For the Next Election, Don’t Recount the Vote. Encrypt It

Let’s be honest: The 2016 election wasn’t a sterling display of American democracy. Its problems extended beyond Russian hackers and trolls trying to thumb the scale, and the winner’s baseless, ongoing claims of voter fraud. For computer scientist Ben Adida, the most troubling part came afterward, when voting security experts and Green Party candidate Jill Stein called for a recount of the vote in three thin-margin swing states, raised millions of dollars to do it—and still mostly failed.

While Stein successfully triggered a Wisconsin recount, federal judges in Pennsylvania and Michigan put an early stop to her efforts. In the latter case, a judge ruled that Stein had “not presented evidence of tampering or mistake” in the electronic voting machines. It was a vexing catch-22, says Adida, an engineer and applied cryptographer at the education startup Clever. If the Michigan vote was tainted, the paper backup ballots Stein wanted to recount were the evidence that could prove it. But Stein didn’t have any evidence to justify looking at the evidence.

“Recounts don’t actually happen, because if you can’t bring a shred of evidence to the table that something went wrong, you sound like a lunatic,” Adida says. “That’s what 2016 proves. We need to build a voting system that inherently provides that evidence in case something goes wrong.”

Encrypt the Vote

At the Enigma security conference next week in Oakland, Adida will make the case for a decade-old voting system that provides that inherent evidence, what Adida and other voting security experts call “end-to-end verification.” Since 2007, thousands of people, including organizations like the Association of Computing Machinery and Greenpeace, have used Adida’s election software, called Helios to solve that core problem. Helios encrypts every vote, and then publishes an online list of encrypted results by voter in a form that allows anyone from an election-monitoring organization to individual voters themselves to check the results.

“The whole idea that paper ballots are going to save us is well-intentioned but flawed,” says Adida. “I think we can do better. We can provide true end-to-end proof that an election works.”

Now that same system will be put into practice for the first time in actual government: A voting scheme, known as STAR-Vote—for Secure, Transparent, Auditable, and Reliable—uses a similar cryptographic system to Helios, but with real, physical voting machines and ballots. One Texas county is even set to implement it before the 2020 presidential election.

“STAR-Vote allows the general public to verify the vote themselves,” says Dana DeBeauvoir, the county clerk of Travis County, Texas, which includes the city of Austin. “We’re trying to build a better mousetrap and share it with everyone else.”

How It Works

Here’s the clever—and somewhat convoluted—way that end-to-end verified voting system works: Registered voters input their vote on a touchscreen machine. When they’re done, the machine prints their ballot with their choices, along with a “receipt” at the bottom that they can take home. That input machine also encrypts the results, shares the encrypted vote data with all the other voting machines at the polling place, and also enters it into a database of all the encrypted votes that will be published online at the end of the election day. Then voters feed their printed ballot into a ballot box with a scanner that reads a barcode on the ballot and confirms to the network that the vote has been cast.

After the votes are published, anyone can use a tracking number on their receipt to look up their vote online and confirm that it was registered. But crucially, no one can see who voted for whom. Not even the voter can decrypt their own vote; if they could prove who they voted for, they might be coerced or paid to vote for a certain candidate.

In fact, thanks to some mathematical sleight-of-hand known as “homomorphic encryption,” not even the election officials counting up the results can decrypt any individual votes. Homomorphic encryption allows simple arithmetic to be performed on encrypted data without decrypting it. So the encrypted votes can be added up and published online to produce an encrypted, public total tally that remains accurate without ever exposing anyone’s vote. Election officials decrypt only that final result, and even they can only do so when a certain number of overseers combine their secret passwords. After the results are decrypted and declared, anyone can re-encrypt them to check that they match the online encrypted tally, to prevent the officials from colluding to falsify the count.

