Orchestrating User Adoption from the Innovators to Laggards

Words like change, transformation, and automation will always generate a wide range of reactions throughout the rank and file of an organization, and our reaction to these words are good indicators as to which group we belong to on the adoption curve.

Innovators and early adopters tend to be motivated by change and late stage adopters and laggards tend to resist it.  Paradoxically, both groups have roles of equal importance in the user adoption process.

Knowing where users and people belong on the adoption curve and organizing them into user groups will enable a phased approach to managing the user adoption process. This approach to user adoption is preferred and will provide a more seamless diffusion of your innovation throughout each stage of the user adoption process. Furthermore, this will ensure that by the time it reaches the laggard group, the bugs will have already been worked out.

Successful innovations will reach a tipping point – which is the point that it is widely accepted and adopted by laggards. However, by the time that happens, count on the next innovation already being in play, and have a plan in place to repeat the cycle of innovation again.


Documenting business processes, conducting frequent business process reviews, building and running knowledge management and collaboration portals, establishing a talent management program, and investing in the professional development of your people will have a direct impact to continued success of any future business transformation initiatives in the future.

As companies transform, it is important to retain a high level of diversity across the organization. Take into account the tacit knowledge that could be lost when choosing to acquire new talent.

Make the language of change pervasive throughout your organization and create a business culture that is comfortable with change and ready to adapt to it.

On a personal level, be prepared for change, because change will happen. Invest time in your own professional development. Always be learning and be willing to step out of the comfort zone.

For more on this topic, I suggest reading the following two books:

“The Tipping Point – How Little Things Can Make a Big difference” by Malcolm Gladwell.

“Diffusion of Innovations” by Everett Rogers

Follow my blog at Social2Direct.com

Artificial Intelligence Potentials in B2B Marketing – D&B

AI Marketing

By Leslie Hancock
Founder & CEO

I did an amusing and highly informal survey, asking various people in my life what they think about when I mention “artificial intelligence” (AI). Not surprisingly, most think of human-like robots or omniscient, self-aware computer systems as portrayed in The Terminator, Ex Machina or Person of Interest. There’s the unavoidable association with a dystopian future in which the machines wake up and decide humans are a blight that must be eradicated. Even luminaries like Stephen Hawking and Elon Musk have warned us of the potential dangers of AI. But the general public still seems to think of AI technology as something to worry about in the distant future, not something that’s already a factor in almost every industry and aspect of our everyday lives.

The truth is that AI is already here, and it’s pervasive. Though most AI tech is nowhere near passing the Turing Test and taking over the world, it’s absolutely already transforming the way we do business. Whether they consider AI technology to be a friend or a foe, many B2B marketing leaders see the potential—and the inevitability—of AI. In fact, most CMOs in five global markets believe artificial intelligence will surpass social media’s influence in the industry. Nearly six in 10 believe that within the next five years, companies will need to compete in the AI space to succeed.

The Dawn of the Cognitive Era

Joanna L. Batstone, PhD, Vice President and Lab Director at IBM Research Australia and Chief Technology Officer, IBM Australia and New Zealand, says those involved in serious information science understand the enormous potential of intelligent systems.

“Cognitive computing systems learn at scale, reason with purpose and interact with humans naturally,” Batstone explains. “They learn and reason from their interactions with us and their experiences with their environment.”

Intelligent systems can go beyond answering numerical problems to offer hypotheses, reasoned arguments and recommendations. However, Batstone reassures anyone who might still be nervous about the nature of AI.

AI Is as Much About “Nurture” as “Nature”

But don’t worry, AI marketing (AIM) isn’t putting human marketers out of work anytime soon. Instead, it has the potential to be the connective tissue among martech systems, augmenting humans’ ability to make sense of and take action on data.

Wayne Sadin, Chief Digital and Information Officer at Affinitas Life, says, “Humans are still very much driving the train. It’s just a much faster, more powerful train now.”

Sadin urges marketers to rely on machine learning to perform rote tasks (like watching social media posts) in order to free teams to do higher-level creative work. By interconnecting social data to web analytics to a CRM database to external data and more, B2B marketers can use AI applications to automate and personalize many interactions that used to be time-intensive and far less efficient.

