UPDATE: The supported language 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 language support as well as changes in Watson Pricing since my last post on Watson pricing from Aug 2016. Only the table is updated (and some typos corrected).
Unstructured data and languages is a tough nut to crack. To support all languages is not easy, but what is a bit frustrating with IBM Watson is that each service support different languages, naturally logical since it is different technologies and as an example it is easier to classify a blogpost as Swedish or Turkish compared to extract relationships or concepts as well as translate a text or sense emotions. Also, there is no Watson language support page that you can go to.
Challenges aside IBM Watson actually already support several languages for most services available today. This is impressive from several angles since many of the services actually require that Watson to be cognitive and learn the language from the beginning, just as we as humans do.
Watson language support per service
What I discovered is that it is really hard to find what language each service support, it is often buried deep in the documentation and only in a few cases listed on the main page for the service.
So, I decided to dig through the documentation and create a document that lists all services and what languages they support.
The list below is the result of me digging through the documentation of each and every Watson Service currently available.
Brazilian Portuguese, English, French, Italian, Spanish, German, Traditional Chinese, Simplified Chinese, and Dutch. Arabic is supported through the use of the Conversation API but not through the tooling interface.
Classify image method: english, spanish, arabic and japanese.
Custom and all others: english
English, Spanish, Portuguese-BR (updated 2017-09-27) and German (Discovery News only english language news).
I hope that IBM and the IBM Watson team soon will publish an official list that they maintain and update. The above list is accurate as of the date this post is published (UPDATE: This table is updated as of 24th of April 2017). I will try my best to update when new services becomes available or an existing one supports additional languages. If I find an official IBM Watson document that lists language support per service I will naturally update as well.
IBM is certainly focusing a lot on IBM Watson, now with 4 locations around the world and more coming. If I remember correct it was almost 20 years ago a new division was created within IBM and now they are launching quite a few, all within Watson.
Watson Headquarters – 51 Astor Place, Manhattan, New York
Watson Commerce – San Fransisco
Watson Health – Cambridge, Massachussets
Watson IoT – Munich
General Manager for the new Watson IoT will be Harriet Green. I will leave you with the fanstastic John Kelly with his keynote and launch speach today. It summorizes and describes it all very well. To finish off I do recommend the other videos on the IBM Watson Internet of Things launch site as well.
When talking IBM Watson the interaction between the user and Watson is mostly done via a “chatty”, Watson assistant like, dialog interface and in natural language. This has created a popular use-case for Watson, the personal assistant.
A dialog is nothing new, there are tons of assistant apps and products out there, it is a big trend, so why is Watson so unique?
The uniqueness is mainly in the department of learning and understanding. The dialog in mainly managed by a service called Dialog. Dialog is actually pretty dumb, or honestly, plain stupid, just like any programmed dialog, nothing new there. What is new is when you add other Watson services to the Dialog experience and enable Watson to learn from the interaction with the human in the other end.
Watson learns from every interaction
If we take a quick look at the list of examples at the end of the post, we quickly see that it is a wide array of verticals that use Watson as an assistant. The greatness surfaces when you realize that Watson can learn domain specific language. An Oncology doctor and a person that want to buy a jacket from North Face do definitely use different and domain specific language. Watson will, overtime, learn this language. Initially Watson will be pretty rigid in terms of what kind of dialog that can be had, it is mainly following the script in the Dialog XML that is used. With some training (look at the Natural Language Classifier as an example) this will improve rapidly and Watson will learn your domain without you lifting a finger.
That is not all that Watson understands how you communicate, he also helps build a better knowledge within the domain, every interaction with Watson will also make Watson smarter. Therefor the interaction is an important part of Watson. It is not necessary though and Watson can learn in may differnt ways. When training Watson, large chunks of data can be uploaded (and updated continously) to Watsons corpus. Watson can read 800M pages per second, that is a bit more than me, but I am a slow reader. When the data is entered into the corpus, Watson need to be trained, now we are back at the “chatty” interface again, but this time only to tell Watson if things he says are good or bad answers The training can be done in several ways.
So again we are back to the fact that Watson actually learns and communicates a lot like a human. Cognitive computing that is.
