Machine learning today not only recommends the things you can buy, or content you can watch, but it is doing wonders in other domains as well. This time it is on a mission to find out something untouched. There are different elements in music that trigger emotion in humans. And machine learning is trying to find out just that — how music affects brain activity, physiological response, and human-reported behaviour. 

Music & Emotions

New research by scholars from the University of Southern California is trying to figure out the elements in a song that triggers different emotions in a human. To be more specific, the project has been carried out considering things like dynamics, timbre, harmony, rhythm, and register. They are also trying to figure out how machine learning could use elements and relate to emotions to predict how people might respond to a new piece of music. 

“This work adds to our understanding of how music affects multimodal human experience and has applications in affective computing, music emotion recognition, neuroscience, and music information retrieval,” researchers stated in their paper.

This is the process that the researchers used:

Using Human Efforts

• They first gathered songs from music streaming sites considering that they have very few plays, and are either tagged with “happy” or “sad”
• Then they sorted the songs (60 pieces for each emotion) and it was done with the help of
• Furthermore, they created three more groups where two groups had triggers sadness and one group had only songs that induced happiness.
• The next thing they did is invited 100 people to listen to those songs from the three groups. And once they were done, they had to fMRI scan. They even had to wear sensors to track pulse, heat, and electricity in order to rate the emotions.

Using Machine Learning

• Once all the data was collected by human testers, they fed that data to a machine learning algorithm.
• That is not all, along with that data, they also fed the machine with features of a song such as pitch, rhythm, harmony, etc.

The early results of this project has shown how in the future machine learning would be able to do so much more in the music and art domain. Despite the fact that it is already doing wonders, the researchers are saying there is still a lot to explore. The project is still in the early stage and it would take a little more time to come to a solid conclusion.

However, they are also optimistic that once it is done and successful, machine learning would not only help you select your bedtime or gym playlist but would also help movie makers, therapists etc. to come up with targeted musical experiences.

Outlook

This is not the first time when machine learning has been used in the music industry, in recent times we have seen machine learning makingheavy metal music,painting portraits, and evenbeatboxing. And with time this sought after tech is just making things easier for the human race.

Despite all this, there are people who would argue that these advancements of technology would soon take up all the jobs of the artists. There would be a time when making music would be so much easier, that the need to have an artist won’t be there. But that’s not the complete story, there are professionals from the industry who believe that these technologies would only empower them to be the best version of themselves. It would make some of the complicated things easier for artists.

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Eager to make extra money on the side, Washington, D.C., resident Paula Alves Silva turned to a gig emblematic of the digital age: She recorded sentences read aloud in the comfort of her home to help train artificial intelligence (AI) software.

Silva completed the tasks in her native Portuguese tongue for Seattle-based startup DefinedCrowd, which develops machine learning algorithms that power products for businesses including heavyweights MasterCard and BMW. Such recordings could be used in voice recognition products introduced in new countries, or to train existing systems to recognize non-native speakers or regional accents, the company says.

Silva earned $20—from 8 to 33 cents per sentence—and considered that satisfactory given the short amount of time it took to complete the tasks. The knowledge that her task would contribute to a new artificial intelligence system was a bonus, she said.

When voice-activated software such as Amazon’s Alexa responds to the simple command of calling Mom, thousands of global workers have helped train the software to ensure that, say, Tom from work isn’t dialed instead. The workers transcribe and annotate recordings that are fed back into the software to improve Alexa’s human speech recognition (sometimes using recordings from unaware consumers, according to Bloomberg).

The rise of AI in the age of the gig economy has ushered in an invisible workforce in which ordinary people, like Silva, train technology to be smarter.

Created in 2015 by CEO and founder Daniela Braga, DefinedCrowd is one of many companies that use so-called crowdworking to teach tech devices how to follow commands. Others including Amazon Mechanical Turk, and Figure Eight, formerly known as CrowdFlower, were established over a decade ago.

In the past two years, DefinedCrowd has grown from 20 employees in 2017 to 110 globally. That’s not counting the 130,000 people across 60 countries that DefinedCrowd says have worked on its tasks. It calls them the Neevo community and says they can work in 50 different languages.

Not all their tasks involve speech: For instance, if the makers of an autonomous vacuum want their machines to clean a living room without running into objects, crowdworkers would annotate the different objects in images of living rooms. Those annotated images would then be used as training data to teach the machines precision, said DefinedCrowd spokeswoman Catarina Salteiro.

“You can think of us as the gasoline of a car,” said Salteiro. “You’ll put good gas in there to make sure that it’s working properly.”

