A Great Simian or just a Monkey

IBM Watson Page 2 of 4

The gap between words and action in cognitive business

One of the biggest challenges with the cognitive era and for enterprises to adopt this shift is in my opinion, two things. The first is the gap between the tech and the business, which leads to the constant question “How do we start?”. The second one is that most companies selling this tech have a salesforce that is used to sell other things and have not had time to adapt to selling cloud-based cognitive solutions to their customers.

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The gap between tech and business

Traditionally companies selling software to the enterprises have a preset business proposition. An example is SAP for financial or Salesforce CRM for sales. If we go a bit more tech it can be a database to store things in or it can be an application server to have our apps running. With AI and cognitive computing, it is something else. We hear all these execs talk and talk about how great cognitive businesses, products and processes will be, but how do we convert these high-level talks to become concrete solutions that will evolve our business? I read a post on the Watson blog the other day and it contained a video of several execs from IBMs Watson unit that talked about how great cognitive business and cognitive computing are (IBM is an example, it could be Google, Facebook or Microsoft, but since I often post pro-IBM posts they can be the example in this post). Try to make something out of the video that you and I can convert into real-life adoption. Really hard, isn’t it?

Additional fun from the video:

  1. The title of the video is “How IT and Watson are partnering in the Cognitive Era”. Ok, what does that even mean? The video does not give a single indication on how IT and Watson even partner, on what level or in what way?
  2. A quote from the video “Adopting cognitive solutions is not as difficult as it sounds!” I agree, but the “how” or “why” is completely absent.
  3. What the heck is Watson anyway? Is it a product, a suite, a platform, APIs, a human, a computer,? If it is supposed to be easy, like the video insinuates, it might be a good idea to make that crystal clear what Watson really is. If you have read some of my posts you hopefully know the answer though.

Number 3 in the list is a big challenge I would say. It needs to be very clear what Watson is for companies to grasp the potential of what they can do with cognitive solutions like Watson (I think it is the same with Googles products within this space). In Watsons case it gives each salesperson the right to interpret this to their benefit. This happens every day at IBM when sales uses the Watson terminology to push clients towards “their” Watson product (if in Analytics, you sell Watson Analytics, if in Cloud you sell Bluemix and the Watson APIs etc). From a short-term pipeline point of view it might be good, but for the customers I doubt it is good.

We need to bridge the gap between high-level executives like those in the video and the APIs, this so it becomes easier for companies to actually adopt and become more cognitive. If this does not happen I am afraid many companies will miss huge opportunities and the cognitive software providers will miss out on great revenues from their great software.

What happens today is that you get either the high-level exec buzzword-ish mumbo jumbo or the deep deep tech talks about how the tech actually work, usually only with one single API (or pre-packaged solution like Watson Virtual Assistant). That will not build great cognitive businesses.

A new salesman / women is required

Selling cognitive solutions requires a new type of salesperson. The classic software sales rep has a portfolio of boxes that is dressed up for each company it is presented to and wrapped in a nice business case, usually a business case that is applicable to many customers, not only one. The new type of salesperson needs to look at things differently, it is mainly about creating the value proposition together with the customer. Cognitive is not a product it is an enabler to make your business better and faster. It is up to us as sales and business developers to actually understand and apply these new technologies to our clients and customers. Why is this a problem? Simple, customers do not get the best solutions presented to them by their salesreps. They get pre-fabricated food, when they could get the two-star Guide Michelin dinner. They would probably pay less as well and get a solution that over-delivered on their expectations.

Why sell pre-fabricated frozen pizza when you can sell a personal dinner at a 2 star Guide Michelin restaurant?

Companies that sell cognitive computing APIs like Watson need to shift to a new salesforce. Another great thing is that this new salesforce will also think cloud-first and not old on-prem software. They will understand how these cognitive APIs exist in the cloud in a secure and compliance ready way. Most of your old salesreps will have a really hard time converting their old way of selling to selling APIs in the cloud (at least in the right way). I would say that the only way to trigger a shift of the old reps is to change how they are compensated and completely shift to cloud based incentives, otherwise they will stick to their old stuff (I sell what I know works).