More use-cases with Watson as an assistant?
To start, I am not a believer in Watson replacing customer service or being the only interface for customers. Watson is very capable of managing specific processes or specific questions like “I have lost my password” or “When do I have to submit my yearly tax report?”and based on those questions provide information and actions. It can definitely replace quite a few of these shitty “Tell me what you want and I will direct you to a customer support agent”-solutions. The great thing there is that Watson can act in all channels with the same solution. Social media, call-center, web, in-store etc. It would cut cost radically (I know quite a few companies are looking at this incl customer support companies).
The greatness does in my oponion not lay in the interaction, it lays in the answers that Watson can provide. A customer service reperesentative, sales person, communication specialist etc do not know every document that might provide an answer, Watson actually knows literally every document. Since Watson also can answer questions and provide evidence for the suggestions he replies with, Watson is a tremendouse help for any individual in an organization that needs to answer a question based on unstrucutred data. Therefor Watson can be a multi-layered implementation for any assistant solution build on Watson. It can help in the first interaction with different tasks as well as direct the client to the right section, person or information. Primarily I would say Watson will provide information to representatives that engage with clients, this to provide the best answer possible and then provide feedback back into Watson. This was a bit of rambling, but I will leave it as is.
IBM already have created best practices within Telco and Insurance. Both are based on the product Watson Engagement Advisor, which is an out-of-the box Watson Assistant. It seems IBM want to start every Watson case with a Cognitive Value Assesment. Have not managed to find that much info on these CVA, except that they should contain these bullets:
Define use case and user personas
Develop benefit model
Map out cognitive journey
Generate stakeholder buy in and confirm commitment
After the CVA a go / no-go decision is made.
What are your thoughts on the Watson Assistant use-case?
Cognition is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and “computation”, problem solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge.
In conclusion, we are finally entering the era of thinking and awareness (which is the original latin meaning of the word), even for computers.
A cognitive process can be divided into 4 steps
Decide (action, present confidence and probability etc)
Research to dive deeper into cognitive
Cognitive is not only a marketing term for selling Watson, it is actually something researchers have worked on for a very long time, this since quite a few IBM researchers work on how the brain works. In the context of this post it naturally gets really intersting when computers and software starts to adopt cognitive functionality. .
According to Arvin Krishna, Senior Vice President of IBM Research, a cognitive technology needs to:
Learn at scale
Reason with purpose
Interact with humans naturally
Has an objective (this bullet was actually not said by Arvin but by Dr John Kelly in a talk at Cognitive Collaquium NY recently)
This is crisp and easy to understand (even though understanding how the hell they got the software to work like that is another thing).
IBM have always been very generous about posting their research online. It can be tricky to find, but it is out there. In terms of cognitive it has its own section on the IBM Research site. So either start on the IBM Research Cognitive Computing section or at the IBM Research homepage. There is droves of info to read up on and hours of video to watch.
Cognitive analytics A set of technologies and processes that analyze data for the purposes of learning, contextualization, and making recommendations.
Cognitive business An IBM strategy that builds on digital business and digital intelligence with systems that can understand, reason, and learn to leverage data to create deeper engagement and personalization, enhanced expertise, and cognitive products, services, operations, and processes.
Cognitive computing A category of technologies that uses natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition.
Cognitive environment An infrastructure that uses specialized software agents and devices that act as one shared integrated resource, enabling fast and efficient human-computer collaboration.
Cognitive system A category of technologies that uses natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition. Watson is an example of a cognitive system.
This is also the second time I borrow a top picture from IBM, this one from their campaign-site “Outthink” that is out there to enlighten us about Cognitive. It is a typical campaign including a darn cool commercial with Bob Dylan talking to Watson..
This is frequent topic. I’ll keep it short. No, Watson was not developed to replace humans. The opposite, one of the reasons was to empower humans, and actually the other way around as well, let humans empower the computers.
Today Watson will help us make better decision, this by learning, reason and communicate in natural language (text and speech). Watson will make us better, not replace us.
But what was is the problem that Watson was set out to solve when it all started?
This is what Dr John Kelly said about that time in history.
We set out to solve the simple problem of massive unstructured data.