To complete tasks for DefinedCrowd, workers register with their email or social media account on the company’s web platform or the recently launched app. After passing a test based on a skill set such as native French fluency, the worker is sent email notifications of available tasks and paid through Paypal at the end of a gig.

Another DefinedCrowd crowdworker, Rakesh Kumar of Delhi, India, said in an email that the recordings he makes in English and Hindi provide a necessary extra income of nearly $10 per month for about six hours of work altogether. It’s helpful, although he noted the payment “is quite less than other freelance work I do.”

A user gets paid only if their work is high quality and matches the requirements. Several members of the Neevo community tweeted at DefinedCrowd that they hadn’t gotten paid yet, nor received notifications of new tasks in a while. “You guys have not paid many completed tasks and the site seems to be down,” wrote one user in December.

The availability of jobs and the skills required for tasks are dependent upon the projects that clients develop, said Salteiro. Certain languages are in a higher demand for tasks than others. “As we grow our client base, the offer of work on our Neevo platform will increase and more tasks will become available for different languages and at a higher frequency,” she said.

A 2018 International Labour Organization report found that crowdworkers who complete tasks for sites such as Amazon Mechanical Turk are paid low wages. Based on two surveys of 3,500 crowdworkers in 75 countries, the report found a third of them relied on the tasks as their main source of income. The report concluded that across five online global platforms—Amazon Mechanical Turk, Microworkers, CrowdFlower, Prolific and Clickworker—the average pay per hour amounted to U.S. $4.43 for work considered payable. When accounting for work that was rejected, pay that wasn’t received, or the amount of time it took to search for tasks, respondents averaged $3.31 per hour. Similar to Kumar, nearly 90% of the surveys’ respondents said they wanted more work than was available, with workers averaging about 25 hours of crowdwork per week.

Labor law professor Charlotte Garden of Seattle University’s School of Law considers crowdworking a form of outsourcing work that was once done by company employees. Such arrangements “can make workers more vulnerable” by preventing them from advancing into higher roles or enjoying the labor protections that regular employees have, said Garden.

Given the absence of clear guidelines on the treatment of crowdworkers, last week the Allen Institute for Artificial Intelligence (AI2) issued a set of ethical recommendations to AI companies on proper pay, privacy and transparency for crowdworkers. In a blog post, it said U.S. crowdworker companies should pay at least the U.S. average minimum wage of $8.50 per hour. Minimum hourly wage in developing countries should be around $4, given the lower prevailing incomes, AI2 recommended. Companies that employ crowdworkers also should be transparent about how long a task will likely take and conditions that may lead to the rejection of work, AI2 urged.

DefinedCrowd is already following the guideline of calculating payment based on the minimum hourly rate of the country where the job is aimed and the estimated time it will take to complete a task, said Salteiro. She wouldn’t be more specific about the pay ranges.

The lack of governance mechanisms in crowdworking has St. Louis University employment law professor Miriam Cherry wondering about the end goal in relying on temporary workers to train AI data systems. “Is it just efficiency for the sake of efficiency … or is it something that really could help people?”

One answer to Cherry’s question could be found in DefinedCrowd’s work with Portugal’s biggest electricity company, EDP, according to company spokesperson Jorge Simões. Last year, DefinedCrowd’s crowdworkers helped the company determine which electricity poles need to be repaired, a once expensive and time-consuming process that required specialists to survey the poles from a helicopter. DefinedCrowd instead devised a machine-learning algorithm to detect defects in poles from images captured by drones and from helicopters.

To create it, crowdworkers identified the type of damage in 900 images of electric poles that were then used to train and test a damage-detection AI system that monitors the state of poles.

EDP was so satisfied with the work that it became an investor in DefinedCrowd last year. The company is now working with DefinedCrowd to create a voice transcription algorithm that works in industrial environments with loud background noise and the use of technical language by service technicians.

To Simões, the work demonstrated that the most difficult tasks could be “successfully automated, optimizing the final result of the inspection by minimizing human error and visual limitations,” even if it took human intelligence to get there.

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If 2017 was the year artificial intelligence rose to prominence, 2018 is when we’re seeing it go mainstream. Whichever area you work in, it’s likely AI will become increasingly prevalent in your everyday activity. Wherever you are in the world – whether you are an expert in AI, someone whose job increasingly uses AI or simply an interested reader we would like to hear from you.

Earlier this year, the Guardian published a long read that asked: Has technology evolved beyond our control? Its author, James Bridle, argued that “our technologies are extensions of ourselves, codified in machines and infrastructures, in frameworks of knowledge and action. Computers are not here to give us all the answers, but to allow us to put new questions, in new ways, to the universe.”

