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IBM Promised Its AI Platform Watson Would Be a Big Step Forward in Treating Cancer. But After Pouring Billions Into the Project, the Diagnosis is Gloomy. (wsj.com)
Can Watson cure cancer? That's what IBM asked soon after its AI system beat humans at the quiz show "Jeopardy!" in 2011. Watson could read documents quickly and find patterns in data. Could it match patient information with the latest in medical studies to deliver personalized treatment recommendations? "Watson represents a technology breakthrough that can help physicians improve patient outcomes," said Herbert Chase, a professor of biomedical informatics at Columbia University, in a 2012 IBM press release. Six years and billions of dollars later, the diagnosis for Watson is gloomy [Editor's note: the link may be paywalled; alternative source]. WSJ: More than a dozen IBM partners and clients have halted or shrunk Watson's oncology-related projects. Watson cancer applications have had limited impact on patients, according to dozens of interviews with medical centers, companies and doctors who have used it, as well as documents reviewed by The Wall Street Journal. In many cases, the tools didn't add much value. In some cases, Watson wasn't accurate. Watson can be tripped up by a lack of data in rare or recurring cancers, and treatments are evolving faster than Watson's human trainers can update the system. Dr. Chase of Columbia said he withdrew as an adviser after he grew disappointed in IBM's direction for marketing the technology. No published research shows Watson improving patient outcomes. IBM said Watson has important cancer-care benefits, like helping doctors keep up with medical knowledge.
after they replaced the HR system, no one could figure out how.
AI is total BS. It is just computer programs running algorithms. There is no intelligence or even learning. And no: intelligent computers will never happen. We have trillions of dollars going into computing and we barely have usable software.
>> Six years and billions of dollars later
If it was IBM's gamble, then nothing of value was lost. If, however, the billions were invested by medical teams duped by impossible promises, then that's a different story.
(Story is paywalled and I'm too lazy to read TFA before commenting.)
I doubt IBM poured billions into oncology Watson. All it was doing was creating recommendations for doctors from reading patient reports and treatment research reports.
IBM sure poured billions into Watson and it's their biggest future product. The oncology seems like a small side project.
I forget the name of the drug, a knowlegeable slashdoter will remind us. The problem is the drug is old and unpatentable, so Big Pharm has no interest in providing it, and it is no wonder oncologists do not often opt to use for treatment because they are being bombarded by Big Pharm with incentives to instead choose less effectice, expensive patented drugs for treating their patients. There is no profit incentive to curing cancer, so that it was cured is ignored.
Judging from the other comments, IBM's AI system has improved human vision to a perfect 20-20 hindsight.
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Watson beat people at Jeopardy because it always got to answer first, because its button-pressing finger was faster than a human's button-pressing response. A fairer assessment of Watson's Jeopardy-playing abilities vs humans would have Watson respond with the same button-mashing-delay profile as its competitors. Beyond that, the relevant question is not whether Watson can beat humans at Jeopardy, or mash a button faster than a human, but whether it can analyze data better than a human to detect cancer (or solve whatever medical problem). And for the most part, it doesn't matter whether the answer comes back in 10 ms, 300 ms, or a a minute, or an hour. Like with any other tool, the question is whether it can help get the job done better for a reasonable price.
because all AI can do is manage the information it is given. Could it make a leap? Yes if the right information is present and the right questions are asked.
;)
Today's AI is not really there yet, today it is just a big automated filter and matching machine there really is not any intelligence behind it ATM.
No one has figured out how to program a concept in to data. No one knows yet how to program thought, consciousness in to an algorithm. At least that I know of.
Just my 2 cents
I know the Washington Post does it but that doesn't mean that putting periods in headlines looks any less fucking retarded. And don't go starting with Huffington Post "This Is The Most Scandalous Detail Of What Happened, And Here's How You Should Feel About It" headlines either. If I wanted entertainment/news I would go to Maddox's site because even though he sucks, he's still better at it than any of you ever will be.
One of the problems with a field like oncology (or medicine in general) is that the AI has to rely on training from humans, using source material generated by humans. Which leaves it with the same problem humans have: research is fast evolving, sometimes biased, incomplete, or experimentally flawed, and oftentimes contradictory from study to study. Seriously, just go look up any complex biomedical subject on pubmed and start reading studies. You will find results all over the place. This is why meta-analysis has become so popular, trying to find consensus in a contradictory jungle. Add in selection biases in its training (something articles have brought up when talking about Watson's oncology uses), and it's no wonder it's spitting out gibberish. The squishy sciences are squishy and amazingly complex.