This post is almost without a specific topic, just one thing I felt needed out of my system. Salesreps, get your act together and start to sell real value to your clients, they will appreciate it. Put in the extra work to build a case for your clients that match their needs and their business, they will value it and it will hopefully be a fruitful relationship for everyone involved.

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Photo credit: Origami T-Rex by Jo Nakashima

How to make a cognitive movie trailer

Cognitive platforms can improve almost every process available. In cooperation with 20th Century Fox, IBM Watson created a movie trailer for the horror movie Morgon. The trailer was created in 24h and by that reducing the production time from weeks to hours. What do you think of the result?

This is the Watson created trailer for Morgan (incl. some comments)

The “classic” trailer is found a bit further down in the post.

So how was this possible and what steps was involved?

First of all, it is important to make it clear that a cognitive solution is almost never acting alone, it is acting in partnership with humans where each part is providing parts that will elevate the other.

The thing that made this project interesting is that there was no existing “ground truth”. This means that there was no right or wrong, no previous training or “how to”, so Watson had to act on it’s own and produce something on its own for the first time. To stitch it all together, IBM provided an internal IBM Research filmmaker to the project for the actual film-cutting.

Watson needed to do a couple of things to make the trailer a reality.

First, as with us humans, they needed to teach Watson what a trailer is and specifically a horror movie trailer. Since no “ground truth” existed, Watson needed to learn what a horror trailer is, so the team pushed 100 horror movie trailers through Watson and while doing that also helped him understand a few things in the trailers. The team used the following.

  1. Learn what a horror movie trailer is
    Needed to understand what was in the trailers and sense the emotion involved. This by both looking at and listening to each trailer and being trained by experts in how a horror trailer is constructed:

    – Visual analytics to identify people, objects, scenery and based on that identify emotions within each scene.
    – Audio analytics to identify tone of voice of the characters as well as the vibe of the soundtrack and sounds during each trailer.

  2. Watch the Morgan movie
    Now that Watson has learned what a horror movie trailer is, it was time to watch the movie that Watson should make a trailer based on. So he leaned back and watched the full Morgan movie.

After watching the movie Watson identified 10 different moments that would be worth including in the trailer. Interestingly Watson chose moments of the movie that other trailers did not include, now compare to the “classic” trailer below.

What is really cool is that this is a “creative project”. In creative projects, you rarely know what to expect as outcome until you see the result. To put Watson to the test on such a creative task is very very interesting.

Creating a movie trailer is usually a very labor-intensive project that involves many manual steps. It usually takes many days or even months to create.

It took Watson 24 hours to create this trailer for the movie Morgan. Reducing the production time from weeks to hours.

This is a great example of an isolated task that can act as a great example where it is beneficial to make a process cognitive, even though this project is pushing the limits given that it is a creative project.

Photo credit: 20th Century Fox

cognitive pre-requisits

Tell me what I do not know!

Artificial Intelligence will be a huge part of our daily lives and in our businesses from now on, but what we see today is not always Artificial Intelligence or my preferred term, Augmented Intelligence. It is just re-packed machine learning with a conversational interface. AI and cognitive are not only algorithms it is so much more. Many high-end AI / Cognitive products today are not mainly algorithm, that goes for Google DeedMind as well as IBM Watson.

Without further rambling, I thought I would state my position on the topic. It is actually pretty simple. A product, service or process that will be an AI-enabled or cognitive shall have one single pre-requisite in my opinion.

An AI, cognitive product or process should tell us things we do not already know. If not, it is not AI or Cognitive!

If it does not meet that “general” term, it is not AI or Cognitive, period! AI and cognitive per se is to think, learn and reason etc, but to provide value in a business and to assist us, we need to guide our software in a way that they provide information, solutions and data that we could not generate ourselves with resources at hand.

The future of software in the enterprise is about elevating both us as humans, and our business. Efficiency and speed is fantastic, but that is nothing new, it is what we have today!