This is a topic of high priority on the international stage. Vladimir Putin has said he believes “the leader of AI will be the ruler of the world”, and China has stated its ambition to be the global AI leader. Meanwhile, Elon Musk has said he fears the threat of unregulated AI and autonomous weaponry and sent a petition to the United Nations calling for regulations on how AI weapons are developed. Mark Zuckerberg has said that Facebook will use artificial intelligence to fight against a vast variety of platform-spoiling misbehaviour, including fake news, hate speech, discriminatory ads and terrorist propaganda. But are governments acting quickly enough? What is the real threat here and what’s the existing framework of governance for AI technology?

With robots increasingly being used to replace the work of humans, what does the future hold? A new report from the Organisation for Economic Cooperation and Development (OECD) looked at the extent to which jobs may soon be automated in 32 different countries and found that 66 million people are at risk of losing their job to machines. That means 14% of jobs currently held by humans could soon be managed by robots. But in the UK, a report from PricewaterhouseCoopers argued that AI would create slightly more jobs (7.2m) than it displaced (7m) by boosting economic growth. So what will it mean for those industries whose employees are being replacedwith robots? Could an AI ever answer questions about the universe that scientists have worked their entire life trying to answer? Are we already seeing changes beginning to happen?

AI is now also being used in healthcare to support the work of medical staff, in surveillance, navigation, gambling and gaming, in banking and finance to predict market trends, in the art world as a tool to spot forgeries, in education and even in care for the elderly.

How do we ensure this new technology remains ethically sound, and that our data remains secure? Are we doing enough to prepare ourselves for the changes that are coming our way? And how will this impact future generations?

The Guardian’s science editor, Ian Sample, will be part of a panel discussion on this topic, and together he and a selection of industry experts and insiders will answer questions from Guardian supporters. We would love to hear from you.

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There are limits to how accurately you can measure things. Think of an X-ray image: it is likely quite blurry and something only an expert physician can interpret properly. The contrast between different tissues is rather poor but could be improved by longer exposure times, higher intensity, or by taking several images and overlapping them. But there are considerable limitations: humans can safely be exposed to only so much radiation, and imaging takes time and resources.

A well-established rule of thumb is the so-called standard quantum limit: the precision of the measurement scales inversely with the square root of available resources. In other words, the more resources – time, radiation power, number of images, etc. – you throw in, the more accurate your measurement will be. This will, however, only get you so far: extreme precision also means using excessive resources.

A team of researchers from Aalto University, ETH Zurich, and MIPT and Landau Institute in Moscow have pushed the envelope and came up with a way to measure magnetic fields using a quantum system – with accuracy beyond the standard quantum limit.

The detection of magnetic fields is important in a variety of fields, from geological prospecting to imaging brain activity. The researchers believe that their work is a first step towards of using quantum-enhanced methods for sensor technology.

‘We wanted to design a highly efficient but minimally invasive measurement technique. Imagine, for example, extremely sensitive samples: we have to either use as low intensities as possible to observe the samples or push the measurement time to a minimum,’ explains Sorin Paraoanu, leader of the Kvantti research group at Aalto University.

Their paper, published in the prestigious journal npj Quantum Information shows how to improve the accuracy of magnetic field measurements by exploiting the coherence of a superconducting artificial atom, a qubit. It is a tiny device made of overlapping strips of aluminium evaporated on a silicon chip – a technology similar to the one used to fabricate the processors of mobile phones and computers.

When the device is cooled to a very low temperature, magic happens: the electrical current flows in it without any resistance and starts to display quantum mechanical properties similar to those of real atoms. When irradiated with a microwave pulse – not unlike the ones in household microwave ovens – the state of the artificial atom changes. It turns out that this change depends on the external magnetic field applied: measure the atom and you will figure out the magnetic field.

But to surpass the standard quantum limit, yet another trick had to be performed using a technique similar to a widely-applied branch of machine learning, pattern recognition.

‘We use an adaptive technique: first, we perform a measurement, and then, depending on the result, we let our pattern recognition algorithm decide how to change a control parameter in the next step in order to achieve the fastest estimation of the magnetic field,’ explains Andrey Lebedev, corresponding author from ETH Zurich, now at MIPT in Moscow.

‘This is a nice example of quantum technology at work: by combining a quantum phenomenon with a measurement technique based on supervised machine learning, we can enhance the sensitivity of magnetic field detectors to a realm that clearly breaks the standard quantum limit,’ Lebedev says.

