Please type the words you see in the image. At some point, we have all completed a captcha to prove we are human when online. So, when a robot successfully completed the test, we were left asking, are our computers secure? Here Jonathan Wilkins, marketing director at obsolete parts supplier EU Automation, explains how machine learning and artificial intelligence (AI) impacts cybersecurity.

A captcha, or Completely Automated Public Turing test to tell Computers and Humans Apart, is designed based on the Turing test. Alan Turing, the founder of modern computing, built a machine that was capable of mimicking human speech in letters, so that outsiders could not distinguish between human and robotic conversations. This machine inspired the field of artificial intelligence, bringing with it security tests to distinguish between humans and machines.

Technology is advancing rapidly, meaning that computers can now solve problems that could only be solved with human intuition traditionally.

But what does a robot beating a captcha have to do with cyber security in manufacturing facilities?

Digitalisation

As manufacturing becomes more digitalised, connected machines collect real-time data that is vital in keeping facilities running at optimum capacity. As more machines become connected thanks to the Internet of Things (IoT), they also become more vulnerable to viruses that can be introduced to the system.

Hacking

The growing use of AI in industry means that manufacturers must do more to secure information. However, manufacturers can look to similar AI technology for help. If it can hack a system by pretending to be human, could it successfully block a similar threat from a human hacker?

Industrial viruses are traditionally introduced from an external source, such as a USB or incoming data file. Both machines and humans will find it difficult to predict how this threat will impact the IT and manufacturing system. However, humans have the upper hand from computers as they can use past experience and knowledge to deal with any system abnormalities.

Robots do not have the same intuition, but advancements in machine learning allow computers to make decisions based on collected data. Each time the machine experiences something new its capabilities will increase.

Security

Some professionals argue that traditional security protocols are reactive and only deal with attacks when they occur. In the past, human hackers have easily broken through barriers such a passwords and firewalls. Now, cyber security companies are offering solutions to this using AI and machine learning technology to introduce more preventative security for manufacturers.

Security company, Darktrace, uses machine learning to create unique patterns of encryption for each machine and detect any abnormalities. The software can then detect emerging threats that may have gone unnoticed and stop them before the damage occurs.

Artificial intelligence is developing rapidly and changing cyber security considerations in manufacturing. It is unclear how much AI will be capable of in the future, but we need to rethink how we distinguish between humans and robots online.

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My spine still shivers when I remember the nuclear stand-off between the Soviet Union and the United States in 1962. As a nine-year-old I felt helpless in the face of two leaders poised to push the button.

It was MAD – mutually assured destruction – but sanity prevailed and by the end of the 1960s we had détente.

In the decades since I have felt comfortable with the dazzling march of technology that has reduced global poverty, given us longer lives, delivered the information superhighway and created my zero-emissions Tesla.

Yes, there are disappointments – the internet, for example, has not raised the calibre of conversation but instead has created echo chambers of bigotry and forums for lies and harassment.

But now for the first time since the 1960s something is tickling my worry beads: artificial intelligence. I fear AI’s capacity to undermine our human rights and civil liberties.

While AI has been in backroom development since the 1950s and increasingly implemented by businesses and government in the past few years, I believe 2018 will go down as the year the AI future arrived.

I am well aware of previous impressive developments such as an AI named AlphaGo beating the world Go champion, but I don’t play Go. I do, however, rely on my executive assistant. So this year, when Google publicly demonstrated a digital assistant named Duplex calling a hairdressing salon to make an appointment for its boss, speaking in a mellow female voice filled with human pauses and colloquialisms, I knew AI had arrived.

Shortly afterwards IBM demonstrated Project Debater arguing an unscripted topic against a skilled human. Some in the audience judged Project Debater the winner.

The simplest definition of AI is computer technology that can do tasks that ordinarily require human intelligence. More formally, AI is the combination of machine learning algorithms, big data and a training procedure. This mimics human intelligence: the combination of innate ability, access to knowledge and a teacher.

Also like humans, when it comes to AI there are the good, the bad and the ugly.

