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Comment by pesenti | Hacker News Reader

The article is scarce in details but this is a real product with real applications. The software uses NLP and ML techniques to sift through large amount of scientific documents, i.e., patents or publications, and extracts relevant entities, e.g., proteins, drugs, genes and their relationships, e.g., how a protein acts on a gene, and lets the user visualize all this and query it using natural language.

I can give more details if anybody is curious. (Disclaimer: I work for Watson)

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Is there an API for Watson? Is it possible as a developer to obtain access?

More abstract: Watson, based on this article (http://www.wired.co.uk/news/archive/2013-02/11/ibm-watson-me...), can already diagnose cancer better than a human doctor. Will we see more of this functionality rolled out publicly? Or will it be reserved for use by healthcare organizations?

Platform 2.0 is coming in October. It will have many new APIs, the docs and code examples will be public, and we will start opening out for beta testing to a much larger set of developers. The goal ultimately is to offer all these capabilities through APIs to anybody.

Thank you!

I went through the paperwork pile to apply to round one access of the Watson APIs and discovered the shocking restrictions/requirement that the system apparently "needs" unstructured text, and if your dataset is more structured than unstructured they won't accept you into to the program.

Needless to say I was a bit annoyed, because I was already using fact extraction in the system I wanted to test Watson's query 'skill' on. I'm in no position to store the raw text. That would require over a hundred times more storage, probably closer to a thousand times the storage costs, making it fiscally untenable for me to even build the database let alone a product with it. Any idea if release 2 in October will change this restriction and give people who aren't sitting on GB of unstructured text, like myself, a chance to apply.

We could already diagnose cancer better than a human using decision trees in the 1960's.

The breakthrough with computational medicine is actually using it.

The techniques of today are actually much more elaborate than decision trees. The key is gathering evidence automatically (i.e., learning) from medical literature - that requires parsing the text through ML techniques that have been invented just recently.

See https://www.youtube.com/watch?v=8lGJ0h_jAp8 for what the Watson medical diagnosis product looks like - it's not just about diagnosis, it's about evidences.

That's definetly progress, but still , the point made by spitfire is true: The real challenge is adoption, not tech.

We even see it with the low acceptance of Watson in medicine and IBM's move into africa.

I wonder thought: why hasn't IBM offered a "second opinion" service directly to consumers ? It could might have sped adoption.

I agree that adoption is a challenge but the tech is also really challenging. Getting a system to a level of accuracy that gives doctors enough confidence to use it is no small task.

A "second opinion" service would have a lot of potential legal pitfalls.

Not necessarily directed at you, but one big frustration of mine while leading a project team was when someone would list off 'potential issues'. If the concept is compelling and the quick estimates look like positive ROI, go get the lawyers/marketers/buyers/executive team to do their job and push forward or come back with logical and specific reasons!

Sorry for the late response - i'm across the Atlantic.

I wonder, with the API , would it be possible for a startup to build such "second opinion service" , with reasonable costs ? will you block it?

What happens in the situation when something Watson has "studied" gets proved wrong or inconclusive say 1 year later. Does Watson have a method to discount that and all subsequent chains based on that research?

My understanding is that if you feed the results of the research into the system then it would be there when the algorithms are run in future and from that point on, the results would be taking the research into account and it's answer would change.

Yes, the system works in a way similar to the Jeopardy Watson. It collects evidences, and weighs them based on their date and possibly source quality. So new results will displace older results at some point.

It sound very impressive and i'm very curious.

A few questions:

1. It feels a lot like literature-based-discovery. Is it ? if so , how is watson better than current literature based discovery methods that did create some scientific hypothesis AFAIK?

If not please share more details(if you can of course) about how it reaches hypothesis).

2. Does it work well in non biological literature ? Are there any examples ?

1. I am not super familiar with the space (I am not responsible for this specific product) so I may not answer your question exactly. The discovery product is an interactive tool more than a static tool generating hypothesis. It lets you search, navigate, visualize, query in natural language the literature and the extracted entities/relationships.

It uses a lot of pretty advanced tech to do that: * State-of-the-art statistical annotators * Some deep domain knowledge understanding of the entities themselves (e.g., how chemical entities are composed) * Some nifty visualizations * Many component from the Jeopardy stack (you can read the Jeopardy papers for more insights) - deep question parsing, candidate generation techniques, scorers, etc. * Down the line it'll integrate more reasoning techniques along the lines of http://www.research.ibm.com/cognitive-computing/watson/watso...

2. Our first version was focused on life sciences and intelligence, we are expanding it to finance, law, and education.

Thanks you!

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