That somewhat mind-bending process still leaves another question: How can voters check that the STAR-Vote machine not only registered their encrypted vote, but registered the correct vote rather than slyly switching it? To solve that problem, the system offers voters one more feature it calls a “challenge.” When the vote is encrypted and declared to the other voting machines—but before the voter scans it and puts it in the ballot box—the voter can choose to challenge it instead of confirming it, essentially declaring the ballot to have been a test of the system. If a ballot is challenged, it’s not counted, and the machine where the voter input their choice uses a special key that only it possesses to decrypt the encrypted vote it just declared to reveal who that challenged vote was for; it’s then shared with the local network and the public database. (The voter, meanwhile, starts over and votes again.)

Before the voter has even left the voting place you have all the information you need to catch the machine cheating. Dan Wallach, STAR-Vote Inventor
Thanks to some proven cryptographic math, there’s no way for the computer to believably decrypt the ballot without revealing which candidate it was about to register a vote for. So if the machine’s answer in the public database doesn’t match the voter’s choices, the voter can look up the challenged vote, spot the mismatch and report the machine’s fraudulent behavior. That makes any attempt at tampering with voting machines highly risky. “Before the voter has even left the voting place you have all the information you need to catch the machine cheating in its electronic representation of your ballot,” says Dan Wallach, a cryptographer at Rice University and one of STAR-Vote’s inventors.

Baking in the Evidence

All of those cryptographic checks aren’t meant to replace paper ballot backups, Wallach and Adida say, which would still serve as the ultimate record in any recount. But with STAR-Vote, the hints of tampering that trigger that recount would be far easier to spot. And just as importantly, says Travis County Clerk Dana Debeauvoir, all that cryptographic complexity remains hidden from any voter that doesn’t want to deal with it. “It has to be something that mom and pop can operate,” she says.

Next month, Travis County, which has about 720,000 registered voters, will reveal the results of a request-for-proposal it issued last year for tech firms to code and build its STAR-Vote machines. Debeauvoir hopes to put the system to use for the first time in local elections in 2019, so that any bugs will be worked out before the 2020 presidential election. She says she expects the system to cost between $8 and $12 million to develop, but argues that’s still less in the long run than licensing the currently available, less-verifiable systems.

Supporters hope that if it catches on, STAR-Vote could serve as a key reassurance for the American electoral system, and save millions of dollars spent on wasted paper recounts. Rather than lawsuits, sore loser accusations, and expensive audits, the audit would be baked into the system, says Adida. “Instead of having to seek the evidence, the system would provide evidence of correct operation by virtue of the process of voting itself,” says Adida. Imagined voter fraud and Russian trolls aside, that might actually be a system all Americans can trust.

Source: Wired-For the Next Election, Don’t Recount the Vote. Encrypt It


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

How accelerating automation is positively disrupting industries

If CIOs are not already looking at RPA as a digital enablement to their digital strategy, they are already behind the eight ball a little bit

A bank slashed the time it would approve an application for credit from 21 days to 11 minutes, by deploying robotic process automation (RPA).

With RPA, the bank was able to process all paperwork and data for credit, criminal and employment checks, to determine whether the bank should be lending more money to a customer.

For David Snell, an independent sourcing advisor, this case study is just one of the ways RPA brings “positive disruption” to industries.

“Its potential to positively disrupt the workplace is incredible, while its potential to disrupt the outsourcing market is far reaching,” says Snell, who was New Zealand country manager for Alsbridge (now part of Information Services Group).

He argues RPA and related automation technologies should already be on the agenda for CIOs.

“If CIOs are not already looking at RPA as a digital enablement to their digital strategy, they are already behind the eight ball a little bit,” Snell tells CIO New Zealand.

“Early adopters are already starting to see significant benefits and not only in their own operations,” he states.

He predicts there will be a rise in the uptake of RPA, because of the net benefits of potential productivity, cost savings and better CX (customer experience).

Apart from banking, RPA has practical applications in insurance, healthcare and manufacturing according to a white paper written by Craig Nelson, Alsbridge managing director and now partner at Information Services Group.

“RPA is a disruptive and game-changing technology that presents an opportunity to adapt, change and achieve significant results,” writes Nelson.