The good: digital assistants, medical AIs to diagnose cancer, satellite navigation that figures out the best way home and systems that somehow know that your credit card has been used fraudulently.

The bad: biases such as that discovered in the COMPAS risk-assessment software used to help judges in the US determine a sentence by forecasting the likelihood of a defendant reoffending. After two years of evaluation COMPAS was found to have overestimated re-offence rates for black defendants and underestimated re-offence rates for white defendants. Every human I know is biased, so why worry when an AI is biased? Because there is a good chance it will be replicated and sold by the millions, thus spreading the bias across the planet.

The ugly: think Orwell’s 1984. Now look at the social credit score in China, where citizens are watched in the streets and monitored at home, losing points for littering or paying their bills late, and as a consequence being denied a bank loan or their right to travel.

So how can we utilise the good but avoid the bad and the ugly? We must actively manage the integration of AI into our human society like we have done with electricity, cars and medicines. Australia can lead the way, as we did for IVF by becoming the first country to collate and report on birth outcomes and the first to publish national ethics guidelines. To capture the benefits and avoid the pitfalls requires a public discussion. In July the Australian Human Rights Commission launched a project on human rights and digital technology. In my keynote speech I finished with the question: “What kind of society do we want to be?”

While the debate unfolds, here a few starting suggestions.

First, adopt a voluntary, consumer-led certification standard for commercial AI akin to the Fairtrade stamp for coffee. I call it the ‘Turing Certificate’, in honour of Alan Turing, the persecuted father of AI. It won’t stop criminals and rogue states but it will help with the smartphones and home assistants we choose to purchase.

Second, adopt the ‘Golden Rule’ proposed by the head of Australia’s Department of Home Affairs, Michael Pezzullo: that no one should be deprived of their fundamental rights, privileges or entitlements by a computer rather than an accountable human.

Third, never forget that AI is not actually human. It is a technology. We made it. We are in charge. Hence I propose the ‘Platinum Rule’: that every AI should have an off switch.

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Artificial intelligence and machines have become a part of everyday life, but that doesn’t mean we understand them well. Do you know the difference between machine learning (ML) and artificial intelligence (AI)?

If you’re hoping to use one or the other in your business, it’s important to know which one to focus on. ML and AI are related, but they aren’t the same, and they aren’t necessarily suited to the same tasks. You can take your business to the next level by knowing when to choose ML or AI.

This guide will walk you through everything you need to know about AI and ML, from what they are to why they’re different. Keep reading to learn how this modern tech can help you and your business.

Machine Learning Vs. Artificial Intelligence: The Basics

Here are two simple, essential definitions of these different concepts.

AI means that machines can perform tasks in ways that are “intelligent.” These machines aren’t just programmed to do a single, repetitive motion — they can do more by adapting to different situations.

Machine learning is technically a branch of AI, but it’s more specific than the overall concept. Machine learning is based on the idea that we can build machines to process data and learn on their own, without our constant supervision.

Let’s take a closer look at what these two concepts really mean and how they developed.

In The Beginning

Needless to say, AI and machine learning are relatively new. The concepts stretch back to certain imaginative individuals from decades, centuries and even millennia ago. But it’s only recently that these dreams became realities.

The concept of AI really solidified with the earliest computers. These first computers weren’t making any decisions on their own, of course. However, they were “logical machines” that were able to remember information and make calculations. The people creating these machines knew that they were working to make a brain-like machine.

However, technology has gotten much more advanced since then, so our ability to make brain-like machines has advanced, too. In the past few decades, we’ve also developed a better understanding of how our own brains actually work.

The more we understand these things, the more the approach to AI changes. Our computers can now make incredibly complex calculations, but developments don’t really focus on those now. Instead, people are seeking to create machines that can make decisions in similar ways to humans and use those decisions to complete tasks.

Types Of AI

There are two major subcategories of AI. The first is applied AI. This is the most common form of AI. It includes everything from intelligent stock-trading systems to automated driving.

