There are myriad issues facing the global patent system which, if not addressed, could lead to a decline in its use.  Put simply, there is way too much data for humans to properly digest.  In this month’s Clarivate Analytics guest piece, Ed White – director of IP analytics at the firm – argues that a closer focus on artificial intelligence could help to solve this existential problem.

The patent system today is challenged as never before. The requirements of novelty and non-obviousness are becoming increasingly difficult to meet and determine with any great level of certainty.  The causes for this are well known.

The volume of information is vast and growing. Last month saw the publication of US patent number 10,000,000.  It joins over 100 million other patent documents, 70 million plus journal articles and over four billion indexed web pages in the corpus of information that potentially needs to be searched to establish novelty.  And the pace is not slackening. It took 122 years to issue the one millionth patent in 1911. It took just over three years to go from nine million to 10 million.

While that growth reflects the increasing ingenuity of civilisation in finding solutions to today’s myriad technical problems, big and small, it comes with a problem. The patent system is based on an exchange – exclusivity, in return for transparency and the benefit of all. While digitisation has meant that most of the world’s patents are readily available, when they come as a firehose, as they do today, we run into some trouble.

Secondly, for some time we have operated in a world where the majority of new inventions don’t have any detail in English. Of the 5.6 million patent documents published globally in 2017, over 62% are in Chinese, Japanese or Korean, often with no English language equivalent.  This too is problematic. It means that the working language of innovation is essentially robbed of the open disclosure intended when patent exclusivity is given out.

Thirdly, innovation is increasingly taking place at the intersection of different technologies and understanding that innovation requires more and more highly complex and specialist knowledge.  The information in patents is complicated. Its value is hidden behind a barrier of required or expected knowledge.

These issues taken together challenge the utility of the patent system. If not addressed, they could potentially devalue it and lead to decline in its use.  There is simply way too much data for a human to read, analyse and understand.  It is clear we need help.  Step up artificial intelligence (AI).

What is AI?

The generally accepted definition of AI is the demonstration of intelligence by machines. More commonly, it’s a term that is used when we use a machine to mimic cognitive human functions such as learning and problem-solving.

Over time, there has been a lot of research done in the areas of mathematics, cognitive science and other areas, but AI really took off in the late 1990s with the advent of computing technology. This allowed AI to make great advances and to result in some tangible applications in various areas.

AI is enabled through machine learning. Training a machine learning system with a pre-determined focused data set provides the computer with the ability to continue learning without being pre-programmed. Machine learning provides algorithms that learn from data and create foresights based on that data.  By using machine learning, AI is able to use learning from a data set to solve problems and give relevant recommendations.

How is AI currently being used?

As applications which demonstrate artificial intelligence become widely adopted, the tendency is to forget that they are actually a part of AI.  Machine translation is an example.

We take for granted that we can copy and paste foreign language text and get back a passable translation instantly, but the machine is truly demonstrating a level of intelligence in performing that feat.  The technology is already being used to address the language issue in patents – specialist machine translation engines are already good and getting much better daily.

Natural language processing is another part of AI that has great potential to help address the twin challenges of volume and complexity.  According to Tom Fleischman, Master Inventor at IBM:

“I believe we must begin to train AI machines to ingest, digest, understand and analyse the tremendous amount of data and to provide insights. This is not necessarily to give us the answer, but to provide insights that help reach towards an answer.  The insights provided should be used as a guide – in this sense, at IBM we call AI ‘augmented Intelligence’ rather than artificial intelligence.  It shouldn’t be used to replace human thinking – it’s meant to augment human thinking.  Think of it as a co-worker.”

Another area where machines have already proved immensely helpful is semantic search, where a section of natural language text is used to search against a data set and return relevance ranked results.  There are various approaches to achieve this – ranging from deep learning techniques and artificial neural networks to Bayesian networks and latent semantic analysis and indexing – but a useful model to look at is the so-called Vector Space Model.

This allows us to take a set of documents and to show the similarity between them by mapping them in multidimensional space.  This is done by considering the terms within the documents and calculating a vector value which places the document at a specific point in space.  The query is then treated the same way as a document and a vector value calculated for the query, and then the vector distance between the query and documents tells us how closely related the answers are to the query. This allows us to rank documents in the dataset – the closer the vector value of the document is to the query, the more highly relevant the answer is to the query.

Vector space model representation, Michael Preminger

Semantic search shows great promise in being able to navigate vast data sets and return useful and relevant results quickly.

How might AI be used in the future?

