Thursday, December 14, 2017

How Artificial Intelligence Will Personalize How We Work

Artificial intelligence in the workplace is here to stay. However, as enterprise technologies continue to develop and evolve, we must understand how AI will affect our roles and responsibilities at work.

The unknowns about the impact of AI has led to the fear that this emerging technology could be a substitute for – or entirely eradicate – existing jobs. Depending on which stats you refer to, AI will replace over 40% of jobs by 2030, or that 165 million Americans could be out of work before 2025.

Yet it is not all doom and gloom. Given the rate of new systems, processes, and data that we’re exposed to each day, AI can deliver tangible benefits in learning our skills, habits and behaviors, upending how we use technology. When companies are spending over $3.5 trillion on IT and use an average of 831 cloud services, it’s no surprise that we forget 70% of what we learn in a day, unless we immediately apply that knowledge into
our workflows.

There are four tectonic shifts happening within businesses that are propelling the need for greater personalization and efficiency in how we use technology:

● Employee expectations and behaviors have shifted. Unlike their predecessors, Millennials and Gen Z employees are accustomed to digital technologies. While they’re resourceful and can easily access information, they aren’t necessarily able to retain it. Generally speaking, they expect consumer-level technologies, are highly distracted and change positions often – and thus expect technology to be quick, efficient and intuitive.
● Organizations are undergoing a sweeping digital transformation. One of the biggest buzzwords of 2017 is “digital transformation” and has been sweeping across all businesses as they look to modernize their activities, processes and models to become completely digitized.
● Decisions are fragmented between departments. As companies move to more digitalized systems, the decision to implement new technologies has been driven by line of business heads. From HR systems, customer relationship management (CRM) tools to ERP solutions, procurement decisions are based on departmental needs, rather than the traditional approach of it being mandated by the CIO or at the organizational level.
● Cloud technologies are creating a training challenge. Cloud-based technologies indicate that systems are undergoing regular improvements and updates, creating a situation where employees must constantly adjust to new changes that they need to learn and adopt quickly.

Based on these changes, AI is a critical component for tomorrow’s organizations. Coupled with deep analytics, AI can greatly affect individual user behavior, identifying barriers to technology adoption and contextually guiding users on how to use any new solution. In doing so, employees can ultimately become instant pros in using a system – even if they haven’t used the technology before.

This contextual, personalized, and just-in- time approach allows us to abandon traditional training and development methods, which can become quickly outdated as we continue to encounter new systems and interfaces. It doesn’t make sense to set up a classroom style training to familiarize your team with a new HR software, for example, when incremental product updates occur so frequently. When employees are stuck
using a system, they’re more apt to ask a colleague for help, search online for the answer, or worst of all, give up on using the system. All are ineffective uses of our time.

Instead of feeling daunted by the onslaught of new systems we encounter, technology should learn about the user to improve their workflows. Creating systems that learn and automate tedious processes will be a major battleground for technology vendors in the next few years. It won’t be long before we can rely on AI to do all the “learning” for us – leading to a workplace where we train the software to adapt to our needs, rather than forcing us to adapt to the software.

Rephael Sweary is the cofounder and president of WalkMe, which pioneered the digital adoption platform. Previously, Rephael was the cofounder, CEO and then President of Jetro Platforms which was acquired in 2007. Since then, he has funded and helped build a number of companies both in his role as Entrepreneur-inResidence at Ocean Assets and in a personal capacity.

How to Not Squander Your AI Investments

We are more comfortable having conversations with machines than ever before. In fact, by 2020, the average person will have more conversations with bots than with their spouse. Twenty-seven percent of consumers weren’t sure if their last customer service interaction was with a human or a chatbot.

To the average person, conversation is simply a more convenient interface, evidenced by messaging services having supplanted social networks in active users. But when it comes to conversational interactions with bots, what exactly do these exchanges mean for machines?

For machines, these conversations are just data. Despite this newfound abundance of ridiculously valuable data, most companies are still just using AI technologies to deflect calls from the contact center.  We have the bigger opportunity to use this data to impact real business decisions across every role, function and department in the enterprise. Yet here we are, about to kick off 2018, and most companies are still leaving the majority of the value of their AI investments on the table.

It’s time to wake up to the data opportunity created by conversational intelligence as a whole.