Generalized AI is less common because it’s more difficult to create. Ideally, a generalized AI would be capable of handling all kinds of different tasks, just like humans are. Although these AIs aren’t common, many researchers have been making advancements in the generalized AI field.

Most importantly, this subsection is what led to the development of machine learning.

Machine Learning’s Growth

Machine learning has developed thanks to certain breakthroughs in the AI field.

The first breakthrough involved realizing that it was more efficient to teach computers how to learn than to teach them how to perform every possible task and give them the information needed to complete those tasks.

The second major breakthrough was the invention of the internet. This led to a massive potential for information storage that had never been seen before. Machines could now look at amounts of data that they’d never been able to access before due to storage limitations. In fact, the amount of data being created is too much for humans to process.

These two breakthroughs made it clear that instead of teaching machines to do things, a better goal was to design them to “think” for themselves and then allow them access to the mass of data available online so they could learn.

The Role Of Neural Networks

The advent of neural networks became essential for this process of teaching computers to think like humans. Neural networks allow computers to more closely mimic human brains while still being faster, more accurate and less biased.

Neural networks are a type of computer system that’s made to classify information like our own brains do. For example, a neural network can look at pictures, recognize the elements in them and classify them according to what they show.

These networks use the data they have access to make determinations. The data doesn’t allow them to be perfectly accurate, but they can make decisions based on what’s most likely to be right.

Most importantly, these systems involve a feedback loop for “learning.” The machine can find out whether or not its decisions were right, and then change its approach to do better next time.

What Can Machine Learning Do?

The possibilities of these systems seem almost endless.

Already, ML allows computers to look at text and determine whether the content is positive or negative. They can figure out if a song is more likely to make people sad than happy. Some of these machines can even make their own compositions with themes that are based on a piece they’ve listened to.

One major application of machine learning is in communication with people. The field of AI called natural language processing heavily uses machine learning. This will someday allow companies to offer automated customer service that’s just as useful as human customer support.

Machine Learning Vs. Artificial Intelligence: Which Is Right For You?

Both AI and ML can have valuable business applications. Determining which one is best for your company depends on what your needs are.

These systems have many great applications to offer, but ML has gotten much more publicity lately, so many companies have focused on that source of solutions. However, AI can also be useful for many simpler applications that don’t require ongoing learning.

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While it’ll be awhile before we all have an IBM Watson Supercomputer on our desks, there are a number of artificial intelligence business tools that property and casualty insurers and insurance professionals can use right now to run smarter, faster — and ahead of the competition.

“The emergence of AI coupled with big data and massive computing power has enabled us to take a different view of our processes,” says Justin Tomczak, CFA, media relations, State Farm. Consider, he added, the “thousands of photos of damage that we receive every day become an amazing set of data for AI to learn from and to serve our customer’s more effectively and efficiently. ”

Jeremiah Bentley, vice president, marketing and customer engagement, Texas Mutual agrees that AI has the power to transform insurance. “We think the greatest potential in the use of artificial intelligence tools in the industry is the opportunity to improve the customer experience, and by extension, the overall impression that the consumer has about the insurance industry,” he says.

Great customer engagement, adds Kevin Kelley, divisional senior vice president, Great American Insurance Group, is what separates today’s disrupters from disrupted in insurance.

“The innovators behind disruptors in the insurance industry are using digital technology, data analytics, artificial intelligence and machine learning to produce a better, faster, and more efficient customer experience,” Kelley says.

Essentially, these next generation AI wonders tap into the technology’s ability to do a lot of the thinking and strategizing for insurers and agents.

Keep reading for a sampling of what the future of insurance business software may look like for insurers.

No. 5: AI app makers

Insurance professionals who want to start dabbling in artificial intelligence right now — for free — might look to such open-source software products as Datumbox. Targeted to businesses with one or more programmers on staff — or an extremely brave PC power-user — Datum is an AI platform that enables the user to design and build their own AI apps from scratch.

Some of the specific tools you can create with Datumbox include:

AI Sentiment Analyzers:  These tools enable you to unleash an app on the Web, social media and similar digital locations that will see what people are saying about your company and/or products and services — and also determine if the sentiments behind those posts are positive, negative or neutral.