As the volume and complexity of patents and the challenge of understanding not just natural language, but patent language continues to increase, AI will be increasingly important in helping to manage the problems and ensure a healthy future for the patent system.

Patent offices themselves are looking to embrace AI as part of the future for their patent, trademark and design application workflow solutions. The Japan Patent Office has announced publicly that it is investing in the use of artificial intelligence technology to automate processes such as screening patent, trademark and design applications.

And just last month, at a meeting in New Orleans of the IP5 patent offices (China, Europe, Japan, Korea, USA), the impact of AI on the patent system was identified as “one of the main IP5 strategic priorities to be the subject of common reflection”.

More speculatively, if we can move towards algorithmically enhanced patentability searching, how long is it until we see the regulatory environment around patents embrace AI techniques – in examination, for example? Or how about automated patent drafting? After all, patent claims and patentability exist within a rules environment.   And if the machine can draft a patent, how about automated inventing? Why not just have the computer create the invention by mining the technology for potential solutions – an algorithm that’s really good at knowing everything that’s been previously invented and potentially inventing on the fly.

These last points open a window on the future, on regulatory and, likely, judicial challenges that await when, due to the scale of our global innovation culture, the patent system itself responds in an automated way. How does the judicial background on obviousness stand up when the “person” skilled in the art is no longer a person, but an algorithm? How does a patent application aimed at an augmented intelligence examination procedure differ from what we have today?

Whatever the future holds for the patent system, it’s a sure bet that AI will be a significant part of it.

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DISCOVERING and harnessing fire unlocked more nutrition from food, feeding the bigger brains and bodies that are the hallmarks of modern humans. Google’s chief executive, Sundar Pichai, thinks his company’s development of artificial intelligence trumps that. “AI is one of the most important things that humanity is working on,” he told an event in California earlier this year. “It’s more profound than, I don’t know, electricity or fire.”

Hyperbolic analogies aside, Google’s AI techniques are becoming more powerful and more important to its business. But its use of AI is also generating controversy, both among its employees and the wider AI community.

One recent clash has centred on Google’s work with America’s Department of Defence (DoD). Under a contract signed in 2017 with the DoD, Google offers AI services, namely computer vision, to analyse military images. This might well improve the accuracy of strikes by military drones. Over the past month or so thousands of Google employees, including Jeff Dean, the firm’s AI chief, have signed a petition protesting against the work; at least 12 have resigned. On June 1st the boss of its cloud business, Diane Greene, conceded to those employees that the firm would not renew the contract when it expires next year.

The tech giant also published a set of seven principles which it promises will guide its use of AI. These included statements that the technology should be “socially beneficial” and “built and tested for safety”. More interesting still was what Google said it would not do. It would “proceed only where we believe that the benefits substantially outweigh the risks,” it stated. It eschewed the future supply of AI services to power smart weapons or norm-violating surveillance techniques. It would, though, keep working with the armed forces in other capacities.

Google’s retreat comes partly because its AI talent hails overwhelmingly from the computer-science departments of American universities, notes Jeremy Howard, founder of Fast.ai, an AI research institute. Many bring liberal, anti-war views from academia with them, which can put them in direct opposition with the firm in some areas. Since AI talent is scarce, the firm has to pay heed to the principles of its boffins, at least to some extent.

Military work is not the only sticking-point for Google’s use of AI. On June 7th a batch of patent applications made by DeepMind, a London-based sister company, were made public. The reaction was swift. Many warned that the patents would have a chilling effect on other innovators in the field. The patents have not yet been granted—indeed, they may not be—but the request flies in the face of the AI community’s accepted norms of openness and tech-sharing, says Miles Brundage, who studies AI policy at the University of Oxford. The standard defence offered on behalf of Google is that it does not have a history of patent abuse, and that it files them defensively in order to protect itself from future patent trolls. DeepMind’s patent strategy is understood to be chiefly defensive in nature.

Whatever Google’s intent, there are signs that the homogeneity of the AI community may lessen in future. New paths are being created to join the AI elite, other than a PhD in computer science. Hopefuls can take vocational courses offered by firms such as Udacity, an online-education firm; the tech giants also offer residencies to teach AI techniques to workers from different backgrounds. That might just lead to a less liberal, less vocal AI community. If so, such courses might serve corporate interests in more ways than one.

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“Artificial Intelligence” is currently the hottest buzzword in tech. And with good reason – after decades of research and development, the last few years have seen a number of techniques that have previously been the preserve of science fiction slowly transform into science fact.