The value of artificial intelligence has compounding interest

In the world of AI, there is a popular concept called the network effect. This is the concept that a good or service becomes more valuable when more people use it. For conversational intelligence, platforms become smarter as it gets more customer data, experiencing the data network effect.

It’s not news that conversations are a great data source. Brands have more information than ever about their customers, especially when paired with other data points such as location, device, and even GPS.

The application of the network effect here is apt. Truly, in the context of conversational technologies, intelligence breeds intelligence. It’s a form of compounding interest in which the principle is the data from systems of record that empower conversations, the interest is the conversation, and the compound interest is the intelligence. This virtuous cycle has been a key driver in the innovation of these technologies.

However, up until now, conversational data has been mostly used to simply have better conversations. The product is getting better and better, the back-and-forths far smoother than ever before. The bottom line, bolstered by the speed of resolution, volume of requests answered, and satisfied customers, is improving.

This is all well and good, but the reality is that hidden in these conversations is business value that to date, brands have let go to waste and not fully capitalized on.

Conversational Intelligence is Real-Time Market Research

Language has always been a tool to better understanding humans – what they need and what they want. Many business use this information in the psychology of marketing and sales, via the arduously conducted market surveys that we are all so apt to ignore. This business process of market research has, until recently, been a separate line item.

Now, however, through the core business process of customer service, the data collection is already happening. As customer service has become more and more automated, we are collecting data more consistently, in real-time, and in a format that is ripe for analyzing. The onus is still on the brand to take advantage of this market research, but brands are in a position to innovate, enhance, and improve at a far more rapid pace.

For example, a large public wing company took advantage of their customer data to unlock an entirely new revenue stream. Through their chatbot, powered by Conversable, they realized that their customers were frequently asking for gluten free options. Now a staple offering, the company was able to unlock this opportunity much faster than the traditional model of market research. Imagine the possibilities hidden in the data, from opening hours, location requests, and even menu items.

Not capitalizing on the information in these conversations means brands are leaving more than half of the value on the table, for no good reason at all.

A story told before

Finding value in unexpected places has happened before. When Dunkin Donuts realized that the hole in the middle of their doughnuts was actually chock full of opportunity in the form of a doughnut hole, their business experienced a massive boost. The waste from their original product that previously ignored or discarded is similar to how brands treat AI. There is latent value in the conversational data not being acted upon.

Which brings us back to the challenge of conversational intelligence today. We’ve reached the point where conversational interfaces are technologically proficient enough to carry out simple tasks and speech recognition has progressed to provide the kind of convenience of customer experience that we expect. Still, we’ve only barely begun to tap the potential of conversational intelligence, and key to its progression, and value, in the future is making real use of the data its producing in near real time.

The criticism of conversational AI to date has often been that these kinds of technologies are over-promised and under-delivered. It is undoubtedly a huge investment, and most people consider it to be the magic solution to their business problems. While I abhor the hype of AI, I also recognize that value is being wasted away in the form of untapped conversational data.

It’s time to start paying attention.

Monday, December 11, 2017

Five 2018 Predictions — on GDPR, Robot Cars, AI, 5G and Blockchain

Predictions are like buses, none for ages and then several come along at once. Also like buses, they are slower than you would like and only take you part of the way. Also like buses, they are brightly coloured and full of chatter that you would rather not have in your morning commute. They are sometimes cold, and may have the remains of somebody else’s take-out happy meal in the corner of the seat. Also like buses, they are an analogy that should not be taken too far, less they lose the point. Like buses.

With this in mind, here’s my technology predictions for 2018. I’ve been very lucky to work across a number of verticals over the past couple of years, including public and private transport, retail, finance, government and healthcare — while I can’t name check every project, I’m nonetheless grateful for the experience and knowledge this has brought, which I feed into the below. I’d also like to thank my podcaster co-host Simon Townsend for allowing me to test many of these ideas.

Finally, one prediction I can’t make is whether this list will cause any feedback or debate — nonetheless, I would welcome any comments you might have, and I will endeavour to address them.

1. GDPR will be a costly, inadequate mess

Don’t get me wrong, GDPR is a really good idea. As a lawyer said to me a couple of weeks ago, it is a combination of the the UK data protection act, plus the best practices that have evolved around it, now put into law at a European level with a large fine associated. The regulations are also likely to become the basis for other countries — if you are going to trade with Europe, you might as well set it as the baseline, goes the thinking. All well and good so far.