Though before embarking implementing RPA, ICT managers need to assess the interests of key stakeholders, their current and desired states of operation, their existing talent pool and senior executive willingness to sponsor the integration of human and virtual labour, according to the paper.

RPA sponsors, Nelson believes, must be prepared to address three questions:

  • How will work processes change?
  • How will skills requirements change?
  • How will the organisation change?

Big data challenge

Snell, on the other hand, links the take-up of RPA to the challenges of data management.

“Getting data into the right place in a usable form is massive and it continues to be a big challenge for a lot of the organisations,” he states.

“Unless you are able to feed that data in those powerful algorithms, you really are not going to get the results that will give you some predictive capabilities.”

For Snell, the most important part of an RPA deployment is change management.

“The technology is not difficult,” he states, but there will always be “people, politics and an appetite for change” that will be involved in the deployment.

Source: accelerating automation is positively disrupting industries

How bots will shape the future of work

Picture this. You are in an elevator and your watch starts vibrating and one swipe tells you that it’s an important alert from your bank. You are facing some issues with your banking transaction and you are connected to a chatbot (an intelligent robot at the bank’s contact centre) and over a series of messages, the bot meticulously addresses your problem. This indicates the onset of a digital era which is set to transform the way people work and interact in daily business activities.

Intelligent automation, software robotics, cognitive computing, augmented reality and machine learning have the potential to increase productivity, improve efficiency, and reduce time-to-market and reshape the manner in which people and enterprises function.

Many industry experts believe that the use of artificial intelligence (AI) will become pervasive across industries in the next decade. Machines with natural language processing skills will help to automate routine and repetitive tasks.

One of the world’s oldest and largest news gathering organizations is using AI to develop more than 4,000 corporate earnings stories.

Leading IT services companies have developed AI platforms to streamline different aspects of their project delivery. For instance, a tier-1 Indian IT services player plans to use its AI platform to automate application maintenance projects. This will help the company save up to $50 million and free around 3,000 engineers from routine software maintenance activities. A leading utility company is using AI in the area of financial reporting and accounting statement preparation.

The future of the workforce is poised to change dramatically. In the new wave of automation led by software robots, AI is also expected to redefine jobs which were previously considered immune to technological displacement such as white-collar work. For instance, algorithms are bringing deeper insights in the financial services space (high-frequency trading) and automation is making its presence felt in the healthcare segment by altering the manner in which healthcare services are being delivered (mobile health apps, robotic surgery and diagnosis by algorithms).

The technological advances will redefine the notion of what a “job” is and change the “eight-hours-a-day-work” paradigm. The workplace will become a hyper-connected network of people and machines and no longer be confined to traditional working hours or places.

However, the proverbial glass is half full. Previous experience has shown that automation has redefined what were then considered as traditional jobs and created new jobs in the manufacturing ecosystem.

Rather, the digital disruption brought about by automation is likely to create new companies and opportunities in hitherto unheard of economic activities. However, it is observed that a majority of the workforce is not adequately prepared to deal with these technology changes.

Both employees and enterprises will have to adapt, reinvent and upskill themselves to stay relevant.

As technology drives and reshapes business needs, enterprises will be required to revamp their learning and development programmes to re-skill their employees and enable them to take on tasks beyond the purview of bots and algorithms. Technical training needs to be oriented towards meeting the dynamic needs of the next-generation workforce. Furthermore, the next-generation workforce will have to take ownership to build their own ‘brand’ to take on jobs which match their new skill-sets.

At the same time, to attract the new-age digitally-connected workforce, companies will need to provide an entrepreneurial environment where employees can experiment innovative ideas. The changing nature of work will free up individuals to pursue other interests and has the potential to spur a new wave of entrepreneurship.

An important stakeholder in the journey to the next-generation workplace is the government, which can act both as a monitor and an enabler. Governments around the world will have an important role to play in the new paradigm as the employer-employee relationship evolves drastically.

