Intelligent adoption of artificial intelligence

Artificial intelligence (AI) is one of the most talked about technologies in recent times. It is capable of increasing enterprise revenue through identifying, analysing and, most importantly, acting on the insights from underlying data. The pertinent question is, “Should we wait for AI to evolve fully and then apply it or should we look at specific applications to solve business challenges?”

Range of AI—Human assistant to human replacement

The combination of parallel processing power, massive data sets, advanced algorithms and machine learning capabilities are spawning varied versions of AI systems.

Today, AI capabilities vary from specific/narrow to super, all-encompassing AI.

Narrow, or specific AI, is an intelligent assistant that can aid humans in making complex decisions and enhance their cognitive powers by processing vast amounts of data. It can conceptualize and correlate data, recognize the patterns and deliver intelligent output.

For instance, soft AI can be used to detect frauds in various sectors such as banks.

A large sample of fraudulent transactions is fed into the AI system, which is trained to look for signs that separate fake transactions from genuine ones.

Another example of soft AI is the voice assistant that can understand voice inputs, analyse data about the users from a variety of sources (social media, smartwatches, etc.) to better understand their behaviour and deliver results tailored to users’ preferences.

Super, or strong AI, aims to make decisions on its own without any external support.

These machines can think, learn, decide and converse like humans. Hence, they have the ability to replace humans altogether.

However, super AI systems are yet to achieve breakthrough improvisation to fully comprehend human mind-maps and replicate human intelligence.

How is AI different from RPA and cognitive?

Though enterprises are increasingly understanding the benefits of AI, there still exists misperception around similar technologies—AI, robotic process automation (RPA) and cognitive.

AI is described as the decision-taking capability based on simulation of human intelligence processes by machines. These machines “can act” as human.

On the other hand, cognitive computing helps humans in fully or partially delivering judgement-based processes and assists in their decision-making. These systems deal with unstructured inputs, and “can think” as humans.

The third type, referred to as RPA, can automate rule-based tasks and “can do” what humans can. Such systems lack self-learning capability and are effectively dumb: they just perform exactly as programmed.

AI-use cases in business

As customers are becoming increasingly demanding, AI offers assistance on key requirements of evolving business:

• People-centric: The AI systems enable the enterprises to shift to a people-centric approach from being process-centric. The decisions are made based on unstructured real-time data rather than pre-defined processes. For instance, ride-sharing companies predict fleet demand based on factors such as weather forecasts, time of the day and historical customer behaviour.

• Ease of use: AI enhances customer experience with offered convenience and assistance. For example, enterprises are using “customer digital assistants” that can recognize customers by face and voice to have relevant conversations, and provide tailored choices to help them make purchasing decisions.

• Self-adaptive: AI has the capability to self-evolve, make connections between data, improve on past decisions and get smarter. For instance, machine learning-based intelligence enables an enterprise to improve sales performance by accurately predicting cross-selling and up-selling opportunities.

AI implementation strategy for enterprises

AI has the potential to disrupt the core of business processes. However, blind adoption of technology and hype-based purchase may not lead to the desired results. Enterprises can ride the wave of success with efficacious adoption of AI technology:

• Getting familiar with the concept: Rather than adopt the technology in haste, enterprises should first educate themselves on the basic concepts and capabilities of AI. The better a company understands what narrow/soft AI does, the more likely is its successful adoption.

• Identifying the problem to which AI is a solution: Enterprises should identify specific use cases in which AI could solve business problems and help them achieve specific project goals. They should further narrow down the possible AI implementations by assessing potential business and financial values.

• Bridging the talent gap: AI requires talent pool with a strong understanding of advanced programming, domain knowledge and business context. Enterprises should bring these skills together instead of waiting for one person to bring all the dimensions.

The importance of AI is well understood. However, its implementation remains limited.

It is imperative for firms to start applying AI for solving narrow-scope problems before expecting it to disrupt the core of the business.

AI can be employed for everything from managing targeted advertisements to optimizing logistics to tracking assets to understanding the customers’ social behaviour. The trick is to get started on the right note.

Source: livemint-Intelligent adoption of artificial intelligence

RPA and AI are not the future – they are the Now!

Robotic Process Automation (RPA) and Artificial Intelligence (AI) are becoming more prevalent in business and society. As the technology becomes more accessible and efficient, more and more organisations are looking at RPA and AI. Although both of these technologies are not new it does seem that they are now beginning to come of age and radically changing the way the world does business.