I think that in 5 years most processes and things we do in a company will be elevated by cognitive elements. AI and Cognitive will be everywhere.

Photo: From a morning walk last week in my new hometown.

watson cost

What does Watson Cost? What is the Price?

UPDATE: The Watson price table below is now updated as of April 24th 2017. Differences from the prior table are documented in my Watson Pricing and Language Updates post, where I have documented the differences in pricing as well as changes in what languages Watson supports since my last post on Watson language support from Dec 2015. Only the table below is updated. The table from Aug 2016 is still available here.

How much does Watson cost? What is the price model? Enterprise software price models are never easy and when entering the API-era it does not get simpler, but it often gets more open. This is all applicable to Watson. Unfortunately, the Watson cost is split, much like what languages that Watson supports, it is listed in different places for each service / API. For my own pleasure (not entirely true) I created a cheat sheet. It is included below both as a link to the actual Google Spreadsheet as well as an embedded table.

Worth noting is that these are the public pricing and I am fairly confident that they stick pretty hard to this structure, but there are discounts involved for partners and enterprises, these are not entirely public, but since being involved with some products / projects I know they are pretty fair.

Also worth noting is that most of the services has a premium offering to move from the public shared cloud and instead go for a “single tenant instance” / private cloud setup that will ensure better encryption and compute-level isolation. I have an upcoming post on security coming up as well.

The source of the prices included in the document is from both the Watson Services Catalog as well as the Watson services on Bluemix.

This post is more of sharing then trying to make a statement or write an intelligent post, it is simply sharing an aggregated sheet I made and maybe others can find it useful in some way.

Watson Cost and Price Model Cheat Sheet

UPDATE: 31st of July: New pricing for Discovery Service and Discovery News. Post from IBM: “New pricing for Watson Discovery Service“.

Watson Price & Cost table

The cover photo is from a visit to NASDAQ in NYC a while back

watson products

Watson Products, what are they?

Is it all about software you download and install or is it APIs in the cloud? Well, depending on who you talk to, you might get different answers on what Watson Products that exists.

Ginni Rometty (CEO and Chairwoman of IBM) always uses the term APIs and that they are services in the cloud. These services are used build the end-customer products. Internally at the local IBM office (non-Watson unit) there is often a mixed voice. Many struggle to change mindset to a API business model instead of traditional software license model and in Watsons case that means talking about Watson Analytics (sigh), Watson Explorer (WEX) or any of the other nifty packaged products they have released, like Watson Company Advisor and Watson Knowledge Studio. Many of these products are hybrid Watson products. A mix of classic software and Watson services.

The pre-packaged way, is in no way a bad approach, but I see it mainly as a way to package and make it easy for the customers to make a decision instead of having to build from scratch.

In my mind the only core Watson products are the services presented at the Watson Developer Cloud Watson service catalog. Those services are the core and the foundation of Watson. Watson is X amount of APIs all available in the cloud. It is also those services I would recommend all companies to primary work with.

14 Watson Services Available today

IBM have been consolidating the services lately and currently there are 14 different services available (from double the amount just a few months ago, the consolidation was needed, there was a big overlap previously). These services then contain from one to many APIs. AlchemyLanguage has 13 APIs and Dialog only one etc.

As of today, the Services are the following, I included an extremely short description, since some can be hard to grasp when reading the IBM lingo on each service.