Already AI techniques are a deep part of our lives: AI determines our search results, translates our voices into meaningful instructions for computers and can even help sort our cucumbers (more on that later). In the next few years we’ll be using AI to drive our cars, answer our customer service enquiries and, well, countless other things.

But how did we get here? Where did this powerful new technology come from? Here’s ten of the big milestones that led us to these exciting times.

Getting the ‘Big Idea’

The concept of AI didn’t suddenly appear – it is the subject of a deep, philosophical debate which still rages today: Can a machine truly think like a human? Can a machine be human? One of the first people to think about this was René Descartes, way back in 1637, in a book called Discourse on the Method. Amazingly, given at the time even an Amstrad Em@iler would have seemed impossibly futuristic, Descartes actually summed up some off the crucial questions and challenges technologists would have to overcome:

“If there were machines which bore a resemblance to our bodies and imitated our actions as closely as possible for all practical purposes, we should still have two very certain means of recognizing that they were not real men.”

He goes on to explain that in his view, machines could never use words or “put together signs” to “declare our thoughts to others”, and that even if we could conceive of such a machine, “it is not conceivable that such a machine should produce different arrangements of words so as to give an appropriately meaningful answer to whatever is said in its presence, as the dullest of men can do.”

He then goes on to describe the big challenge of now: creating a generalised AI rather than something narrowly focused – and how the limitations of current AI would expose how the machine is definitely not a human:

“Even though some machines might do some things as well as we do them, or perhaps even better, they would inevitably fail in others, which would reveal that they are acting not from understanding, but only from the disposition of their organs.”

So now, thanks to Descartes, when it comes to AI, we have the challenge.

The Imitation Game

The second major philosophical benchmark came courtesy of computer science pioneer Alan Turing. In 1950 he first described what became known as The Turing Test, and what he referred to as “The Imitation Game” – a test for measuring when we can finally declare that machines can be intelligent.

His test was simple: if a judge cannot differentiate between a human and a machine (say, through a text-only interaction with both), can the machine trick the judge into thinking that they are the one who is human?

Amusingly at the time, Turing made a bold prediction about the future of computing – and he reckoned that by the end of the 20th century, his test will have had been passed. He said:

“I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about [1GB], to make them play the imitation game so well that an average interrogator will not have more than 70 percent chance of making the right identification after five minutes of questioning. … I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.”

Sadly his prediction is a little premature, as while we’re starting to see some truly impressive AI now, back in 2000 the technology was much more primitive. But hey, at least he would have been impressed by hard disc capacity – which averaged around 10GB at the turn of the century.

The first Neural Network

“Neural Network” is the fancy name that scientists give to trial and error, the key concept unpinning modern AI. Essentially, when it comes to training an AI, the best way to do it is to have the system guess, receive feedback, and guess again – constantly shifting the probabilities that it will get to the right answer.

What’s quite amazing then is that the first neural network was actually created way back in 1951. Called “SNARC” – the Stochastic Neural Analog Reinforcement Computer – it was created by Marvin Minsky and Dean Edmonds and was not made of microchips and transistors, but of vacuum tubes, motors and clutches.

The challenge for this machine? Helping a virtual rat solve a maze puzzle. The system would send instructions to navigate the maze and each time the effects of its actions would be fed back into the system – the vacuum tubes being used to store the outcomes. This meant that the machine was able to learn and shift the probabilities – leading to a greater chance of making it through the maze.

It’s essentially a very, very, simple version of the same process Google uses to identify objects in photos today.

The first self-driving car

When we think of self-driving cars, we think of something like Google’s Waymo project – but amazingly way back in 1995, Mercedes-Benz managed to drive a modified S-Class mostly autonomously all the way from Munich to Copenhagen.

According to AutoEvolution, the 1043 mile journey was made by stuffing effectively a supercomputer into the boot – the car contained 60 transputer chips, which at the time were state of the art when it came to parallel computing, meaning that it could process a lot of driving data quickly – a crucial part of making self-driving cars sufficiently responsive.

Apparently the car reached speeds of up to 115mph, and was actually fairly similar to autonomous cars of today, as it was able to overtake and read road signs. But if we were offered a trip? Umm… We insist you go first.

Switching to statistics

Though neural networks had existed as a concept for some time (see above!), it wasn’t until the late 1980s when there was a big shift amongst AI researchers from a “rules based” approach to one instead based on statistics – or machine learning. This means that rather than try to build systems that imitate intelligence by attempting to divine the rules by which humans operate, instead taking a trial-and-error approach and adjusting the probabilities based on feedback is a much better way to teach machines to think. This is a big deal – as it is this concept that underpins the amazing things that AI can do today.