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If you believe the hype, then Artificial Intelligence (AI) is set to change the world in dramatic ways soon. Nay-sayers claim it will lead to, at best, rising unemployment and civil unrest, and at worst, the eradication of humanity. Advocates, on the other hand, are telling us to look forward to a future of leisure and creativity as robots take care of the drudgery and routine.

A third camp – probably the largest – are happy to admit that the forces of change which are at work are too complicated to predict and, for the moment, everything is up in the air. Previous large-scale changes to the way we work (past industrial revolutions) may have been disruptive in the short-term. However, in the long term what happened was a transfer of labor from countryside to cities, and no lasting downfall of society.

However, as author Calum Chace points out in his latest book ‘Artificial Intelligence and the Two Singularities’  this time there’s one big difference. Previous industrial revolutions involved replacing human mechanical skills with tools and machinery. This time it’s our mental functions which are being replaced – particularly our ability to make predictions and decisions. This is something which has never happened before in human history, and no one exactly knows what to expect.

When I recently met with Culum Chase in London, he told me “A lot of people think it didn’t happen in the past, so it won’t happen now – but everything is different now.

“In the short run, AI will create more jobs as we learn how to work better with machines. But it’s important to think on a slightly longer timescale than the next 10 to 15 years.”

One guiding idea has always been that as machines take care of menial work (be that manual labor, augmenting the abilities of skilled professionals such doctors, lawyers, and engineers, or making routine decisions), humans will be free to spend their time on leisure or creative pursuits.

However, as Chace says, that would require the existence of the “abundance economy” – a Star Trek-like utopia where the means of filling our basic needs – sustenance and shelter – are so highly available that they are essentially free.

Without this happening, humans will find themselves in a situation where they have to go out and compete for whatever paid jobs are still available to humans in the robot-dominated workforce. As a simple example, a fully automated farm would, in theory, provide food at a far cheaper cost than one staffed with human farm hands, machinery operators, administrative staff, distributions operatives and security guards. However, if the owner of the farm still parts with his goods to the highest bidder, there would be inequalities in how that food is distributed among the populace and the potential for a poverty-struck underclass which lacks access to adequate sustenance. Nothing new there – of course, this underclass has always existed throughout history. However, it doesn’t exactly fit with the idea of the Star Trek utopia we need to have in place before we can comfortably hand the reigns to the machines.

This makes it something of a “chicken and egg” problem, and the ideal way for it to play out would seemingly be a gradual and managed transition to a smart machine-driven economy. This process would involve careful oversight of which human roles were being automated, and ensuring that the “plentiful” resources are in place to support those who unfortunately do find that they are being replaced, rather than merely “augmented.”

The problem is that this would require two elements: A concerted and informed effort from governments and regulators to understand the scale of the challenge and enable the right framework for it to happen. And an acceptance by those leading the charge – the tech industry – that there is a more important motive than profit for getting the change right.

Neither of those seems likely to happen any time soon. Despite the “make the world a better place” ethos, big tech’s overriding aim is still to generate growth and profit for their enterprises.

Also, managing the political change could be an even tougher job than persuading a tech CEO that she shouldn’t be focusing on revenue or profits.

“People aren’t stupid,” Chace says, while discussing how automated driving systems look set to erode the employment opportunities for humans whose trade is driving.

“They will see these robots driving around taking people’s jobs, and think ‘it won’t be long until they come for mine’ – and then there will be a panic. And panics lead to very nasty populist politicians, of the left or the right, being elected.”

Chace also doesn’t believe that the concept of universal basic income – currently being trialed in some Scandinavian countries – is the right answer, or at least not in its current form.

“The problem with universal basic income is that it’s basic. If all we can do is give people a basic income, we’ve failed, and society probably isn’t saveable.”

A future where the majority of humans live a subsistence-level income funded by the fruits of a robotic labor force, while a “1 percent” upper class – those in control of the robots – build their empires and reach for the stars – isn’t appealing to those with an egalitarian mindset. However, it could be the direction we’re heading in.

However, argues Chace, it’s not too late to plot a better course.

“We’ve all got a job to do – to wake up our political leaders who are not thinking about this, and wake up our tech leaders – who seem to be deeply in denial.

“If we do grasp the challenge we can have an amazing world for ourselves, our kids and our grandkids, a world where machines do the boring stuff and humans do the worthwhile, interesting stuff.”

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Artificial Intelligence (AI) appears to be suffering from an image crisis. Many of the most vocal commentators seem to believe it will ultimately cause more harm than good. While fear of new technology is nothing new, it doesn’t help when thought leaders like Elon Musk join the prophets of doom. But guess what? Sometimes even Elon Musk is wrong. There I said it.

