Artificial intelligence (AI) is not a new term. It has been around for years and first used in the mid-1950s. Since its inception, AI has been successful in enabling computers to perform some tasks that are normally done by humans.

Since AI became more prevalent in the last few years, there are myths around this technology. The idea of machines learning and making decisions like the human brain is itself seen as the biggest threat. Scientists around the world have been warning about the dangers of AI for ages.

The first such claim was put forth in 1958 by Herbert Simon and Allen Newell. They wrote, “There are now machines in the world that think, learn, and create. Furthermore, their ability to do these things will rapidly increase until – in the visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied.”

Myth #1: AI is smarter than people.
There is no intelligence without the human brain. The people who create the algorithms and provide information to it make up the AI. To build and teach it, you need to feed information. AI is as smart as you program it.

Myth #2: AI will make medical diagnoses
Medical professionals use technology for efficiency. A radiologist who is an expert in the evaluation of X-rays, CT scans and other medical imagery will use AI for easy primary level of diagnosis. However, a human doctor will be the one determining a diagnosis and making medical decisions.

Myth #3: Modeling determines the outcome
AI initiatives begin as test projects. You may get excellent results during the testing phase but the final results come after you deploy it to production. Training your AI model is never complete. As you feed more data to the model, it evolved and accuracy goes up.

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Artificial intelligence (AI) seems to be taking over the world and is even helping us combat the ongoing coronavirus pandemic, but so far it has been a product of human supervision – we teach computers to see patterns, just like we teach children to read. However, researchers believe the future of AI depends on systems that are capable of learning on their own, without any supervision.

What is supervised learning?

When a parent points towards a dog and tells the baby to “Look at the doggie,” the child learns and understands what to call the furry four-legged friends. This is an example of supervised learning, as pointed out by New York Times. However, when the baby stands and stumbles, over and over again, before she learns how to walk, that is something else.

Computers and humans are quite similar when it comes to learning. Just as we learn mostly through observation or trial and error, computers also have to pass through the stage of supervised learning before they can reach the human-level of intelligence.

Even if a supervised learning system reads all the books in the world, it would still not be able to achieve human-level intelligence because a large chunk of our knowledge and expertise is not penned down.

Limitations of human supervision

Supervised learning comprises of feeding data, including images, audio, or text that is fed into computer algorithms, which teams machines to do what they do. However, this learning method has its restrictions.

“There is a limit to what you can apply supervised learning to today due to the fact that you need a lot of labeled data,” said Yann LeCun, an expert in the field of machine learning and artificial intelligence, and a recipient of the Turing Award, the equivalent of a Nobel Prize in computer science, in 2018. He is also the vice president and chief A.I. scientist at Facebook.

Although learning methods that are not dependent on such human intervention are less explored, they have been overshadowed by the success of supervised learning and its many practical applications in the real world, from self-driving cars to smart speakers. But supervised learning still can’t do many of the tasks that are simple enough even for a toddler.

Artificial intelligence that learns on its own

Therefore, scientists leading the charge of artificial intelligence research have shifted their focus to less-supervised learning methods in which the artificial intelligence develops a common sense or sorts and carries out tasks by learning on its own.

“There’s self-supervised and other related ideas, like reconstructing the input after forcing the model to a compact representation, predicting the future of a video or masking part of the input and trying to reconstruct it,” said Samy Bengio, a research scientist at Google.

Scientists are also exploring reinforcement learning, which requires very limited supervision and does not rely on data. This learning method, pioneered by University of Alberta’s Richard Sutton, follows a reward-driven learning mode, essentially like a dog performing a trick to earn a treat. The strategy has been developed to teach computer systems to learn new actions on their own.

All they need to do is set a goal, and a reinforcement learning system will try to achieve the said goal through trial and error until it is consistently receiving a reward. A more appropriate term for this future AI is “predictive learning,” which means that systems not only recognize patterns but also predict outcomes and choose a course of action autonomously.

