Am exploring what really can be said for AI in medicine. There are lots of good things going on … but some reality seems to have set in.
I ran into this conclusion in the paper Deep Learning for Genomics: A Concise Overview by Yue and Wang at Carnegie Mellon. [Yue, Tianwei and Haohan Wang. “Deep Learning for Genomics: A Concise Overview.” CoRR abs/1802.00810 (2018):]
Current applications, however, have not brought about a watershed revolution in genomic research. The predictive performances in most problems have not reach the expec- tation for real-world applications, neither have the interpretations of these abstruse models elucidate insightful knowledge. A plethora of new deep learning methods is constantly being proposed but awaits artful applications in genomics.
I was really hoping we were farther along. Maybe there’s hope … there’s always hope [Elvis: Farther along we’ll know more about it. Farther along we’ll understand why. Cheer up my brother live in the sunshine]. Right now, what I am seeing with Watson for Genomics, and other ‘production systems ‘ suggest lots of work ahead.
Now added a recent search that looked for recent USPTO grants that specified Deep Learning and Medicine …
PATEX 2: Patent Exploration for Artificial Intelligence in Medicine (AIM)
As an amusing element, it picked up this:
IBM Watson’s foundations certainly had and still have a lot of promise! Perhaps a more thorough ‘lessons learned’ is on order.
(web article states article appears in the April 2019 print issue as “IBM Watson, Heal Thyself.”)
Fascinating insights by Computer Science / Artificial Intelligence profs …
some have summarized their insights by writing: “only by doing activities that fulfill our curiosity without any pre-defined objectives, true creativity can be unleashed. They call this the ‘Myth of the Objective’: Objectives are well and good when they are sufficiently modest … In fact, objectives actually become obstacles towards more exciting achievements, like those involving discovery, creativity, invention, or innovation—or even achieving true happiness… the truest path to “blue sky” discovery or to fulfill boundless ambition, is to have no objective at all.”
some of Stanley’s and Lehmans insights:
- “The flash of insight is seeing the bridge to the next stepping stone by building from the old ones. ”
- “[Picbreeder] is just one example of a fascinating class of phenomena that we might call non-objective search processes, or perhaps stepping stone collectors. The prolific creativity of these kinds of processes is difficult to overstate”
- “ measuring success against the objective is likely to lead you on the wrong path in all sorts of situations”
- “You can’t evolve intelligence in a Petri dish based on measuring intelligence. You can’t build a computer simply through determination and intellect—you need the stepping stones. ”
- “ambitious objectives are the interesting ones, and the idea that the best way to achieve them is by ignoring them flies in the face of common intuition and conventional wisdom. More deeply it suggests that something is wrong at the heart of search. ”
I find their books inspiring and insightful. Reframing questions and providing different lines of attack on AI and Search Optimization to Ambitious Goals …
according to popular legends and urban myths … Amos Tversky is said to have said …
My colleagues, they study artificial intelligence; me, I study natural stupidity.
this, from CoEvolving Innovations which seems like a fascinating resource.
The blog entry there talks about Daniel Kahneman and Amos Tversky.
The topic is fascinating. The question of how intelligence and stupidity are related is fascinating.
There’s also a reference to Daniel Kahneman, Paul Slovic, and Amos Tversky book Judgment under uncertainty: Heuristics and biases, that I now feel compelled to investigate
interesting factoid …Kahneman was awarded the 2002 Nobel Prize in economic sciences despite being a psychologist, not an economist. Which goes to show you … that Forrest Gump’s Mom was right “Life is like a box of chocolates. You never know what you’re gonna get.”