I tried to take a long holiday weekend. A quick trip to the beach with a friend, slow breakfasts with my family, and a much needed long run with the dog.
Back to reality and there is a lot of late breaking news and advancements in oncology (and beyond).
You can read all of that in your favorite science news channel. My thinky thoughts arrive at the edges of science and deep learning algorithms.
Machine learning dictates that we need to evolve our thinking beyond single objectives. Maybe we are too focused on the solution we hope to see and missing insights along the way?
It seems that precise and well defined objectives carefully placed on a path to robust discoveries may be self-limiting. What if the most innovative discoveries aren't waiting to be predicted in advance? I would argue that maybe we need to identify the steps along the way--not knowing where they might lead. For example, perhaps there are populations of cell types or immuno-signatures potentially missed if we don't redefine what success looks like along the way to a singular objective--improving overall survival for example.
What does it mean to make progress in neuroevolution? In general, it involves recognizing a limitation on the complexity of the ANNs that can evolve and then introducing an approach to overcoming that limitation. For example, the fixed-topology algorithms of the '80s and '90s exhibit one glaring limitation that clearly diverges from nature: the ANNs they evolve can never become larger.
In contrast, brains in nature have increased in size and complexity in many lineages, and our distant ancestors had orders of magnitude fewer neurons in their cranium than the 100 billion neurons (and 100 trillion connections) in ours. Clearly, if the topology of the ANN is fixed from the start, no such complexity can ever evolve.-- Why Greatness Cannot be Planned: The Myth of the Objective
Chimeric antigen receptor (CAR)-T cell infusion has demonstrated an increase of many genes in predefined immune signatures, including t-cell related genes, chemokines, checkpoint inhibitors, and lymphocyte-activation protein 3 (LAG3).
Maybe there are too many clinical steps along the path for a singularly-defined objective to serve research goals or more importantly--patients.
For example, wouldn't it be informative to address the complexity of cancer as a system? We have multiple measures rolled into a clinical trial endpoint that decides whether a investigational product progresses to the next phase, what patient may benefit, and who funds the research.
Meanwhile we must consider pre-existing immunity, type and density of immune cells, spatiotemporal dynamics of intratumoral immune cells, cancer vaccines, the role of cytokines, CD122, T cell bispecifics, and a host of other immune factors.
Bringing to Life the Science around Innovative New Drugs, Gene and Cell Therapies--noveltargets.com
Interesting thoughts by Ken Stanley on how we might be too reliant on carefully constructed objectives. Not to say we don't need them, but what have we learned from artificial intelligence?
Do we focus on mechanical metrics to our own detriment?
"Controversial at its heart, yet refreshingly provocative, this book challenges readers to consider life without a destination and discovery without a compass."
A straight path never leads anywhere except to the objective.--Andre Gide