For instance, if a self-supervised computer system “watches” millions of videos on YouTube, it will gather a representation of the world from the clips and when the machine is asked to perform a particular task, it can take action based on what it has learned from the videos – in other words, teach itself.

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When chief information officers think about their organizations and where machine learning might be deployed, the process often begins with an inventory of tasks. 

The CIOs and department leaders identify routine, repeatable processes that humans can pass off to computers. Then the operations and IT teams set up targeted programs to make those tasks more efficient. 

As legendary CIO Paul Strassmann has pointed out — not without controversy — it’s a piecemeal approach that has become standard practice in most businesses. It’s leading CIOs down a path of marginal returns and surprisingly limited innovation. 

Strassmann’s career includes serving as NASA’s CIO from 2001 to 2003 and serving in an equivalent role in the Pentagon before that. As far back as 1998 he has been on record suggesting software should be seen as a storehouse of knowledge and experience in an enterprise — what he calls “knowledge capital.” Software should not be the equivalent of a new forklift.

A new forklift does a job faster and better. But it does not learn or improve with every use. It doesn’t learn how it fits into the workflows of the business where it’s used, or how its work fits with the work of other machines. An even faster and better forklift is eventually bought, and the formerly new forklift is scrapped. All the use put into the scrapped forklift is lost, because obviously the machine never had the ability retain that knowledge capital. Strassman argues too many companies use enterprise technology this way, using it and then replacing it, rather than using it as a store for knowledge capital that becomes smarter and smarter.

That’s true for machine learning as well. It’s used as a tool to make tasks more efficient and faster, but it is not used enough as a store of knowledge capital not only for that task, but for how that task and others fit together, and can fit together better.

CIOs planning their organization’s evolution to machine learning, along with machine learning developers, need to dust off their Strassmann books.

More learning

CIOs should push to empower machines to do more learning, better, ahead of the task. This requires rethinking how machines take in data. Businesses should not think of themselves as a collection of tasks, but rather view their operations as brought to life by streams of data that run through workflows made up of those tasks. The tasks are just the muscles of the corporate body. Data is the blood flow and nervous system.

Focusing on how to turn that data into useful information and unique insights horizontally across the organization, no matter the task, is where CIOs can get a competitive edge and expand the return on machine learning investments. Deploy a smarter system for how data is ingested and interpreted by machines, and it will inevitably introduce greater efficiency and accuracy to the many tasks it touches. The goal is to move from a one to one benefit, to a one to many benefits.

Slow on the uptake

CIOs are having a tough time persuading skeptical business leaders to deploy machine-based intelligence in their organizations, and appropriately so. Enterprise tech marketers say the words “machine learning” very easily. But it’s harder to back those words with sustained, high quality results. Business leaders want more show, less tell.

A recent CFA Institute survey found that in the financial world, only 10% of investment professionals use machine learning. Instead they rely on traditional spreadsheets and desktop data tools. Across industries, only 50% of large businesses have artificial intelligence strategies. About 80% of enterprise businesses that have rolled out artificial intelligence or machine learning projects report stalled progress. And CIOs will continue to have a hard time modernizing their organizations and showing a return on the investment, if the effort remains task oriented.

As a team from Deloitte Australia writes, “if our social and economic systems persist in framing work in terms of tasks completed, and to value labor in terms of its ability to prosecute these tasks — then we can expect AI & ML solutions to continue to be used as they often are today: as cost-cutting enablers, substitutes for humans instead of partners with humans.”

The question should be: How will the entire organization benefit from smarter data systems that pervade across workflows? And if humans are not spending their time collecting and sorting data, what else can they be doing to add value to the organization?

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ClearDATA®, the leader in healthcare public cloud security, compliance and privacy, today expanded their ClearDATA Comply™ Software as a Service (SaaS) compliance management product to include Microsoft’s Azure Cloud Services. With this new addition to the ClearDATA portfolio of products, more healthcare providers, payers and life sciences organizations can adopt Azure’s Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) cloud services while mitigating risk of security vulnerabilities with real-time compliance checks and automated remediation.