So, what is RPA and AI? Let’s consider RPA first of all. Perhaps the most important thing to say about RPA is that it is not a robot! At least it is not a physical robot. RPA is a type of software that is able to interface with computer systems in the same way as a person does. RPA software is able to ‘type’ and is able to ‘click’ and is able to move a cursor. This enables it to open and close programs and to use programs. This is why the term ‘robotic’ was coined – there’s no physical robot but the software behaves in a robotic way. What is key though is that the RPA software is able to carry out tasks with a much greater level of efficiency than a human operator – and it never gets tired.

RPA has been shown to be a highly effective option for carrying out certain types of tasks. It has delivered huge cost savings for organisations and eye wateringly massive returns on investment of 100s of per cent in some instances. It is certainly worth every organisation taking a serious look at how they might take advantage of what it is able to do.

Shop Direct, Telefonica, RAC and nPower are just some of the organisations that have reported substantial benefits to their businesses. Some of those benefits include the reduction of costs of processes. It isn’t however only a matter of cost reduction. Using RPA helps to further improve the quality and consistency of outputs – why wouldn’t it? It also provides organisations with greater auditability / trackability of their processes down to key stroke level. A boon for those in say the financial services sector.

Neither is RPA just about the commercial sector. In 2015, Sefton Council became the first local authority in the UK to trial RPA in its revenues department. At the beginning of the project, leading international service provider, Arvato automated three processes in Sefton’s revenues department to ensure the RPA solution was accurate, robust, auditable and scalable, before extending it to cover a number of high-volume tasks across the department. The tasks vary in complexity, from indexing documents and assigning them to specific workflows to signing up people to direct debit payment of Council Tax and processing discount applications.

Alastair Bathgate, CEO of Blue Prism is confident that RPA has a greater role to play for local government in the future, “We believe there is huge potential for RPA to make a difference in local government thanks to the large number of repetitive back office tasks which can be automated”. His view is support by a a recent study by PricewaterhouseCoopers it was estimated that 45% of work activities could be automated, creating $2 billion of savings in global workforce costs.

RPA is clearly an important tool for organisations to consider. There is though considerable confusion that surrounds it. Many wonder what all the fuss is about given that most organisations already have very high levels of automation and have had for many decades. This gets us to the key to RPA’s appeal. We pointed out that RPA software uses other systems, like a human operator. This is crucial. It means that we can improve the efficiency of a process that is largely automated without necessarily having to make any changes to the existing legacy systems. For anyone who has wrestled with old legacy systems and over stretched IT teams the prospect of being able to make improvements, without instigating costly and resource hungry IT projects, is a very attractive proposition indeed.

RPA often acts as a link, bridging the gaps between systems or it can act as an effective work around for a system that could not quite accommodate a particular set of tasks. Often these system shortcomings have been addressed by getting people to fill the gap. Not only is this often quite inefficient it also creates mind numbingly dull tasks that someone has to do. RPA offers the opportunity to improve the efficiency of a process, reduce cost and often take away tedious tasks, allowing people to focus on more added value, more highly skilled tasks – like talking to customers.

This takes us nicely to the difference between RPA and AI. RPA is a dummy. It is pretty stupid. It does exactly what it is told to do – exactly. There’s no thinking – no judgement – just a set of rules which it blindly follows. It can only work with structured data. If the task requires working with less structured data RPA is struggling. If the rules for what it needs to do – down to individual key strokes – cannot be defined, then RPA is going to struggle.

Enter Artificial Intelligence. AI does have the capacity to work with less structured data. It can find patterns in data and be programed to make choices. AI can learn, based on what it experiences and that learning can then inform future choices. AI is advancing quickly and has evolved into an incredibly useful tool for organisations. Unstructured data such as emails and phone calls can be sifted with AI programmes by identifying key words or phrases before checking parameters and categorising what is required. Virgin Trains have been using AI for some time now to analyse emails received from customers. They are able to make sense of what the email is about by analysing key words. They are then able to decide to make for example further checks on the validity of the email content by perhaps checking if a specific train journey mentioned in the email actually exists. It might then go on to check if there was any reported issue with that train journey. It can then pass the task on to person who is now able to make a judgement about what needs to happen next having been saved the chore (and the time) of checking key facts.

In some instances AI can be used to help structure data allowing it then to be offered perhaps to an RPA solution to progress further. A combination of AI and RPA can potentially transform a process, massively reducing costs through reducing FTE’s while being more efficient and effective at completing tasks than human employment.

What has happened alongside the growing popularity of exploring and implementing RPA and AI is a growing appreciation of not only the benefits of these solutions but also the challenges. Understanding implementation and the processes that RPA and AI can improve is crucial to getting the best return on your investment. There has perhaps been a view that RPA certainly could be almost bought off the shelf and implemented by a school leaver with a GCSE in woodwork. The reality is somewhat different. More people are recognising that there is in fact a lot to consider to get the most from RPA. How to choose the best processes to RPA. How to gain buy-in and support. How to design the new target operating model. What software to choose. How to manage long term. How to ensure the fit with IT . How to set up effective governance.