I browse on +1 so AC's need not respond, I won't see it.
For AI to shine with anything this complicated, you really need detailed and consistent testing. This is where tech needs to go first in medicine. In very many areas, it would help to have a massive increase in testing and fidelity of the tests.
After the success of the genome project, we should have moved to a massive effort to develop cost efficient full system scanning and testing instead of starting the brain project.
We should launch a project to figure out how to measure every aspect of health imaginable in a fashion that allows it to be done as a checkup once a year. It should include things like somehow measuring gene activity at thousands of points within the body, very detailed measurement of cognitive performance to detect changes, detailed hearing and sight measurements (not just clarity but speed of focus, comprehension, etc.), checking the performance of nerves throughout the body, etc. - detail like never before.
Then, we would have the data needed to advance medicine and we'd definitely have a need for AI to interpret that volume of data.
In short, our medical sensor technologies suck. Take $50 billion per year away from defense and spend it on this until the problem is fixed. The payoff would change the world.
To cure cancer and to "match patient information with the latest in medical studies" are completely different things. No wonder they suck, if they can't even get that distinction.
I've said it before, I'll keep saying it: until we actually understand how a biological brain produces the phenomena we call 'thinking', we will not be able to create 'machine intelligences' that match or exceed human beings. Period. It's 'magical thinking' to keep hooking up more and more processors and throw more and more data at the same half-assed software and expect it to suddenly be smart and cognitive like a human brain. 'Deep learning algorithms' are just a very small part of the total answer, and that's all they've been obsessively focusing on.
Now, what they should be investing 'billions and billions of dollars' in, is research and development of newer, better instrumentation for observing a living brain in action (and I do NOT mean 'a better fMRI, I mean invent something that's a new and different approach). Only when we can see the total system in action will we even have a chance to understand how it works, the problem being that once it's dead, it's dead, and dissecting it isn't going to show you what you need to see.
Poured billions into an assumption that genes cause cancer.. phew..
They should have researched "WFPB"
Hippocrates: “Let food be thy medicine and medicine be thy food.”
Maybe because finding patterns without actually understanding anything is not really "intelligence". The AI hype is slowly dying and even non-IT/non-science-related people finally have finally come to a realization that ... not understand how these trained networks operate and that turns them into a black box you cannot really trust and which is bound to give absolutely wrong results.
1) AI is not a magical pill that can solve all the problems in the world
2) There isn't too much "intelligence" in AI
3) Coding real intelligence is a lot harder than using throwing reinforced convolutional neural networks at everything
4) We do
It's not like we understand how the human brain operates but we have certain reasons to believe it's mostly rational, intelligent and infallible (with exceptions, of course) since it has got us here - the age of technology and an improved quality and increased length of life which no other animal has been able to achieve.
I'm not against reinventing the biological intelligence that the human beings possess but it surely looks like we haven't come close to it.
Why does this sound like code whereby "helping doctors keep up with medical knowledge" really means "spending hours determining just how fucking badly Watson is at treating cancer".
I swear, everyone runs around saying "yarg, teh apps and teh AI" when they really have no fucking clue but they're hoping the idiots will keep throwing money at them.
People, stop listening to the tech pundits ... they don't know a fucking thing.
"No published research shows Watson improving patient outcomes."
Would a doctor want to publish research showing that their expensive need is diminishing?
What you mean by "common sense" is really just information gained from years of training which the human brain has. Hence, when even the most unusual situation occurs, the human brain has got something to compare it with and can come up with some sort of reasonable action even though it might not be the optimal one.
This is the advantage we have over AI algorithms: our brains can receive, interpret and learn from a massive variety of data and they do this every hour we are awake. This gives us a huge breadth and depth of experience to draw on when handling unknown situations. Unfortunately, AI systems can only currently learn from the limited set of data that they have been programmed to understand and, until that changes, humans will always have the upper hand dealing with rare/unusual situations.
Well professional bionformaticians had already been working on the problem of personalized medicine and medical diagnosis before IBM and Watson got involved. If you listen to them, there is a clear consensus of how that is is going to work in the future.
Part 1: Because of a dependence of both disease and the effectiveness of treatments upon personal genetics, every person will get sequenced at birth. That will do at least three things: reduce what otherwise appears as statistical noise in assessing treatment efficacy by resolving interdependencies between the treatment and personal genetics, improve estimates of the likelihood of any individual developing a disease or disorder, and help to identify the best treatments for specific individuals.