Gil Press at Forbes argues that this switch was heralded in 1988, as IBM’s TJ Watson Research Center published a paper called “A statistical approach to language translation”, which is specifically talking about using machine learning to do exactly what Google Translate works today.

IBM apparently fed into their system 2.2 millions pairs of sentences in French and English to train the system – and the sentences were all taken from transcripts of the Canadian Parliament, which publishes its records in both languages – which sounds like a lot but is nothing compared to Google having the entire internet at its disposal – which explains why Google Translate is so creepily good today.

Deep Blue beats Garry Kasparov

Despite the shift in focus to statistical models, rules-based models were still in use – and in 1997 IBM were responsible for perhaps the most famous chess match of all time, as it’s Deep Blue computer bested world chess champion Garry Kasparov – demonstrating how powerful machines can be.

The bout was actually a rematch: in 1996 Kasparov bested Deep Blue 4-2. It was only in 1997 the machines got the upper hand, winning two out of the six games outright, and fighting Kasparov to a draw in three more.

Deep Blue’s intelligence was, to a certain extent, illusory – IBM itself reckons that its machine is not using Artificial Intelligence. Instead, Deep Blue uses a combination of brute force processing – processing thousands of possible moves every second. IBM fed the system with data on thousands of earlier games, and each time the board changed with each movie, Deep Blue wouldn’t be learning anything new, but it would instead be looking up how previous grandmasters reacted in the same situations. “He’s playing the ghosts of grandmasters past,” as IBM puts it.

Whether this really counts as AI or not though, what’s clear is that it was definitely a significant milestone, and one that drew a lot of attention not just to the computational abilities of computers, but also to the field as a whole. Since the face-off with Kasparov, besting human players at games has become a major, populist way of benchmarking machine intelligence – as we saw again in 2011 when IBM’s Watson system handily trounced two of the game show Jeopardy’s best players.

Siri nails language

Natural language processing has long been a holy grail of artificial intelligence – and crucial if we’re ever going to have a world where humanoid robots exist, or where we can bark orders at our devices like in Star Trek.

And this is why Siri, which was built using the aforementioned statistical methods, was so impressive. Created by SRI International and even launched as a separate app on the iOS app store, it was quickly acquired by Apple itself, and deeply integrated into iOS: Today it is one of the most high profile fruits of machine learning, as it, along with equivalent products from Google (the Assistant), Microsoft (Cortana), and of course, Amazon’s Alexa, has changed the way we interact with our devices in a way that would have seemed impossible just a few years earlier.

Today we take it for granted – but you only have to ask anyone who ever tried to use a voice to text application before 2010 to appreciate just how far we’ve come.

The ImageNet Challenge

Like voice recognition, image recognition is another major challenge that AI is helping to beat. In 2015, researchers concluded for the first time that machines – in this case, two competing systems from Google and Microsoft – were better at identifying objects in images than humans were, in over 1000 categories.

These “deep learning” systems were successful in beating the ImageNet Challenge – think something like the Turing Test, but for image recognition – and they are going to be fundamental if image recognition is ever going to scale beyond human abilities.

Applications for image recognition are, of course, numerous – but one fun example that Google likes to boast about when promoting its TensorFlow machine learning platform is sorting cucumbers: By using computer vision, a farmer doesn’t need to employ humans to decide whether vegetables are ready for the dinner table – the machines can decide automatically, having been trained on earlier data.

GPUs make AI economical

One of the big reasons AI is now such a big deal is because it is only over the last few years that the cost of crunching so much data has become affordable.

According to Fortune it was only in the late 2000s that researchers realised that graphical processing units (GPUs), which had been developed for 3D graphics and games, were 20-50 times better at deep learning computation than traditional CPUs. And once people realised this, the amount of available computing power vastly increased, enabling the the cloud AI platforms that power countless AI applications today.

So thanks, gamers. Your parents and spouses might not appreciate you spending so much time playing videogames – but AI researchers sure do.

AlphaGo and AlphaGoZero conquer all

In March 2016, another AI milestone was reached as Google’s AlphaGo software was able to best Lee Sedol, a top-ranked player of the boardgame Go, in an echo of Garry Kasparov’s historic match.

