Ugh... no operator overloading, no efficient generic programming and no lambda expressions... Only time will tell, but I don't understand who the intended audience is: I think that Python guys won't care about the (supposedly) increased performance (and you can interface C/C++ with Python easily) and that C++ programmers (I mean, the hardcore serious C++ Boost-like programmers, no the Java-like whiners :P) won't have their beloved templates pried from their cold dead hands with ease.
yeah though I think especially operator overloading is not going to be a main problem, it is as with the JS library though quite thinkable that lots of users will switch or use it (or being put to use it...) because it is done by Google
Having Google backing it will certainly help, even though they are presenting it as a "system level" (i.e., hard-core) language, and in that domain it is much more difficult to bullshit your way to a position of relevance.
Look at Java: Sun pushed it like hell and it is certainly widely used in many contexts (corporate, web and embedded markets mostly), yet it completely failed to win the hearts of "open-source" developers (or, more generally, of those developers who are not forced to use it by virtue of some management-driven decision).
Eve, an artificially-intelligent 'robot scientist' could make drug discovery faster and much cheaper, say researchers writing in the Royal Society journal Interface. The team has demonstrated the success of the approach as Eve discovered that a compound shown to have anti-cancer properties might also be used in the fight against malaria.
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
Research in active-matter systems is a growing field in biology. It consists in using theoretical statistical physics in living systems such as molecule colonies to deduce macroscopic properties. The aim and hope is to understand how cells divide, take shape and move on these systems.
Being a crossing field between physics and biology "The pot of gold is at the interface but you have to push both fields to their limits." one can read
Maybe we should discuss about this active matter one of these days?
"These are the hallmarks of systems that physicists call active matter, which have become a major subject of research in the past few years. Examples abound in the natural world - among them the leaderless but coherent flocking of birds and the flowing, structure-forming cytoskeletons of cells. They are increasingly being made in the laboratory: investigators have synthesized active matter using both biological building blocks such as microtubules, and synthetic components including micrometre-scale, light-sensitive plastic 'swimmers' that form structures when someone turns on a lamp. Production of peer-reviewed papers with 'active matter' in the title or abstract has increased from less than 10 per year a decade ago to almost 70 last year, and several international workshops have been held on the topic in the past year."