Part 2: Every patient treatment and its outcome become a trail logged into a massive database along with the patient's medical history and genetics. Currently, massive amounts of information about the effectiveness of treatments is discarded because the records of treatment after a drug is released are not accumulated. Now, before a drug is introduced to the market, there are clinical trials on a subpopulation, and that becomes an authoritative record of the drug effectiveness. That is a tiny fraction of the potential information out there and insufficient to assess interactions of drugs with other factors such as genetics.
One of the barriers to implementing that system is the price of sequencing, about $1000.00/person. Prices are projected to fall until sequencing becomes ubiquitous.
The other barrier is privacy legislation (HIPAA) and financial incentives acting on institutions against information sharing. Despite endless funded government initiatives to implement sharable electronic medical records, patient medical information remains siloed within provider and insurance networks. Rather than work to share information, those institutions are competing to build the largest silo. (This circumstance exemplifies a typical type of government ineptitude, which is to continuously and futilely throw enormous sums of money at a problem rather than simply and cheaply reforming the legislation and regulation giving rise to the perverse incentives which created the problem. Information sharing for research medical use to benefit personalized medicine was the main driver behind the U.K. slackening medical records privacy, demonstrating that in the U.K. not all government officials are complete idiots.)
Finally, the main point of this post: The bioinformaticians have wished for that future because they knew that the problem of personalized medicine was information-starved before IBM threw billions at the problem. Given adequate information, the computational solutions of personalized medicine are already known by those humans with domain-specific expertise.
If IBM had instead invested those billions in reducing the cost of sequencing further and in lobbying government to fix the stupid incentives and restrictions acting against medical information sharing, the problem could have been solved by now. Another case of someone with a hammer looking for nails by pounding on things to see if they move.
Had Watson been genuinely intelligent, it would explained all that that IBM.
Ceci n'est pas une signature.
Over-hyped products and deceitful marketing failed us again.
Seems like they should have started with differential diagnosis or something more suited to Watson
Difference between this and Theranos is that I hope IBM can give back some invested money. Probably they don't care that much as any day or another we'll see a "Watson AI will solve problem X" news again and people will put money on it again.
The traditional modus operandi of the AI community remains the same as it was from its inception: some problems are solved initially with spectacular results, and optimistic extrapolations are made on the basis of such successes to other problems - which, invariably, turn out to be far more difficult to tackle, with the ensuing disappointing results. The AI community seems to have forgotten its past, and is therefore condemned to repeat it, as we are seeing with Watson and with the digital assistants, the usefulness of which remains extremely limited.
"A 1.6 Billion-Year-Old Accident Waiting to Happen" http://bit.ly/18a3ul5
"Tempers are wearing thin. Let's just hope some robot doesn't kill everybody." --Bender
They are missing out on a major opportunity here.
IBM has this AI thingie called Watson. Right now they are tasking it with cancer related programming.
Don't they have enough nerds there to convince their bosses to give it proctology programming?
Then after they feed the patient a laxative that they can call No Shit Sherlock, the AI's controller can put it to work with the command, "Dig Deeper Watson."
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He's just not as good as a young experienced one. Old doctors assume they've seen it all and 70% of the customers are diagnosed as 'stomach flu' anyway, just on general principle.
And nobody is as old as IBM.
If "treatments are evolving faster than Watson's human trainers can update the system", wouldn't the same be true for oncologists trying to keep up with the latest and greatest?
When they came for the communists, I said "He's next door. Take him away. Goddam commies."
"...treatments are evolving faster than Watson's human trainers can update the system"
And if you do away with the mass of humans doing a particular area of expertise and turn it over to "AI" you will freeze that area at that level of AI expertise. The AI has no motivation array. It can't look for ways to "do it better," as humans constantly do. It cannot advance the field. AI is potentially a human disaster if too much trust is given it. Remember, Watson was built, programmed, turned on to play Jeopardy by humans. Then it was turned off when it had satisfied the motivations of its creators. It did not WANT to play Jeopardy, or anything else. It is a rock, a tool. Nothing else. Unmotivated intelligence is not intelligence. We must not rely on it.
E Proelio Veritas.
Just to be clear, the system that beat Ken Jennings has very little to do with Watson in its current incarnation. Much of the team was splitup after the Jeopardy demonstration and IBM decided there wasn't much market for a question/answer system such as this.
So they reused the name, banking on the general population having heard of Watson Jeopardy to drive sales. Watson in its current incarnation is actually mostly off-the-shelf open source and existing IBM tools. Apache Spark and IBM's SPSS are currently under the Watson umbrella.
Watson Jeopardy was interesting for the time, but other companies are doing much more interesting things these days.