Next-generation enterprises, whose businesses are based mainly on AI/automation, argue that existing regulations were designed for another era and are not in line with the demands of the industry. There is a need to re-examine existing regulations and develop new regulatory regimes which are designed for the future—nimble, real time and responsive to the needs of the work force.

Source: Livemint-How bots will shape the future of work

The regulatory automation ecosystem: Reinventing monitoring and testing programmes

KPMG’s Todd Semanco discusses how the leveraging of regulatory technology, digital labour and data analytics is transforming compliance programmes, and why cognitive automation is seen as the future of regulatory infrastructure.

In today’s environment, risk and compliance leaders are increasingly challenged to take on more responsibility though funding levels often do not keep pace. This is prompting many to pursue sustainable means of becoming more efficient while improving quality and broadening capabilities. Two areas rife with opportunity for redesign are monitoring and testing, and leaders are considering how digital labour and automation can transform the function in a variety of ways: broader, deeper and more frequent coverage; enhanced synergy across multiple assurance functions; a more dynamic and robust risk assessment process; and more meaningful and streamlined reporting, including data visualisation.

What should organisations consider when transforming a monitoring and testing programme?

Todd Semanco: In order to maximise the value in such a transformation, it is essential at the outset to confirm an enterprise view of all the various testing activities across the organisation. Not only will this be critical in identifying gaps and redundancies in coverage, but is also required to evaluate and determine an optimal target-state model – whether it be a centralised monitoring and testing function or a decentralised function operating under a common mandate and set of standards. Test scripts and procedures should be analysed to confirm appropriate alignment to regulatory obligations and policies, and such analysis should include the availability and integrity of testing data, as well as documentation of the products, activities and systems covered by the respective testing and monitoring plans. Considering these elements, leaders can develop tangible, prioritised automation road maps focused on addressing gaps and/or shifting execution from human capital to digital labour.

The foundation of monitoring and testing programmes is a robust compliance risk assessment. What are the recent trends in this area?

Todd Semanco: Risk assessment continues to represent an area of heightened attention for many. Among the top focus areas are the breadth and frequency of risk assessments, key variables and drivers considered during the risk assessment process, the qualitative and quantitative mechanics, and, of course, the alignment of risk assessment results to testing and monitoring efforts. Risk assessment is a dynamic exercise in which numerous inputs such as testing results, complaints, hotline calls, investigations, etc, are considered ‘real time’, and testing and monitoring plans are either confirmed or adjusted accordingly. The reality is, given the volume and pace of change of this data, many organisations struggle to surface and apply insights at an appropriate rate. For many, it is becoming clear that enhanced automation, leveraging integrated regulatory technology and digital labour, is a ‘must-have’ to sustainably assess risk in a timely manner.

What role is technology playing in monitoring and testing programmes?

Todd Semanco: Regtech – broader than fintech and not limited to a specific technology – and digital labour are being leveraged to transform programmes end-to-end. From the way organisations monitor and manage global changes, to regulatory obligations and how testing is performed, analysed and reported, all components of managing the function are under review. The demands, from internal and external stakeholders alike, to demonstrate adequate coverage and provide precise impact and root-cause analysis are very high. To meet these demands, leaders are increasingly turning to technology to collect, consolidate and map key data elements together – for example, obligations, policies, risks, controls, process detail – at a granular level. This capability supports not only dynamic regulatory change management activities, but also the oversight of business process and technology changes. Further, by consolidating and integrating monitoring and testing scripts within this technology, forming rules engines, outcomes and impacts may be more immediately assessed and remediated while data is accumulated to support predictive analytics.

With the speed of technology advancement, how are organisations leveraging digital labour?

Todd Semanco: We’re seeing all levels of organisations – from the business to risk, compliance and internal audit partners – intensifying their efforts to further cognitive automation, integrate digital labour and establish an enterprise-wide automation infrastructure. Efforts are typically phased along a continuum – from basic process automation of repeated high-volume transactions to enhanced robotic process automation reliant upon both structured and unstructured data sources, through to cognitive automation. In the monitoring and testing space, digital labour is driving a shift from sample-based testing to the testing of full populations, and resultant outcomes are increasingly available for use in analytics, predictive forecasting and enhanced monitoring and surveillance. Leading organisations are viewing this as an investment opportunity to revamp monitoring and testing capabilities in a sustainable way while operationalising compliance – often while yielding a compelling return on investment and competitive advantage.