What made it significant was not just that Go is an even more mathematically complex game than Chess, but that it was trained using a combination of human and AI opponents. Google won four out of five of the matches by reportedlyusing 1920 CPUs and 280 GPUs.

Perhaps even more significant is news from last year – when a later version of the software, AlphaGo Zero. Instead of using any previous data, as AlphaGo and Deep Blue had, to learn the game it simply played thousands of matches against itself – and after three days of training was able to beat the version of AlphaGo which beat Lee Sedol 100 games to nil. Who needs to teach a machine to be smart, when a machine can teach itself?

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Digital transformation, the notion of harnessing advances in data science to create value, is being reimagined through applications of artificial intelligence (AI). Focusing AI on the field of intelligent content analytics, the world’s largest banks, technology companies and life sciences organizations are taking it out of the lab and putting it into the hands of business to reduce risk, maintain compliance, increase profitability and optimize faster.

While AI is not—at least yet—a precise augur for predicting the unknown, companies now use advanced AI techniques such as machine learning to cogently search, codify, reveal and extract the hidden economic and operational data that is contained within hundreds of thousands of documents, especially contracts. A system can even be designed to teach itself once it has enough examples.

This use of AI to automate traditionally mundane, manual work and reveal future, actionable insights is where it is making perhaps its most remarkable impact. In one survey of professionals from 14 different sectors, conducted by employment specialist Emolument, jobs in the legal profession were voted the dullest in the world with 8 of 10 legal pros indicating that they are are bored. Similarly, 60 to 70 percent of experts in financial services, banking, sales and operations indicated that their jobs are tedious and unexciting.

What they all have in common is the dull repetitiveness of document review such as researching cases and rulings, reading contracts, and analyzing impenetrable blocks of text. So, let’s get those paralegals, operational experts and procurement specialists riveted to their work by helping them eliminate monotony and reveal the future using AI.

At the fundamental level, for example, data from sell-side contracts can be exposed to highlight which customer contracts have volume pricing language, which are coming up for renewal, which have license limitations and so on. On a more enhanced level, combining contractual data with data from other enterprise applications can reveal significant opportunities for cross-sell and up-sell.

Doing this with thousands of contracts has been well-nigh impossible in the past, and secondary to ensuring compliance and risk mitigation. But the latest capabilities of intelligent content analytics (ICA) technology, driven by AI, is giving birth to real digital transformation of the enterprise.

So, what exactly is intelligent content analytics? According to Jim Lundy, lead analyst of Silicon Valley-based Aragon Research, ICA refers to the use of analytics to derive insights from content “where the text or a higher-level abstraction of meaning—called a concept—has been organized in a model that can be mechanically processed.” He sees this as a ‘third era’ in unstructured content, where the focus has shifted from storing and tracking data to extracting and analyzing it.

This shift evolves from what has traditionally been called enterprise content management (ECM), which has been around for nearly two decades but is no longer sufficient. Web content management, document management and digital asset management morphed into singular enterprise-wide platforms to handle nearly all types of unstructured content. Mostly they had a bias, with some focused on the regulatory nature of critical content with capabilities like auditing, workflow and security as their strengths, while others focused on managing website content and the creation, workflow and approval processes around it.

ECM has universally relied on metadata to categorize content and understand the relationships between unstructured data in documents, and to describe the main features of the content objects themselves. However, ECM systems provided extremely limited intelligence into what these content objects contain, and virtually no level of analysis was being applied to them. That is, what the words in the documents said, what the sentiment might be, and what those words meant for the owner of those objects.

With the development of AI and machine learning models, and the massive recent improvements in computer processing power, it is now feasible to apply analytics to hundreds of thousands of pieces of content in parallel, and to extract the cognizant information hidden inside.

And, crucially—and this is key—it is now possible to derive insight from them which leads to better decision-making, risk mitigation and opportunity-taking. This is something ECM systems were never designed to do, and not unsurprisingly, documents such as contracts have emerged as the obvious place to start for this level of deep analysis.

There is a lot at stake. For instance, contract documents very often contain legally enforceable clauses that may pose both risk and opportunity for one or more contracting parties. They could contain revenue-generating opportunities, such as negotiated pricing agreements, or risk in terms of obligations associated with a data breach.

This is why gaining insight into contracts is proving to be the perfect foundation for the even bigger market of content analytics where, with technologies like SAP HANA and Hadoop, ICA providers will emerge to do the same thing for unstructured data that business intelligence tools have done for structured data at a massive scale.