Meanwhile, it’s an incredible, expensive (and necessary, if you’re a consumer that cares about your data rights) mountain to climb for any organisation that processes or stores your data. The deadline for compliance is May 25th, which is about as likely to be hit as I am going to finally get myself the 6-pack I wanted when I was 25.

No doubt GDPR will one day be achieved, but the fact is that it is already out of date. Notions of data aggregation and potentially toxic combinations (for example, combining credit and social records to show whether or not someone is eligible for insurance) are not just likely, but unavoidable: ‘compliant’ organisations will still be in no better place to protect the interests of their customers than currently.

The challenges, risks and sheer inadequacy of GDPR can be summed up by a single tweet sent by otherwise unknown traveller — “If anyone has a boyfriend called Ben on the Bournemouth – Manchester train right now, he’s just told his friends he’s cheating on you. Dump his ass x.” Whoever sender “@emilyshepss” or indeed, “Ben” might be, the consequences to the privacy of either cannot be handled by any data legislation currently in force.

2. Artificial Intelligence will create silos of smartness

Artificial Intelligence (AI) is a logical consequence of how we apply algorithms to data. It’s as inevitable as maths, as the ability our own brains have to evaluate and draw conclusions. It’s also subject to a great deal of hype and speculation, much of which tends to follow that old, flawed futurist assumption: that a current trend maps a linear course leading to an inevitable conclusion. But the future is not linear. Technological matters are subject to the laws of unintended consequences and of unexpected complexity: that is, the future does not follow a linear path, and every time we create something new, it causes new situations which are beyond its ability to deal with.

So, yes, what we call AI will change (and already is changing) the world. Moore’s, and associated laws are making previously impossible computations now possible, and indeed, they will become the expectation. Machine learning systems are fundamental to the idea of self-driving cars, for example; meanwhile voice, image recognition and so on are having their day. However these are still a long way from any notion of intelligence, artificial or otherwise.

So, yes, absolutely look at how algorithms can deliver real-time analysis, self-learning rules and so on. But look beyond the AI label, at what a product or service can actually do. You can read Gigaom’s research report on where AI can make a difference to the enterprise, here.

In most cases, there will be a question of scope: a system that can save you money on heating by ‘learning’ the nature of your home or data centre, has got to be a good thing for example. Over time we shall see these create new types of complexity, as we look to integrate individual silos of smartness (and their massive data sets) — my prediction is that such integration work will keep us busy for the next year or so, even as learning systems continue to evolve.

3. 5G will become just another expectation

Strip away the techno-babble around 5G and we have a very fast wireless networking protocol designed to handle many more devices than currently — it does this, in principle, by operating at higher frequencies, across shorter distances than current mobile masts (so we’ll need more of them, albeit in smaller boxes). Nobody quite knows how the global roll-out of 5G will take place — questions like who should pay for it will pervade, even though things are clearer than they were. And so on and so on.

But when all’s said and done, it will set the baseline for whatever people use it for, i.e. everything they possibly can. Think 4K video calls, in fact 4K everything, and it’s already not hard to see how anything less than 5G will come as a disappointment. Meanwhile every device under the sun will be looking to connect to every other, exchanging as much data as it possibly can. The technology world is a strange one, with massive expectations being imposed on each layer of the stack without any real sense of needing to take responsibility.

We’ve seen it before. The inefficient software practices of 1990’s Microsoft drove the need for processor upgrades and led Intel to a healthy profit, illustrating the vested interests of the industry to make the networking and hardware platforms faster and better. We all gain as a result, if ‘gain’ can be measured in terms of being able to see your gran in high definition on a wall screen from the other side of the world. But after the hype, 5G will become just another standard release, a way marker on the road to techno-utopia.

On the upside, it may lead to a simpler networking infrastructure. More of a hope than a prediction would be the general adoption of some kind of mesh integration between Wifi and 5G, taking away the handoff pain for both people, and devices, that move around. There will always be a place for multiple standards (such as the energy-efficient Zigbee for IoT) but 5G’s physical architecture, coupled with software standards like NFV, may offer a better starting point than the current, proprietary-mast-based model.