  1. Alchemy Language
    A great service that analyzes text and sense sentiment, companies mentioned, person mentions, language used etc. It can also extract keywords which is very useful.
  2. Conversation
    A graphical interface to help designing Bots. A combo of Dialog and NLC I would say.
  3. Dialog
    A very “simple” API that only manages a scripted dialog.
  4. Document Conversion
    Converts text and documents to a format that Watson can use to learn from.
  5. Language Translation
    A service that translates text from one language to another.
  6. Natural Language Classifier
    Helps with deciding the intent of the input a user sends to Watson. Very useful since we all use different ways to express in different domains etc.
  7. Personality Insight
    A very cool API that helps us understand what type of person we interact with or receive information from.
  8. Retrieve & Rank
    The Jeopardy brain. This API is the one that answer your question and replies with a recommended solution on your problem, including evidence and ranking.
  9. Tone Analyzer
    What tone do clients use on your Facebook page or in customer service?Separate the happy clients from the angry ones etc. This API helps you understand.
  10. Speech to Text
    Obvious I guess
  11. Text to Speech
    Also obvious I guess
  12. Visual Recognition
    What is in the picture, is it a 40 year old man or a product from your company, this API helps you recognize things in images.
  13. AlchemyData News
    An archive of news and blogs. One of the least impressive APIs imho.
  14. Tradeoff Analytics
    Which product of these 5 is best for ME? TA helps me decide.

If you wonder what languages each API supports, read my post “What languages does Watson support” where I list language support per API.

4 Watson Products that are pre-packaged

As of today the Watson offerings (pre-packaged products) are:

  1. Watson Engagement Advisor
    Essentially a pre-built bot framework for e.g customer service
  2. Watson Explorer
    Search on steroids. Search both structured and unstructured data, build dashboards etc.
  3. Watson Knowledge Studio
    A graphical interface to build and train your machine learning models.
  4. Watson Company Advisor
    A high-octane version of company profiles. Find out whatever you want about any company and compare to yours etc.

These are the Watson Products as of today.

 

Augmented Intelligence instead of Artificial Intelligence

Artificial Intelligence is a term that, for me, only causes confusion and also builds up caution towards new tech. It indicates that AI will replicate our intelligence and some are even afraid of AI taking over our jobs etc. It is a term that stunts the development and our progress to more successful companies. A term that much better describes what it is about is Augmented Intelligence.

Augmented Intelligence is actually what it is all about. Augmented send the notion of evolving and increasing our existing knowledge. The intelligence that systems will assist us with is not artificial, it is real and elevated. It is systems that are taught by humans and then elevated to assist and scale human expertise to help businesses and applications to assist people with well-defined tasks and help them make more informed decisions.

This heavily connects to the fact that collaboration between human and computer is a very important part of becoming a successful business in the future. This collaboration is about systems helping humans and humans helping systems.

As a side note, augmented intelligence also fits much better as term in the landscape of cognitive. Artificial Intelligence is one part of cognitive, but many tend to mix cognitive with Artificial Intelligence. If we replace Artificial Intelligence with Augmented Intelligence, it is much easier and more relevant…..at least for me.

 

Microsoft Cortana vs IBM Watson

IBM Watson is a brain and Microsoft Cortana is a personal assistant. That is the simple conclusion of Yesterdays Cortana vs Watson (sort of) at The Conference in Malmö. My biggest surprise was that Microsoft Cortana is 100% scripted. Watson is a cognitive platform that actually could complement Cortana’s great features as personal assistant.

Currently in Malmö in Sweden, the conference The Conference (ehh) is happening and I was very much looking forward to the session Artificial Intelligence in Services. Primarily to listen to Deborah Harrison from Microsoft, since my knowledge about Cortana is limited. To summarize, Cortana was a huge disappointment to me. After listening to Deborah I posted the below tweet that described my immediate feelings about Cortana (excuse the typo).

So, Cortana is:

  • Scripted
  • Only a personal assistant
  • Personality and tone driven

As Deborah say already in the first minute of her talk (embedded at the end of the post) “Nothing is programmatic, we write it all”.

This leads me back to my recent post where I say that the future is about cognitive platforms and not about Bots (aka personal assistants).

Cognitive Platforms are the future, not Bots and AI

And to continue with the embeds, the comparison between Cortana and Watson was by Felix Segerbrecht described in a true and funny (with a touch of sexism irony included) way.

Cortana is a Personal Assistant and Watson is a Brain.

The speakers were senior and knew the topic way better than most (both speeches embedded below). Deborah Harrison from Microsoft is one of the original architects behind Cortana as well as head of the team that creates all the content that Cortana is communicating (again, Cortana is all scripted). Her talk was focused on how Cortana communicates and how she interprets different situations. It seems that Cortana is more of a Siri for the enterprise (guess that Microsoft is still enterprise focused).

