Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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Because artificial intelligence (AI) has become so buzzy, and applied so indiscriminately-AI for pot, AI for beer brewing, AI for horse care, AI for sex ed (all examples courtesy of CB Insights)-it's easy to dismiss as just another passing trend, like slap bracelets, Fitbits or a dignified presidency.
As Venrock VC and physician Bob Kocher recently commented:
"I get pitched at least two companies per day claiming to have AI. I always ask them, "Tell me what AI is," and they have yet to say the same thing. I have not seen one [product] that is truly learning or is truly intelligent.
That's the bear view, and fairly representative of how many in healthcare see AI-just the latest bright, shiny object that's unlikely to meaningfully impact their daily work.
What's so striking to me out here in Silicon Valley, however, is not so much the brash claims of disruption-of which there are many-but rather what I'm hearing in more considered conversations with experts who've been involved in AI for years, through previous bursts of optimism followed by long periods of disappointment. Almost to a person, these veterans see the emergence of the discipline from the latest "AI winter" as both hard-won and well-deserved. These experts believe the discipline has made incredible progress and is truly, finally positioned to deliver on its extraordinary and long-awaited promise.
"Take any old classification problem where you have a lot of data, and it's going to be solved by deep learning," computer scientist Geoffrey Hinton told New Yorker writer Sid Mukherjee. "There's going to be thousands of applications of deep learning."
In fact, some technologists and data scientists believe, ardently, that the future is already here-that we already have ability to use AI to solve important problems in healthcare, and that it's intransigent docs who are standing in the way.
It would only be a slight exaggeration to say that from the perspective of many of these data jocks, an ideal healthcare system would consist of a front end of diligent data gatherers, to collect as much information as possible. These data would then be fed into a giant data warehouse, where they could be thoughtfully analyzed, and result in significantly improved clinical recommendations that would either be returned directly to the patients themselves (ideally), or to a health provider (reluctantly) who could then relay the information, with requisite empathy, back to the patient. At best, the health professional in this context would be a friendly and agreeable customer service representative, while the insight would be provided by the data scientists and the computational horsepower behind them.
Surprisingly enough, most health professionals haven't been particularly enthralled by this scenario, and have questioned many of the fundamental assumptions. For starters, many wonder whether the technology is as a good as promised; as MedCityNews recently reported:
"Although medical image analysis has proved a popular area of investment for investors, [Venrock VC] Kocher doesn't believe the technology from these companies has reached the point where it's better at assessing these images than radiologists.
A second group of concerns, raised with characteristic eloquence by Mukherjee in the New Yorker, relate to the idea that reducing the medical problems to data that can be fed into a computer inevitably removes critically important elements. For instance, in eliciting a patient's history, a skilled dermatologist may pull out a relevant feature that rote data analysis might have missed. The interaction an engaged doctor has with her patient, including "laying on of hands," may also have important therapeutic value in itself, as Mukherjee nicely conveys. Read More