I struggled to find consensus on an antonym for AI. So we’re calling it natural intelligence. That is, the stuff that’s supposed to be crammed into our brains making us top of the food chain. But it’s overrated. And the fact that so many have blindly concluded AI will be the death of civilisation as we know it is one part humanity’s inclination to fear the unknown, and three parts The Terminator movies. Thanks Arnie.

If AI does learn how to self-evolve and, therefore, think for itself, who is to say it wouldn’t develop consciousness that was genuinely altruistic, compassionate and fair-minded? Right now, the people building AI do so with unconscious bias, limited intelligence and are frequently driven by personal gain rather than the welfare of others. That’s why the greatest achievements have come from corporate entities like Facebook, who use it for targeted advertising, photo tagging and news feeds. Microsoft and Apple need AI to make their digital assistants, Cortana and Siri, wow us by turning on the immersion.

Google is by far one of the hardest at work in its efforts to create the kind of self-teaching AI that might one day outsmart us all. It recently promoted one of its own whizz kids to be the new lead of its AI division. While not a kid at 50 years of age, Jeff Dean had been impressing his co-workers at Google with his robotics skills since 1999. So he was an obvious choice.

Machine-driven apocalypse

A position like this at a company like Google isn’t one of those “made-up” titles like vice-president of customer development or chief innovation officer. AI strategy is at the heart of everything the company does. So if anyone is to inadvertently cause a machine-driven apocalypse, it’ll be these guys.

We’re not there yet though. AI’s greatest screw-ups have also come from the corporate sector. In 2015, Google’s photo-organising product tagged some images of black people as gorillas.

News that a robot figured out how to autonomously assemble an Ikea chair without malfunctioning, like most humans do, is kind of impressive. But it’s not enough to run screaming to the hills. Researchers at Nanyang Technological University in Singapore used a couple of bog standard industrial robot arms with force sensors and a 3D camera to build a robot that had a Stefan Ikea chair assembled in 20 minutes.

It was programmed to build the chair. It knew no other option than this. So given the choice, would a conscious machine decide not to help a human in distress assemble a chair? No one is wildly speculating on the possibility that robots that can think for themselves might choose to be altruistic, compassionate and fair. That they might protect the most vulnerable in society, distribute wealth equally, and put criminal, narcissistic, incompetent leaders of the free world, for example, into recovery treatment rather than a jail cell which is what humans would consider doing first.

Machines vs myopia

There are already solutions to many of the world’s ills – wealth inequality, environmental damage, racial and cultural discrimination etc – at our disposal. We as a species choose not to implement them because of the potential negative impacts – financial loss, time-consumption, not to mention apathy – they might have on us as individuals in the short term. Machines might not be so myopic.

Of course, taking a cold, rational approach to decision-making isn’t necessarily the best idea for society’s ills either. The debate really centres around what we constitute as consciousness. Can a robot develop a sense of itself – and of those around it – while continuing to deliver a purely logic-based approach to “choice”? Were this the case, artificial decision-making could decide eugenics is back in vogue.

All humans have managed to achieve thus far though is a kind of organised chaos. People will stop at a red light and wait till it’s green before driving through an intersection. But in the back of everyone’s mind is the knowledge that it would take very little for civil society to fall apart and have us all at each other’s throats. That’s why so many take comfort in organised religion as it offers answers to many of our questions. They might not be the right answers but sometimes living a lie is easier than accepting the harsh reality that we have little or no control over our lives.

From what I can tell, machines aren’t big on chaos either. They prefer order, logic and fully formed Ikea chairs. At a recent talk he gave in Austin, Texas, Elon Musk said, “Smart people who know they’re smart have a tendency to define themselves by their intelligence meaning they don’t like the idea that machines could ever be smarter than them.” I’m no psychologist but Musk himself happens to be a smart man who is clearly aware of his own intelligence. The engineer doth protest too much, methinks.

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While AI-controlled doomsday machines are considered unlikely, the hazards of artificial intelligence for nuclear security lie instead in its potential to encourage humans to take potentially apocalyptic risks, according to the paper.

During the Cold War, the condition of mutual assured destruction maintained an uneasy peace between the superpowers by ensuring that any attack would be met by a devastating retaliation. Mutual assured destruction thereby encouraged strategic stability by reducing the incentives for either country to take actions that might escalate into a nuclear war.

The new RAND publication says that in coming decades, artificial intelligence has the potential to erode the condition of mutual assured destruction and undermine strategic stability. Improved sensor technologies could introduce the possibility that retaliatory forces such as submarine and mobile missiles could be targeted and destroyed.