Maya Weinstein is design lead at IBM Watson in New York. Maya talked to the Robot Neo (the real robot, not an app), gave the standard examples of oncology (cancer treatment) and kids education (Element Path) etc. She gave the standard variety of examples of use cases for Watson from concierge services (Connie the Concierge at Hilton Hotels as an example) to dating apps and medical examples. Naturally, Jeopardy was mentioned as well.

To put it blunt, Cortana could be built with just three Watson APIs, if you add a few more Watson could do even more, but again, I hope that does not happen, they should not compete, rather compliment each other. Watson is a cognitive platform and not a product in that way. Watson could even be the cognitive parts (aka the Brain) of a very good personal assistant (Cortana). Cortana is the UI / UX and asks Watson for help with all the cognitive parts she is asked to help her users with.

Nevertheless, it is interesting times and this is a true paradigm shift in technology and business…..and it is starting now.

We are now entering the World of Cognitive!

Maya Weinstein’s talk at The Conference in Malmö 16th of August 2016

 

Deborah Harrison’s talk at The Conference in Malmö 16th of August 2016

Top image is from my daughters origami book

ai chatbots and cognitive platforms

Cognitive Platforms are the future, not Bots and AI

Wherever you turn, whatever magazine you read, you see Bots, more Bots. Most of them sprinkled with Artificial Intelligence. In my view, this is a flawed and wrong assumption. The future is a platform that makes processes cognitive. A cognitive platform that will enable processes to think, learn, reason, solve problems and communicate in the expected domain and in the expected language. Bots and Artificial Intelligence are part of this platform.

The future is cognitive platforms!

Just a bot is just plain stupid, it does what you tell it to do, it is very much like traditional programming, the program does what it is programmed to do, nothing more, nothing less. It is a fun interface and an innovative communicative interaction. To escape the risk of endless loops and repeating the same stupid standard answer over and over again, we sprinkle some artificial intelligence on the poor Bot. Now we have a Bot that understands what you are saying to it and also can reply with a bit more elaborative answer then the basic standardized sentences. Does that mean that the Bot will actually assist us in our daily life and improve our performance as humans?

The above is true to a certain degree, in some cases, it will improve our lives, but mainly in the way of speed to information, the Bot will not tell us things we could not find out in other ways, it will just find it fast and deliver it to us. Such Bots are already here and have been for decades. Bots are simply put another type of interface.

The sprinkled AI is mainly about language and algorithms, a subset of the real meaning of AI. Languages in the sense that the Bot will understand you, classify what you intend to communicate and also often do this in several languages. A flaw today is that we often confuse machine learning and algorithms with the true meaning AI. It cannot be AI when most of the time it is a formula created in a spreadsheet that we then put to work on extensive amounts of data to help us to provide insight and conclusions to the user or the company. That is just machine learning. This is often done in very impressive ways (Spotifys Discover Weekly is a good example here), I have personally built companies mainly based on machine learning, so I am not in any way negative to the practice. It is just that it often (not in the Spotify case, just to be clear, they use machine learning) is described as AI and that it is the overall future of tech and in that I disagree. Why? Well, let’s start by looking at what AI is according to Wikipedia

Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

So, according to Wikipedia all is fine, cognitive and AI is the same, but that is not the way it is translated into today’s tech-world. Most describe AI as a combination of Machine Learning, Natural Language Processing and Algorithms. With this in mind, I prefer the “cognitive” term instead of AI. The AI term is in many ways already destroyed by those who claim to do AI, but mainly does Machine Learning and NLP based on structured data.

Lets not forget that 80% of todays information today is unstructured, not even possible to interpret for most systems today.

Today 80% of the information we have at hand is unstructured, that means that very few computers can work with that data in a valuable way. To be able to understand millions of medical journals or law books from all over the world the computer need to be able to learn, reason etc as well as to learn the domain specific language. You cannot apply an algorithm from a spreadsheet to accomplish this, a software that is very close to a human brain is needed. A cognitive platform learns, communicates and does problem-solving.