For this reason, Sadin thinks of the “A” in “AI” as augmented instead of artificial. “AI is part of the trend towards ‘Augmented Everything’: brains (AI), muscles (robots), vision (AR/VR),” Sadin says. “It makes workers smarter, stronger and faster. It’s not some central overlord machine, though. It’s just smart people taking advantage of data that’s already there and intelligent martech that is getting more and more capable of adapting to changing customer behaviors and expectations.”Sadin cautions that AI has to be carefully nurtured and guided. It can only do what we tell it to do and learn what we tell it to learn. AI can’t connect data and martech systems on its own without a human telling the technology how to make the connections and what to do with the data it takes in. (Maybe Maciej Ceglowski puts it best: “I find it helpful to think of algorithms as a dim-witted but extremely industrious graduate student, whom you don’t fully trust.”)

To train AI apps to be genuinely useful and not just more chaos cluttering up the martech landscape, we have to tie machine learning to business goals and ethical standards and be very, very specific about the data we feed into the AI to train it. We have to set limits and maintain good data hygiene so the AI makes the connections we want and stays on track. Without reasonably clean, complete data, AI is just garbage in, garbage out. And without encoding careful ethical parameters to nurture the AI’s learning and development.



AI Marketing Requires a Different Mindset

AI is already proving to be more of a friend than a foe to B2B marketers. To get the desired results, though, not only do marketers have to properly nurture AI technologies, we also have to embrace their potential—even when there’s the possibility that AI will eventually render our current jobs obsolete.

Paul Greenberg, independent consultant and author of the best-selling book CRM at the Speed of Light, points out that marketers have to embrace a very different mindset moving forward. Marketers are trained to target personas—representations of broad groups of people with similar characteristics and drivers—and produce content that appeals to them. But now customers expect an extremely high level of personalization and real-time interactions with brands across a multitude of devices and channels.

These technologies will nevertheless grow in popularity. Greenberg expects these applications to be acquired by, and absorbed into, big marketing clouds like IBM, Oracle and Salesforce, so you should expect to see them soon in your martech portfolios if you don’t have them already.

As for what we can and should expect AIM technology to do today and in the future, Greenberg points to one of his favorite marketing videos of the past few years, Corning’s “A Day Made of Glass.” This video was not produced using AI, but it’s the kind of thing Greenberg believes we can and should be leveraging AI to create. He says the Corning video brilliantly connects with consumers’ emotions to create a human-centric vision of the future with extremely broad appeal.

“AIM technology can already do this if we ask it to. It’s not way off in the future,” Greenberg says. “Intelligent systems can already make the connections between systems of record and systems of engagement to understand what would emotionally engage people. These technologies can independently test and learn from people’s reactions enough to change their approach and build highly personalized, relevant and contextual custom content for prospects and customers in real time.”

So is AI a friend or a foe to B2B marketers? The answer is yes. AI is what you make it. It all goes back to how you feed, nurture and train your intelligent systems. Just as you wouldn’t toss a five-year-old child into a mosh pit to learn how to dance, you can’t leave AIM technology on its own to learn from unlimited inputs and “dirty” data sources. If you train it well and use it to build interconnections across your organization and beyond, AIM can significantly augment your human marketing team’s capabilities to anticipate and connect with much larger and more diverse audiences over time.

Source: Artificial Intelligence Potentials in B2B Marketing – D&B

Amplifying human cognition with cognitive computing


By Rob High


Throughout history, humankind has created technologies that amplified our strengths. As an extension of the strength of our arms, we created the hammer; as an extension of the strength of our backs, the steam engine was born; and as an extension of our intelligence and skills, we created cognitive computing, a form of artificial intelligence (AI).

When we think about AI, it’s often about technology like natural language processing, smart homes and cars and virtual personal assistants. These solutions make people’s lives easier, and some may think they replace the need for human intervention altogether. But rather than replacing human minds, the purpose of cognitive computing is to make human cognition even stronger, even better. Cognitive computing enables people to see a perspective they wouldn’t have seen on their own; to recognize something they otherwise would have missed; to help them build an idea; to strengthen their creative processes.

Using cognitive computing to help save lives
IBM Watson for Oncology is able to assist oncologists when making decisions on how to treat their patients. Doctors do not have to rely solely on reading medical journals or finding treatments. By using cognitive computing, doctors can start with an understanding of the patient by extracting information from medical records. IBM Watson for Oncology is able to linguistically analyze clinical literature to recognize the intended meaning in the literature and whether it is relevant to the patient’s case, rather than processing a straight translation like a simple keyword search.

By performing micro-segmentation for population similarities and combining that with an analysis of the patients’ current disease states, possible treatments and regimes, and by monitoring progress, this cognitive system allows oncologists to predict and better prepare for treating side effects. The system is also able to analyze all clinical trials a patient may be eligible for to quickly get patients placed in clinical trials that best fit them. With less time analyzing reports on their own, oncologists are able to spend more time with their patients and making decisions, knowing they have all the crucial information they need.