Nations may be tempted to pursue first-strike capabilities as a means of gaining bargaining leverage over their rivals even if they have no intention of carrying out an attack, researchers say. This undermines strategic stability because even if the state possessing these capabilities has no intention of using them, the adversary cannot be sure of that.

“The connection between nuclear war and artificial intelligence is not new, in fact the two have an intertwined history,” said Edward Geist, co-author on the paper and associate policy researcher at the RAND Corporation, a nonprofit, nonpartisan research organization. “Much of the early development of AI was done in support of military efforts or with military objectives in mind.”

He said one example of such work was the Survivable Adaptive Planning Experiment in the 1980s that sought to use AI to translate reconnaissance data into nuclear targeting plans.

Under fortuitous circumstances, artificial intelligence also could enhance strategic stability by improving accuracy in intelligence collection and analysis, according to the paper. While AI might increase the vulnerability of second-strike forces, improved analytics for monitoring and interpreting adversary actions could reduce miscalculation or misinterpretation that could lead to unintended escalation.

Researchers say that given future improvements, it is possible that eventually AI systems will develop capabilities that, while fallible, would be less error-prone than their human alternatives and therefore be stabilizing in the long term.

“Some experts fear that an increased reliance on artificial intelligence can lead to new types of catastrophic mistakes,” said Andrew Lohn, co-author on the paper and associate engineer at RAND. “There may be pressure to use AI before it is technologically mature, or it may be susceptible to adversarial subversion. Therefore, maintaining strategic stability in coming decades may prove extremely difficult and all nuclear powers must participate in the cultivation of institutions to help limit nuclear risk.”

RAND researchers based their perspective on information collected during a series of workshops with experts in nuclear issues, government branches, AI research, AI policy and national security.

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As we enter into the next revolutionary age, the age of artificial intelligence (AI), it’s no surprise fear often guides the mainstream narrative. Fear of massive job loss and millions unemployed as AI and robots are implemented on a global scale. But as the CEO of Mondo, a niche tech and digital marketing staffing agency, I envision this future as one of major job creation and opportunity. This future, however, is only possible if we work together to guide AI and robotics innovation responsibly throughout all industries.

Take A Note From History

To see proof of why AI won’t take all our jobs, you only need to look at history. Predictions of doomsdays for the workforce are common during ages of innovation, like the Industrial Revolution. Although these predictions are common, they’re also unsubstantiated. Whenever there is a major advancement in how we work, there is a fear that all the jobs will go away. While there is always an impact, the tail-end of the Industrial Revolution, for example, led to the assembly line; the workforce remains stable throughout these changes because new jobs are created as a result.

Automation is a perfect example of recent tech innovation that led to more jobs, rather than fewer. I find this to be especially true in the digital marketing space as companies began (and continue) to hire technology-specific experts to help them best utilize the automation tools they’ve integrated. The digital marketing roles they are hiring for, the roles my company recruits for, were nonexistent a few years ago and are thus a direct result of the innovation of automation for business purposes.

A recent study surveying 992 companies in a variety of sectors and countries with revenues of $500 million that are implementing AI as either a pilot project or “at scale” by technology consulting group Capgemini, supports the belief that the integration of AI will create jobs, rather than destroy them. 83% of respondents in the survey reported new jobs were created as a result of AI implementation. Additionally, 63% reported AI had not destroyed jobs in their organization. These findings support the recent analysis by Gartner that AI will create 2.3 million jobs in 2020 while eliminating 1.8 million.

It also comes down to basic economics. If companies replace all of their employees with AI or robots and a large part of the workforce becomes unemployed, there will be no consumers left to purchase the products these companies produce.

It’s Not Humans Vs. Machines, It’s Humans And Machines

To drive job creation rather than job loss through the adoption of AI, the responsibility will fall on innovators and industry leaders to open new windows of opportunity by training workers for new and emerging skill sets that will grow in demand as AI becomes more widely adopted. Society as a whole also needs to adjust the way we define and stereotype the near, AI-driven future. We need to change the perception of the future from humans vs. machines to humans and machines working together to accomplish a task.

A recent McKinsey study found that few occupations are fully automatable, but 60% of all occupations have at least 30% technically automatable activities. Alphabet’s Executive Chairman Eric Schmidt cited the study at the 2017 Viva Tech conference in Paris when he contended that the problem with AI wouldn’t be massive job loss but instead would be the creation of new jobs with AI-driven components that can’t be filled, adding to the tech talent gap in the U.S. as demand for tech-based skill sets continues to outpace available talent.

