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On Tech & Vision With Dr. Cal Roberts

Oct 7, 2022

This podcast is about big ideas on how technology is making life better for people with vision loss.

In 1997, Gary Kasparov lost an epic chess rematch to IBM’s supercomputer Deep Blue, but since then, artificial intelligence has become humanity’s life-saving collaborator. This episode explores how AI will revolutionize vision technology and, beyond that, all of medicine.

Karthik Kannan, co-founder of AI vision-tech company Envision, explains the difference between natural intelligence and artificial intelligence by imagining a restaurant recognizer. He describes how he would design the model and train it with positive or negative feedback through multiple “epochs” — the same process he used to build Envision. Envision uses AI to identify the world for a blind or visually-impaired user using only smartphones and smart glasses.

Beyond vision tech, AI enables faster and more effective ophthalmic diagnosis and treatment. Dr. Ranya Habash, CEO of Lifelong Vision and a world-renowned eye surgeon, and her former colleagues at Bascom Palmer, together with Microsoft, built the Multi-Disease Retinal Algorithm, which uses AI to diagnose glaucoma and diabetic retinopathy from just a photograph. She acquired for Bascom Palmer a prototype of the new Kernal device, a wearable headset that records brain wave activity. Doctors use the device to apply algorithms to brainwave activity, in order to stage glaucoma, for example, or identify the most effective treatments for pain.

Finally, AI revolutionizes drug discovery. Christina Cheddar Berk of CNBC reports that thanks to AI, Pfizer developed its COVID-19 treatment, Paxlovid, in just four months. Precision medicine, targeted to a patient’s genetic information, is one more way AI will make drugs more effective. These AI-reliant innovations will certainly lower drug costs, but the value to patients of having additional, targeted, and effective therapies will be priceless.


The Big Takeaways:

  • Natural vs. artificial intelligence, and the “restaurant recognizer.” Karthik Kannan, CEO and co-founder of Envision explains the difference between natural and artificial intelligence by describing how humans recognize restaurants in a foreign city and comparing that to how he’d train a “restaurant recognizing algorithm.” Here’s a hint: the algorithm needs a lot more data.
  • Sensor fusion AI. AI developers are interested in using different types of sensors together to give the algorithms a sense of the world closer to human intelligence. One example is the use of LiDAR in the Envision app, in addition to the phone camera.
  • Transhumanism. Humans don’t have LiDAR. Does that mean AI will surpass human capability? Karthik offers that some radiology AI have higher accuracy than human radiologists, but he thinks it will be much more of a partnership between the human and the machine.
  • Multi-Disease Retinal Algorithm. Dr. Ranya Habash and her colleagues at Bascom Palmer Eye Institute worked with Microsoft on an AI diagnostic tool. They fed the algorithm 86,000 images of eyes, labeled with relevant diseases, and taught the machine to diagnose eye disease with just a photograph, making remote diagnosis not just possible but inexpensive.
  • The Brain-Machine Interface. Dr. Habash wrote a grant that earned Bascom Palmer a prototype of the Kernal device, a helmet-like device that measures brainwave activity. Doctors used this device to create a “brain-machine interface” which advances brain research on glaucoma, diabetic retinopathy, Alzheimer’s, and pain management.
  • Bias in AI. Karthik Kannan reminds us that the biggest threat that humanity faces from AI is bias encoded in the algorithms. This is a real harm that humans have already experienced, and AI engineers need to be extremely sensitive to ensure they are not encoding their own biases.
  • AI for Drug Discovery. Christina Cheddar Berk, a reporter for CNBC, shares how the pace of drug discovery is set to speed up, thanks to AI algorithms and supercomputing power that can cycle through millions of possible chemical compounds per second to I.D. effective options. Pfizer used a similar process to develop Paxlovid, in a process that took only four months.



  • “The secret sauce is always in the data.” — Karthik Kannan, CEO and Co-Founder of Envision
  • “Human intelligence is so holistic. We have so many sensors on our bodies. […] Whereas an AI is taught only images.” — Karthik Kannan, CEO and Co-Founder of Envision
  • “I know what’s going to work and what’s not going to work within thirty seconds of seeing it. […] They need to show up with a smartphone. Then I’ll take them seriously.” — Dr. Ranya Habash, CEO, Lifelong Vision
  • “I don’t think there’s anything more powerful in medicine than to be able to treat a patient and get rid of a problem that is plaguing them so much.” — Dr. Ranya Habash, CEO, Lifelong Vision
  • “If you can measure it you can control it.” — Dr. Ranya Habash, CEO, Lifelong Vision
  • “It strongly takes over the bias of whoever is actually feeding the data […] and I think that has much, much more potential for harm than an AI taking over humanity.”  — Karthik Kannan, CEO and Co-Founder of Envision
  • “The value for patients of having those additional therapies available; it’s hard to put a price on.” — Christina Cheddar Berk, reporter, CNBC


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