By investing deeply in cloud and healthcare compliance expertise, ClearDATA protects sensitive healthcare data in the cloud by strategically mapping critical standards and regulations like HIPAA, GxP and GDPR to relevant technical controls. The company’s flagship SaaS product, Comply is designed to detect and immediately remediate compliance deviations, while providing near real-time reporting with clear, auditable and visual confirmation of the users’ compliance status. This helps healthcare organizations to meet compliance obligations throughout the lifecycle of an application and frees IT assets to invest in the latest cloud technology.

“PHI is extremely sensitive and requires a sophisticated marriage of deep cloud expertise and comprehensive compliance to keep secure. Most healthcare providers and life science organizations do not have the expertise or in house capability to tackle such a complex objective,” said Suhas Kelkar, ClearDATA’s Chief Product Officer. “With Comply, healthcare organizations can leverage the power, flexibility and inherent security of the Azure cloud with even greater confidence – thus accelerating adoption of Azure and increasing the speed of innovation.”

As of today, the solution automatically configures over 70 controls across 32 of the most commonly used Azure services for sensitive patient data (PHI/PII) in healthcare including Azure Kubernetes Service (AKS), Azure Machine Learning as well as PaaS based services like Azure SQL. ClearDATA is a Microsoft Gold Partner and has offered managed security, compliance and privacy solutions on Microsoft Azure since 2015. ClearDATA will further collaborate with Microsoft to support its sensitive data workloads, with plans to add new HIPAA-eligible Azure services to ClearDATA Comply every quarter.

“As digital patient care solutions provide more data to doctors, healthcare organizations are leveraging the Azure cloud to gain insight from their data and transform to a value-based care model focused on improving patient outcomes and experience. With ClearDATA Comply’s availability in the Azure Marketplace, we’ve brought together Microsoft’s strength in cloud services and ClearDATA’s deep understanding of complex healthcare regulations to offer a solution that allows healthcare to innovate and mitigate risk,” said David Houlding, Microsoft’s Director of Healthcare Experiences.

“Platform as a Service (PaaS), machine learning (ML) and other advanced Azure services are driving rapid healthcare innovation,” said ClearDATA Chief Technology Officer and Co-Founder, Matt Ferrari. “ClearDATA sees a need to invest in our longstanding strategic partnership with Microsoft to apply our best security, compliance and privacy practices on top of them.”

The collaboration between ClearDATA and Microsoft Azure is evidenced by the value healthcare organizations such as BehaVR are realizing by implementing ClearDATA Comply for Azure. A provider of VR digital therapeutics and wellness programs for behavioral health, BehaVR uses Comply for Azure to manage and enforce compliance and security of their application. This frees their development team to focus on continued innovation in virtual reality for healthcare versus navigating how to configure a compliant cloud environment.

ClearDATA launched its Comply multi-cloud solution in December 2019 with early availability for Amazon Web Services (AWS). With its expansion to include support for Microsoft Azure, ClearDATA Comply provides a multi-cloud view of compliance within a single pane of glass.

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Security robots are slowly becoming a more common sight in malls, offices, and public spaces. But while these bots are often presented as replacements for human security guards — friendly robots on patrol — they’re collecting far more data than humans could, suggesting they’re more like mobile surveillance machines than conventional guards.

A new report from OneZerosheds some light on the scope of the data collection, featuring marketing material and contracts between Knightscope and various city councils. Both show that the main purpose of these robots is gathering data, including license plates, facial recognition scans, and the presence of nearby mobile devices. It’s the sort of constant low-level surveillance that only a machine can perform.

Exactly what each robot collects differs, as Knightscope leases its bots rather than selling them outright, tailoring each contract to customers’ needs. But it’s a fair bet that if you’ve seen one of these machines in person, it’s recorded your presence in one way or another.

