IBM Watson is just fine, thank you !


ibm-bets-watson

Over the last couple of days, I have seen a bunch of articles on my social media feed that are based on a research report from Jefferies' James Kisner criticizing IBM Watson.

I am a big fan of criticism of technology – and as folks who have known me over time can vouch, I seldom hold back what is in my mind on any topic. I strongly believe that criticism is healthy for all of us – including businesses, and without it we cannot grow. If you go through my previous blogs, you can see first hand how I throw cold water on hype.

Unlike my usual posts, I cannot claim to be an impartial observer in this case. As much as I am a geek who wants to make my opinions known on technology topics, I am also an IBM executive , and I run a part of IBM GBS business in North America that also includes services on IBM Watson (including Watson Health) . I also own IBM stock via ESPP and RSU. I don't set product direction for Watson – but my team does provide input to the product  managers. So I was in two minds over the weekend about blogging about this – but net net, I think I will go ahead and say some things about this , and as always I am happy to debate it and stand corrected as need be. So here we go.

IBM Watson's primary focus is on enterprise, not consumer !

This should be obvious to most people but perhaps the technical and use case implications are not super clear when they conclude Watson is in trouble.

Lets take an example of something that is often used to make the point in favor of consumer AI tech – Alexa. I often get asked Watson versus Alexa/Google assistant questions. You can tell Alexa or Watson to check the weather and they will both do it. The big difference is – Watson keeps the context of the first question while you ask the second question, and Alexa treats the second question as if the first one was independent of the second one. In the set of use cases Alexa solves, this is not a big problem – but the ability to keep context is important for the use cases that Watson solves, like customer service. In a customer service scenario, you cannot engage in a conversation without knowing and interpreting what has already been said.

That said – it is very easy to combine Watson and Alexa. For example , if you have echo installed at home, you can invoke Watson via a voice command and keep having a conversation without even knowing it is Watson that you are talking to.

While Watson cannot solve every possible customer service scenario – it can solve several and deliver very high value. For example – lets say you are a utility company that gets calls from clients who want to pay a bill, check a balance, find outage restorations etc. Those are all things Watson can do just fine, and leave the high value tasks – like being an energy advisor , or a retention specialist – to expert humans. Imagine the type of value generated for that utility, and the consistent and fast customer service for their clients . Consumer AI does not tackle these kinds of problems – and that is a big difference. There are many such examples like this in enterprise side of the house – like this video about how Watson acts as an expert engineering advisor for Woodside, and H&R block using Watson as a tax expert.

IBM Watson does not share one client's data with another client

This design principle is very key to enterprise clients. Data security and privacy drives a lot of AI decision making. Consumer AI generally keeps the data all users give it and uses it to learn and get better. I am sure those companies have high ethical standards and the data won't get misused. But that is not how enterprises look at their data. It is important for clients to have full trust that their data is not shared with others that they don't want to see it.

A lot of the criticism that Watson takes a long time to learn and needs data in a specific format that is hard to do for clients come from this principle being not fully understood. Watson can learn from a given client's data – usually unstructured data – and keep getting better, but will not use company A's data for Company B's system to learn. Even if we ignore Watson and look at data science as a general topic – there is no way to shy away from an AI model having to learn. That is the core of the value prop of AI.

This is not to say every client starts from scratch. In many cases, there is a well established starting point. Lets take a Telco call center as an example. If a client wants to put Watson to augment a telco call center, they don't need to build intents from scratch. Instead, they can use "Watson for Telco" that has hundreds of prepackaged intents and just add of change as needed. Over time, this will be applicable to all industries. These are all repeatable patterns – another point that observers don't seem to notice.

IBM Watson has plenty of successful implementations , including Healthcare 

The Jefferies report calls out MD Anderson project uses that to extrapolate that Watson is doomed. I don't see any mention of Mayo Clinic trials,  Or Barrow ALS study, or  Memorial Sloan-Kettering-IBM Watson collaboration   .  Where is the balanced analysis that led to the dooms day conclusion ?

Watson is in clinical use in the US and 5 other countries, and it has been trained on 8 types of cancers, with plans to add 6 more this year. Watson has now been trained and released to help support physicians in their treatment of breast, lung, colorectal, cervical, ovarian, gastric and prostate cancers. Beyond oncology, Watson is in use by nearly half of the top 25 life sciences companies.

IBM Watson is delivered as APIs that its ecosystem can easily use

When Watson won Jeopardy, that incarnation was largely monolithic. But that is not how Watson works now. It is now a set of APIs. I am under no illusion that IBM will be the only game in town, although I strongly believe we are one of the best. Partners and clients will build Cognitive applications using Watson in a much more productive way because the functionality is exposed as APIs.

This gets painted as a negative by some of the articles. You can't have it both ways. As I mentioned above, where it makes sense to package something for a given industry or domain, IBM or someone in the ecosystem will of course package it. But the decoupled nature is the most flexible way of innovating fast and at scale in my opinion. The fact that billions of dollars of investment is directed into this field is good for IBM and its ecosystem – let the market decide on merits who succeeds and who does not.

IBM Watson some times needs consulting , but it only helps adoption

Let me also point out the role of consulting – be it my team at GBS or another consulting company. Clients are still largely tip toeing into Cognitive computing. They need significant help to understand what is possible and what is not in their industry and their specific company – which is what we call advisory services. The actual integration work is not complex and can be done by in house teams or a qualified SI. The other service I often see that is requested by clients is for design. In some other cases, they also need services for instrumentation (like in IOT use cases).