RPA and AI are not the future, they are the present. A great return on your investment that efficiently and effectively gets the job done. Whether or not you and your organisation ultimately invest in RPA and AI solutions importance of investing in finding out more about it the case for investing some time and energy in finding out more about it and how it might add value to your business is extremely compelling. Those that don’t run the risk of missing out on a very good thing.

Source: and AI are not the future – they are the Now! 

AI: What it Can Bring and How to Prepare for the Future

Every decade a major disruption has occurred that altered the digital landscape: from the PC revolution, to the internet boom, to the mobile-first rise. Each development brought powerful opportunities for businesses that were smart enough to change. So what is next? The artificial intelligence (AI) boom is upon us and technology driving AI is becoming more accessible and affordable for businesses, opening doors for new use cases and workforce augmentation.

By the year 2020, if you aren’t AI-first, it will be too late, much as it was for any business that failed to make the leap to digital in decades past. Organizations have a brief window to experiment and become familiar with the strategies and technologies to get ready for the AI-first world. The emerging AI-first era is already creating new ways for organizations to interact with, serve, and empower customers and employees. For example, by augmenting employees’ capabilities using AI-specifically across intelligent automation, Robotic Process Automation (RPA) and physical automation-organizations will enable workers to achieve far more, faster, with intelligent action and better results.

Additionally, as cloud, big data, and mobile continue to converge, AI-driven user interfaces will lead to ever-deeper, more meaningful interactions-a “situational centricity” tailored not only to each individual customer or employee, but also to his or her unique situation.

An augmented workforce powered by AI will help organizations attract and retain new generations of workers

Currently, there are five actions that companies can take to survive and thrive in the new AI-first era:

1. Embrace AI as the new experience layer: Customers won’t just be on apps or the internet. They will expect AI-powered assistants and invisible user interfaces, as well as differentiated experiences such as voice, mixed reality, and haptics.

2. Augment your workers: The gains we’ve made from innovating workplace productivity have hit a plateau, but AI will help organizations reach new levels of efficiency and effectiveness. An augmented workforce powered by AI will also help organizations attract and retain new generations of workers.

3. Plug in to the Platform Economy: Organizations must be ready to create and join the AI-driven borderless platforms in their industry-and others-in order to reach customers where they want to be.

4. Take a DesignOps approach, everywhere: Combining design thinking and modern engineering principles will be necessary to the digital enterprise’s transformation as a completely user-centric entity. Organizations should start now to build up a culture, mindset, and business model ready for a DesignOps revolution-where everyone is focused on the user and value.

5. Act with responsibility and plan for secondary consequences: The rise of AI is fundamentally changing everything about the way we live, work, and understand our world. Organizations must develop a digital ethics framework that addresses issues like data security, trust and privacy, and provides guidelines about how data should be obtained and used.

As companies move down the path to digital transformation, there is a growing need for organizations to act with responsibility and adopt digital ethics as every digital action can have an equal and potentially unintended consequence. The rise of AI is fundamentally changing the way we live, work, and understand our world, and this “digitization of everything” requires a new level of corporate accountability. Just because something can be done with digital innovation doesn’t mean that it should. Each organization must be prepared to continuously assess how smart machines and humans can best work together to drive productivity and innovation. To maintain the trust of employees, partners and customers, investment and focus is required now to address the ethical issues arising from smart machines in the workplace.

Source: What it Can Bring and How to Prepare for the Future

Bring on the Bots

Artificial intelligence is moving from science fiction to practical reality fast.

AI — technology that teaches machines to learn so they can perform cognitive tasks and interact with people — is suddenly accessible to many companies. Costs associated with the advanced computing and data-storage hardware behind AI are plummeting. A growing number of vendors also offer AI tools such as robotic processing automation that can be configured without the help of a rocket scientist.

So this is clearly an area more banks will need to pay attention to going forward.

Already some AI pioneers have emerged in the financial industry just over the past year: Bank of New York Mellon‘s use of robotic process automation in trade settlement and other back-office operations; Nasdaq‘s search for signs of market tampering with an assist from AI; UBS’ initiative to answer basic customer-service questions through Amazon’s virtual assistant, Alexa; and USAA‘s development of its own virtual assistant.

Most large banks are considering using AI wherever mundane or repetitive tasks could be offloaded to a computer fairly easily.

What It Can Do

Here are some examples of where AI could make the biggest difference.