As an internal report by California’s Huntington Park Police Department (HPPD) published by MuckRockbackin August noted, “Knightscope’s secret to the K5 robot is simply sensors — lots of them.”

HPPD started leasing a Knightscope K5 robot to patrol parks and buildings this June, and the robot soon made headlines when a passerby pressed its emergency button to report a nearby fight, to no effect. According to NBC News, the bot ignored the woman and continued moving down its preprogrammed path “humming an intergalactic tune” and pausing to tell visitors to “please keep the park clean.”

Stories like this suggest that, as a replacement for human security guards (people who can respond intelligently and spontaneously to emergency situations), Knightscope’s machines are lacking. But as surveillance devices, they have a lot of potential.

The report from the HPPD notes that the robots can identify nearby smartphones over an unknown range, recording their MAC and IP addresses. In Knightscope marketing material published by OneZero, this is a central part of the company’s sales pitch, with one slide telling customers: “90%+ of Adults Have Smartphones And Use WiFi When Available.” Recording the presence of cellphones is a subtle form of surveillance

Scanning phones is a subtle form of surveillance with a far-reaching impact. It’s not as invasive as identifying someone by name, but it can be a rich source of information, telling you a lot about someone’s daily routine, like how often they visit a certain area and how long they stay there. As Knightscope says, it can also be used as a proxy to keep out unwanted individuals: just create a whitelist of approved devices, and scan for unfamiliar ones.

It’s a job these robots are well-suited to. They’re dogged and consistent, with the patience of a machine. They can run 24 hours a day, have infrared cameras to see in the dark, and are, in a way, are less conspicuous than humans performing similar surveillance duties. A robot might be a novelty the first few times you see it, but machines become invisible, blending into the background while continuing to scoop up data.

Knightscope’s robots certainly aren’t physically capable enough to apprehend wrongdoers. They can’t run down criminals or even navigate stairs. And when they’ve made headlines in the past, it’s usually for some sort of pratfall, like when one of their bots drowned itself in a fountain or when another knocked down a toddler in a mall.

So what are they good for? Knightscope maintains that its robots are essentially supplementary devices, meant to compensate for a lack of personnel, to spot trouble and call the police. But in an age when automated systems are replacing humans in more and more fields (think: algorithms making decisions in areas like hiring and benefits), it’s likely they’ll gradually take on a more prominent role, leaning on their surveillance skills.

As roaming security cameras, they’ll continue to make an impact. As John Santagate, an analyst at IDC, told Recode last year, these robots can’t respond to emergencies, but they can intimidate people. “I use the analogy of the police car parked at the corner,” said Santagate. “Even when no one is in it, people around the car adjust their behavior.”

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Microsoft really wants you to know that its anti-malware, once an industry laggard, is today the best option for Windows 10 machines.

Tanmay Ganacharya, general manager of Microsoft ATP security research, told ZDNet there are sound reasons why its defenses are now the primary antivirus on more than half a billion devices. 

Barely a week goes by without Microsoft posting a new blog boasting about sophisticated attacks that its cloud and on-machine anti-malware systems have stopped. It argues its use of machine-learning detection models succeed where traditional antivirus fails. 

In June, the company recounted how it foiled Astorath malware targeting organizations in Brazil and attempting to evade detection by using tools like the Windows Management Instrumentation Command-line (WMIC). 

The attacks exclusively used legitimate Windows tools to download code that executes only in memory, making it one of a growing number of so-called fileless attacks, since no executable runs on disk. Using legitimate tools – a strategy called ‘living off the land’ – also makes it harder for antivirus to detect. 

More recently, the Microsoft Defender ATP Research team explained how its machine-learning models have been hardened against a specific type of adversarial attack that did work against the detection models used by BlackBerry-owned security company Cylance. 

Microsoft employs a particular ‘monotonic’ machine-learning model that is resistant to gaming by attackers who are stuffing malware with ‘clean’ signals, with the knowledge that most machine-learning malware detection models are trained on a mix of malicious and clean signals. 