The Cognitive Doctor vs the Human Doctor (example)

Over the years we have done tremendous research on cancer. We also have a huge amount of medical journals on cancer patients. Let’s play the role of the doctor. You think you have read a lot about cancer, and this specific version of cancer. You have also been in practice for 20+ years so you have seen your fair share of cancer patients passing by as well as written a lot of research papers on the topic, which would classify you as a very knowledgeable and experienced doctor within the field.

A patient walks in with the diagnoses of cancer, the specific cancer type that you are experienced in. You look at the journal and talk to the patient. Given the research reports you have done over the years, as well as the one you have read from others in combination with your experience in the field you come to the conclusion that a specific type of treatment would be the best way to progress. Hopefully, the patient gets well and all is good.

Now, if the doctors working process would be cognitive the cognitive parts of the process would read ALL available research, ALL available medical journals. This in combination with getting all the data and info about the person incl. historical sickness and traumas etc that the patient might have. Again, let’s not forget that most of this data is actually unstructured and not even readable to most computers today. The cognitive process looks at all data (structured and unstructured) and swiftly replies to the Human Doctor with three alternatives (including the info these recommendations are based upon) for treatments for the patient, including a ranking on which alternative might be most suitable for this patient.

Now, the truth is that no doctor in the world can keep all information inside his / her head, the truth is that most of us tend to do what we have seen previous success with. What a cognitive enabled process now have given us is three alternatives for the Human Doctor to consider. It is still the Human Doctors decision, but the help presented might give new angles and new info that the Human Doctor actually had not considered. The decision is still the doctors, the decision is now only based on a more trustworthy base of data and information. Still presented to the patient by its doctor without even knowing that a computer actually helped a lot in providing the best treatment for you.

Is this pure fiction? Actually not! 

What is the future then? It is cognitive platforms!

For me, the success in this space lies in how well a company makes its processes cognitive. Cognitive will never be a specific product (like a Smart Bot) it will be integrated into existing (or new) business processes. Damn, that sounds boring, but let’s be honest here, most companies have defined ways of working aka processes. Most of these processes can be vastly improved by making them cognitive. To be clear, it is not new processes it is about making existing processes cognitive and by that more efficient, more valuable and more productive.

To be able to do this a process needs to be able to access different cognitive functions and consume them where appropriate. I could be to read, learn and understand a law book (and solve legal obstacles) or thousands of medical journals (like the cancer doctor example above) or it could be to find certain objects in millions of images or to analyze incoming communication, voice or e-mail, to be able to give a better customer experience, it could also be to give a quick, accurate  and personal (tone, personality) reply (in the right language) to a customer that interacts with the company.

This is only provided by a cognitive platform and not by a bot or AI (as positioned today by tech companies), but both are important parts of the cognitive platform.

PS I think I will break down this rant rambling post in a few more specific posts on the topics of cognitive platform, cognitive process etc Stay tuned DS

Photo from the movie Big Hero 6 by Disney

cognitive transparency

Transparency is the future

Transparency will be the key to success in the era of artificial intelligence and cognitive. With all new technology also comes new challenges. In the artificial intelligence and cognitive area of technology one main challenge is how to manage privacy and integrity.

Currently artificial intelligence and cognitive products are the new hot and trendy topic and also it seems to be the new hot area for new startups.

Disclaimer: The company I have co-founded, Monies, is one of those. Our goal is to unify your financial life and bring meaning and consciousness to your financial life. By that we are naturally in the needles eye of this post and transparency will be one of a few keys to our success.

Since these systems and technologies are a lot about emotions and to learn about how we as humans work and to learn, reason and have a conversation based on the input you provide, it is essential that we trust the technology and that we know 100% that the info is secure, not shared and private only to me. Already at this stage there are a lot of risk and many will fail.