The key to prepping the current workforce for this is AI augmentation, arguably the greatest AI benefit and job creator. AI augmentation is the combination of human and AI, where the two work together to supplement each other and make up for what the other lacks. As populations are getting older, the total number of people working has gone down, which means the key to continued economic success is boosting productivity through AI augmentation.

We need to look at AI as an opportunity to find ways to do our jobs more efficiently and effectively, without replacing the human component. Instead, AI can free the individual up to focus on tasks they do better than an AI system.

The only potential job disruption on a major scale resulting from AI will be a lack of qualified talent skilled in collaborating with AI components, tools or systems due to a lack of training and adequate educational opportunities available now. To prevent the doomsday AI fears keeping us all up at night, we need to start investing in and providing opportunities to our current employees now so they can begin developing the AI-driven skills we’ll rely on later.

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Not for the first time in its history, artificial intelligence is rising on a tide of hype. Improvements to the technology have produced some apparently impressive advances in fields such as voice and image recognition, predictive pattern analysis and autonomous robotics. The problem is that people are extrapolating many unrealistic expectations from these initial successes, without recognizing the many constraints surrounding their achievements.

Machine intelligence is still pretty dumb, most of the time. It’s far too early for the human race to throw in the towel.

People are misled by artificial intelligence because of a phenomenon known as the Eliza effect, named after a 1966 computer program that responded to people’s typed statements in the manner of a psychotherapist. The computer was executing some very simple programming logic. But the people interacting with it ascribed emotional intelligence and empathy to its replies.

The same phenomenon happens today in our reactions to the apparent successes of machine learning and AI. We overestimate its achievements and underestimate our own performance because we rarely stop to think how much we already know. All of the context we bring to interpreting any situation is something we take for granted.

Machines are good at pattern matching

Computers are much better than us at only one thing — at matching known patterns. They can only match the patterns they have learned, and they have limited capacity to learn more than just a few patterns. Humans are optimized for learning unlimited patterns, and then selecting the patterns we need to apply to deal with whatever situation we find ourselves in. This is a skill that’s been honed by millions of years of evolution.

This is why Buzzfeed writer Katie Notopoulos was able to crack Facebook’s new algorithm and wind up her friends the other week. She successfully matched a pattern in a way that Facebook’s algorithm couldn’t fathom — as she explains, the Facebook machine doesn’t really know what it’s doing, the best it can do is to just try and match patterns that it’s been told look like friendship:

This algorithm doesn’t understand friendship. It can fake it, but when we see Valentine’s Day posts on Instagram four days later, or when the machines mistake a tornado of angry comments for ‘engagement’, it’s a reminder that the machines still don’t really get the basics of humanity.

This echoes Douglas Hofstadter’s far more erudite takedown of AI for The Atlantic last month, The Shallowness of Google Translate. If you understand both French and English, then just savor for a moment this put-down of Google’s translation skills:

Clearly Google Translate didn’t catch my meaning; it merely came out with a heap of bull. ‘Il sortait simplement avec un tas de taureau.’ ‘He just went out with a pile of bulls.’ ‘Il vient de sortir avec un tas de taureaux.’ Please pardon my French — or rather, Google Translate’s pseudo-French.

A takedown of Google Translate

Hofstadter is generous enough to acknowledge Google’s achievement in building an engine capable of converting text between any of around 100 languages by coining the term ‘bai-lingual’ — “‘bai’ being Mandarin for 100” — yet thoroughly demolishes its claim to be performing anything truly intelligent:

The bailingual engine isn’t reading anything — not in the normal human sense of the verb ‘to read’. It’s processing text. The symbols it’s processing are disconnected from experiences in the world. It has no memories on which to draw, no imagery, no understanding, no meaning residing behind the words it so rapidly flings around.

A friend asked me whether Google Translate’s level of skill isn’t merely a function of the program’s database. He figured that if you multiplied the database by a factor of, say, a million or a billion, eventually it would be able to translate anything thrown at it, and essentially perfectly. I don’t think so. Having ever more ‘big data’ won’t bring you any closer to understanding, since understanding involves having ideas, and lack of ideas is the root of all the problems for machine translation today.

Enterprises are constantly encountering the limitations of that lack of ideas in their quest to apply machine learning and artificial intelligence to today’s business problems. Last year I listened to a presentation at the Twilio Signal conference in London by Sahil Dua, a back-end developer at Booking.com. He spoke about the work the travel reservation site has been doing with machine learning to autonomously tag images.