We are now poised to give key unstructured content objects, such as insurance claim forms, medical records, marketing content, and financial documents, the same attention with deep analysis that contracts have enjoyed over the last five or six years.

And much to the relief of those employed in the dullest professions in the world, automating the mundane using advanced techniques in AI is not only useful for revealing the future, but it also makes their jobs a lot more interesting.

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MONTREAL — Technological advances in artificial intelligence are fuelling a new race between hackers and those toiling to protect cybersecurity networks.

Cybersecurity is always a race between offence and defence but new tools are giving companies that employ them a leg up on those trying to steal their data.

Whereas past responses to cybercrimes often looked for known hacking methods long after they occurred, AI techniques using machine learning scan huge volumes of data to detect patterns of abnormal behaviour that are imperceptible to humans.

Experts expect machines will become so sophisticated that they’ll develop answers to questions that humans won’t clearly understand.

David Decary-Hetu, assistant professor of criminology at the University of Montreal, says defenders have an edge right now in using artificial intelligence.

“But who knows what’s going to happen in a few years from now,” he said in an interview.

“The main issue is that if you’re defending a system you have to be good 100 per cent of the time, but when you’re attacking the system you only have to be successful once to get in.”

Decary-Hetu said a growing list of corporate and government officials, including Bank of Canada governor Stephen Poloz, who say infiltrations are their top worry have a very good reason to fear.

The Bank of Canada warned in its semi-annual review released this month that the high degree of interconnectedness among Canadian financial institutions means any successful cyberattack could spread widely throughout the financial system.

Reports suggest cybercrime costs the Canadian economy between $3 billion and $5 billion a year, including ransom paid to foreign criminals.

Hacks of Sony Pictures, Uber, Ashley Madison, Yahoo and multinational retailers have sparked unsettling headlines about security of personal information.

One of the latest to face scrutiny is global credit-reporting firm Equifax. Hackers accessed the personal information, including names, social insurance and credit card numbers, as well as usernames, passwords and secret question/secret answer data of 19,000 Canadians and 145.5 million Americans.

Current detection systems tend to only recognize improper activity based on past events, often long after the damage is done.

An example of this is Equifax, which discovered the breach in July, months after hackers first infiltrated the system. It only notified the public in early September.

Niranjan Mayya, founder and CEO of Toronto-based Rank Software, said it takes on average 143 days for a breach to be detected.

The challenge is growing as the number of connected devices in the world continues to soar.

“Clearly the old style techniques of looking at cybersecurity threats and having people go through each threat aren’t working anymore, so automated means of detecting threats has become more and more important,” he said.

David Masson, Canadian manager for U.K.-based Darktrace, said artificial intelligence will help to keep up with threats by quickly identifying and stopping attacks by picking up on subtle markers that identify bad behaviour.

He said his company’s systems map a customer’s entire network, including every user and device, to discern even the slightest deviations as they emerge.

Masson said AI is needed to keep up with threats by automating defence responses to growing machine-on-machine attacks launched by sophisticated hackers.

“You’re kind of looking at a cyber arms race,” he said in an interview.

“If you want to keep up with this threat and put the advantage back in the hands of the defenders you’re gonna have to use AI.”

Ontario-based utilities company Energy+ Inc. said installed Darktrace technology alerted it to a user going to a malware site in Russia and uploading undisclosed sensitive data to a third-party cloud provider that its existing security was unable to catch.

Some observers temper the current exuberance about AI, saying it’s not a silver bullet and these are nascent days for the technology.

Receptiviti CEO Jonathan Kreindler says the hype around artificial intelligence has accelerated and has almost become a branding exercise for some companies that aren’t even offering truly leading edge technology.

“The term AI is now being applied to any sort of algorithmic reasoning unfortunately,” said Kreindler.

His firm uses AI to scour writings for unconscious use of language to understand the psychological state of company insiders who are responsible for 80 per cent of cybersecurity issues.

Canada’s largest IT company, CGI Group, said artificial intelligence is a growing field of interest for customers, although the average client is in the fairly early stages of considering AI adoption in cybersecurity.

CGI cybersecurity expert Andrew Rogoyski said that still puts them one step ahead of most hackers, who are typically interested in stealing data using the cheapest tools possible.

Rogoyski added that he expects a strengthening of defensive mechanisms might force hackers to also adopt innovative techniques such as AI.

“There’s a race, it’s been going on for 20 years plus and the race just keeps evolving. We keep leapfrogging each other,” he said.

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If my email inbox is anything to go by, a technology revolution is under way that is going to transform all of our lives very soon and it is called artificial intelligence.