We did not set out to replicate the human brain or map the brain or a form of artificial intelligence that replicates what humans do.
The project that today has become Watson started in August 2007 and was then called BlueJay. The IBM Researchers had realized that the amount of unstrucured data was increasing with an ever before seen volume and speed. The systems at that time (and most systems today) did simply not handle that amount and kind of data. Data was not only text, it was images and sound etc. None of this could be read by computers.
Today the analyzing of unstructured data is an underlaying core part of what Watson is. It is also probably the most desired and valuable selling point. Just imagine all the data in your office that just lays there and is never utilized by computers to empower us and our business. Today 80% of all data produced is unstrucutred, and Watson can handle it.
For some more on the background and purpose of Watson, I recommend this talk by Dr John Kelly, held at Cognitive Era Colloquium in November 2015.
Personally I am surprised how good it was and (for those who know me) I think you will recongnize my personlity pretty good in the summary that Watson Personality Insight put togheter on me based on text from this blog.
You are inner-directed and skeptical.
You are independent: you have a strong desire to have time to yourself. You are philosophical: you are open to and intrigued by new ideas and love to explore them. And you are authority-challenging: you prefer to challenge authority and traditional values to help bring about positive changes.
You are motivated to seek out experiences that provide a strong feeling of efficiency.
You are relatively unconcerned with tradition: you care more about making your own path than following what others have done. You consider independence to guide a large part of what you do: you like to set your own goals to decide how to best achieve them.
There is naturally tons of other data, a few examples of “me” in the list below:
Big 5. I am 96% openness
Needs. Challenge and Practicality is very high.
Values. Openness to change is 96%, Self-enhancement is 91%, Conservation …. 1%.
With this post I will try to bring some light on what Watson is. What are the components and products etc. I will also dive into the partner channel and price-model. IBM have really tried to make Watson easy to understand, almost too easy, which is very good, being IBM and all. Watson will likely assist us in ways we have not seen before.
As IBM have described it in the header-image of this post, that I have borrowed:
Watson is a cognitive technology that processes informaiton more like a human than a computer – by understanding natural language, generating hypotheses based on evidence, and learning as it goes. And learn it does. Watson “goes smarter” in three ways: by being taught by its users, by learning from prior interactions, and by being presented with new information. This mean organizations can more fully inderstand and use the data that surrounds them, and use that data to make better decisions
Watson has the ability to reason and work with hypotheses. It means that Watson will not always respond with the, according to the raw data, best suited answer, but will instead reason and weight other dependencies into the picture, just like we humans do.
As humans we base our decisions on a wast amount of data, a huge amount of data naturally, but we also take outside things into considerations, that are not pure data, but related info that we kind of know anyway and then we put all the alternatives on the table and reason about the best solution. That is how Watson does it as well.
The impressive thing with Watson is that when most of us actually forget things, Watson has access to all the data, always and dont forget, on top of that he reason and work with hypothesis. A great example here is how Watson works with oncology. Watson has access to huge amount of data and then the entire journal of the patient.
When Watson is asked about the best treatment for the patient he produces a couple of suggestions, including the evidence, and ranks them. The Doctor naturally makes the final decision, but with this, it is no risk that the Doctor misses out on some information and go on routine as “he has always done”, the Doctor now has several options that probably will contain information and suggestions that he / she (as the human being he / she is) probably did not consider nd even if he did, he gets all the supporting evidence to make the right decision for the patient.
Was that not possible prior with the classic big data / analytics tools we hear all about. Well, I would argue NO. This since most of the data in healthcare research and medical records are simply just text and images. Traditional, yet smart, analytics tools cannot read that kind of data, Watson can. it is not only about being able to read the information, it is also about actually understanding it, learning from it and draw conclusions from it, that makes the big difference and also what makes Watson so impressive. Agian, Watson think and acts a lot like a human, or the work IBM is pushing, being cognitive.