Customer conversations. Chatbots, natural language processing and speech processing could all be used to improve social interactions. In addition to USAA, Bank of America, Capital One Financial, Barclays and BBVA are experimenting with AI-powered virtual assistants.

“The vision that excites me is the one where we have seamless interactions, where I’m interacting with people, with the bank, with systems in the bank, and at the end of the day what the bank is giving me is exactly what I want,” said Marco Bressan, chief data scientist at BBVA. “We shouldn’t have a fixed idea of what the customer wants. There are some customers that the less they see their banks the better, as long as their money is well taken care of. Other customers want to see their bank every day. We have to serve both. And communicating with each of those from an AI perspective is very different. One has to do with full automation, and the other has to do with a smart interface.”

Automated investment advice. AI is used to help investment advisers and robo-advisers make better recommendations to customers. Australia’s ANZ Group has been using IBM’s Watson in its wealth management division for three years. Watson can read and understand unstructured data found in contracts and other documents, comb through millions of data points in seconds, and learn how to draw conclusions from the data. It can assess a new customer’s financial situation more quickly and comprehensively than a human being, and it never forgets anything.

BlackRock uses AI to improve investment decision-making. The startup Kensho combines big data and machine-learning techniques to analyze how real-world events affect markets.

Faster, better underwriting. BBVA uses artificial intelligence to improve its risk scoring of small and midsize businesses. “We realized we could update data in real time and integrate it with what the risk analysts were doing to have a much deeper understanding of their own portfolio,” Bressan said.

Some online lenders use AI to speed up their process. The software can look at hundreds or thousands of attributes, such as personal financial data and transaction data, to determine creditworthiness in a split second. The system learns as it goes — when a lender gets payment information on loans, that information gets fed back into the system, so its knowledge evolves.

However, some people question whether AI programs can be trusted to make sound, unbiased lending decisions.

Streamlined operations. BNY Mellon, Deutsche Bank and others are using bots in their back offices to automate repetitive tasks like data lookups.

Assisted account opening. Account origination can be a slow, cumbersome process. Some banks are experimenting with robotically automating some elements, such as data verifications.

Fraud detection. Card issuers and payment processors like PayPal use AI to compare current card transactions to the user’s past behavior as well as to general profiles of fraud behavior. Human analysts teach the model to discern the difference between legal and fraudulent transactions.

General efficiency. “The financial industry is an enormous percentage of the GDP,” said Robin Hanson, an associate professor at George Mason University. “A lot of it is due to various regulations and rules about who has to do what and how. It’s entrenched in regulatory practices, and it’s really hard to innovate in finance because you run into some of these obstacles.”

For example, Hanson wanted to sell some books at a convention. To do so, he had to apply for a tax ID, pay a fee and cover $25 in sales taxes. That required him to go to his bank to get a cashier’s check, for which he had to pay a $5 transaction fee and postage. “That’s an enormously expensive, awkward process,” he said. “If we had an efficient financial system, that would cost pennies.”

Unintended Consequences

As AI is used to improve the speed and efficiency of tasks now performed by humans, there are potential unintended consequences. For one, people in lower-paying jobs in operations, branches, compliance and customer service are likely to lose those jobs.

“Bank executives say they’re going to take those people and put them into high-tech, high-pay jobs to help us code, help us do this, help us do that. It’s just not going to happen,” said Christine Duhaime, a lawyer in Canada with a practice in anti-money-laundering, counterterrorist financing and foreign asset recovery and the founder of the Digital Finance Institute. However, “the bank may end up with the same number of employees,” as it sheds customer-facing jobs and hires trained software developers to code.

There are also privacy concerns around the use of AI in financial services. “From a consumer protection point of view, there are concerns people need to take into account when it comes to AI, machine learning and algorithmic decision-making,” said Steve Ehrlich, an associate at Spitzberg Partners, a boutique corporate advisory and investment firm in New York. “Say a company wants to look at your social media or your search engine history to determine your creditworthiness. They go into Facebook and find a picture of you that you didn’t upload. It’s a picture of you at a bachelor party or gambling at a casino. That data gets fed into the algorithm. For one, they should tell you they were going to be taking that information.”

There is also the chance that bots and AI engines could run amok and make poor lending decisions, or commit an operations error that a human with common sense could have averted.

What Banks Can Do

These caveats aside, banks’ wisest course is to prepare to be part of the revolution.

One thing they can do is create an internal center of excellence where a group of people become experts and help bring AI to other parts of the company. They could test technology and use cases and guide the business units in their adoption of bots and AI. Citigroup and BBVA are among the banks doing this. BNY Mellon has a robotics process automation team that partners with businesses and has come up with eight pilots, including settlement and data reconciliation.

