Tag: ai

ML planet Hunting

Using a dataset of more than 15K labeled Kepler signals, we created a TensorFlow model to distinguish planets from non-planets. To do this, it had to recognize patterns caused by actual planets, versus patterns caused by other objects like starspots and binary stars. When we tested our model on signals it had never seen before, it correctly identified which signals were planets and which signals were not planets 96% of the time. So we knew it worked!

ML vulnerabilities

Identifying vulnerabilities in the ML model supply chain

we show that maliciously trained convolutional neural networks are easily backdoored; the resulting “BadNets” have state-of-the-art performance on regular inputs but misbehave on carefully crafted attacker-chosen inputs. Further, BadNets are stealthy, .i.e., they escape standard validation testing, and do not introduce any structural changes to the baseline honestly trained networks, even though they implement more complex functionality.

TFX

TFX also includes a suite of data transformations supporting feature wrangling. As an example, TFX can generate feature-to-integer mappings, known as vocabularies. It’s easy to mess things up when transformations differ in subtle ways between training and serving. TFX automatically exports any data transformations as part of the trained model to help avoid these issues.

Developing World AI

The relation between AI and the developing world is thus a subtle one, with multiple intertwined dimensions that will change as technology and society unfold. There are manifold ways in which AI can benefit the developing world, and some of these have been reviewed above. There are also numerous ways in which the developing world can and will contribute powerfully to the advancement of AI and associated technologies – including obvious ones such as the brainpower of young scientists emerging from developing-world universities and startup incubators, and less obvious ones such as the contribution of the developing-world spirit of mutual aid to the psyches of powerful AGIs as they emerge.

AI taxation

if most countries will not be able to tax ultra-profitable AI companies to subsidize their workers, what options will they have? I foresee only one: Unless they wish to plunge their people into poverty, they will be forced to negotiate with whichever country supplies most of their AI software — China or the United States — to essentially become that country’s economic dependent, taking in welfare subsidies in exchange for letting the “parent” nation’s AI companies continue to profit from the dependent country’s users. Such economic arrangements would reshape today’s geopolitical alliances.

Automated diagnosis

“I think that if you work as a radiologist you are like Wile E. Coyote in the cartoon. You’re already over the edge of the cliff, but you haven’t yet looked down. There’s no ground underneath.” Deep-learning systems for breast and heart imaging have already been developed commercially. “It’s just completely obvious that in 5 years deep learning is going to do better than radiologists. It might be 10 years. I said this at a hospital. It did not go down too well.”

2022-10-05: The deep learning dividend for medicine

Today’s report on AI of retinal vessel images to help predict the risk of heart attack and stroke, from 65k UK Biobank participants, reinforces a growing body of evidence that deep neural networks can be trained to “interpret” medical images far beyond what was anticipated. Add that finding to last week’s multinational study of deep learning of retinal photos to detect Alzheimer’s disease with good accuracy. AI models have been shown to be quite useful for detecting eye diseases, such as diabetic retinopathy. But this is about the indirects, the not so obvious. That work has now extended to detection of kidney disease, control of blood glucose and blood pressure, hepatobiliary disease, a previous study on predicting heart attack, close correlation of the retinal vessels with the heart (coronary) artery calcium score


2023-07-31: Misdiagnosis is one of the biggest causes of death, yet doctors think they’re better than AI

~800k Americans are permanently disabled or die each year from diagnostic medical errors. “Our results demonstrate that, unless the documented mistakes can be corrected, the optimal solution involves assigning cases either to humans or to AI, but rarely to a human assisted by AI.”