AI Text Readability Analysis:  This tool can be used to ensure the marketing copy for your insurance business is extremely accessible — or conversely, appeals to a more discriminating audience.

AI  Gender Analysis:  Whether its soaring praise or withering criticism, this tool will enable you to determine whose behind posts about your company — a man or a woman.

No. 4: AI dashboard maker

Qlik enables your insurance business to develop AI dashboards that can monitor dozens, hundreds — or even thousands — of web sites and/or web properties across cyberspace, and then bring back all that data for instant analysis.

With Qlik, you’ll be able to compare and contrast the performance of all your Web sites in terms of clicks, visits, purchases, successful calls-to-action, and more. Plus, the software promises to bring back associations and insights you may not have thought to consider.  Similar products include Metric Insights  and Tableau.

No. 3: AI self-designing websites

Grim fact: Not all of us are Da Vinci’s in the making.

Fortunately, with Grid — an online service that will auto-design a web site for your insurance business — that doesn’t matter anymore.

This tools allows users to simply upload the content you want on your web site — text, images and video — and the service does the rest, placing everything just where it’s supposed to go. Once all your components are in place, you also have the ability to tweak the resulting design.  You can get an in-depth look at how Grid works with its introductory video (56 minutes) on YouTube. Wix offers a similar online service.

No. 2: AI call center matchmaker

Any insurance business exec who has winced listening to a call center rep clashing with a customer will want to look into Affinti.

Designed to find ‘birds-of-a-feather’ personality matches between your call center reps and your customers, Affiniti processes more than one billion calculations-a-second in its never-ending quest to sniff out the personality of anyone who happens to be calling your business.

Essentially, the AI software works by retrieving, storing and analyzing psychographic and demographic data on customers across the U.S., which it sources from the world’s identity data brokers, including Allant, Axciom, Experian, Facebook, LinkedIn and Targus.

Specific data Affiniti is incessantly gobbling up includes income level, credit card  usage,  profession,  gender,  telecommunication  usage  patterns, responsiveness to marketing, political persuasion and travel habits.

Most likely, it also knows if your toenails need trimming.

Meanwhile, Affiniti analyzes the other side of the equation — the personalities of the call center reps at your insurance business — by studying how your reps interact with customers over a 60-90 day period, and by crunching data from a 20-minute survey that you can administer to your call center reps when they’re first hired.

The result: In a perfect world, you get a match made in bits-and-bytes heaven that hopefully will result in a better customer service experience and perhaps heavier sales.

No. 1: AI early warning lawsuit alerts

When it comes to lawsuits, the only thing better than an attorney who strikes sheer terror in the opposition is one who can scope-out potential lawsuits before they happen — and steer you clear of any trouble.

That’s the premise behind Intraspexion, ingenious lawsuit-prevention software developed by seasoned attorney Nick Brestoff.

Intraspexion works by relentlessly analyzing every single email your employees send or receive from the outside world, and then studying those emails for telltale signs of trouble ahead.

As soon as it finds an email it believes could be the start of an impending lawsuit, it instantly alerts your attorney or in-house counsel, requesting human intervention.

According to the company’s founder, Nick Brestoff, Intraspexion’s accuracy had been verified by a third party source at 99%.

Interestingly, Intraspexion is built on Google TensorFlow — a free, open source, deep learning software developed by researchers and engineers on the Google Brain Team.

“TensorFlow is quickly becoming a viable option for companies interested in deploying deep learning,” says Rajat Monga, engineering leader, TensorFlow at Google.

Currently, Brestoff’s software — which is being pilot-tested by a New York Stock Exchange level company — is only programmed to analyze employee emails for potential employee discrimination suits, simply because those suits are among the most common.

But Brestoff says he can easily rework his code for insurers to do the same kind of monitoring for breach-of-contact suits, fraud suits and more than 150 other categories of lawsuits that businesses must dodge every day.