Banks also can try to encourage people to embrace AI — even if their jobs are at risk. It helps to communicate that there could be some benefit to them. “People in operations and data analysts don’t want to be doing this work anyway — swivel-chair work, mindless copying and pasting and keying in data,” said Adam Devine, head of marketing at WorkFusion, a robotics process automation software provider that competes with Blue Prism and Automation Anywhere.

David Weiss, senior analyst at Aite Group, also sees the trend as an eventual positive for employees. “I personally argue for human augmentation — go after the peak human problems first,” he said. “There, you’re not going to cut jobs, you’re just going to make people more functional, and leverage their inorganic intelligence more.”

But there’s no question the workplace will change and people will have to adapt.

Source: on the Bots

What Is The Difference Between Deep Learning, Machine Learning and AI?


Over the past few years, the term “deep learning” has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries.

Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is. But what exactly is it? And is it just another fad being used to push “old fashioned” AI on us, under a sexy new label?

In my last article I wrote about the difference between AI and Machine Learning (ML). While ML is often described as a sub-discipline of AI, it’s better to think of it as the current state-of-the-art – it’s the field of AI which today is showing the most promise at providing tools that industry and society can use to drive change.

In turn, it’s probably most helpful to think of Deep Learning as the cutting-edge of the cutting-edge. ML takes some of the core ideas of AI and focuses them on solving real-world problems with neural networks designed to mimic our own decision-making. Deep Learning focuses even more narrowly on a subset of ML tools and techniques, and applies them to solving just about any problem which requires “thought” – human or artificial.

How does it work?

Essentially Deep Learning involves feeding a computer system a lot of data, which it can use to make decisions about other data. This data is fed through neural networks, as is the case in machine learning. These networks – logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them, and classify it according to the answers received.

Because Deep Learning work is focused on developing these networks, they become what are known as Deep Neural Networks – logic networks of the complexity needed to deal with classifying datasets as large as, say, Google’s image library, or Twitter’s firehose of tweets.

With datasets as comprehensive as these, and logical networks sophisticated enough to handle their classification, it becomes trivial for a computer to take an image and state with a high probability of accuracy what it represents to humans.

Pictures present a great example of how this works, because they contain a lot of different elements and it isn’t easy for us to grasp how a computer, with its one-track, calculation-focused mind, can learn to interpret them in the same way as us. But Deep Learning can be applied to any form of data – machine signals, audio, video, speech, written words – to produce conclusions that seem as if they have been arrived at by humans – very, very fast ones. Let’s look at a practical example.

Take a system designed to automatically record and report how many vehicles of a particular make and model passed along a public road. First, it would be given access to a huge database of car types, including their shape, size and even engine sound. This could be manually compiled or, in more advanced use cases, automatically gathered by the system if it is programmed to search the internet, and ingest the data it finds there.

Next it would take the data that needs to be processed – real-world data which contains the insights, in this case captured by roadside cameras and microphones. By comparing the data from its sensors with the data it has “learned”, it can classify, with a certain probability of accuracy, passing vehicles by their make and model.

So far this is all relatively straightforward. Where the “deep” part comes in, is that the system, as time goes on and it gains more experience, can increase its probability of a correct classification, by “training” itself on the new data it receives. In other words it can learn from its mistakes -just like us. For example it may incorrectly decide that a particular vehicle was a certain make and model, based on their similar size and engine noise, overlooking another differentiator which it determined had a low probability of being important to the decision. By learning that this differentiator is, in fact, vital to understanding the difference between two vehicles, it improves the probability of a correct outcome next time.

So what can Deep Learning do?

Probably the best way to finish this article and give some insight into why this is all so ground breaking is to give some more examples of how Deep Learning is being used today. Some impressive applications which are either deployed or being worked on right now include:

Navigation of self-driving cars – Using sensors and onboard analytics, cars are learning to recognize obstacles and react to them appropriately using Deep Learning.

Recoloring black and white images – by teaching computers to recognize objects and learn what they should look like to humans, color can be returned to black and white pictures and video.

Predicting the outcome of legal proceedings – A system developed a team of British and American researchers was recently shown to be able to correctly predict a court’s decision, when fed the basic facts of the case.

Precision medicine – Deep Learning techniques are being used to develop medicines genetically tailored to an individual’s genome.

Automated analysis and reporting – Systems can analyze data and report insights from it in natural sounding, human language, accompanied with infographics which we can easily digest.

Game playing – Deep Learning systems have been taught to play (and win) games such as the board game Go, and the Atari video game Breakout.

