the cognitive labour that characterizes strategic and administrative work in our globalized society.
Look at trucking: it currently employs millions of individuals in the United States alone. What will happen to them if the self-driving trucks promised by Tesla’s Elon Musk become widely available in the next decade? But on the other hand, if we consider the lower risk of accidents, self-driving trucks seem like an ethical choice. The same scenario could happen to office workers, as well as to the majority of the workforce in developed countries.
This is where we come to the question of how we are going to spend our time. Most people still rely on selling their time to have enough income to sustain themselves and their families. We can only hope that this opportunity will enable people to find meaning in non-labour activities, such as caring for their families, engaging with their communities and learning new ways to contribute to human society.
If we succeed with the transition, one day we might look back and think that it was barbaric that human beings were required to sell the majority of their waking time just to be able to live.
Inequality. How do we distribute the wealth created by machines?
Our economic system is based on compensation for contribution to the economy, often assessed using an hourly wage. The majority of companies are still dependent on hourly work when it comes to products and services. But by using artificial intelligence, a company can drastically cut down on relying on the human workforce, and this means that revenues will go to fewer people. Consequently, individuals who have ownership in AI-driven companies will make all the money.
We are already seeing a widening wealth gap, where start-up founders take home a large portion of the economic surplus they create. In 2014, roughly the same revenues were generated by the three biggest companies in Detroit and the three biggest companies in Silicon Valley … only in Silicon Valley there were 10 times fewer employees.
If we’re truly imagining a post-work society, how do we structure a fair post-labour economy?
Humanity. How do machines affect our behaviour and interaction?
Artificially intelligent bots are becoming better and better at modelling human conversation and relationships. In 2015, a bot named Eugene Goostman won the Turing Challenge for the first time. In this challenge, human raters used text input to chat with an unknown entity, then guessed whether they had been chatting with a human or a machine. Eugene Goostman fooled more than half of the human raters into thinking they had been talking to a human being.
This milestone is only the start of an age where we will frequently interact with machines as if they are humans; whether in customer service or sales. While humans are limited in the attention and kindness that they can expend on another person, artificial bots can channel virtually unlimited resources into building relationships.
Even though not many of us are aware of this, we are already witnesses to how machines can trigger the reward centres in the human brain. Just look at click-bait headlines and video games. These headlines are often optimized with A/B testing, a rudimentary form of algorithmic optimization for content to capture our attention. This and other methods are used to make numerous video and mobile games become addictive. Tech addiction is the new frontier of human dependency.
On the other hand, maybe we can think of a different use for software, which has already become effective at directing human attention and triggering certain actions. When used right, this could evolve into an opportunity to nudge society towards more beneficial behavior. However, in the wrong hands it could prove detrimental.
Artificial stupidity. How can we guard against mistakes?
Intelligence comes from learning, whether you’re human or machine. Systems usually have a training phase in which they “learn” to detect the right patterns and act according to their input. Once a system is fully trained, it can then go into test phase, where it is hit with more examples and we see how it performs.
Obviously, the training phase cannot cover all possible examples that a system may deal with in the real world. These systems can be fooled in ways that humans wouldn’t be. For example, random dot patterns can lead a machine to “see” things that aren’t there. If we rely on AI to bring us into a new world of labour, security and efficiency, we need to ensure that the machine performs as planned, and that people can’t overpower it to use it for their own ends.
Racist robots. How do we eliminate AI bias?
Though artificial intelligence is capable of a speed and capacity of processing that’s far beyond that of humans, it cannot always be trusted to be fair and neutral. Google and its parent company Alphabet are one of the leaders when it comes to artificial intelligence, as seen in Google’s Photos service, where AI is used to identify people, objects and scenes. But it can go wrong, such as when a camera missed the mark on racial sensitivity, or when a software used to predict future criminals showed bias against black people.
We shouldn’t forget that AI systems are created by humans, who can be biased and judgemental. Once again, if used right, or if used by those who strive for social progress, artificial intelligence can become a catalyst for positive change.
Security. How do we keep AI safe from adversaries?
The more powerful a technology becomes, the more can it be used for nefarious reasons as well as good. This applies not only to robots produced to replace human soldiers, or autonomous weapons, but to AI systems that can cause damage if used maliciously. Because these fights won’t be fought on the battleground only, cybersecurity will become even more important. After all, we’re dealing with a system that is faster and more capable than us by orders of magnitude.
Evil genies. How do we protect against unintended consequences?
It’s not just adversaries we have to worry about. What if artificial intelligence itself turned against us? This doesn’t mean by turning “evil” in the way a human might, or the way AI disasters are depicted in Hollywood movies. Rather, we can imagine an advanced AI system as a “genie in a bottle” that can fulfill wishes, but with terrible unforeseen consequences.
In the case of a machine, there is unlikely to be malice at play, only a lack of understanding of the full context in which the wish was made. Imagine an AI system that is asked to eradicate cancer in the world. After a lot of computing, it spits out a formula that does, in fact, bring about the end of cancer – by killing everyone on the planet. The computer would have achieved its goal of “no more cancer” very efficiently, but not in the way humans intended it.
Singularity. How do we stay in control of a complex intelligent system?