A popular method to boost clean signals is to digitally sign files with trusted but fraudulently obtained code-signing certificates. The infamous LockerGoga ransomware that wreaked havoc on metal maker Norsk Hydro earlier this year used this tactic.            

And yesterday the Microsoft Defender ATP team talked up a feature it introduced last year, a ‘hardware-rooted’ virtualization-based security called ‘runtime attestation’. The technique tripped up a kernel-based token-swap attack. The token includes details about the privileges of the user account associated with a process. 

“Token theft attacks are rampant because they can allow adversaries to use access tokens to operate using different user accounts or under different system security contexts to perform malicious actions and evade detection,” explained researchers from Microsoft Defender ATP. 

According to Ganacharya, these investments in machine learning and cloud-based security, plus improvements to its client-side software, have helped drive the share enjoyed by Windows Defender, now known officially known as Microsoft Defender, to over half the Windows ecosystem. 

“Windows Defender already has more than a 50% share in the Windows ecosystem. So that’s more than half a billion machines that are running Windows Defender in an active mode as the primary antivirus. And it has grown pretty significantly and is among the best now.” 

Despite improvements to its own security products, the sheer size of Windows Defender’s market share now threatens to make its machine-learning models a greater target. 

“Windows Defender is protecting more than 50% of the Windows ecosystem, so we’re a big target, and everyone wants to evade us to get the maximum number of victims,” said Ganacharya.  

“We’ve predicted this is going to happen, and this is why we invested in this before it happened.”

But more danger lurks around the corner as advanced techniques used by state-backed hackers, for example, to steal information, filter down to financially-motivated attackers. This threat applies to the continuing growth of fileless malware, supply-chain attacks, and phishing. 

“We’re seeing the trend of advanced techniques being used to deliver commodity malware. Once the advanced technique becomes public knowledge, this next section of actors use it, like Dofoil,” Ganacharya said.  

“It was a coin-miner. It wasn’t trying to steal valuable information, it was just trying to make money by mining coins.”

A massive Dofoil outbreak occurred in early 2018, infecting 400,000 PCs within hoursthrough a popular BitTorrent client. Just over half a year earlier, NotPetya spread rampantly across several global companies, including Maersk and Mondalez, after their Ukraine-based offices installed an update from a widely-used Ukraine accounting software package. 

“Supply-chain attacks are also a really great way to attack because you’re leveraging trusted channels already established in customers’ networks to deliver your payload from. I don’t think we’re past the rise of the supply-chain attack,” said Ganacharya. 

And the one style of attack that isn’t going away any time soon is phishing, which Ganacharya notes is useful when exploitation becomes hard. 

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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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Human civilization has come a long way since the times of using Stone Age and Iron Age implements for hunting, agriculture, mobility and in general, for addressing all aspects of human existence . The basic urge to improve the quality of life by using tools is perhaps hardwired into the human brain as evidenced by many inventions across all the cultures of the world, including primitive societies of today. With the advent of industrial revolution in England in 17th century, machines came to be invented and the power of coal, and later electricity and other sources of energy came to be harnessed for purposes which were until then undertaken by manual or animal labour or even impossible to be done.

One basic characteristic of all human inventions which continue unabated till date has been that though they helped reduce the drudgery of humankind, they were designed to merely obey human commands and often perform repetitive jobs with immense power, precision, unerring accuracy and efficiencies bordering on the unfathomable . In other words, most machines were “dumb” and possessed no “native” intelligence that would enable them to learn, memorize and execute tasks of ambiguity like humans do.