It is somewhat easy to get carried away with the hype and hyperbole which is often used when these cutting edge technologies are discussed (and particularly, sold). But in truth, it’s often deserved. It isn’t uncommon to hear data scientists say they have tools and technology available to them which they did not expect to see this soon – and much of it is thanks to the advances that Machine Learning and Deep Learning have made possible.

Bernard Marr is a best-selling author & keynote speaker on business, technology and big data. His new book is Data Strategy. To read his future posts simply join his network here.

Source: Is The Difference Between Deep Learning, Machine Learning and AI?

Will Automation will take away all out jobs?

TEDx Cambridge Sept 2016 by David Autor

Here’s a paradox you don’t hear much about: despite a century of creating machines to do our  work for us, the proportion of adults in the US with a job has consistently gone up for the past 125 years. Why hasn’t human labor become redundant and our skills obsolete? In this talk about the future of work, economist David Autor addresses the question of why there are still so many jobs and comes up with a surprising, hopeful answer.

Source: TEDx Cambridge- Will Automation will take away all out jobs?

Artificial intelligence will increase productivity

Research shows that software robots will soon automate 80% of repetitive tasks currently being done by people and increase productivity by freeing up humans to use their brains.

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Businesses will need to develop a balance of artificial and human intelligence as different roles require a mix of the two, found the academic study by Goldsmiths, University of London and artificial intelligence (AI) supplier IPsoft,

It said by automating and redeploying humans away from repetitive jobs to tasks that require creativity and innovation, organisations can increase productivity three times over.

The FuturaCorp: Artificial Intelligence & the Freedom to be Human report outlines the future workplace where humans and machines together increase output.

The report described three tasks requiring a different mix of human and artificial intelligence.

It said deterministic tasks are repetitive and process-oriented, while probabilistic tasks require a human in concert with machines. Then there are cross-functional reasoning jobs that rely on connections that can only be made by the human brain.

The report said that 80% of deterministic tasks will be done by machines in the not-too-distant future, probabilistic jobs will be shared 50:50, while humans will do 80% of cross-functional reasoning tasks.

Read more about artificial intelligence in banking

  • SEB bank is currently integrating AI into its customer services channels, following an internal trial of the technology.
  • Ahead of its annual meeting in Davos, the World Economic Forum warns that AI needs strong governance.
  • Read why enterprises need to worry just as much – if not more – about the business implications, rather than the technical challenges when implementing cognitive software.
  • Middle East banking group Emirates NBD is piloting an intelligent virtual assistant with selected customers and plans to launch it soon.

“The real productivity benefits of AI will not be simply a factor of automating existing processes. The arrival of AI will engender entirely new, unknown possibilities for humans and what they can achieve,” said Chris Brauer, senior lecturer at Goldsmiths, University of London.

“It is this new configuration of humans working alongside intelligent machines that will be the source of sustained competitive advantage. The result will be FuturaCorp – a Fortune 500 with the innovative flexibility of a Silicon Valley startup, or a startup with the IT power of a Fortune 500.”

Chetan Dube, CEO at IPsoft, said CEOs must be prepared to redefine their business in order to capitalise on the productivity potential of AI. “That journey begins with fundamental change to organisation structure, who they hire for which roles, and how they use the new relationship between humans and machines to maximise efficiency and innovation.”

“AI engenders emergent individual qualities, which push us to access the more complex parts of our minds. When routine work is automated, we will be able – and required – to flex our most human of skills. The future of society relies on individuals accessing higher reasoning, critical thinking and complex problem-solving skills,” said Dube.

Amelia’s reading power

IPSofts AI platform, known as Amelia, was launched in 2014. It has an understanding of the semantics of language and can learn to solve business process queries like a human. It can read 300 pages in 30 seconds and learn through experience by observing the interactions between human agents and customers.

If Amelia can’t answer a question, it passes the query on to a human, but remains in the conversation to learn how to solve similar issues in future. It understands 20 languages, as well as context, can apply logic and infer implications.

The software is used for services such as technology helpdesks, contact centres, procurement processing and to advise field engineers, among other business processes.

AI’s benefit to productivity is now being predicted. According to recent research by Vanson Bourne for Infosys, businesses that have adopted AI technologies expect their revenues to increase by 39% and costs to drop by 37% by 2020. Some 64% say their future growth depends on large-scale AI adoption.

But there are hurdles to overcome. The World Economic Forum’s Global Risk Report 2017 has highlighted risks associated with AI. Based on a survey of 750 experts, the report warned that AI, biotech and robotics have among the highest benefits to society, but they also require the most legislation.

The World Economic Forum warned that governance of emerging technologies is patchy. Some are regulated heavily, and others hardly at all because they do not fit under the remit of any existing regulatory body.