The reason humans are on top of the food chain is not down to sharp teeth or strong muscles. Human dominance is almost entirely due to our ingenuity and intelligence. We can get the better of bigger, faster, stronger animals because we can create and use tools to control them: both physical tools such as cages and weapons, and cognitive tools like training and conditioning.
This poses a serious question about artificial intelligence: will it, one day, have the same advantage over us? We can’t rely on just “pulling the plug” either, because a sufficiently advanced machine may anticipate this move and defend itself. This is what some call the “singularity”: the point in time when human beings are no longer the most intelligent beings on earth.
Robot rights. How do we define the humane treatment of AI?
While neuroscientists are still working on unlocking the secrets of conscious experience, we understand more about the basic mechanisms of reward and aversion. We share these mechanisms with even simple animals. In a way, we are building similar mechanisms of reward and aversion in systems of artificial intelligence. For example, reinforcement learning is similar to training a dog: improved performance is reinforced with a virtual reward.
Right now, these systems are fairly superficial, but they are becoming more complex and life-like. Could we consider a system to be suffering when its reward functions give it negative input? What’s more, so-called genetic algorithms work by creating many instances of a system at once, of which only the most successful “survive” and combine to form the next generation of instances. This happens over many generations and is a way of improving a system. The unsuccessful instances are deleted. At what point might we consider genetic algorithms a form of mass murder?
Once we consider machines as entities that can perceive, feel and act, it’s not a huge leap to ponder their legal status. Should they be treated like animals of comparable intelligence? Will we consider the suffering of “feeling” machines?
Some ethical questions are about mitigating suffering, some about risking negative outcomes. While we consider these risks, we should also keep in mind that, on the whole, this technological progress means better lives for everyone. Artificial intelligence has vast potential, and its responsible implementation is up to us.
IDC recently released a report, “IDC FutureScape: Worldwide Manufacturing Predictions 2018,” surveying the global manufacturing landscape. When creating its predictions the firm examined ecosystems and experiences, greater intelligence in operational assets and processes, data capitalization, the convergence of information technology (IT) and operations. Most of the group’s predictions refer to a continuum of change and digital transformation (DX) within the wider ecosystem of the manufacturing industry and global economy.
“Manufacturers of every size and shape are changing rapidly because of new digital technologies, new competitors, new ecosystems, and new ways of doing business,” said Kimberly Knickle, research vice president, IT Priorities and Strategies, IDC Manufacturing Insights. “Manufacturers that can speed their adoption of digital capabilities in order to create business value will be the leaders of their industry.”
Technologies that will have the greatest impact include cloud, mobile, big data and analytics, and internet of things (IoT). Manufacturers also have high expectations for the business value of technologies that are in earlier stages of adoption, such as robotics, cognitive computing/artificial intelligence (AI), 3D printing, augmented reality/virtual reality (AR/VR), and even blockchain.
Over the next few years, IDC identified some of the most notable changes in the industry:
Redefining how businesses design (or define), deliver and monetize products and services
Developing new contextualized and customized experiences for customers, employees and partners
Increasing coordination and collaboration between IT and line-of-business organizations, as well as among ecosystem participants
Changing the nature of work and how it’s accomplished with people, process, and technology coming together
While the predictions offered largely focus on the near- to midterm (2018–2021), the impact of many of these will be felt for years to come. IDC’s worldwide manufacturing 2018 predictions are:
Prediction 1: By 2020, 60% of the top manufacturers will rely on digital platforms that enhance their investments in ecosystems and experiences and support as much as 30% of their overall revenue.
Prediction 2: By 2021, 20% of the top manufacturers will depend on a secure backbone of embedded intelligence, using IoT, blockchain, and cognitive, to automate large-scale processes and speed execution times by up to 25%.
Prediction 3: By 2020, 75% of all manufacturers will participate in industry clouds, although only one-third of those manufacturers will be monetizing their data contributions.
Prediction 4: By 2019, the need to integrate operational technology and information technology as a result of IoT will have led to more than 30% of all IT and OT technical staff having direct project experience in both fields.
Prediction 5: By 2019, 50% of manufacturers will be collaborating directly with customers and consumers regarding new and improved product designs through cloud-based crowdsourcing, virtual reality, and product
Prediction 6: In 2020, augmented reality and mobile devices will drive the transition to the gig economy in the service industry, with “experts for hire” replacing 20% of dedicated customer and field service workers, starting with consumer durables and electronics.
Prediction 7: By the end of 2020, one-third of all manufacturing supply chains will be using analytics-driven cognitive capabilities, thus increasing cost efficiency by 10% and service performance by 5%.
Prediction 8: By 2020, 80% of supply chain interactions will happen across cloud-based commerce networks, dramatically improving participants’ resiliency and reducing the impact of supply disruptions by up to one-third.
Prediction 9: By 2020, 25% of manufacturers in select subsectors will have balanced production with demand cadence and achieved greater customization through intelligent and flexible assets.
Prediction 10: By 2019, 15% of manufacturers that manage data-intensive production and supply chain processes will be leveraging cloud-based execution models that depend on edge analytics to enable real-time visibility and augment operational flexibility.