Source: regulatory automation ecosystem: Reinventing monitoring and testing programmes

Cybersecurity in the Age of Digital Transformation

Technologies such as big data analytics, the Internet of Things (IoT), blockchain, and mobile computing are reinventing the way companies handle everything from decision making to customer service. The automation of virtually all business processes and the increasing digital connectedness of the entire value chain create agility, but they also significantly raise cybersecurity risks and threat levels.

The key to addressing those risks and threats is building security into applications, as well as into interconnected devices, right from the start.

Running IT systems in the cloud supports organizational flexibility. To that end, companies are increasingly moving both data and business functions (e.g., human resources and procurement) between the cloud and on-premises legacy systems.

But as companies embark on their journeys of digital transformation, they must make cybersecurity a top priority, says Michael Golz, CIO, SAP Americas. “We have to maintain confidentiality, integrity, and availability of data in all these contexts: on premises, in the cloud, and in hybrid environments,” Golz says.

Both the value and the volume of data have never been higher, and end points are more vulnerable than ever. That’s especially the case with the IoT, which is still in its infancy. As the IoT is extended to everything from industrial equipment to consumer devices, attacks are growing not just in number, but also in sophistication. Next-generation devices are now deployed in potentially vulnerable environments such as vehicles, hospitals, and energy plants, vastly increasing the risks to human welfare. Concerns about such devices being hacked, turned into botnets, and used to attack targeted computers and organizations are growing as well.

“Any vulnerabilities in the supply chain now have a wildfire effect that results in millions of dollars being lost and trust being destroyed on impact,” says Justin Somaini, global CSO, SAP. “It used to take a while to exploit these weaknesses. Nowadays, it’s very fast and the damage is immediate.”

With the stakes so high, senior IT leaders, including both CIOs and CSOs, need to adopt a more proactive approach to securing critical data. Forensic analysis of what went wrong after a breach won’t be enough to save lives—or C-level careers.

Focusing on Both Applications and Data

Cybersecurity professionals are accustomed to securing access to their networks and applications. But digital transformation leads to an explosion of connected environments where perimeter protection is no longer enough. Attackers and other malicious individuals will continue to compromise weak links, resulting in deep access to companies’ networks, systems, and data.

In a digital world, the classic, contained enterprise network no longer exists. For that reason, security must be embedded into all applications as the first line of defense, Somaini says. To achieve that level of security, SAP favors the “security by default” approach, in which an application’s embedded security controls are, by default, set at the highest levels of protection. “The idea is to build in security, rather than asking users to opt in,” he says. That’s one of the hallmarks of being more proactive in securing data: protection is the default posture.

So-called “self-defending apps” are another example of proactive security. This active-protection technique provides applications with advanced access-control capabilities, allowing them to react to malicious source-code modifications and debugging at runtime. Encryption of all data in transit is another core tenet of preemptive cybersecurity, according to Somaini. SAP HANA, for example, features encryption services for data both at rest and in flight.

Among the most important factors for heading off insider threats are two-factor authentication (which verifies a user’s identity via two different methods) and role-based access controls (which limit the user’s access to data by job role), Golz says. “The insider threat is very real. There are a lot of data breaches today by people who have a legitimate authorization that is too broad. They get to see more than they are entitled to. Two-factor authentication dramatically increases the security of the communications.”

Bringing Two Worlds Together

The cybersecurity issues raised by digital transformation are driving the need for a better understanding between the organization’s cybersecurity professionals and those who provide application security. “Traditionally, those groups don’t speak the same language and don’t understand what the other side is doing,” Golz says.