4. Attitudes to autonomous vehicles will normalize

The good news is, car manufacturers saw this coming. They are already planning for that inevitable moment, when public perception goes from, “Who’d want robot cars?” to “Why would I want to own a car?” It’s a familiar phenomenon, an almost 1984-level of doublethink where people go from one mindset to another seemingly overnight, without noticing and in some cases, seemingly disparaging the characters they once were.  We saw it with personal computers, with mobile phones, with flat screen TVs — in the latter case, the the world went from “nah, thats never going to happen” to recycling sites being inundated with perfectly usable screens (and a wave of people getting huge cast-off tellies).

And so, we will see over the next year or so, self-driving vehicles hit our roads. What drives this phenomenon is simple: we know, deep down, that robot cars are safer — not because they are inevitably, inherently safe, but because human drivers are inevitably, inherently dangerous. And autonomous vehicles will get safer still. And are able to pick us up at 3 in the morning and take us home.

The consequences will be fascinating to watch. First that attention will increasingly turn to brands — after all, if you are going to go for a drive, you might as well do so in comfort, right? We can also expect to see a far more varied range of wheeled transport (and otherwise — what’s wrong with the notion of flying unicorn deliveries?) — indeed, with hybrid forms, the very notion of roads is called into question.

There will be data, privacy, security and safety ramifications that need to be dealt with — consider the current ethical debate between leaving young people without taxis late at night, versus the possible consequences of sharing a robot Uber with a potential molester. And I must recall a very interesting conversation with my son, about who would get third or fourth dibs at the autonomous vehicle ferrying drunken revellers (who are not always the cleanliest of souls) to their beds.

Above all, business models will move from physical to virtual, from products to services. The industry knows this, variously calling vehicles ‘tin boxes on wheels’ while investing in car sharing, delivery and other service-based models. Of course (as Apple and others have shown), good engineering continues to command a premium even in the service-based economy: competition will come from Tesla as much as Uber, or whatever replaces its self-sabotaging approach to world domination.

Such changes will take time but in the short term, we can fully expect a mindset shift from the general populace.

5. When Bitcoins collapse, blockchains will pervade

The concept that “money doesn’t actually exist” can be difficult to get across, particularly as it makes such a difference to the lives of, well, everybody. Money can buy health, comfort and a good meal; it can also deliver representations of wealth, from high street bling to mediterranean gin palaces. Of course money exists, I’m holding some in my hand, says anyone who wants to argue against the point.

Yet, still, it doesn’t. It is a mathematical construct originally construed to simplify the exchange of value, to offer persistence to an otherwise transitory notion. From a situation where you’d have to prove whether you gave the chap some fish before he’d give you that wood he offered, you can just take the cash and buy wood wherever you choose. It’s not an accident of speech that pond notes still say, “I promise to pay the bearer on demand…”

While original currencies may have been teeth or shells (happy days if you happened to live near a beach), they moved to metals in order to bring some stability in a rather dodgy market. Forgery remains an enormous problem in part because we maintain a belief that money exists, even though it doesn’t. That dodgy-looking coin still spends, once it is part of the system.

And so to the inexorable rise of Bitcoin, which has emerged from nowhere to become a global currency — in much the same way as the dodgy coin, it is accepted simply because people agree to use it in a transaction. Bitcoin has a chequered reputation, probably unfairly given that our traditional dollars and cents are just as likely to be used for gun-running or drug dealing as any virtual dosh. It’s also a bubble that looks highly likely to burst, and soon — no doubt some pundits will take that as a proof point of the demise of cryptocurrency.

Their certainty may be premature. Not only will Bitcoin itself pervade (albeit at a lower valuation), but the genie is already out of the bottle as banks and others experiment with the economic models made possible by “distributed ledger” architectures such as The Blockchain, i.e. the one supporting Bitcoin. Such models are a work in progress: the idea that a single such ledger can manage all the transactions in the world (financial and otherwise) is clearly flawed.

But blockchains, in general, hold a key as they deal with that single most important reason why currency existed in the first place — to prove a promise. This principle holds in areas way beyond money, or indeed, value exchange — food and pharmaceutical, art and music can all benefit from knowing what was agreed or planned, and how it took place. Architectures will evolve (for example with sidechains) but the blockchain principle can apply wherever the risk of fraud could also exist, which is just about everywhere.