Other AI tools on the horizon

“There are a variety of potential use cases that span improved customer service; risk, price, fraud, demand modeling; improved underwriting practices; identifying behavioral insights and driving optimized segmentation,” says Tim Cunningham, CIO, Grange Insurance.  “The capabilities exist today and the barriers to experiment and learn continue to diminish.”

John Tramonti, AVP, product implementation, MetLife Auto & Home, agrees:  “Artificial intelligence — specifically cognitive technologies — has the potential to redefine both how we interact with consumers and support our workforce.”

Concludes Bill Bloom, executive vice president, Operations, Technology & Data, The Hartford on the coming Age of AI:  “In the near-term, robotics and natural language processing offer the greatest opportunities within service operations and claims, but it’s clear that the value will soon be felt in areas such as underwriting, actuarial and finance.

“The vendor landscape for these tools is broad and evolving rapidly. Therefore, we’ve architected our solutions to allow us to swap software providers over time as the market changes. ”

Adds Luyang Fu, vice president, predictive analytics, The Cincinnati Insurance Company: “My impression is that property casualty insurers are working to integrate these AI tools into smaller projects first, and building up to full integration. Artificial intelligence tools will be an essential part of many operations, but it will take time.”

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ST. LOUIS — Big Data in agriculture isn’t really new. The technology used to collect it may be, but common sense still rules its use.

Steve Sonka realized the value of ag data during his childhood on the family farm in Iowa, where he milked cows and fed chickens. He learned early on to reward cows that filled buckets with milk with an extra scoop of grain.

At the same time, some of the 100 laying hens in the chicken house weren’t producing the same as others. But there was no feasible way of adjusting feed for them, since it was impossible to determine which ones were the under-producers. Developing a system to segregate the birds in order to adjust feed would not have been practical.

“Just because technology exists, that doesn’t mean it’s economical to use it,” Sonka said at an ag information conference here. “That should permeate what we understand about technology.”

Sonka left the farm and became an economist, now serving as emeritus chaired professor of agricultural strategy at the University of Illinois. He also has a post at the University of Maryland and is founder of an ag consulting company.

In agriculture and other industries, data quality carries a high cost. But the adoption of digital knowledge and communication offers farmers an opportunity to be more efficient, even with incomplete information.

“Perfect data is very expensive,” Sonka said.

“One of the things that big data does is it lets us make inferences from less-than-perfect data. That’s important, because less-than-perfect data is less expensive.”

Digital technology has dramatically streamlined business the world over, inside and outside agriculture.

One example is the airline reservation system. In the past, a traveler would contact a travel agent who would contact an agent at an airline, who would then enter the data on a computer.

An executive at American Airlines got the idea of having the travel agent type in the information directly, saving time and eventually making many data-entry jobs unnecessary. Now consumers enter the information themselves online.

The advent of digital data in the business world has turned economic models upside-down. It is no longer a zero-sum game. And incomplete information still has value.

“In basic economics, if I give you an ice cream cone, you’re happy, but I’m not. But if I give you a copy of data on a USB device, I’m still happy,” Sonka said.

“… When data is digitized, it becomes free. If you use it for one purpose, it’s free to use for another purpose. But just because we capture data, not always is it economical. We still need to be driven by whether we’re using it to make positive decisions.”

Sonka said analytics is key, and that process is different today and “gives us more power than it used to.”

“We’ve always used the term Garbage In, Garbage Out. That’s not necessarily the case today,” he said.

Sonka has served in a consulting role in Australia, where he has seen the value of data used to increase efficiency.

Ranchers who graze cattle on large paddocks have always used weather observations to make decisions about when to move them off a pasture. Satellite technology has provided more precise information, saving the ranchers tens of thousands of dollars, in some cases.

“They’re using satellite information to understand the vitality of grass,” Sonka said.

“They are making individual animal observations, linking those to the vitality of grass and using that information to decide when to make this important decision — when to move the animals.

“One farmer says it makes him $40,000 a year in net profit, even though he may not have complete information.”