Looking toward a future with cognitive computing AI is used to inspire and assist creative processes. It doesn’t just perform individual tasks or answer single questions, it shapes conversations with people that help to build out ideas. People work collaboratively to come up with and build on ideas in the presence of a cognitive system. Rather than thinking about AI like natural language processing — as a simple back-and-forth conversation — we look at it as a conversation between human and machine. The outcome of this dialogue is an amplification of human intelligence.

In our session at Mobile World Congress 2017, we will discuss how cognitive computing is evolving to further amplify human cognition. We will describe how, with devices that people carry or locate in the world, cognitive systems will create a presence with people, whose presence can be useful in activating and accelerating human creativity.

Cognitive computing is set to revolutionize how we interact with our world (in fact, it’s already started). Join me at Mobile World Congress 2017 at the session, “Artificial Intelligence: Chatbots and Virtual Assistants” on 27 February to discover more.

The rise of the chief marketing technologist | IBM THINK Marketing


Source: The rise of the chief marketing technologist – IBM THINK Marketing

This article was written by Marco Antonio Cavallo from CIO and was legally licensed through the NewsCred publisher network.

Whenever we hear the word “digitalization,” we must understand that it is the sound of inevitability and irreversibility. The digital economy isn’t on the horizon anymore, it′s here and it is here to stay. It’s no longer a secret that the digital economy is changing the world at an unprecedented rate. Companies that are looking to succeed in this fast emerging new economy must transform themselves by reinventing their business models, strategies, processes, and practices, and that impacts on the roles of all of its employees, as well as bringing departments to work together, once everyone is more and more dependent of technology to function.

It’s no surprise that marketing is rapidly becoming one of the most technology-dependent functions across all businesses. Gartner has predicted that by 2017, a company’s chief marketing officer (CMO) would be spending more on technology than its CIO, and that is becoming more credible every day, as many CMOs have adopted technology in their everyday activities, showing that technology became the core of marketing nowadays. Every year, CMOs are globally directing their budgets to the usage of technology or software in many different marketing areas.

IDC Research has released a few predictions on how marketing will strategically use technology to accelerate client acquisition, brand awareness, to gather and analyze market and customer information and even to optimize its operational efficiency in order to generate more revenue for companies and be more accurate when directing resources, mainly by enhancing customer experience.

1. In 2017, CMOs will spend more on content marketing assets than on product marketing assets: For decades, the product launch has reigned as the kingpin content event. With a “bill of materials” stretching through multiple Excel pages, product marketing assets suck up a major portion of the marketing budget – and much of that content is wasted. The days of product content dominance are numbered. Product content will remain important, but it will take its place behind the content marketing assets matched to decision-journey stages.

  1. By 2020, 50 percent of companies will use cognitive computing to automate marketing and sales interactions with customers: A few leads go right to sales. But the majority need further qualification and extended nurturing. Companies will increasingly turn to smart systems that automatically assess and respond to buyers at the point of need. IBM recently added Watson to its marketing cloud offerings. The question is not when cognitive marketing will become mainstream – but rather, will anyone notice?
  2. In 2017, 20 percent of large enterprise CMOs will consolidate their marketing technology infrastructure: Marketing has been absorbing marketing technology a bite at a time for more than a decade. Many organizations now manage dozens (if not hundreds) of point solutions. Just as marketing environments are hitting the wall of this operational complexity, marketing tech vendors are building solid integrated platforms – able to be tailored through a partner eco-system. A fortuitous convergence of supply and demand.
  3. By 2018, predictive analytics will be a standard tool for marketers, but only a third will get optimal benefit: Early adopters of predictive analytics for buyer behavior report amazing results. The benefits come from the ability to discover hidden segments that have a high propensity to buy. Marketers can also better serve these segments with behavioral targeting. However, the majority of marketers face big challenges to achieving the benefits. Chief inhibitors? Lack of statistical skills, stubborn organizational silos that won’t integrate data, and a culture that resists truth when it goes against tradition.
  4. By 2018, 50 percent of CMOs will make significant structural changes to their “intelligence” operations and organizations: “Intelligence” as a capability is growing in importance in modern marketing organizations. Intelligence includes market intelligence (MI), business intelligence (BI), competitive intelligence (CI), and social intelligence (SI). In the past, these four functions were spread around the enterprise. Now, IDC sees more companies consolidating into a larger, single, intelligence group – often combining with intelligence functions from other areas like sales. The elimination of silos in this important area is a positive sign.

