Marketing Transformation and Artificial Intelligence

Artificial Intelligence and Marketing
Artificial Intelligence and Marketing

7 examples where artificial intelligence is transforming marketing:

1. Content curation

Predictive analytics allows Netflix to optimize its recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Uniting information from diverse datasets is a common use of AI.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

2. Search

In 2015, Google admitted it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries. RankBrain utilizes natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

3. Predictive customer service

Knowing how a customer might get in touch and for what reason is obviously valuable information.

Not only does it allow for planning of resource (do we have enough people on the phones?) but also allows personalization of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analyzing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

4. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research, tells Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimizing bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimizing what product mix to display when retargeting, or what ad copy to use for what demographics.

5. Customer segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or customer experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior.

6. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analyzed and appropriate action taken based on past behaviors and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

7. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognizes faces, as does Snapchat Face Swap.

Perhaps the most exciting implementation of image recognition is DuLight from Baidu…Designed for the visually impaired, this early prototype recognizes what is in front of the wearer and then describes it back to them.

Artificial Intelligence Potentials in B2B Marketing – D&B

ai-dnb
AI Marketing

By Leslie Hancock
Founder & CEO
CreativeCafeHQ.com

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-friend-foe

 

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

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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.

Marketing Transformation and Artificial Intelligence

Artificial Intelligence and Marketing
Artificial Intelligence and Marketing

Here are seven examples where artificial intelligence is transforming marketing:

1. Content curation

Predictive analytics allows Netflix to optimize its recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Uniting information from diverse datasets is a common use of AI.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

2. Search

In 2015, Google admitted it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries. RankBrain utilizes natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

3. Predictive customer service

Knowing how a customer might get in touch and for what reason is obviously valuable information.

Not only does it allow for planning of resource (do we have enough people on the phones?) but also allows personalization of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analyzing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

4. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research, tells Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimizing bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimizing what product mix to display when retargeting, or what ad copy to use for what demographics.

5. Customer segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or customer experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior.

6. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analyzed and appropriate action taken based on past behaviors and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

7. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognizes faces, as does Snapchat Face Swap.

Perhaps the most exciting implementation of image recognition is DuLight from Baidu…Designed for the visually impaired, this early prototype recognizes what is in front of the wearer and then describes it back to them.

Will You Lose Your Job to Artificial Intelligence? Here’s What the Experts Really Think | Inc.com

Credit Getty Images

By Kevin J. Ryan is a staff writer for Inc. He has Kevin J. Ryan is a staff writer for Inc. He has written for ESPN The Magazine and the Long Island Press and contributed to Mental Floss. He lives in Queens, New York.

An insurance firm in Japan is replacing 34 claims adjusters with artificial intelligence. What does it mean for the future of work?
A few months into my time as an insurance claims adjuster, a customer called and said he thought his house was breaking apart. He’d heard what sounded like wood beams snapping in the basement, so he went downstairs and crept into the crawl space to investigate.

While he was lying in the dark surrounded by cement, he told me, he started to panic. It brought him back to years earlier when, as a firefighter with the New York City Fire Department, he served as a first responder on September 11, crawling through the giant blocks of brick and mortar that had collapsed hours earlier.

This revelation came 10 or 15 minutes into our conversation, after I’d gathered his basic information and logged the details of the case. He’d been friendly, even lighthearted (if a little excitable) for the first part of it. But recalling his moment of anxiety, his voice quivered.

To an extent, I was able to relate. My father was a captain in the FDNY on September 11. He, too, spent the hours, days, and weeks that followed at Ground Zero, digging around in the rubble and coming home covered in dust. I know that he, too, has painful memories stored away in the deep parts of his mind.

I relayed this to the man on the other line. We talked for a few minutes about that day and its aftermath, and then I brought the conversation back to what was going to happen with his claim. He asked if it was OK if he called back just to chat if he needed to.

A few hours later, he did just that. I don’t remember exactly what we talked about, but I remember he was calm, he used my name a lot, and he thanked me.

These are the experiences I’d argue that artificial intelligence cannot fully replace. Or can it?

Case in point: Fukoku Mutual, an insurance firm in Japan, is replacing 34 claims adjusters with A.I. According to a press release from the company, the system, which uses the technology found in IBM’s Watson, will be able to study factors including the length of a hospital stay, procedures performed, and the patient’s medical history to determine a payout.

Benefits of using the system, according to the release, include improving operating efficiency by 30 percent. Japanese publication The Mainichi reports that it will save Fukoku Mutual $1.2 million (140 million yen) in wages annually.