With the exploding scientific, medical knowledge on human brain anatomy , neural networks, and the computational power being made available these days, it is now becoming possible to design machines that think like humans, are self-learning and will possess all the traits that a human brain encompass. But the more important thing is that these machines are embedded with machine learning statistical algorithms that enable the machines to improvise on their tasks by discovering new and better ways of execution, which humans may take more time and intuition to evolve. In the process they can almost completely eliminate the common errors prevalent in human transactions and improve the quality of the job execution manifold. Taken to its conclusion, a fully human like machine in thought, look and action can be a troubling philosophical proposition on what this constitutes to humanity’s own perception of itself, but it opens up exciting possibilities on many fields which hitherto required human intervention at different levels and hence prone for errors.

For example, humanoid robots (machines which resemble humans in look and behaviour) can be designed to undertake surgeries which need be done with precision that are not available to human hands and eyes. Similarly humanoid caretakers can assist with geriatric care in a rapidly aging population and even comfort the lonely and depressed by offering much more humane care and succour. Humanoid pilots can possibly fly airplanes without the rigor of training and wont sleep off on long duration flights and the future armed forces may likely be manned by humanoid “beings” who will carry out war operations on behalf of the country owning them. Mundane operations like banking are no exception and will be completely owned, operated 24*7*365 with little human involvement.

The difference between humans and humanoid “creatures” will blur with the evolution of artificial intelligence and one will never know if the humanoid capabilities may even outstrip the human brain’s capabilities, and one wonders if they will eventually take over the earth from human beings. Overall, the future of artificial intelligence looks both exciting and foreboding. Humankind seems to have turned full circle with machines based on artificial intelligence because it imitates human beings themselves. Only time will tell the course and impact of this technological innovation on the future evolution and existence of humankind.

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The internet changed travel planning for most people years ago, but even a high-powered search engine has its limitations when it comes to plotting out the perfect weekend getaway or lengthy European excursion.

Yes, an online search provides plenty of travel options. In fact, it may be too many, so that the would-be traveler is drownin g in information and tempted to settle for the hammock in the backyard.

Online travel planning can be time consuming and impersonal. Travelers are forced to search through dozens of results before finding an offer that matches their needs.

But add a touch of artificial intelligence — or AI — to that search, and things change. Artificial intelligence is essentially a computer system that can perform tasks that normally would require a human, such as voice recognition, visual perception and decision making.

Bringing AI into travel planning means those searches won’t just pick up random key words. AI can figure out the kind of experience a traveler is really after and home in on the best matches.

In other words, artificial intelligence won’t just help you land the lowest rate for a hotel or assist with airline reservations. It also can provide the inspiration for just what kind of adventure you want.

This isn’t quite like hiring C-3PO as your travel agent, but it’s a step in that direction, minus the shiny humanoid form and British accent. In fact, travel agents themselves are able to rely on AI to give their customers better service.

There are plenty of reasons artificial intelligence will transform the travel-planning process. Here are a few:

Machines can learn. A computer program doesn’t exactly learn the way people do, but over time it does detect patterns in the data it collects and makes adjustments in its “knowledge.” So every time a traveler searches, the AI system gets a little smarter. The result is a recommendation engine that understands travel like an expert, factoring in both context and search intent to the results a traveler gets.

A photo finish. Online searches typically involve keywords that need to match words on websites. But artificial intelligence can “look” at a photograph and match it to what you’re searching for. For example, those artificial intelligence algorithms will figure out that a photo of a couple on a beach matches your interest in a romantic anniversary trip.

AI is conversational. With searches assisted by artificial intelligence, travelers don’t have to type the exact right words to get the desired results. They can search in their own words and AI figures out how to get them what they want.

The travel industry hasn’t always kept pace with technological advances. Part of the reason may be that the avalanche of information a typical online search provides is a pain for consumers, but not for the brands themselves. But hotels, tour operators and travel destinations of all sorts may be missing out on business because of the travel-search overload that becomes a game of “Where’s Waldo” for the traveler.

A lot of brands are figuring out that customers want a better experience in the planning stage — and the vacation-inspiration stage — and want to help them. If they can do that, and provide a more pleasant experience every step of the way, then those customers are going to be a lot happier.

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