Today, responsibility for cybersecurity is generally shared by the application team, which tends to focus on hardening and securing enterprise applications, and the cybersecurity professionals, who handle aspects such as access controls and firewalls. “Those are different roles, and they use different technologies and terms,” Golz says. Going forward, with the focus shifting from traditional network-perimeter security to securing application data, those two worlds need to join forces to prevent issues from falling through the cracks, he adds.

Digital transformation makes it essential that the cybersecurity and IT teams find a common understanding, a shared terminology, and a unified approach to securing applications and data. “Systems are being opened in ways that they weren’t before,” Golz explains. “There is more direct connectivity with suppliers, partners, customers, and consumers. There are tighter connections between a company’s Web presence and back-end systems. The seamless process flows mean more things can go wrong.”

When it comes to digitally transforming a company’s business, cybersecurity must be part of that conversation from the start. As a case in point, many companies now sell software along with their products. For example, a large industrial vendor such as GE today provides not just the equipment used in production environments but also subscription-based monitoring and maintenance services to ensure that equipment does not experience an unexpected outage. “That means all the challenges and requirements a software company faces now apply to you. The way you protect the data is paramount. It’s a whole set of new challenges,” Golz says.

As one of the top providers of business-critical applications, SAP will continue to build security into the heart of its applications and to secure cloud operations to protect content and transactions, Golz says. “We are working to help customers define, plan, and execute measures for their secure digital transformation.”

Source: Technology Review-Cybersecurity in the Age of Digital Transformation

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.

For more about artificial intelligence, watch:

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

Beyond robo-compliance: How bots will soon permeate banking

Banks are already experimenting with robotic process automation in areas like their compliance functions. But robotics technology may soon find its way to nearly all aspects of running the bank.

As banks become more comfortable with the relying on software robots to replicate the actions of a human interacting with machines to handle rote tasks, experts say they will be quick to deploy the technology companywide as a way to trim expenses and redirect employees to more crucial tasks.

“I think we’re going to see it move from a few narrow functions to across the enterprise,” said Alan McIntyre, the industry managing director for banking at Accenture. “It’s going to become an indispensable technology for banks, rather than an interesting experiment.”

This could include areas in finance departments that are heavily manual, such as accruals and managing and clearing payments. Human resources and administrative functions is also an area where robotics can be deployed. For example, robots could take over payroll and managing employee incentive programs.

And even if robots don’t fully run these departments, they can be used to assist human employees during periods when workloads get large, said Alastair Bathgate, CEO and co-founder of Blue Prism, a robotic process automation firm, which has partnered with Accenture to help financial services and other industries implement robotics.

“When used to help with large or temporary workloads, [robotics] can reduce costs,” since additional temporary employees wouldn’t need to be hired for that period, he said.

Austria’s Raiffeisen Bank International AG is among the first to work with Accenture and Blue Prism to automate various business functions. Additionally, it is in the process of creating an in-house robotics center dedicated to experimenting with how the technology can be used in different functions at the bank.

The bank started out with four pilots implementing robotic process automation in tasks that had “low-to-medium complexity; rule-based processes with a logical order of steps, repetitive process patterns with clearly defined process options,” said Markus Stanek, head of group efficiency management at the bank.

With the center, Raiffeisen will experiment with how to implement robotics in in a whole host of banking functions.

“RPA allows us to introduce automation where expensive and heavy IT solutions do not pay off,” Stanek said. “RPA as a tool and as we understand it, is to be deployed like a surgical intervention, automating specific process steps…given the speed of implementation it should allow us to start thinking and implementing automation in a couple of weeks rather than months.”

Of course, the technology also has potential uses for consumer facing functions, not just back-office automation and staff efficiency. The most notable use thus far has been so-called robo-advisers, which use simple, rules-based models to choose investment vehicles for an investor after that person inputs basic information about their risk tolerance and investing goals. But beyond robo-advisers and even more complicated wealth management services, the technology can be used to enhance and improve customer relationship management for banks, said Mark Schwanhausser, director of omnichannel financial services at Javelin Strategy & Research.