6. The world will keep on turning

There we have it. I could have added other things — for example, there’s a high chance that we will see another major security breach and/or leak; augmented reality will have a stab at the mainstream; and so on. I’d also love to see a return to data and facts on the world’s political stage, rather than the current tub-thumping and playing fast and loose with the truth. I’m keen to see breakthroughs in healthcare from IoT, I also expect some major use of technology that hadn’t been considered arrive, enter the mainstream and become the norm — if I knew what it was, I’d be a very rich man. Even if money doesn’t exist.

Truth is, and despite the daily dose of disappointment that comes with reading the news, these are exciting times to be alive. 2018 promises to be a year as full of innovation as previous years, with all the blessings and curses that it brings. As Isaac Asimov once wrote, “An atom-blaster is a good weapon, but it can point both ways.”

On that, and with all it brings, it only remains to wish the best of the season, and of 2018 to you and yours. All the best!


Photo credit: Birmingham Mail

Thursday, December 7, 2017

EaseUS Data Recovery Wizard Professional Review  

Originally Published Here: EaseUS Data Recovery Wizard Professional Review  

EaseUS Data Recovery Wizard Professional Review   posted first on

The AI Revolution for Recruitment

In the era of autonomous vehicles, food delivery by drones, and swiping our way to love, it’s clear that the tedious, time consuming, and often fruitless job recruitment system of old is in need of a tech-makeover. Who better to do it than the all mighty AI.

AI is already affecting the technology powering digital advertising, vehicle connectivity, and financial services. The recruitment industry is also ripe for an AI revolution.

In fact, 15% of HR leaders in 40 countries shared that they believe AI is already impacting the workplace, and an additional 40% believe that AI will significantly influence their decision making, in the coming two to five years. And it should, as implementing AI promises to maximize efficiency, reduce annual business costs, and tackle workplace inequality and discrimination, and more.

AI can process tasks at a scale that most HR teams would struggle with, including quickly and efficiently analyzing thousands of candidates’ applications, saving valuable time and money when searching for talent with the most relevant competencies and experienceNot only does this streamline the recruitment process, it also helps companies hire the most suitable candidates, drastically reducing the chances of hiring an ill-suited employee.  

while maximizing resource utilization enabling the best use of resources.

Hiring the wrong person can be crippling for companies, particularly smaller businesses. A bad hire not only presents a wasted opportunity cost, but equally troubling bi-products such as low productivity, and negative morale. When AI takes over the process of sifting through resumes, it will free up managers to refocus their attention on crucial matters such as employee retention, office morale, and of course productivity.

Job seekers are also looking for more efficient methods of job hunting. Tired of dealing with grueling applications, they too are turning to easy-to-use technologies which smartly match them with suitable companies. The level to which AI is empowering candidates goes as far as pinpointing the traits and trajectories of top performers, to tailoring job searches for opportunities that will result in a strengthened career path, ultimately providing an edge when it comes to securing the role and future they want.

While AI is remarkable in many ways, it’s not full-proof and requires oversight and vigilance to make sure conscious and unconscious human bias doesn’t seep into the hiring process. AI systems needs to be programmed what to learn and what data is important. But because they need to be programmed by humans, there is a risk of AI emulating existing human bias. For instance, if a company’s culture is already predominately made up of white males, with similar backgrounds, there is a danger that AI would exacerbate the problem by selecting candidates that match that company’s existing make up -not based on the variable of their actual ethnic background, but rather traits that tend to be more popular among members of a certain group.

As a National Academy of Sciences research paper shows, both male and female managers are twice as likely to recruit men, as opposed to women, based on paper resumes alone. AI can tackle this problem by focusing solely on experience and ability, ignoring demographic information such as name, nationality and/or gender.

Don’t fret! There are ways to consciously program the system to eliminate, or reduce, existing and inherent biases. This is a perfect example of how humans and AI will collaborate in recruitment. So, for those of you who are worried that AI will put you out of a job, it won’t. It will however, change the nature of your day to day work – and for the better.

The AI recruitment revolution is still in its infancy, but HR teams know that there is no better combination than artificial intelligence and emotional intelligence to build a winning team.