Source: intelligence will increase productivity

When Thinking About Artificial Intelligence, Don’t Forget the Peopl

Businesses that adopt artificial intelligence technology to help with jobs like automating call center activity must also consider giving employees education and training so that those who are displaced by innovation can still work.

That’s one of the takeaways from Accenture’s annual report on Thursday about technology trends. In short, companies should realize that innovation can cause human pain and that they should do something to minimize it.

Accenture joins the countless other analysts, technologists, and researchers who claim that the rise of artificial intelligence technologies like deep learning is ushering a new age. Deep learning, when done right, can help developers build software that can sift through mountains of data, recognize patterns, and take action.

Companies like Amazon (amzn) and Google (goog) are using AI to improve their digital assistants, those voice operated helpers on smartphones and home automation hubs, said Accenture chief technology officer Paul Daugherty during a Wednesday media event. Digital assistants are an example of the change in how people interact with their devices, Daugherty explained.

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And while AI can make people worry about losing their jobs, it’s up to business leaders to instill a workplace culture that encourages constantly learning new skills. Additionally, business leaders must be more involved with public education to ensure that it is properly training the next generation to become “life-long” learners who are willing to adapt as technology continuously advances.

“We need to invest in technology, but the real opportunity is to invest in people,” Daugherty said.

Companies looking to invest in AI should be aware that the hype behind it has led vendors to claim they have the latest answer to every business problem, said Jerry Kaplan, a computer scientist and entrepreneur who spoke at the event. But he cautioned that artificial intelligence “is not magic” and it’s not something you can easily install into an app to give it super powers, he said.

“If someone comes in and says you should buy this because it has AI in it, I’d be extremely skeptical,” said Kaplan.

Kaplan also opposes the notion that businesses will need to have “chief AI officers” in charge of directing an AI strategy, as Baidu chief scientist Andrew Ng has espoused.

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AI is just one element within software and one part of a company’s overall technology strategy, Kaplan said. Ultimately, it’s up to businesses to decide the best way to implement it, along with other technologies. Additionally, Kaplan doesn’t believe that AI at its core is a moral issue; it’s just a type of technology, no different than other tech in that it’s ultimately how it’s used that’s important.

“You don’t worry whether relational databases are forces of good or evil,” Kaplan joked.

Source: Fortune-When Thinking About Artificial Intelligence, Don’t Forget the People

How Cybersecurity Will Be Transformed By AI?

The emerging field of artificial intelligence technologies can lead to a revolution in cybersecurity.

Detecting And Blocking Hacked IoT Devices

Any individual and business needs to ensure securing their digital assets, whether they are looking to protect company’s intellectual property, personal photos, customers’ sensitive data, or anything else that can affect business continuity or individual reputation.

According to Venture Beat, while billions of dollars are spent on cybersecurity, the magnitude of breaches and the number of reported cyberattacks still keep rising. Fortunately, there are hopes this situation will suddenly change.

There are many ways to take advantage of the predictive power of artificial intelligence (AI) for developing new advanced cybersecurity apps. Artificial intelligence might give security experts as well as businesses and individuals the upper hand in the field by implementing crucial keys of cyberdefense innovation.

Cisco estimates that the number of Internet of Things (IoT) connected devices in the world will increase to 50 billion by 2020 from 15 billion today. Due to limited software and hardware resources, most of these devices do not have basic security measures.

The recent massive denial of service attack issued against KerbsOnSecurity was a strong demonstration of the power of hacked IoT devices. But the fact that the source code for the Mirai malware used in the attack was soon released to the public is even more frightening.

AI technologies have one of most prominent arenas of developing in the field of IoT security. Lightweight AI-based prediction models that can reside and operate autonomously even on devices with low computing power are able to enable detection and blocking of suspicious activity in real time on either at network level or on the device. Several startup companies implement AI technologies for IoT security applications, including PFP Cybersecurity, CyberX and Dojo-Labs.

Preventing Execution Of Malicious Files And Software

One of the leading cyberattack vectors remain the file-based attacks. These file-based cyberattack most commonly use Acrobat Reader (.pdf), executables (.exe) and MS Office file types.

A new malicious file with different signature but the same malicious intent can be created with just a small change in line of code. These small changes in code cannot be detected by advanced heuristic-based endpoint detection and response (EDR) solutions, legacy signature-based antivirus programs, as well as even network level solutions such as sandboxing.

Such problem can be solved by harnessing AI power and there are already a few startups that address this. They leverage the AI’s great capability to evaluate millions of features per suspicious file. This way can be detected even the slightest code mutations. Among the leader companies in implementing file-based AI security are included Cylance and Invincea.