Think of a human financial adviser, said Schwanhausser, after a major event that affects the stock market (such as Brexit). The adviser may have 30 clients to call who have questions about how this affects their portfolio, a major task for one person. But using robotics, the adviser could send a message tailored to each individual client telling them how the event affects their portfolio, along with a “click to call” button if they do want to talk to the adviser more in depth.

Similar ideas can be used to enhance personal budgeting and financial management tools for customers, Schwanhausser said. For example, when logging onto mobile banking, a customer can view personalized information on how they are doing achieving their financial goals with customized insights into spending and saving habits.

“Maybe [robotics] could even take your financial information and boil it into a useful infographic,” he said. “It can provide very personalized service that can be delivered without interacting with a human.”

But, like Accenture’s McIntyre, Schwanhausser believes this doesn’t mean the human element will be removed from banking, but rather deployed in smarter ways.

“It is a move to automation, but it’s a move to automation that complements the human element,” he said. “You may not want to talk to someone every time you have any financial question; [robotics] can provide you with a lot of daily information you wouldn’t have thought to ask for to begin with. If it just represents an interaction that never would have happened anyway, then it’s not a threat to any employee.”

Regardless of how it plays out, Schwanhausser said the move to using robotics in financial services has already passed the point of no return.

“What you are seeing now is the beginning stages of what is going to increasingly become the norm,” he said.

Source: robo-compliance: How bots will soon permeate banking

The Robot Work Revolution, How Most Jobs Will Change

The robot worker has arrived and many more are coming — impacting the stock boy and the chief executive officer in fashion.

The good news is that jobs aren’t expected to go away as much as they’re going to change, with human workers relying more on automation, according to a new report on the future of work from the McKinsey Global Institute.

Artificial intelligence and machine learning are all jumping ahead and have opened the door for continuing change.

“Automation now has the potential to change the daily work activities of everyone, from miners and landscape gardeners to commercial bankers, fashion designers, welders — and ceo’s,” McKinsey said.

“Almost half the activities people are paid almost $16 trillion in wages to do in the global economy have the potential to be automated by adapting currently demonstrated technology,” the report said.

While less than 5 percent of all occupations could be entirely given over to robots, the research found that a third of tasks that make up 60 percent of jobs could be handled by machines.

That could help companies do more, and better.

“At a time of lackluster productivity growth, this would give a needed boost to economic growth and prosperity and help offset the impact of a declining share of the working-age population in many countries,” McKinsey said.

That’s the utopian vision, more “Star Trek” than “Terminator,” but it would be a tricky feat for companies and policy makers to make the transition in the generations ahead.

McKinsey said the tasks that are most susceptible to automation are “physical activities in highly structured and predictable environments, as well as the collection and processing of data.”

All of that should be ringing bells for retailers.

Such tasks make up 51 percent of the U.S. work life, accounting for almost $2.7 trillion in wages, and are centered in manufacturing, accommodation and food service, and retail trade.

Many tasks in the retail trade could be automated, according to the report, which pointed to work in a predictable physical environment and the collection and processing of data.

That doesn’t necessarily mean that Apple’s Siri, Amazon’s Alexa or the Google Assistant will be bucking for employee of the month at Macy’s Inc. anytime soon. Other key attributes in retail don’t lend themselves to robots, including management and interfacing with people.

All told, half of today’s work activities could be automated by 2055, the report said. That’s a shift in the labor force that would rival the move away from an agricultural-based economy and one that was based on manufacturing — a sector President Trump is talking about trying to bring back.

Agricultural and manufacturing jobs that were lost in those massive changes were replaced by other jobs that didn’t exist earlier. (That doesn’t mean that the march of progress includes everyone, with some workers leaving the factory finding themselves working at fast-food joints or nowhere at all).

“We cannot definitively say whether historical precedent will be upheld this time,” McKinsey said. “But our analysis shows that humans will still be needed in the workforce: the total productivity gains we estimate will come about only if people work alongside machines.”

As long as machines want them to.

Source: -The Robot Work Revolution, How Most Jobs Will Change