With that perspective, it is clear that technology has turned a black art into hard science. Marketing now must be well versed in customer data, analytics, mobile, social and marketing automation tools, and that requires new type of executive. The Chief Marketing Technologist is emerging at the center of this transformation as a part strategist, part creative director, part technology leader, and part teacher professional. Its mission is very clear: align marketing technology with business goals, serving as a liaison to IT, and evaluating and choosing technology providers. About half are charged with helping craft new digital business models as well.

The best CMTs are able to set a technology vision for marketing in the digital age. They champion greater experimentation and more agile management of that function’s capabilities, as well as act as transformation agents, working within the function and across the company to create competitive advantage and collaboration. It is not difficult to enlist some of the main reasons why this new executive has emerged:

  • Software became the chief means of engaging prospects and customers: A marketing team’s choice of software and how to configure and operate it, along with how creatively the team applies it, materially affects how the firm perceives and influences its audience and how the audience sees the firm.
  • Digital marketing and e-commerce skills: once those two methodologies increasingly augment or replace traditional touch points, the importance of mastering those capabilities grows. Digital marketing budgets are expanding annually at double-digit rates, and CEOs say that digital marketing is now the most important technology-powered investment their firms can make.
  • The rise in digital budgets: it is not merely a migration of spending from traditional to digital media. A growing portion of marketing’s budget is now allocated to technology itself. A recent Gartner study found that 67% of marketing departments plan to increase their spending on technology-related activities over the next two years. In addition, 61% are increasing capital expenditures on technology, and 65% are increasing budgets for service providers that have technology-related offerings.
  • Efficiently manage all this technology: there are now well over 1,000 marketing software providers worldwide, with offerings ranging from major platforms for CRM, content management, and marketing automation to specialized solutions for social media management, content marketing, and customer-facing apps. Relationships with agencies and service providers now include technical interfaces for the exchange and integration of code and data. And bespoke software projects to develop unique customer experiences and new sources of advantage are proliferating under marketing’s umbrella.

The reason why this is a growing role within companies is very simple. In this new digital economy environment, the CMO and the CIO must collaborate closely, although this executive-level cooperation isn’t just enough. A supporting organizational structure is also needed and vital for this collaboration to work properly. A company can’t simply split marketing technology down the middle and declare that the CMO gets the marketing half and the CIO gets the technology half. Such division might look good on paper, but it leaves yawning knowledge gaps in practice.

Marketing might not understand how to fully leverage what IT can offer, and IT might not understand how to accurately translate marketing requirements into technical capabilities. Instead, marketing technology must be managed holistically. In a virtuous cycle, what’s possible with technology should inspire what’s desirable for marketing, and vice versa. The right structure will help marketing become proficient with the array of software it must use to attract, acquire, and retain customers. It will help marketing leadership recognize how new technologies can open up new opportunities and allow marketing to deftly handle the technical facets of agency and service provider relationships in both contract negotiations and day-to-day operations.

The chief marketing technologist role itself is already an acknowledgement of just how important the marketing group is to driving revenue within the organization and, when properly resourced, how today’s marketing information systems are driving the current and future growth of the business. Only by bringing the CIO and the CMO together can the CEO have a complete picture of what insights must be acted upon quickly in order to establish or maintain the top market position. In a nutshell, the power comes from the intersection between marketing and IT.

Today, companies can no longer afford separate silos between marketing and IT. The rapid collapse of these silos means that one person must be able to converse seamlessly between both groups. While many CMOs are getting their arms around the technology side of their business, the natural evolution of this role is for the CIO to improve its marketing skills in order to grow into the Chief Marketing Technologist role. The faster we embrace these trends, the bigger the impact we will have on our bottom line. This is the imminent future of the industry, and it’s the reason chief marketing technologists will be in high demand within 2017 and in the years to come.

Data Driven V. Predictive Marketing: BEWARE JETSON’S MARKETING!

The Big Willowby: Charlie Tarzian

My son came to me one day in early December and said:  ‘Hey, Dad, let’s get Mom one of those robotic vacuum cleaners.  You know, the ones you switch on and they vacuum your whole floor!’  He could not contain his enthusiasm – this was going to be great – no one would have to vacuum our floors ever again!!!

So we went to Amazon (of course) and two days later our round disc of a maid showed up via FEDEX.

Come Christmas Day, the robot fully charged, off we went to the kitchen to marvel at what was certain to be a life changing event.  We turned it on and put it down on the floor and the vacuum swung into action.  It crossed the floor, sensed it was coming to a wall, made a pivot, chugged in another direction…and got stuck on the slight incline by the fireplace…then stuck again on the floor mat by the stove…then got caught between a chair and a table and went into a break dance that would make R2D2 jealous.