If we rewind couple of decades and go to the time when SAP was just starting out in ERP, What was the role of consulting ? Did consulting  services help or hinder the adoption of SAP globally ? None of this is any different from any other technology at this stage of its life cycle. So I am not sure why there is an extra concern that adoption will tank due to consulting.

IBM Watson team does great marketing, and we already have amazing AI talent 

To be perfectly clear, I am not a marketer – nor do I have any serious knowledge of marketing other than a couple of classes I took in business school many years ago. However, I am VERY proud of the work IBM Marketing has done about Watson. Its an early stage technology – and that needs a certain kind of messaging to get clients to take notice. If all we did was fancy videos and panel discussions and there were no customers using Watson today, I would have gladly joined the chorus to boo Watson. But that is not the case – All over the place leading companies are using it and as I have quoted above, several are public references.

From what I could learn internally, there are about 15000 of us working on this at IBM. This includes about a third of IBM Research. And we are hiring top AI talent all the time. In fact if you are an AI developer and want to work on Watson, shoot me an email and I will get you interviewed right away. While we of course use job boards etc to attract talent, that is not the only way we find people. We already have more AI folks than a lot of our competition – so perhaps that should be factored in to the discussion on "look at job postings, IBM Watson is short on talent" part of the story.

So why is IBM not publishing Watson revenue specifically ?

I am not an official IBM spokesperson – and I am not an expert on this topic. So this one aspect – I have to direct you to people with more stars and stripes than me in the company.

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IBM Watson – what better use of analytics than fighting cancer ?


From 1992 – when I joined the mechanical engineering degree class in TKM College, till today – I have been a fan of Analytics.In fact, I am pretty sure it is the engineering education that put this fascination in me. And it was my statistics professor Mr. Kalyanaraman who took it to the next level.

Nothing fascinated me more than numbers and making inferences based on them. It was not as if I didn’t realize that texts and pictures and all the so called “unstructured” data was very important – it is just that I always felt that there was plenty more to be done in the “small data” world of numbers, before any one worried about “big data” . I have kept on questioning the value of the insight that will come out of big data for most companies, since they cannot even make decisions based on relatively small and highly structured data from pre-defined sources.

And then, along came IBM Watson and it changed my perspective on analytics and big data completely. Although I work in IBM, I don’t work in the team that works on Watson directly. If I am envious of any one professionally – it is that group of colleagues who get to work on Watson. Watson captured my imagination from the first day I heard about its plans to play Jeopardy on public TV.

Now, god knows how I don’t suffer marketing . I attribute it to the compulsory marketing classes I had to take in B School. And the irony is that IBM has world class marketing. So when IBM trumpeted Watson from the roof tops, my natural instinct was to cringe. But as I thought through the implications – it became clear that Jeopardy was the perfect way for IBM to avoid evolutionary steps, and make a grand leap into the future of analytics. Jeopardy needed everything – ability to consume big data with no structure, ability to understand natural language, truly massively parallel processing, ability to work on commodity hardware, lightning speed, ability to make a decision, ability to learn and many more. And it was a safe test bed to see if technology can stand up to that stress in an environment that is not “life and death” types, but useful enough to make a determination if this has a future.

Right after Watson won Jeopardy against the “human” champions, the IBM team started focusing it on real world problems. And this is where I was most fascinated by the choices of that team and its leader, Manoj Saxena.

IBM has a huge army of smart sales people, who could have very easily sold Watson in some form to a large number of clients across the globe.It would not have been hard at all – my own clients have asked me multiple times how they can use Watson to help their business, without me having to bring it up. As we know, IBM is a publicly traded company with a published roadmap for earnings till 2015. But instead of taking a short term view of cashing in right away, they took a long term view of proving it out thoroughly in the real world with real customers.

Instead of trying to do too many things across all the geographic regions that IBM does business in, they chose to focus on a small number of very specific high value use cases in healthcare, insurance, banking etc. And they partnered with some of the best in class clients in those industries to do so – and in public,not behind closed doors. Now, that is good marketing – the kind I can relate to. Let the customers declare the vision and the success, not the vendor.

The use case that makes me most excited is the cancer treatment one where IBM is teaming with Memorial Sloan-Kettering Cancer Center. Like everything else, there is of course a commercial angle to this – and I can imagine this to earn IBM good revenue. But that revenue could also have come from many other use cases. It is the humanitarian angle that impresses me the most. Cancer research and knowledge can now be spread across the world in very short time once this project succeeds. Doctors outside major research hospitals can reduce a lot of dependence on opinions and guess work and experience, and do a lot more “evidence based” decisions. Of course I don’t expect Watson to ever replace a doctor, but Watson has the potential to be the strongest weapon an oncologist has in the fight against the deadly disease. That is not evolutionary – it is revolutionary. It makes me wonder how many other big problems can be solved by the judicious use of analytics theory and technology.

Please watch this and listen to Dr Norton explain this

And finally, I like the sense of reality the IBM team and the clients who are partnering with them display. They clearly explain what Watson can and cannot do , and how long it will take to get there. Now, I know a lot of my friends like innovations to be out in the market quickly – and I understand where they come from. So on this cancer treatment use case, I pinged a few friends who are doctors in India, who I know from childhood to understand more. It sounded like on an average it takes anywhere from 8 to 12 years according to them for information on diagnosis and treatment to become common knowledge from the time it is published. According to them, they will be thrilled if they can cut that time by a third. So even if Watson can start being of help to cancer patients in couple of years, it will be a big deal, and quite fast in “time to market” .

I am sure the Watson team will have its ups and downs in this journey – but I think it will be well worth the proverbial blood, sweat and tears. I wish them the best. And tonight I will dream of doctors in my Grandom’s village having a pocket Watson with them when they help their patients fight their diseases.