A neat thing with most solutions that are built on Watson is that we can communicate in natural language. We can simply write or talk in a natural way. Watson will even learn how we talk in different domains. This is very good since a doctor has one type of vocabulary and there is another in school or in the military. Again, as with us humans, Watson needs to be taught every language and every domain. This might sound cumbersome, but think about it like this. A traditional software is as stupid or intelligent as it is programmed, it simply only does what it is programmed to do. It is comparable to a person being told “Read what is on page 32, second row”. Watson on the other hand will after he is taught be able to answer “Watson, can you explain to me how subject X works?”, which is the summary of the data on page 32 but also with additional info from other things he has learnt.
Above is what Watson is mainly known for and we all want to know how we get our hands on that nifty software, so how do we?
The picture communicated from IBM usually looks similar to the below chart.
The reason for the confusion is pretty simple I would guess. Watson is developed in such speed that documentation and communication is not always keeping up with the updates that are being implemented, I would say that is a very good thing, rather some confusion about the amount of APIs then a technology that is updated at a slow pace. A few examples that might confuse are the following:
Message Resonance. It is a service that is available in the IBM Watson Developer Cloud, have a demo and some documentation. It is not listed on the services page, but hidden.
All APIs are avialble to play with for free in the Watson Developer Cloud. A Bluemix account is all that is needed. Every API has documentation and most have tutorials and example code. Additional code examples can be found on the IBM Github site.
High and low!
Herewithin lies a small challenge. I have followed the progress of Watson over some time now and the “Jeopardy” vs “28 APIs” is the challenge. On a highlevel it seems like a magic machine (which it really is), but scratching the surface reveals an API based technology. To start, that is fantastic since the tech-world we live in today is an API-based world. The tricky thing is to connect the one-box-solution aka Jeopardy or Oncology with these 28 APIs. When we come up with a brilliant use case and want to start exploring how to build it, it takes a lot of time to realize how the APIs connect and which ones that work best if combined with other APIs etc (Dialog and NLC as an example). When that phase is over it all becomes clearer, but to draw the lines between the goal and the services can be a challenge.
IBM Watson Knowledge and Expertice
Knowledge is naturally a challenge for IBM. A new product has been released and it takes time before it generates revenue, therefore, IBM sales will still focus on opportunities that will generate revenue now. With that said, there is no doubt that IBM is putting in huge amount of resources to make Watson succeed, but from a knowledge perspective it is still 51 Astor Place, Manhattan, NYC that is the epicentre or knowledge, with is smaller sibling in the new Watson office located in San Fransisco since recently.
The rest of IBM seems to be in a learning phase currently, but I except that to be improved significantly shortly.
In terms of support in EU, I think the UK office has a small team in place. I am also starting to see quite a few IBM Watson-positions available in most countries incl Europe.
What about partners, Watson Partners?
Since I have been around the block a few laps in terms of the IBM partner-channel, I could probably write a book about that, but lets keep focus on Watson. It seems that the channel-strategy for Watson is somewhat different and I am not even sure the classic IBM partner organisations around the world have started to look at Watson just yet.
It might even be that the partner-channel is different with Watson. It is all new models and there is also the IBM Watson Ecosystem.
IBM Watson Ecosystem
There will be a seperate post on the Ecosystem soon. This since the company I have co-founded Monies, just have been accepeted as an IBM Watson Ecosystem partner. All papers are signed, but we have not really started yet, so more info will follow.
As a summary the IBM Watson Ecosystem consist of about 400 companies that have been accepted and fulfilled the pre-requisits that IBM has in place. It has a kind of incubator / accellerator 3months program included as well, this to get your product out the door quickly, this with support from IBM in many different ways, not only from a tech-perspecitve.
It also opens up the opportunity to pitch for a piece of the $100M that IBM has put aside to invest in companies within the Watson Ecosystem.
The Watson Ecossytem is fully managed from Watson Group in NYC. The agreements are partially local, which I guess is for legislation reasons. In our case no one from our local country have been involved, 100% New York.
How much does IBM Watson cost? Pricemodel?
As usual with these large corporations it is a jungle to find out, but let me give it a try. Currently there are three price-models.
PayGo. A straight-forward pay-as-you-go subscription model based on usage. It has two tiers, one free and one paid. The free one is usually limited to amount of API calls or similar. A modern model. Most prices are public and can be found on each service, like Natural Language Classifier as an example.
