[hooande]

Watson could be described as a natural language search engine. This is no small thing. It's linguistic abilities were showcased on jeopardy, though it's wins might have had more to do with speed of processing and "buzzing in" than it did with being really smart. Watson is quite possibly the most sophisticated specific use natural language program to ever exist (as opposed to general use nlp, which is star trek level problem).

That said, the approach and subsequent utility might not live up to the hype that IBM is pumping out. It's one thing to search very quickly. Being able to discover patterns that lead to new levels of understanding and predictable relationships is another thing entirely. IBM is more search vs predict in part because they only have so much data to work with. All of the medical books in the world are a drop in the bucket in terms of algorithmic understanding. Watson has mastered working with all available information. Collecting and processing massive data sets is another challenge that IBM hasn't been willing to tackle yet.

IBM is billing Watson as the all singing, all dancing solution to the world's data problems. They're tackling a lot of problems in diverse areas. I hope it works out, the world needs as much help as it can get. But IBM has shifted their core mission to be consulting and I wonder if Watson's purpose will be to support that more than becoming a Super Siri type software project that could do the most good.

[taejo]

> its wins might have had more to do with speed of processing and "buzzing in" than it did with being really smart.

It may only be better than humans at buzzing in, but being as good as humans at natural language search, but faster and more consistently (doesn't make mistakes when tired; works just as well in Kampala as New York; can be audited when it makes mistakes) is already better than humans.

[bnegreve]

> That said, the approach and subsequent utility might not live up to the hype that IBM is pumping out. It's one thing to search very quickly. Being able to discover patterns that lead to new levels of understanding and predictable relationships is another thing entirely.

I don't get this, Watson might not be able to provide new levels of understanding but it still does a much better job than current search engines, so why do you think it cannot live up to the hype? Why do you think that it is not a significant improvement? What makes you think the approach is wrong? I need some clarifications :)

[macspoofing]

>but it still does a much better job than current search engines

Does it?

[bnegreve]

If you query Google with a Jeopardy style question you won't have a valid answer as the first result, so at least for this particular application, yes it does a better job.

I'm sure there are plenty of reason to believe that this cannot be generalized to real world problem but no one here has given these reasons. So I am not really sure why people claim that it might not live up the the hype. (And I would genuinely like to understand).

[jijiwaiwai]

Watson is trained for answering those questions ,google is not. this is why some one can pee all his urine into a small cococola bottle on the ground from upstairs while others can,t:he trained himself and practiced a lot. if you need Watson help you with other things, you need to train him again.that's why you need apps for: do the training. you will find there's nothing different with training a SAS or R program. you are still on your own. there's no help you can get from Watson

[mikeash]

Regarding Watson's speed on Jeopardy, that was certainly a big advantage for it. However, consider that no matter how fast it is, a machine that "only" gets 50% of the questions right after it buzzes in (which would be an amazing accomplishment already) would lose the game horribly. That it won so solidly shows that it goes well beyond mere speed.

[AJ007]

From the consumer's end, we expect computers to produce accurate calculations almost always. If a calculator produced the wrong answer to a basic mathematical function we would throw it out.

Humans are error prone, even when doing things they know and are good at.

Artificial intelligence is marketed as being a machine that is as smart as a human, but somehow we infer that because AI is a machine it will not make human mistakes. Mistakes are what produces learning.

The question becomes, do we only release AI for public use when it is assigned to a narrow range of problems and trained to 99.9% accuracy? Or does a consumer just throw AI at unknown, or even non trainable, problems and we take the result with a grain of salt? (Non trainable being something like predicting the value of the S&P 500 in 24 months.)

Perhaps a new words will be formed to describe AI, its behavior, accuracy, and experience? For now there is a lot of "one size fits all" and "holy grail" seeking. Big companies with armies of sales people seem to prefer this.

[TallGuyShort]

I think a search engine is the appropriate metaphor. Google's first result isn't always what you're looking for, but if you have no idea where to start, you can just type some words and Google will give you 10 pretty good ideas. You can then easily know if those 10 ideas are what you needed, or at least have a better idea of how to modify your query.

Watson, I think, will be the same way. Say for medical diagnoses - you don't just feed it observations and prescribe whatever it says. But if you want to ask about an unusual combinations of symptoms you've never encountered, it'll come back with something you can then go research. How you could make that connection before systems like Watson or Google (or similar medically-focussed systems if they exist) is beyond me - but they'll probably never replace the doctor's judgement. They're just a tool and should be treated that way.

[eru]

Watson can even come back with a suggestion of further tests to do.

[jijiwaiwai]

Watson is is now only a search engine you need to train and define your own domain models and logics in side the App. The bloom depends on wether there can be enough Apps on the platform. Remember: those Apps are enterprise level and will always be developed by companies. Why don't these companies just deploy their app in other clouds and connect with a 'siri' like voice interface? I can't see any values inside this platform. Vertical search and IP is not hard for developers today. Small companies can do that without IBM's help.