A Welsh company is using AI to detect North Korean bio-weapons.

I could pop over to California to hear about “AI wearable solutions for aging population”.

And Lloyd’s of London has unveiled its first artificial intelligence deal. It promises that “in a decade a significant part of the insurance industry will be powered by AI.”

These represent just three of the innumerable AI press releases aimed at me and other technology journalists over recent days.

Last week also saw the London premiere of AlphaGo, an excellent and surprisingly touching documentary about one of the great recent triumphs of artificial intelligence, Google DeepMind’s victory over the champion Go player Lee Sedol.

And then, over the weekend, as if to confirm this is a subject that should occupy politicians and policymakers as well as journalists, a major report on what the UK should be doing to nurture AI was published.

It was commissioned by the government and authored by two distinguished computer scientists, Prof Dame Wendy Hall and Dr Jerome Pesenti.

They say the UK is already well placed as a centre of artificial intelligence, and the government should act to cement its position.

Their recommendations include:

• more investment in academic research
• developing more skills throughout industry and the education sector
• throwing open more datasets for AI scientists to work with
• encouraging the uptake of AI techniques by all kinds of companies

All of these ideas seem eminently sensible and uncontroversial. But they are also predicated on a belief that this is urgent – that we are making very rapid progress, not just in developing artificial intelligence but in applying it in areas that will transform the economy.

It certainly appears to be the case that rapid advances in processing power, coupled with access to vast amounts of data and smart new algorithms are helping computers carry out all sorts of tasks once restricted to humans. But so far the impact on everything from jobs to the way industries such as healthcare and transport work appears minimal.

So, is there a danger that AI is being overhyped?

Let’s dissect a few of the bold statements in that government AI report:

“We are at the threshold of an era when much of our productivity and prosperity will be derived from the systems and machines we create.”

Well, it has always been the case that the machines we create – from the wheel, to the spinning jenny, to the dishwasher – drive increases in productivity and prosperity.

Are we clear that the AI revolution will deliver the kind of boost to living standards we saw in the 1950s and 1960s as mass production and the use of consumer goods took off?

“We are accustomed now to technology developing fast, but that pace will increase and AI will drive much of that acceleration.”

First, you can question how fast technology has developed in recent years. Yes, we have computers that can differentiate between a cat and a dog and understand any language, but our physical infrastructure is not being rapidly transformed.

Indeed, when it comes to air travel or building new railways, you could argue that we are going backwards. Software is racing ahead, hardware not so much – just watch robots trying to play football if you are worried about them threatening to replace us.

So saying that the pace of change will increase, driven by AI, may be little more than a leap of faith.

“[Accenture] estimated that AI could add an additional $814bn (£630bn) to the UK economy by 2035, increasing the annual growth rate of GVA from 2.5 to 3.9%.”

GVA – gross value added – is close to gross domestic product (GDP), the headline measure of a country’s economic activity, including all the services and goods produced in a year.

Suggesting that its trend growth rate could rise to 3.9% – more than during boom decades the 1950s and 1960s – is, in the words of an economist of my acquaintance, “staggering”. All the more so when you look at the UK’s recent record, which has seen productivity growth flat-lining.

Now, big advances in technology can take time to show up in productivity growth – it took decades for factory owners to reorganise production around electric rather than steam power.

So, maybe we will see law firms become more efficient as AI lawyers assess contracts for risk, hospitals cut waiting lists as robot doctors examine scans, and cities cut congestion as autonomous cars and buses waft us from home to work.

All of these advances are already technically possible – but you have to be quite an optimist to believe that the changes in our infrastructure, regulation and social attitudes needed to make them a reality will happen quickly.

Last week a House of Lords select committee on artificial intelligence heard evidence from three leading scientists, including Prof Hall.

They spoke of the UK’s potential as a centre of AI excellence, and the urgent need for government to start thinking about both the benefits and the risks of the technology.

Then, the committee heard from three journalists – including me. And their lordships seemed rather startled to find that, by contrast with the scientists, we were pretty sceptical about the speed of this revolution.

We were not convinced that driverless cars would be on our roads very soon, but that meant we were also more optimistic that the threat to jobs from the robots had been exaggerated.

One of the peers mentioned that quote about futurology – that we tend to overestimate the effect of a technology in the short run and underestimate it in the long run.

The scientists will no doubt be proved right about AI one day, but the cynical old journalist in me thinks we can afford to relax for a while yet.