IBM Watson Ecosystem. This is an entirely new model and I like it a lot. It is a revenue-share model where you share a part of the revenue with IBM based on how much of the product that relies on Watson (or if the full product does). The best thing is that you do not pay until the product generates revenue. This creates a good partnership with shared interest in the success.
Enterprises. I have not found any formal info on this and I actually guess it is pretty much up-in-the-air still. My personal guess is that IBM currently creates custom packages until they have found a good model for Watson within the enterprise segment.
But what about Watson Analytics and Watson Explorer etc?
In short it is the APIs that are the Watson product. There are also some support services like the Watson Experience Manager. Watson Group have also created a few applications based on the APIs, like Watson for Oncology, Chef Watson (together with Bon Appetite and I recommend a dinner based on suggestion by Chef Watson, great fun) and Watson for Wealth Management, but they are still based on the 28 APIs….or the 19.
Watson Analytics and Watson Explorer are simply already existing products that has been sprinkled with some magic Watson-dust. Watson Analytics has gotten itself a natural language interface, but underneeth it is still an SPSS predicitive analytics product. Watson Explorer is the old IBM Infosphere product etc. These products are also sold as classic software within (I guess) the Analytics Software brand.
That concludes this humungusly long post. I will try to keep them shorter in the future. I have simply been digging in the throwes of Watson info for a while and thought it was time to let some steam out and share my thoughts. Almost 2000 words.
We hear about the IBM Watson everywhere. It began with Watson winning Jeopardy in 2011, then the message and the technology behind has been pollished to now being commercially viable both as strategy, technology, marketing, and now also as a busienss.
The IBM story that led to IBM Watson
Let’s start with a little flashback. Generally IBM is perceived as best (only) suitable for large and bulky corporations. It is in fact a very true reality-based picture. IBM, however, has driven innovation in a way that few other companies have. Fluffy concepts that few have understood have after a few years become a reality, and IBM has rarely received any credit for it, despite the fact that they pushed much of the early innovation and research in the field. Terms such as e-business and on-demand were both coined by IBM. IBM has had its fingers in most big shifts in technology over the years.
It began with the tabulating machines and punched cards. The tabulating machines and punched cards are kind of the first computer ever made.
Programmable systems. IBM with their computers and their databases, programming languages and operating systems have helped to create this era, it is quite natural. It was IBM who introduced the magnetic hard drive, DRAM, etc., etc., but also the relational database in 1970 and that they actively contributed to several programming languages.
There are of course several other companies that have affected the industry over the years, but I argue that no other company has done it the same way as IBM. Today, IBM is the world’s largest IT company (usually # 1, but it shifts between IBM and HP). IBM has about 400,000 employees and are in the leading position in most areas they operate in.
The toughest time for IBM was during the 1990s, when IBM was very heavy and was stuck in history. Some of the same challenges we see that IBM have today.
The share price today is the lowest in five years, analysts thrashes quarterly reports and the future success is constantly questioned. Is IBM a dead horse?
I’m not an analyst and do not want to be either, but in my world, the stock market is a rigged game, it is financial models more than if companies are doing well or not. IBM is the only listed share I own and I doubled up my little possession as late as last week. Why? Well, I believe in IBM and I think Watson is a big part of IBM’s future success.
IBM suffer from being big (big blue) and have been around for over 100 years, although the IBM name came first in 1924. It all started when Charles Flint merged together four companies, and in 1911 Thomas J. Watson, Sr. became General Manager of CTR, IBM name at that time.
This allows IBM to drag on a lot of legacy. It’s almost a little scary when you read in the business press and comments from analysts that IBM is a hardware company. It was a very long time ago IBM had hardware that some kind of core business and today it is just over 10% of revenue coming from hardware and margins I guess are at a minimum, which IBM realized in time and sold off most to Lenovo. The cash-cow mainframe is kept and doing very well as far as I have understood.
Why are the analysts wrong?