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Imagine that tomorrow, some smart kid invented a technology that let people or physical goods pass through walls, and posted instructions for how to build it cheaply from common household materials. How would the world change?

Lots of industries would probably become more productive. Being able to walk through walls instead of being forced to use doors would make it easier to navigate offices, move goods in and out of warehouses and accomplish any number of mundane tasks. That would give the economy a boost. But the negative might well outweigh the positive. Keeping valuables under lock and key would no longer work. Anyone could break into any warehouse, bank vault or house with relative ease. Most of the methods we use to keep private property secure rely on walls in some ways, and these would be instantly made ineffective. Thieves and home invaders would run rampant until society could implement alternative ways of keeping out intruders. The result might be an economic crash and social chaos.

This demonstrates a general principle — technological innovations are not always good for humanity, at least in the short term. Technology can create negative externalities — a economics term for harm caused to third parties. When those externalities outweigh the usefulness of the technology itself, invention actually makes the world worse instead of better — at least for a while.

Machine learning, especially a variety known as deep learning, is arguably the hottestnew technology on the planet. It gives computers the ability to do many tasks that only humans were able to perform — recognize images, drive cars, pick stocks and lots more. That has made some people worried that machine learning will make humans obsolete in the workplace. That’s possible, but there’s a potentially bigger danger from machine learning that so far isn’t getting the attention it deserves. When machines can learn, they can be taught to lie.

Human beings can doctor images such as photographs, but it’s laborious and difficult. And faking voices and video is beyond our capability. But soon, thanks to machine learning, it will probably be possible to easily and quickly create realistic forgeries of someone’s face and make it seem as if they are speaking in their own voice. Already, lip-synching technology can literally put words in a person’s mouth. This is just the tip of the iceberg — soon, 12-year-olds in their bedrooms will be able to create photorealistic, perfect-sounding fakes of politicians, business leaders, relatives and friends saying anything imaginable.

This lends itself to some pretty obvious abuses. Political hoaxes — so-called “fake news” — will spread like wildfire. The hoaxes will be discovered in short order — no digital technology is so good that other digital technology can’t detect the phony — but not before it puts poisonous ideas into the minds of people primed to believe them. Imagine perfect-looking fake video of presidential candidates spouting racial slurs, or admitting to criminal acts.

But that’s just the beginning. Imagine the potential for stock manipulation. Suppose someone releases a sham video of Tesla Inc. Chief Executive Officer Elon Musk admitting in private that Tesla’s cars are unsafe. The video would be passed around the internet, and Tesla stock would crash. The stock would recover a short while later, once the forgery was revealed — but not before the manipulators had made their profits by short-selling Tesla shares.

This is far from the most extreme scenario. Imagine a prankster creating a realistic fake video of President Donald Trump declaring that an attack on North Korean nuclear facilities was imminent, then putting the video where the North Koreans can see it. What are the chances that North Korea’s leadership would realize that it was a fraud before they were forced to decide whether to start a war?

Those who view these extreme scenarios as alarmist will rightfully point out that no fake will ever be undetectable. The same machine learning technology that creates forgeries will be used to detect them. But that doesn’t mean we’re safe from the brave new world of ubiquitous fakes. Once forgeries get so good that humans can’t detect them, our trust in the veracity of our eyes and ears will forever vanish. Instead of trusting our own perceptions, we will be forced to place our trust in the algorithms used for fraud detection and verification. We evolved to trust our senses; switching to trust in machine intelligence instead will be a big jump for most people.

That could be bad news for the economy. Webs of trade and commerce rely on trust and communication. If machine learning releases an infinite blizzard of illusions into the public sphere — if the walls that evolution built to separate reality from fantasy break down — aggregate social trust could decrease, hurting global prosperity in the process.

For this reason, the government should probably take steps to penalize digital forgery pretty harshly. Unfortunately, the current administration seems unlikely to take that step, thanks to its love of partisan news. And governments like Russia’s seem even less likely to curb the practice. Ultimately, the combination of bad government with powerful new technology represents a much bigger danger to human society than technology alone.

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