I bring this up because a colleague sent me this little snippet from the website of a Predictive Marketing vendor:

“Predictive Marketing doesn’t need to be a services heavy engagement to get you up and running. With CompanyX (name of company withheld) and our push button integration, we can integrate with your existing systems and build your predictive model in under a day. – See more at: http://www.companyx.com/what-we-offer/#sthash.vBzkU18n.dpuf

There you have it: Jetson’s Marketing – just push our one little button and off you go:  great leads, great meetings, great website experiences – in fact all your marketing/sales problems solved in ‘under a day’.  All that is left to do is fire your staff, except for that one person in charge of pushing the button when you run out of leads, meetings and website visitors.

Look, I know what I don’t know, but I can tell you this: whatever you’re thinking the new generation of transformative marketing is – one thing it isn’t is automated bliss.  It takes a fair sized village to make things happen.  And herein lies the huge disconnect between data driven marketers and the shiny new object called Predictive Marketing.  Data driven marketers know that data can and should be utilized across the marketing/sales continuum – but it is more about data orchestration than anything else.  Therefore, one button, add water and stir does not take into consideration any of the cause and effect across all the communications and transactive channels that marketers rely on.

Marketing is services heavy (sorry, Company X) because at the nexus of MarTech, AdTech and Sales Enablement sits quite a bit of cause and effect.  And unless you aspire and build towards using predictive data to positively impact all channels aligning as one – then what you are predicting is a very small sliver of what could be.  In other words, if the connectivity and synapsis among outward facing channels are not orchestrated and optimized using predictive data and you are not feeling good that all channels are working in sync – then how can you predict a scaled outcome?  The predictions you are making will reflect a small percentage of the whole – and so instead of widening your funnel and increasing your opportunities along every step in a buying journey, you are narrowing that funnel based on a flawed assumption that you are predicting against a full boat of reliable data.

On the other hand, Data Driven Marketing  sets up to be based on solving for the cause and effect of what is less than optimized (can anyone say, broken?)  It attempts to determine (not predict) what works and doesn’t and then – as a village – cohesively knits together a response to results that can be repeatable but certainly is not a just add water, one button pushed result.

So – are we starting to see a difference:  Predictive Marketing – a push button approach to a complicated set of executional events and response, or, Data Driven Marketing – a human driven (sorry robots!) approach to the cause and effect of humans communicating to other humans about things that may or may not be important to the recipient (we always hope for the former)?

What do you think?  We would love to know.  Have any stories to share – we would love to hear from you.

by: Charlie Tarzian, Founder, The Big Willow

Inside The Growing Social Media Skills Gap – FastCompany

Fast Company LogoBY RYAN HOLMES

On February 4, 2004, a handful of Harvard students logged onto a newly launched website called thefacebook.com. Just a dozen years later, some 2 billion people—nearly a third of the planet’s population—are social media users.

So if companies are having trouble keeping up with that pace of adoption, it’s no surprise. Businesses have overcome their earlier skepticism and raced head-on into the social arena, chasing the estimated three-quarters of consumers who now say social media influences their buying decisions. Nearly 90% of U.S. companies are currently using Twitter, Facebook, and other networks—all jockeying for their share of the estimated $1.3 trillion in value that social media stands to unlock.

There’s just one small problem: The contemporary workforce is woefully ill-equipped to help companies unlock it.


While social media races ahead, formal training and education programs are lagging seriously behind. If that isn’t making headlines, it’s testament to social media’s comprehensive mainstreaming: “Facebook? I use that everyday. Who needs to be trained in it?”

Yet a meager 12% of the 2,100 companies in a 2010 Harvard Business Review survey said they’re using social media effectively. And more recent research by Capgemini and others show that confidence gaining only incrementally.


Reports of social media gaffes and blunders in the workplace are still routine. Meanwhile, the real price of the skills gap often goes unnoticed—billions of dollars in missed opportunities and lost revenue.WHAT’S BEHIND THE SHORTFALL

The clearest culprit is the breakneck proliferation of new platforms and features. Around a year ago, Snapchat was still a toy for teens to trade disappearing messaging; today it’s the latest way to reach young customers on their own turf. As more platforms incorporate more sophisticated features, even the most plugged-in users are struggling to keep up.