Of course we all know that the likes of Google, Amazon and Microsoft Azure already offer generic image tagging services. But the problem Booking.com encountered was that those services don’t tag images in a way that’s useful in the Booking.com context. They may identify attributes such as ‘ocean’, ‘nature’, ‘apartment’, but Booking.com needs to know whether there’s a sea view, is there a balcony and does it have a seating area, is there a bed in the room, what size is it, and so on. Dua and his colleagues have had to train the machines to work with a more detailed set of tags that matches their specific context.

Why humans will always be smarter than AI

This concept of context is one that is central to Hofstadter’s lifetime of work to figure out AI. In a seminal 1995 essay he examines an earlier treatise on pattern recognition by Russian researcher Mikhail Bongard, a Russian researcher, and comes to the conclusion that perception goes beyond simply matching known patterns:

… in strong contrast to the usual tacit assumption that the quintessence of visual perception is the activity of dividing a complex scene into its separate constituent objects followed by the activity of attaching standard labels to the now-separated objects (ie, the identification of the component objects as members of various pre-established categories, such as ‘car’, ‘dog’, ‘house’, ‘hammer’, ‘airplane’, etc)

… perception is far more than the recognition of members of already-established categories — it involves the spontaneous manufacture of new categories at arbitrary levels of abstraction.

For Booking.com, those new categories could be defined in advance, but a more general-purpose AI would have to be capable of defining its own categories. That’s a goal Hofstadter has spent six decades working towards, and is still not even close.

In her BuzzFeed article, Katie Notopoulos goes on to explain that this is not the first time that Facebook’s recallbration of the algorithms driving its newsfeeds has resulted in anomalous behavior. Today, it’s commenting on posts that leads to content being overpromoted. Back in the summer of 2016 it was people posting simple text posts. What’s interesting is that the solution was not a new tweak to the algorithm. It was Facebook users who adjusted — people learned to post text posts and that made them less rare.

And that’s always going to be the case. People will always be faster to adjust than computers, because that’s what humans are optimized to do. Maybe sometime many years in the future, computers will catch up with humanity’s ability to define new categories — but in the meantime, humans will have learned how to harness computing to augment their own native capabilities. That’s why we will always stay smarter than AI.

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2017 was a formative year for artificial intelligence – not just in terms of the advancement of the technology itself, but also for the evolution of our understanding of AI’s impact on our society. Here are my predictions on the two topics that will become the primary focuses of AI in 2018.

Algorithms will change the way we work

When we talk about AI, often we focus on the belief that it is going to take away certain types of jobs (like cashiers or janitorial services). However, we often ignore the fact that this technology will also slowly permeate into most of our lives during work.

I refer to this as the distinction between AI and IA — artificial intelligence vs. intelligence augmentation. Despite the strides we’ve made in AI development and will continue to make in 2018, we are still years away from AI fully replacing human jobs. However, we are much closer to seeing the impact of AI permeate into almost every job and augment human intelligence.

Think about how you spend your days at work. There are likely parts of your day that require repetitive tasks or pattern recognition that could be delegated to someone without as much context and creativity. Take a doctor for example — AI will soon be able to detect and diagnose common diseases more quickly and accurately than humans. Imagine a world where computers read in a patient’s medical history, test results, and scans, and they are able to present doctors with various possible diagnoses and the likelihood that each of them is correct. Now, doctors can spend more of their time interpreting the possible diagnoses, communicating with patients, and developing unique, sustainable treatment plans that are most effective for each specific patient.

A world where doctors are replaced by AI — if that ever happens — is far off. However, a world where doctors are more efficient and effective with the help of AI is coming very soon. In cases like these, AI isn’t replacing anyone’s job; it’s simply allowing people to do their job better.

AI will be held against more scrutiny

AI is incredibly powerful and its adoption will only accelerate as it begins to augment our work and allow us to focus on the parts of our job that are most important. But part of why AI is so attractive is also why it’s so dangerous.

Algorithms are desirable for their ability to make well-informed decisions quickly and accurately. But that same power allows algorithms that are making incorrect decisions (and perpetuating biases) to do so with greater velocity and widespread impact than humans have ever had. As I mentioned in my earlier post about the Dark Side of Artificial Intelligence, there is potential for homogenous data sets to produce biased algorithms that are making important decisions about people’s lives — and we have already started to see the impact of these today (see Microsoft’s Twitter bot “Tay” and Google’s image results for Gorilla).

This is why, as AI becomes more prolific in 2018, it will also become more closely audited and scrutinized. Unfortunately, we will probably endure a few more experiences detailing what happens when AI is left unchecked. However, 2018 will be the year that companies purchasing AI products don’t just ask about the predictive power of an algorithm — they will stipulate that algorithms are tested in advance, interrogating and minimizing their potential unexpected impact.

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