more later … if you really want the details .. look at
Li, Jun-Yan, Hsin-Yi Chen, Wen-Jie Dai, and Calvin Yu-Chian Chen. “Deep Learning to Investigate Longevity Drug.” Available at SSRN 3361157 (2019).
in any case, from the virtual experiment it looks like Antifebrile Dichroa, ArecaeSemen and Gelsemium sempervirens are part of the mystery.
Next on the research reading queue, pointers to applications of AI in Regenerative Medicine. We’ll be including this in discussions.
Wikipedia: Regenerative medicine is a branch of translational research ] in tissue engineering and molecular biology which deals with the “process of replacing, engineering or regenerating human cells, tissues or organs to restore or establish normal function”. This field holds the promise of engineering damaged tissues and organs by stimulating the body’s own repair mechanisms to functionally heal previously irreparable tissues or organs.
Regenerative medicine also includes the possibility of growing tissues and organs in the laboratory and implanting them when the body cannot heal itself. If a regenerated organ’s cells would be derived from the patient’s own tissue or cells, this would potentially solve the problem of the shortage of organs available for donation, and the problem of organ transplant rejection.
Some of the biomedical approaches within the field of regenerative medicine may involve the use of stem cells. Examples include the injection of stem cells or progenitor cells obtained through directed differentiation (cell therapies); the induction of regeneration by biologically active molecules administered alone or as a secretion by infused cells (immunomodulation therapy); and transplantation of in vitro grown organs and tissues (tissue engineering). ]
along these lines, I encountered this interesting title:
Zhavoronkova, Anna A., Polina Mamoshinaa, Quentin Vanhaelena, Morten Scheibye-Knudsene, Alexey Moskalevf and Alex Alipera. “Artificial intelligence for aging and longevity research.” (2018).
Abstract: The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tre- mendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models—extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and re- inforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and ger- oprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker develop- ment, target identification, drug discovery and real world evidence pipeline that may help accelerate and im- prove pharmaceutical research and development practices
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:
the Beyond topics
- Gelernter, D. (2016). The tides of mind: Uncovering the spectrum of consciousness. WW Norton & Company.
- Marquis, P., Papini, O., & Prade, H. (2014). Some Elements for a Prehistory of Artificial Intelligence in the Last Four Centuries. ECAI.
- Scheutz, M. (Ed.). (2002). Computationalism: new directions. MIT Press.
- Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach.
This is an updated edition of the 2010 version containing extensive current references. [note the book is getting hard to find sometimes due to demand, and its being the definitive AI textbook. Check the edition you are using/getting]
- Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT Press. This is an updated (2nd) edition of the 1998 version
- Nilsson, N. J., & Nilsson, N. J. (1998). Artificial intelligence: a new synthesis. Morgan Kaufmann.
- Poole, D. L., Mackworth, A. K., & Goebel, R. (1998). Computational intelligence: a logical approach (Vol. 1). New York: Oxford University Press.
see also Artificial Intelligence: Foundations of Computational Agents 2nd Edition by the same authors.
- Pratt, V. (1987). Thinking Machines—The Evolution of Artificial Intelligence. Oxford: Basil Blackwell. – this is a general history of earlier machines … great reference to get historical insights not easily obtained elsewhere.
- Turing, A. M. (1948). Intelligent machinery. NPL. Mathematics Division. See also, Turing, A. (2004). Intelligent machinery (1948). The Essential Turing: Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus The Secrets of Enigma B. Jack Copeland, 395 which provides context and pointers to additional Turing resources.
- B. Jack Copeland (2004), Computability: Turing, Gödel, Church, and Beyond, The MIT Press.
Hard(er) Core Science Fiction and Speculative Fiction works
- John C. Wright’s Count to the Eschaton series is worth reading … provides interesting glimpse into a possible (far) future. It’s also fun to read … so good ideas and an interesting, universe spanning plot.
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 …
Demis Hassabis: Towards General Artificial Intelligence – talk at Center for Brains, Minds and Machines (CBMM). [Background: r. Demis Hassabis is the Co-Founder and CEO of DeepMind, the world’s leading General Artificial Intelligence (AI) company, which was acquired by Google in 2014 in their largest ever European acquisition.
The talk draws on Demis’ eclectic experiences as an AI researcher, neuroscientist and video games designer.
u/kmario23 over at reddit points to a wonderful new resource m the deep learning drizzle. [on Github]
I have collected a list of freely available courses on Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing, Computer Vision, Probabilistic Graphical Models, Machine Learning Fundamentals, and Deep Learning boot camps or summer schools.
So I checked it and immediately got involved watching Ian Goodfellow …
Ian and his advisor wrote this book …. take a look at it.
Goodfellow posted pdfs of his talks here
https://www.reddit.com/r/MachineLearning/ is worth following