At the same time, how social media is used in the workplace is fundamentally changing. Just a few years ago, social media in the office was the domain of specialized social media managers, the gatekeepers who owned a company’s public face on the leading platforms. In a short time, however, social media duties have been radically democratized and decentralized. The number of job descriptions on Indeed.com mentioning social media skills is booming: “[We’re] seeing this demand span many levels, from executive assistants to senior vice presidents,” Amy Crow, Indeed’s then communication director told Quartz a few years ago.

Since then, employees have been asked to use social media in ever more numerous and unfamiliar ways. The standard marketing functions are just the tip of the iceberg. Social tools are being used to streamline customer service, drive sales, improve HR processes, and build employee brand advocacy programs.

Meanwhile, platforms like Facebook at Work (in beta now and expected to roll out this year) and Slack (which boasts millions of users, from NASA to your corner coffee shop) are quickly changing how workers collaborate. By bringing social messaging inside the office, these technologies are breaking down silos and boosting productivity (although some disagree). Social media is no longer a discrete thing that certain people do in certain jobs, and more of an integral component of work itself.


But this approach only works if employees are on board and up to speed. “The real problem is that we expect people to know these skills without providing any training,” William Ward, professor of social media at Syracuse University, recently told me. Social media know-how isn’t something you just pick up as a casual user. And it isn’t just older employees who are in the dark—millennial hires need training, too.”

Because somebody grows up being a social media native, it doesn’t make them an expert in using social media at work,” Ward says. “That’s like saying, ‘I grew up with a fax machine, so that makes me an expert in business.’”


Fixing this social skills gap is no small task. In the long term, social media coursework is slowly being incorporated into university programs, and not just for students pursuing marketing and communications degrees. Here at Hootsuite, for instance, we’ve developed a social media syllabus that’s now being used in more than 400 universities around the world by 30,000 students. Programs like these offer a foundation of social media skills for the workplace and may one day be as commonplace as introductory college writing and computer skills classes.

But what about employees struggling right now with the growing demands of social business? The good news is that companies are beginning to acknowledge social media literacy as a critical job skill (just like Internet and basic computer literacy back in the day) and are starting to offer on-the-job training programs. Altimeter reports that almost half of the companies it surveyed are planning on rolling out some kind of internal social education program for employees, while overall spending on corporate training is on a serious upswing, rising 15% in the U.S. in a recent year to $70 billion.

The challenge, of course, is how to teach social media in such a mercurial environment. In the last year alone, for instance, we’ve seen the meteoric rise of “social video” and a whole new crop of one-to-one messaging apps, while Twitter has struggled to reinvent itself.

But few employees have time for in-depth courses or bootcamps. Ultimately, the right training solution needs to be on-demand and mobile-friendly. Currently, some of the bestpaid options are coming not from traditional educational sources, but from companies immersed in the social and digital media space, offering real lessons from the front lines. (Hootsuite’s own online course, Podium, is one free alternative, with 50,000 users and counting.)


Ultimately, though, any investment in upgrading social media skills in the workplace is likely to be money well spent. Twitter, Facebook, Instagram, and other networks aren’t going away. Social business has become business as usual. Indeed, social media budgets at companies are expected to double in the next five years.

To avoid throwing good money after bad, companies need to ensure that their employees actually know how to use new and emerging social technologies. Those that succeed in closing the social media skills gap will discover new ways to reach and retain customers, engage and recruit employees, and boost productivity. Those that fail will miss out on their chunk of a multitrillion-dollar pie, and might not be around long enough to regret it.

Google’s Artificial Intelligence Masters Atari Video Games

atari-brainDeep learning, one of the hottest topics today in artificial intelligence (AI), has taken another leap forward with DeepMind’s latest announcement.

Source: Google’s Artificial Intelligence Masters Atari Video Games

Think you’re good at classic arcade games such as Space Invaders, Breakout and Pong? Think again.

In a groundbreaking paper published yesterday in Nature, a team of researchers led by DeepMind co-founder Demis Hassabis reported developing a deep neural network that was able to learn to play such games at an expert level.

What makes this achievement all the more impressive is that the program was not given any background knowledge about the games. It just had access to the score and the pixels on the screen.

It didn’t know about bats, balls, lasers or any of the other things we humans need to know about in order to play the games.

But by playing lots and lots of games many times over, the computer learned first how to play, and then how to play well.

A Machine That Learns From Scratch

This is the latest in a series of breakthroughs in deep learning, one of the hottest topics today in artificial intelligence (AI).

Actually, DeepMind isn’t the first such success at playing games. Twenty years ago a computer program known as TD-Gammon learned to play backgammon at a super-human level also using a neural network.

But TD-Gammon never did so well at similar games such as chess, Go or checkers.

