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.

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.

The Best BPM Platforms for Digital Business

In the last quarter of 2015, Forrester Research identified Pegasystems, Appian, and IBM as leading the field of suppliers of business process management platforms for today’s digital business.

This post focuses on the value that Pegasystems delivers to today that is transforming how their clients engage with their customers throughout each stage of engagement.

photoSource: Pega.com The complexity of today’s business makes it hard to truly know a person across marketing, sales and service. There are too many customers, too many permutations of what they need and too many obstacles. Your customer base has grown, and so has your need for more sophisticated technology that not only understands prospect and customer demands but also helps accomplish your business objectives. Initially, your systems did what you needed: track customers and help you market and sell to them. But as your enterprise acquires companies and systems, technology becomes a barrier to how you engage with customers — across departments, time zones and geographies. Complexity has also brought inflexibility, making it hard for systems to adapt to changing needs, changing markets and changing regulations. It also makes it difficult to train employees because they’re battling systems, not servicing customers..Click here to read more.

The 8 white boarding videos that follow will help you visualize the unique value that Pega is delivering value today to their clients customer engagement management initiatives around the customer experience…

Build for Change: Directly Capture Objectives (DCO)

With Pega 7, you capture the policies and procedures that define your business – including rules, data models, UIs, integrations, reports, and organizational structures – in the model. Pega 7 automates the code generation. As the requirements change, a change in the model equates to an immediate system change.

Build for Change: Situational Layer Cake

Situational Layer Cake™ (SLC) architecture enables organizations to differentiate, specialize, and reuse their business applications. Pilot projects can grow into enterprise transformation programs overnight. Instant reuse dramatically accelerates the time to value for organizations seeking to be more agile in response to changing market and regulatory demands.

Build for Change: Case Lifecycle Management™

When a business person starts explaining their needs for an enterprise, they don’t generally dive into process details. And they certainly don’t describe “transactions.” They think in terms of the case and its stages. Rather than drawing an end-to-end process, Pega 7 provides tools for business people to define the major steps of how work gets done – essentially building the skeleton on which you hang the more detailed process. You establish a business view of the data before debating the details.

Build for Change: Mashup

Traditional service or API-based architectures result in hard-coding the UX logic into each channel independently. Process changes must therefore be made in multiple places, making it impossible to deliver a consistent customer experience. By embedding the Pega UX directly into the mobile or web channels, all of the intelligence and capability of Pega 7’s Case Management is brought directly to the customer touchpoint.

Build For Change: Omni-Channel UX

Pega’s Omni-Channel UX delivers an optimized and consistent user experience in every channel. Learn more at http://www.pega.com/platform

Build For Change: Event Strategy Manager

Pega’s Event Strategy Manager gives you the tools you need to turn streams of customer data into valuable business decisions and actions. Learn more at http://www.pega.com/platform

Build for Change: Pega Live Data

Pega Live Data allows users to quickly and easily define the data required to build the apps they need, and then access that data in their running application – all without having to worry about how and where the data is actually stored and accessed.

Build for Change: Next Best Action

The real value from Big Data and analytics comes when every customer conversation delivers exactly the right message, the right offer, and the right level of service to both give the customer a great experience and maximize the customer’s value to the organization. With Pega’s Next Best Action, business experts develop decision strategies that combine predictive analytics,
adaptive analytics, traditional business rules.

 

The Forrester Wave™: BPM Platforms For Digital Business, Q4 2015

Key Takeaways: Pegasystems, IBM, and Appian lead the pack

Forrester’s research uncovered a market in which Pegasystems, Appian, and IBM continue to lead the pack. Software AG, Oracle, Newgen Software, OpenText, Bizagi, K2, and DST Systems offer competitive options. Red Hat and TIBCO Software lag behind. The BPM Platforms Market Is Growing As EA Pros Accelerate Digital Transformation The BPM platforms market is growing because more EA professionals see BPM as a way to address emerging challenges for customer experience and digital business. This market growth is, in large part, because EA pros increasingly trust BPM platform providers to act as strategic partners, helping them transform how they use technology to win, serve, and retain customers in the digital age. Differentiators Include Rapid Development, UX Design, And Case Management As legacy BPM technology becomes outdated and less effective, improved delivery speed and process flexibility will dictate which providers will lead the pack. Vendors that can provide fast ramp-up, flexible mobile experiences, and dynamic case management position themselves successfully to deliver speed and business agility to their customers.

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Why Marketers Should Consider Artificial Intelligence When Reaching Out To Consumers

Robots are often the first point of contact in the process of customer engagement.

Source: Why Marketers Should Consider Artificial Intelligence When Reaching Out To Consumers

Predictive Analytics | Applying it to #Sales and #Marketing

Several weeks ago, I was in a meeting with a group of senior executives at one of the oldest business information companies in the United States, and the conversation shifted to lead generation:

“Results are horrendous, incredibly weak. Much of these leads are unusable. High percentages from Gmail, AOL, Hotmail… So many unknowns and, well, at least some decision makers, along with the rest of usual useless information.”

Anyone who works in today’s digital marketing space knows this is a common conversation that is hard to fix.

So, is bad #data the real issue for us or is it that we are chasing down the wrong path?

Think about it. We spend millions of dollars chasing individuals who download content, attend webinars or throw business cards into fishbowls at conferences and shows. We spend very little trying to figure out what is really going on inside a company of interest.