In my opinion we will also need to be really assured that my data is not mixed with other persons or organizations data. This is important since most of the AI / cognitive platforms are….platforms. This means that all data goes into “one” place and not on your own dedicated instance or server. Most platforms, Facebook, Google, Amazon or IBM are physically based in huge data centers and all data is physically stored in the same environment as other individuals or organizations data. For these organizations to be able to build the necessary trust that our data is secure and private to us or our organization, transparency will be of outmost importance to establish this trust.

If we push the topic it is the same as when you share information with another human, you need to feel that you trust that individual prior to sharing sensitive information. Since these technologies replicates a lot of the behavior we have as humans. Would you share your private info if you knew that the other person immediately shared that info with the rest of his company or even other companies that they are also doing business with, would not think so.

Even though machines replicate a lot of our behavior today, some things are hard to establish, like trust. Enter the revenge of transparency. Transparency have long be pushed down in the value-chain in favor of capitalizing on our private data, companies like Facebook, Google and others that have as a business-model to re-sell your data to companies that want to reach you with their message. Their business model is to sell your data, you are the product.

This will not compute in the era of AI and cognitive. We will, in my opinion, be much more restricted in what we share about ourselves if we do not feel safe and secure.

Ai and cognitive as technologies are already here to stay so this is not a post against these technologies, the opposite, I believe strongly in them, even so much that I have started a company in the segment.

We and everyone else in the segment need to be fully transparent about the following:

  1. How we store the data
  2. How we keep it safe
  3. That personal information is not shared and truly personal
  4. Provide info on the reasoning on the answers provided
  5. Provide evidence that backs the answers that are provided

Only then can a artificial intelligence and cognitive technologies really become as successful as they are expected to become.

I will end this post with a quote rom Rob High, IBM Fellow and CTO of IBM Watson, in a Mashable article on the topic recently.

They’re subject to the human condition — that is, all of the forms of expression that we leverage to communicate our thoughts, ideas and knowledge, and all of the experiences that we’re exposed to that shapes those thoughts — cognitive systems don’t behave like other deterministic (mathematically modeled) computing systems. They are subject to the same ambiguities, nuances, subtleties and lack of universal truth that we as humans are subject to. They, like other human experts, are only really held up as an expert when we develop trust in them. Cognitive systems, like other human experts, have to establish that trust by being transparent about why they believe what they believe — answer what they answer. And in doing so, they will reveal whether they are acting nefariously or not.

Photo: The top photo is taken from a train entering Stockholm a rainy winter day in December. I thought the wet window could symbolize some transparency.

Cognitive Business Part 2 – Marketing

Cognitive Marketing will bring a lot of vagueness out of digital marketing and digital business. Most areas of marketing will benefit from becoming cognitive. Lets start with four bullets that you will recognize.

  1. Conversation
  2. Personalization
  3. Retention
  4. Conversion

Now, these are in no way new to anyone who have worked in digital marketing or anything digital for that matter, what is new though is naturally how it is done. Prior to this new era we have struggled to talk to people in a way so they feel engaged and intrigued to continue (1), this manly since the information that has been talked about have not been “for me”, we want the “other end” to know me that much so the information is very much personalized (2), it often feels like it is for someone else and often oversold as well, it is simply not engaging enough and it is often hard to find why I should continue, easier to unsubscribe / cancel / de-register etc (3). we struggle with conversion rates and penetrate all goals and metrics in Google Analytics to see why people drop off at different places, trying to iterate with small technical and graphical details, all to convert more people, that is after all the end-game. As a potential customer we often feel like we are in a real-life store and the staff is putting things in our basket and then pushing us towards the cashier (4), this without knowing what we want or if we want to ask something etc, if we just could get some personal service, we would “convert” much faster…and naturally return to us over and over again (3 again).

I know what people from the different marketing department are saying. “We are doing that, we are listening, we are personal, we do participate in conversations and are accessible….and our conversion-rate is way higher then average in our vertical / category”.

Let me say this, you are all doing a great job, this considering the tools at hand, but if you are honest with yourself, how much do you really know about your customers, how individual is the information you are pushing, how good is your retention or is it mainly campaign-spikes….and lastly, if you think about it a bit, the conversion-rate you are so proud of, if being really honest towards yourself, is it high or simply extremely low, just high compare to competition?