In a few years time, though, you’re likely to see such deep learning in your Google search results. Early last year, inspired by results like these, Google bought DeepMind for a reported $400 million.

Many other technology companies are spending big in this space. Baidu, the “Chinese Google”, set up the Institute of Deep Learning and hired experts such as Stanford University professor Andrew Ng. Facebook has set up its Artificial Intelligence Research Lab which is led by another deep learning expert, Yann LeCun. And more recently Twitter acquired Madbits, another deep learning startup.

The Secret Sauce of Deep Learning

Geoffrey Hinton is one of the pioneers in this area, and is another recent Google hire. In an inspiring keynote talk at last month’s annual meeting of the Association for the Advancement of Artificial Intelligence, he outlined three main reasons for these recent breakthroughs.

First, lots of Central Processing Units (CPUs). These are not the sort of neural networks you can train at home. It takes thousands of CPUs to train the many layers of these networks. This requires some serious computing power.

In fact, a lot of progress is being made using the raw horse power of Graphics Processing Units (GPUs), the super fast chips that power graphics engines in the very same arcade games.

Second, lots of data. The deep neural network plays the arcade game millions of times.

Third, a couple of nifty tricks for speeding up the learning such as training a collection of networks rather than a single one. Think the wisdom of crowds.

What Will Deep Learning Be Good For?

Despite all the excitement about deep learning technologies, there are some limitations to what it can do.

Deep learning appears to be good for low-level tasks that we do without much thinking. Recognizing a cat in a picture, understanding some speech on the phone or playing an arcade game like an expert.

These are all tasks we have “compiled” down into our own marvelous neural networks.

Cutting through the hype, it’s much less clear if deep learning will be so good at high level reasoning. This includes proving difficult mathematical theorems, optimizing a complex supply chain or scheduling all the planes in an airline.

Where Next for Deep Learning?

Deep learning is sure to turn up in a browser or smartphone near you before too long. We will see products such as a super smart Siri that simplifies your life by predicting your next desire.

But I suspect there will eventually be a deep learning backlash in a few years time when we run into the limitations of this technology. Especially if more deep learning startups sell for hundreds of millions of dollars. It will be hard to meet the expectations that all these dollars entail.

Nevertheless, deep learning looks set to be another piece of the AI jigsaw. Putting these and other pieces together will see much of what we humans do replicated by computers.

If you want to hear more about the future of AI, I invite you to the Next Big Thing Summit in Melbourne on April 21, 2015. This is part of the two-day CONNECT conference taking place in the Victorian capital.

Along with AI experts such as Sebastian Thrun and Rodney Brooks, I will be trying to predict where all of this is taking us.

And if you’re feeling nostalgic and want to try your hand out at one of these games, go to Google Images and search for “atari breakout” (or follow this link). You’ll get a browser version of the Atari classic to play.

And once you’re an expert at Breakout, you might want to head to Atari’s arcade website.

CMO Council: Marketers are struggling with Customer Engagement – Thunderhead

imgresBlog summarizing the CMO Council new report on the good, the bad and the ugly of how marketers feel they are coping with improving customer engagement

Source: CMO Council: Marketers are struggling with Customer Engagement – Thunderhead

The Skills The World Will Need In The Future (+infographics) – Innovation for Development

By Enrique Rubio

engine-289x300The Fourth Industrial Revolution was the topic for the 2016 World Economic Forum. Developments in areas such as robotics, artificial intelligence, machine learning, and others, are driving fast-paced change that will radically affect the way we work and live.

There are many unknowns about the future, but what we do know for sure is that reality as we know it today will be very different. Jobs will disappear (the WEF has calculated that about 5 million jobs will be lost), new jobs will be created and some skills will become obsolete, whereas others will be highly demanded.

The report from the World Economic Forum lists the ten critical skills that will be needed in the workforce in 2020: 1) Complex problem saving; 2) Critical thinking; 3) Creativity; 4) People management; 5) Coordinating with others; 6) Emotional intelligence; 7) Judgment and decision-making; 8) Service orientation; 9) Negotiation; and 10) Cognitive flexibility.

Between today and 2020, we have a little bit more than 1400 days. Most of us are trying to live a fulfilling life, exploring and discovering our potential, and thriving in an environment in which we opportunities to maximize that potential. But 1400 days is not much time, and we need to plan and act upon our professional and career future starting right now. I can’t stress enough the urgency of my words.