IBM is today in terms of sales about 50% Consulting, 35% Software, the rest is small numbers. What should worry the analysts, if one should find something to complain about, is that the kind of consultants that IBM is offering is moving toward decreasing demand in a world where companies buy more and more SaaS solutions, those solutions are not in need of lots of consultants in nice suits for extremely expensive hourly rates. Sure, I generalize, but the fact is that the demand decreases, and right from the start, consulting is no high-margin product, so personally, I see a transformation like Lou Gerstner did, when he shifted from hardware to consulting 15 years ago. For those interested, his book “Who says elephants can’t dance” is a really good read about that transformation.
Just to be clear, both GBS and GTS have a lot of geat offerings which are still very valuable and affordable for customers including outsourcing. It is the actual hourly consulting I refer to.
Software, on the other hand, is about 80% margin (GP Margin 3Q15 was up 86.4%). Software is the new black, but there are clouds in the sky, the license-model that IBM offers are from the stonesge. IBM’s licensing model is based on selling large volumes at discounted price (this is strucutred in a system, called Passport Advantage) and with volumes the customer can grow with, that they do not need now, but maybe in the future, aka the shelfware. In Sweden (that I now have retured to after 3 years in southeast asia), I would guess that these transactions corresponds to approximately 80% of the revenue and only comes from about 20 customers (maybe should have added a “Hello ELA” in the title). It is not the future!
IBM have had the SMB sector as a major focus for about 10 years now, unfortunately, it is nice words than operational reality, IBM goes where the money is and in their case that is the big corporations. Even here I think that Watson will be able to bring a change, both in the business model as well as he products.
Since I wrote above from the top of my head, I felt that it might be worth checking facts about the numbers, so I checked the IBM 3Q15 earnings presentation from October 19, and my numbers correspond well with the 3Q15 earnings numbers. It again illustrates my skepticism towards the stock market. The overall profit margin for IBM is impressive 50%, it’s a pretty nice margin. If you move to higher margin products which require less human effort and away from products that generate the most revenue (hour-based-consultants and hardware) it will increase margin but sales go down, why do not the analysts see this, but what do I know, I’m just thinking logical and that is the opposite of the stock market.
But are the analysts right after all? Oh no, they’re wrong, I say optimistic, but there are some challenges? A look at their product portfolio and ignore the different IBM divisions, one sees quickly that IBM must manage to turn its business model and its products to the cloud and valuebased subscription models. Sounds buzzwordy, but it is crass. With competitors in the cloud as Amazon, but also Google, Microsoft and Salesforce et al. Personally, I think that this particular infrastructural change in terms of the cloud, will be the toughest challenge of them all for IBM.
With the above in the rearview mirror, I’m very happy to see the intense focus IBM management has on Watson. Every time IBM CEO and Chairman Ginni Rometty speaks she always mention Watson, and when you see Ginny it really feels that she is passionate and sincerely believe in Watson and its development.
Watson is not a temporary marketing fix, it is IBM’s future, or at least a very important component in IBM’s future.
My personal opinion is that she invests her name, her success as well as IBMs in the success of Watson. I believe Watson to be the single most important part of IBM to be successful long-term.
Why is Watson so important to IBM?
Why this exposition on the share price, turnover, success with cloud etc? What does that have to do with IBM Watson? Well, Watson represents a new era in technology, so if we link together IBM’s history with this new era, the cognitive era, this picture is very simple and descriptive about the different periods we had and the one we are now entering.
But it is not just by what Watson can do in cognitive analysis, but also the business model. Watson lives in the cloud, has an extremely flexible and adaptable business model and that Watson is not only aimed at large companies, but also for startups and others. For me Watson represents IBM’s future at all levels.
Watson’s success will be based on Watson’s part in other companies products and that requires a good genetic price / licensing model, and that services are available in the cloud. There we have a future for IBM that I believe in.
This post was intended as a “Hello World”-post in terms of Watson, but now when I have written it, it should rather act as a philosophical baseline of my attitude towards IBM and its future. I have a few upcoming posts on the topic and those will guaranteed be more related to Watson. It all sticks together though since Watson is IBM’s future. IBM Watson Group with about 2,000 employees has now existed for almost 2 years, the recently founded a consulting unit of GBS, IBM Cognitive Business Solutions, with about 2000 consultants who solely will work with cognitive and further 25,000 IBMers are to be trained in the field this autumn, over time all IBMers will most probably get their fair share of Watson.