Quantifying Risks

Another challenging task within the field of cybersecurity is quantifying organizations’ cyber risks. This is mainly due to the vast number of variables that need assessed and the lack of historic data.

Many of today’s organizations as well as cyber insurers and other third parties that want to assess these organizations must go through a tedious cyber risk assessment process if they are interested in quantifying their risks. This assessment process is mainly based on questionnaires that evaluate an organization’s governance and risk culture as well as qualitative measures of compliance with available cybersecurity standards.

For a genuine representation of cyber risks, this kind of approach might be insufficient. A better way is to take advantage of AI technologies’ ability to generate predictions by processing millions of data points. This way, cyber insurers and organizations can come at the most accurate cyber risks estimation. Among the startup companies that are approaching this task are included Security Scorecard and Cisco.

Source: – How Cybersecurity Will Be Transformed By AI?

Hiring Your First Chief AI Officer

A hundred years ago electricity transformed countless industries; 20 years ago the internet did, too. Artificial intelligence is about to do the same. To take advantage, companies need to understand what AI can do and how it relates to their strategies. But how should you organize your leadership team to best prepare for this coming disruption? Follow history.

A hundred years ago, electricity was really complicated. You had to choose between AC and DC power, different voltages, different levels of reliability, pricing, and so on. And it was hard to figure out how to use electricity: Should you focus on building electric lights? Or replace your gas turbine with an electric motor? Thus many companies hired a VP of Electricity to help them organize their efforts and make sure each function within the company was considering electricity for its own purposes or its products. As electricity matured, the role went away.

Recently, with the evolution of IT and the internet, we saw the rise of CIOs to help companies organize their information. As IT matures, it is increasingly becoming the CEO’s role to develop their companies’ internet strategy. Indeed, many S&P 500 companies wish they had developed their internet strategy earlier. Those that did now have an advantage. Five years from now, we will be saying the same about AI strategy.

AI is still immature and evolving quickly, so it is unreasonable to expect everyone in the C-suite to understand it completely. But if your industry generates a large amount of data, there is a good chance that AI can be used to transform that data into value. To the majority of companies that have data but lack deep AI knowledge, I recommend hiring a chief AI officer or a VP of AI. (Some chief data officers and forward-thinking CIOs are effectively taking on this role.)

The benefit of a chief AI officer is having someone who can make sure AI gets applied across silos. Most companies have naturally developed siloed functions in order to specialize and become more efficient. For the sake of argument, let’s say your company has a gift card division. There is a reasonable chance that AI could make the selling and processing of gift cards much better. If the team has the expertise to attract and deploy AI talent, by all means let them do so! However, in most cases, that’s unrealistic. Because AI talent is extremely scarce right now, it is unlikely that they will attract top talent to work on gift cards at the division level.

A dedicated AI team has a higher chance of attracting AI talent and maintaining standards than a single gift card division does — and anyway the new talent can be matrixed into the other business units in order to support them. But the dedicated team needs leadership, and I am seeing more companies hire senior AI leaders to build up AI teams across functions.

Hiring the right AI leader can dramatically increases your odds of success, but only if you pick the right person. Here are some traits I recommend you look for in a chief AI officer or a VP of AI, based on my experience in leading and nurturing some of the most successful AI teams at Google, Stanford, and Baidu:

  • Good technical understanding of AI and data infrastructure. For example, they should ideally have built and shipped nontrivial machine learning systems. In the AI era, data infrastructure — how you organize your company’s databases and make sure all the relevant data is stored securely and accessibly — is important, though data infrastructure skills are arguably more common.
  • Ability to work cross-functionally. AI itself is not a product or a business. Rather, it is a foundational technology that can help existing lines of business and create new products or lines of business. The ability to understand and work with diverse business units or functional teams is therefore critical.
  • Strong intrapreneurial skills. AI creates opportunities to build new products, from self-driving cars to speakers you can talk to, that just a few years ago would not have been economical — or might even have been in the realm of science fiction. A leader who can manage intrapreneural initiatives will increase your odds of successfully creating such innovations for your industry.
  • Ability to attract and retain AI talent. This talent is highly sought after. Among new college graduates, I see a clear difference in the salaries of students who specialized in AI. A good chief AI officer needs to know how to retain talent, for instance by emphasizing interesting projects and offering team members the chance to continue to build their skill set.

An effective chief AI officer should have experience managing AI teams. With AI evolving rapidly, they will need to keep up with changes, but it is less important that they be on the bleeding edge of AI (though this helps attract talent). What’s more important is that they can work cross-functionally and have the business skills to figure out how to adapt existing AI tools to your enterprise.

Source: Harvard Business Review-Hiring Your First Chief AI Officer