In the future, either in 2020, 2030 or 2050, but definitely not too far from now, working class will be further divided into low-paying low-skilled jobs, and high-paying high-skilled jobs. An engineer of today, if he or she doesn’t increase the skills needed in the future, could potentially be placed in the low-skilled job band. On the other hand, a clerk of today, who is deciding to begin right now the learning process to strengthen the skills and capacities that match his or her potential, with the needs in the future, could potentially be placed in the high-skilled job band. The difference in both is not academic training or diplomas, but long term professional planning.

Below is how I see the extremes in the top five of these 10 skills needed for the future. Where are you standing? What do you need to do in order to navigate the path between a low extreme and a high one? What urgency will you consider in order to learn and move from one place to the other?

Source: The Skills The World Will Need In The Future (+infographics) – Innovation for Development

By Enrique Rubio

I’m an HR Professional with background in Electronic Engineer and a Fulbright scholar with an Executive Master’s Degree in Public Administration from Syracuse University. I’m passionate about development, innovation, leadership and neuroscience. I’m also a competitive ultrarunner.

How Big Data Will Shape Industry in 2016 | Datafloq

how-big-data-will-shape-the-it-industry-in-2016Accurately predicting how Internet trends and future technology will play out is no easy task. This year alone there has been an increase in cyber threats and hacks, new software from Windows and the introduction of smart watches, and of course, a spike in big data. As technology advances, so does its growth for possibilities.

Below are six sure-fire predictions made by analysts for 2016:

1. A Huge Decline in Legacy Vendors

As indicated by a report released by the IDC, almost a third (30 percent) of IT vendors will cease to exist by 2020. As it appears now, many legacy vendors, especially larger ones, will either have to shut down completely, downgrade or partner with another company due to sluggish growth and lost earnings.

Good examples of this trend can be seen with Dell which is rumored to buy out EMC, and HP splitting its operations in half. In a nutshell, legacy vendors as a whole are missing the mark when it comes to delivering practical solutions in the tech industry—a red flag that many vendors will eventually end up being archived in exchange for private equity.

2. Appearance of More Wearables

Within the next two years, wearable health and fitness tracking devices will take the critical role workforce by storm. By 2018, it is estimated two million people will be required to wear health and fitness tracking devices as a safety measure. This includes firefighters, law enforcement, paramedics, remote field workers, airline pilots, industrial workers, professional athletes, and even political leaders.

This expected boom in wearable tech calls for IT professionals that are adept in device discovery. Network and device discovery at this level usually calls for a more formal network device discovery platform, and IT professionals that understand how to implement it.

3. Big Data Gets Bigger

In 2016, big data will have an even greater impact on how many industries function. Diverse industries are seeing the benefit of analyzing large amounts of data from healthcare to language translation. Understandably, more and more companies are adopting big data services and customizations; they’re catching on that utilizing insights based on algorithms is a much more practical strategy to successful marketing and business expansion as opposed to trial and error. By the year 2018, analytics will be embedded in every application to enhance functionality or convenience.

4. Cloud Providers Will Diminish

In reaction to the big data explosion, many big cloud providers will be vying for the chance to host big data storage. Google, Microsoft, and AWS all provide machine learning services as well as access to a range of massive data groups that can be used for analytics. Major public cloud providers will gain momentum and strength, with Amazon, IBM SoftLayer, and Microsoft grabbing a large percentage of the business cloud services market.

Unfortunately, smaller cloud service providers just won’t be able to invest in hosting machine learning services; this will likely be the catalyst for such companies to bail out of the market altogether. The volume of options for cloud management software and general infrastructure-as-a-service (IaaS) cloud services will significantly decline at the end of 2016.

5. More Business Content Generated by Machines

Technologies possessing the ability to proactively assemble and send information through automated composition engines will take a more active role. Business content such as legal documents, market reports, shareholder reports, press releases, articles and white papers will be generated more frequently by machines.

This shift in operations will increase by 20 percent in 2018, this includes machine learning in the IT sector as a whole. The initial companies predicted to expedite and implement smart machine technologies effectively will be startups and other fresh-out-the box companies.

6. Increase of Artificial Intelligence Implementation

According to esteemed analyst Daryl Plummer, the artificial intelligence (AI) trend is the result of enterprises and consumers willingly embracing the advancement of machine-driven technologies. Since the capability of applying smart technology for specific tasks dramatically improves the time, cost and energy generally contributed to recruiting, hiring, training and expansion demands associated with human labor, it’s little wonder, then, that artificial intelligence will play a larger role in a company’s infrastructure in 2016.

Source: Datafloq – How Big Data Will Shape the IT Industry in 2016

Article Author:

Xander SchofieldFollow Xander SchofieldLinkedIn Xander Schofield