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We live in a world of chatbots. Chatbots improve human interaction with systems by giving a response based on the user input. This means chatbots are simple automated programs that can process simple user inputs and provide a meaningful output.
But with time, chatbots evolved and now are impacting the industries around us. The last decade has been glorious for chatbots. In fact, the growth has already prompted the increased availability of learning material on the internet.

This being said, current chatbots work in a very simple way. They take input in the form of language which is then processed by the computer using natural language processing. Once they understand the meaning, they give the most accurate answer to the user. The accuracy of the chatbot interaction depends on the algorithm being used. Chatbots also use expert systems to make decisions and act accordingly.

The most recent chatbots are using deep learning to break the next barrier of communication between humans and machine. The aim is to create a chatbot that can completely imitate a human response and not let the user understand if he/she is talking to a chatbot.

Right now, chatbots are used in almost every sector. From social media to management to healthcare, chatbots just cannot be ignored anymore. For example, Siri, a personal assistant helps iOS users to get answers. It uses a natural language user interface to respond to questions from users.

Limitations and Opportunities

There are limitations to what has been currently achieved with chatbots. The limitations of data processing and retrieval are hindering chatbots to reach their full potential. It is not that we lack the computational processing power to do so. However, there is a limitation on “How” we do it. One of the biggest examples is the retail customer market. Retail customers are primarily interested in interacting with humans because of nature of their needs. They don’t want bots to process their needs and respond accordingly.

So what is the solution? Artificial Intelligence (AI).

Artificial intelligence has been evolving at a rapid pace. The growth has been so substantial that the best AI minds are concerned with how journalists are interpreting information without understanding it. Despite the misinterpretation by journalists, it is evident from the current progress that AI has the potential to completely make chatbots more mainstream and indistinguishable from a real human interaction.

If you are curious on how modern chatbots work, you can try out these chatbots built by Cuor99, Cookkkie, and DasCode. All of them work at a different level and try to solve a problem which would require real human interaction.

The future of AI and Chatbots

The future of AI bots looks promising and exciting at the same time. The limitation in regards to accessing big data can be eradicated by using AI techniques.

The ultimate aim for the futuristic chatbot is to be able to interact with users as a human would.

Computationally, it is a hard problem. With AI evolving every day, the chances of success are already high. The Facebook AI chatbot is already showing promises as it was able to come up with negotiation skills by creating new sentences.

E-Commerce will also benefit hugely with a revolution in AI chatbots. The key here is the data  collection and utilization. Once done correctly, the data can be used to strengthen the performance of highly-efficient algorithm, which in turn, will separate the bad chatbots from the good ones.

Emotional Processing

Automation is upon us, and chatbots are leading the way. With a fully-functional chatbot, e-commerce, or even a healthcare provider can process hundreds of interactions every single minute. This will not only save them money but also enable them to understand their audience better.

Nevertheless, for this to happen, companies should restructure their approach and start investing in the Machine Learning (ML) infrastructure so that they can gather new data on a regular basis, feeding the algorithm and improving its performance over time.

In the end, the only thing that matters is how chatbots fulfill the emotional needs of the people for whom they are made for.

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Signifying the growing popularity of Artificial Intelligence (AI), global research firm Gartner has predicted that AI will be virtually pervasive in almost every new software product and service by 2020.

Owing to its market hype, almost all established software vendors are working to introduce AI into their product strategies which is creating considerable confusion in the process.

The term ‘artificial intelligence’ was not even in the top 100 search terms on gartner.com in January 2016 but by May 2017, it ranked at number 7, indicating the popularity of the topic.

“As AI accelerates up the ‘Hype Cycle’, many software providers are looking to stake their claim in the biggest gold rush in recent years,” said Jim Hare, Research Vice-President, Gartner, in a statement.

“AI offers exciting possibilities, but unfortunately, most vendors are focused on the goal of simply building and marketing an AI-based product rather than first identifying needs, potential uses and the business value to customers,” he added.

Instead of using cutting-edge AI techniques for every solution, Gartner recommends vendors to use the simplest approach that can do the job.

“Software vendors need to focus on offering solutions to business problems rather than just cutting-edge technology. Highlight how your AI solution helps address the skills shortage and how it can deliver value faster than trying to build a custom AI solution in-house,” suggested Hare.

The survey also indicated that lack of necessary staff skills was the top challenge in adopting AI in the organisations.

Gartner said that AI can greatly augment human capabilities and the combination of machines and humans can accomplish more together.

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