Fukoku is just the latest example of a company testing the promises of artificial intelligence. It’s clear that A.I. is getting increasingly sophisticated at doing what humans do–but more efficiently and cheaply. What’s less clear is whether those gains trump the huge implications it would have for the future of work.


A.I. gets smarter

There’s a big temptation for businesses to use artificial intelligence to shave off time and money wherever they can, but experts say that’s not the smartest use of the technology.

“I actually think that’s the worst reason to automate things,” says Josh Sutton, global head of data and A.I. at marketing giant Publicis-Sapient. “Over time, it’s a losing proposition. It’s a race to the bottom.” Improving the customer experience and creating new revenue sources are much better applications of A.I., he says.

In the case of Fukoku Mutual, the more noble objective is getting customers a decision–and a payout–faster, which the company claims the system will accomplish. The company I worked for, which was one of the largest insurance providers in the U.S., made this the No. 1 priority, citing evidence that customer satisfaction is most closely related to the speed with which a payout is made.

But, as adjusters, we were also trained incessantly on our bedside manners. Someone who calls a home insurance firm is usually in a vulnerable place–and sometimes in full-blown crisis mode–so it was essential that a touch of humanity was included.

The question, then, is whether computers will ever have an emotional intelligence interchangeable with that of humans.
“There’s no question in my mind that the technology is moving in that direction,” says David Schatsky, managing director and emerging technology analyst at Deloitte. “A.I. can already perceive and understand emotional cues, based on factors like tone of voice and word choice. And it’s getting better at projecting them back at users.” So what does this mean for the work force? Until now, many of the jobs that have been displaced by machines are of the manual-labor kind: bots that fulfill orders in Amazon warehouses, for example, and machines that move products along an assembly line. The assumption has been that those jobs that require more training would be safe for some time. Arguably, that’s no longer the case.

“We’re starting to see the effects of technology automating cognitive work–things we used to think only people could do,” Schatsky says.

A 2015 Forrester analysis predicted automation would replace 25 percent of all job tasks by 2019. Losing some specific roles, like the country’s 1.6 million truck-driving jobs, seems like a foregone conclusion with the rise of self-driving cars. (Recently, an autonomous tractor trailer from Otto completed a 120-mile beer delivery.) Other occupations are using A.I. in tandem with people: Lawyers use software that can analyze cases and search for relevant past rulings; pharmaceutical firms use algorithms to aid in drug discovery.

Some experts argue that even humans in some of the highest-paying roles could become redundant. Last year, a team of British and American researchers fed an A.I. system the details of a series of court cases. The computer reached the same verdict as the judge 79 percent of the time. Other jobs, like those in the medical field, could eventually be replaced by faster, more accurate machines.
“Many of us in the A.I. field believe that physicians will be replaced long before nurses are replaced,” says Andrew Moore, dean of Carnegie Mellon’s School of Computer Science. ”

The parts of medical care to do more with interacting with the patients, making them comfortable, and communicating clearly with them are going to turn out to be the things that only humans can do well. The diagnostics, coming up with a theory as to what’s going on, or what a good subsequent test would be–those are the things that look very promising for automation. Compared with human experts, computers are doing a very good job.”

What happens next?

If it’s cost effective, it’s hard to imagine companies passing up the opportunity to take advantage of A.I. That’s especially true for positions like customer service reps, which require problem solving and a strong knowledge of the company’s operating standards. “The cost of both finding and retaining people with that combination of skills is pretty high,” Moore says.

At the insurance company where I worked, for example, training took anywhere from six to 12 weeks, and the employees turned over at a very high rate–sometimes just months after the program, which included a weeklong trip to company headquarters, was complete.
Sourcehttp://www.inc.com/kevin-j-ryan/artificial-intelligence-replaces-insurance-workers.html

Marketing and Artificial Intelligence

Artificial Intelligence and Marketing

7 examples where artificial intelligence is transforming marketing:

1. Content curation

Predictive analytics allows Netflix to optimize its recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Uniting information from diverse datasets is a common use of AI.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

2. Search

In 2015, Google admitted it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries. RankBrain utilizes natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

3. Predictive customer service

Knowing how a customer might get in touch and for what reason is obviously valuable information.

Not only does it allow for planning of resource (do we have enough people on the phones?) but also allows personalization of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analyzing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

4. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research, tells Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimizing bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimizing what product mix to display when retargeting, or what ad copy to use for what demographics.

5. Customer segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or customer experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior.

6. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analyzed and appropriate action taken based on past behaviors and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

7. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognizes faces, as does Snapchat Face Swap.

Perhaps the most exciting implementation of image recognition is DuLight from Baidu…Designed for the visually impaired, this early prototype recognizes what is in front of the wearer and then describes it back to them.

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

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

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:

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