Cognitive marketing is mainly about taking all the techie stuff out and putting some humanity in there. Make our potential customers feel like they are seen and heard and that we offer them things they really want to buy.

As with most cognitive this is a way to make the digital world very much more like the real world, so if you think of my last section and the numbers you are so proud of, if you had the same amount of visitors in your “real-life-store” would you still be proud of how you communicate, sell and the conversion-rate you have?

  • Offline world: Low amount of visitors, more personal, recognition, high retention and conversion.
  • Online world: High amount of visitors, little to not at all personal, limited recognition, ok retention, low conversion.
  • Cognitive world: High amount of visitors, personal, recognition, high retention, high conversion.

Naturally above is simplified, but you get the picture, now lets dig into who it actually could play out.

Cognitive Marketing

Let´s immediately look at an example. You want to sell more ecological clothes, but do not know how to reach your potential customers, get their attention, keep it and then hopefully get them to like your products, buy once and then have an ongoing relationship that includes frequent purchases in your store. You want to get to know your customers and have a conversation with them so they feel engaged and recognized.

Your goal is to attract X customers within X months. First we need to figure out who is most likely to buy our products (below are facts made up by me, I have not done any digging into who want to buy eco-friendly clothes, so entirely made up).

  • Female 20-25
  • Often express opinion on politics, relationships and culture.
  • Open to change, curious and often follow their own path. Can be philosophical.
  • …etc

To be able to match these details we also need to know how our product is liked and disliked.

  • Overall people are positive towards our product
  • Our product is often mentioned and shown in pictures with X and Y products
  • Our product is often mention in information that is classified as cultural, sub.cultural, design and environment.
  • In pictures our products are often seen together with X and Y and in outdoor environment.

Given that we also have access to a simple monitoring solution that monitors our own brand / products as well as maybe some competitor (not necessary) we have everything we need to get started.

Traditionally we do not know most of the above, we are simply screaming of joy over the number of sign-ups or subscribers we have, we rarely know even a subset of above.

In cognitive marketing we actually do.

Lets play

To play with the entire process would be a 10.000 word post and you would put in the time to read it so I used the identification and personalization part as examples. The “who to engage” part that is. One of the most important parts to have a high retention and conversation rate, get the right people in the door and talk to them in the right way.

So, when I found someone that is interested in this area of products I simply run them through Personality Insight, all we need to get an analytics like below in a Twitter username, but naturally the more info you feed the better.

watson personality insight

This person seem to be quite a good fit for our company, lets see if we can find out even more so we can be even more precise when reaching out, so we can adress the person in the right tone and message etc.

Lets continue with the case where we only have the Twitter username. Now lets grab the Twitter avatar and analyze that. From before we knew a lot about the personality, needs and values. Not so much facts, but with the avatar we get some interesting facts, even without asking.

  • Gender
  • Age
  • …and that the photo actually contains a person

alchemy vision

This process is a start of building something really great. As you see in the image above the person that I have used is Lady Gaga (both for personality insight and for the image), which was also discovered in the analysis. I think she is slightly younger though, but a hard life in the music industry might impact the, by Watson, presumed age.

Just to be certain of what tone to use when interacting you can always analyze the tone in the text that we found written by the person about our products or area of products, this so we address with the expected tone. It could look something like this.

watson tone analyzer

I could create examples like above for the entire chain I described above, but I hope and guess that the description might suffice as an overview of how Cognitive Marketing could look like in real-life.

To summarize, cognitive marketing will bring out a lot of vagueness and replace that with a relationship that is very similar to how we interact in real-life.

Even though I am not entirely comfortable with IBM CEO Ginny Rometty in her statement Digital Intelligence + Digital Business = Cognitive Business it is really applicable to marketing.

This is the second post in my Real-life Cognitive Business series. Below is part 1 and the other parts will be added as they are posted.

Cognitive Business Part 1: Internal Communication

Page 2 of 4

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