Things that sales and marketing agree on:

  • Purchasing is a team exercise
  • Figuring out what the consensus inside of buying teams is a tough job to figure out
  • There is a value to downloaded content as a proxy for team interest
  • An individual act tells us nothing about its organizations intent.

Is it time to devalue the downloaded white paper as our lead generation currency? (Sales people will love this one, Marketing, maybe not) 

More to the point, isn’t it time to evolve and begin the process of understanding the corporate body language through a variety of data points that are already available to us? Would it not be better to understand that the almighty download can and should be part of a larger canvas where a broader, more accurate picture emerges?

Even at it’s broadest level, #predictive #analytics can come in some simple forms.

6 examples of simple forms that apply basic predictive analytics:

  1. You can use any number of competitive search tools to understand what keywords and key phrases are collectively perceived as important when prospects begin their journey to find you
  2. And if you look historically backwards, you will be able to see what changed and potentially why
  3. You can also develop an understanding for funnel position (where companies are in the sales funnel by segmenting out keywords based on a natural progression of educating oneself.
  4. You can then use that analysis to make sure your own website is in tune editorially – are you mapping to what is important at that moment in time to companies that are consuming the content aligned with your objectives?
  5. You can find sites where these keywords exist ON PAGE in ways that align with your objectives. Page Indexing has grown up and become very sophisticated.
  6. Just this simple knitting together of these two components begin to give you an indication of trends and volume of content that is out there and that your prospects are consuming

Then do this:

  • Use IP identification and targeting to match who you see on your site and who is consuming the relevant content across the Web. This type of targeting will enable you to report back on which companies are most active in consuming specific keywords across contextually aligned sites.
  • This gives you a marriage of your data and external data that help you develop prioritization for sales, messaging across marketing, content development and most of all – IT GETS YOU OUT OF DEPENDING ON WHITE PAPER DOWNLOADS as your proxy for interest.
  • Once you add your crm and marketing automation data, revealing what companies you currently talk to are most engaged – you have a clear path to a strategy.

To review:

  • Analyze the competitive set to understand how everyone is deploying search and keywords
  • Utilize page indexing to understand where the content is
  • Use IP identification and targeting to tell you who and what and how many from where
  • Knit your own data in to complete the virtuous circle

The age of #Predictive #Automation is upon us. Take the initial steps needed to understand organizational #intent and funnel position, and your sales organization will stop complaining about those lousy leads you send them.

Predictive Analytics | Applying it to #Sales and #Marketing

Several weeks ago, I was in a meeting with a group of senior executives at one of the oldest business information companies in the United States, and the conversation shifted to lead generation:

“Results are horrendous, incredibly weak. Much of these leads are unusable. High percentages from Gmail, AOL, Hotmail… So many unknowns and, well, at least some decision makers, along with the rest of usual useless information.”

Anyone who works in today’s digital marketing space knows this is a common conversation that is hard to fix.

So, is bad #data the real issue for us or is it that we are chasing down the wrong path?

Think about it. We spend millions of dollars chasing individuals who download content, attend webinars or throw business cards into fishbowls at conferences and shows. We spend very little trying to figure out what is really going on inside a company of interest.

Things that sales and marketing agree on:

  • Purchasing is a team exercise
  • Figuring out what the consensus inside of buying teams is a tough job to figure out
  • There is a value to downloaded content as a proxy for team interest
  • An individual act tells us nothing about its organizations intent.

Is it time to devalue the downloaded white paper as our lead generation currency? (Sales people will love this one, Marketing, maybe not) 

More to the point, isn’t it time to evolve and begin the process of understanding the corporate body language through a variety of data points that are already available to us? Would it not be better to understand that the almighty download can and should be part of a larger canvas where a broader, more accurate picture emerges?

Even at it’s broadest level, #predictive #analytics can come in some simple forms.

6 examples of simple forms that apply basic predictive analytics:

  1. You can use any number of competitive search tools to understand what keywords and key phrases are collectively perceived as important when prospects begin their journey to find you
  2. And if you look historically backwards, you will be able to see what changed and potentially why
  3. You can also develop an understanding for funnel position (where companies are in the sales funnel by segmenting out keywords based on a natural progression of educating oneself.
  4. You can then use that analysis to make sure your own website is in tune editorially – are you mapping to what is important at that moment in time to companies that are consuming the content aligned with your objectives?
  5. You can find sites where these keywords exist ON PAGE in ways that align with your objectives. Page Indexing has grown up and become very sophisticated.
  6. Just this simple knitting together of these two components begin to give you an indication of trends and volume of content that is out there and that your prospects are consuming

Then do this:

  • Use IP identification and targeting to match who you see on your site and who is consuming the relevant content across the Web. This type of targeting will enable you to report back on which companies are most active in consuming specific keywords across contextually aligned sites.
  • This gives you a marriage of your data and external data that help you develop prioritization for sales, messaging across marketing, content development and most of all – IT GETS YOU OUT OF DEPENDING ON WHITE PAPER DOWNLOADS as your proxy for interest.
  • Once you add your crm and marketing automation data, revealing what companies you currently talk to are most engaged – you have a clear path to a strategy.

To review:

  • Analyze the competitive set to understand how everyone is deploying search and keywords
  • Utilize page indexing to understand where the content is
  • Use IP identification and targeting to tell you who and what and how many from where
  • Knit your own data in to complete the virtuous circle

The age of #Predictive #Automation is upon us. Take the initial steps needed to understand organizational #intent and funnel position, and your sales organization will stop complaining about those lousy leads you send them.