Technology, Science and Culture

From the success of the first and second volume of this series, we are enthusiastic to continue our discussions on research topics related to the fields of Food Science, Intelligent Systems, Molecular Biomedicine, Water Science, and Creation and Theories of Culture. Our aims are to discuss the newest topics, theories, and research methods in each of the mentioned fields, to promote debates among top researchers and graduate students and to generate collaborative works among them

Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy

In recent years, deep learning has infiltrated every field it has touched, reducing the need for specialist knowledge and automating the process of knowledge discovery from data. This review argues that astronomy is no different, and that we are currently in the midst of a deep learning revolution that is transforming the way we do astronomy. We trace the history of astronomical connectionism from the early days of multilayer perceptrons, through the second wave of convolutional and recurrent neural networks, to the current third wave of self-supervised and unsupervised deep learning. We then predict that we will soon enter a fourth wave of astronomical connectionism, in which finetuned versions of an all-encompassing 'foundation' model will replace expertly crafted deep learning models. We argue that such a model can only be brought about through a symbiotic relationship between astronomy and connectionism, whereby astronomy provides high quality multimodal data to train the foundation model, and in turn the foundation model is used to advance astronomical research.Comment: 60 pages, 269 references, 29 figures. Review submitted to Royal Society Open Science. Comments and feedback welcom

Using Deep Learning to Explore Ultra-Large Scale Astronomical Datasets

In every field that deep learning has infiltrated we have seen a reduction in the use of specialist knowledge, to be replaced with knowledge automatically derived from data. We have already seen this process play out in many ‘applied deep learning’ fields such as computer Go, protein folding, natural language processing, and computer vision. This thesis argues that astronomy is no different to these applied deep learning fields. To this end, this thesis’ introduction serves as a historical background on astronomy’s ‘three waves’ of increasingly automated connectionism: initial work on multilayerperceptrons within astronomy required manually selected emergent properties as input; the second wave coincided with the dissemination of convolutional neural networks and recurrent neural networks, models where the multilayer perceptron’s manually selected inputs are replaced with raw data ingestion; and in the current third wave we are seeing the removal of human supervision altogether with deep learning methods inferring labels and knowledge directly from the data. §2, §3, and §4 of this thesis explore these waves through application. In §2 I show that a convolutional/recurrent encoder/decoder network is capable of emulating a complicated semi-manual galaxy processing pipeline. I find that this ‘Pix2Prof’ neural network can satisfactorily carry out this task over 100x faster than the method it emulates. §3 and §4 explore the application of deep generative models to astronomical simulation. §3 uses a generative adversarial network to generate mock deep field surveys, and finds it capable of generating mock images that are statistically indistinguishable from the real thing. Likewise, §4 demonstrates that a Diffusion model is capable of generating galaxy images that are both qualitatively and quantitatively indistinguishable from the training set. The main benefit of these deep learning based simulations is that they do not rely on a possibly flawed (or incomplete) physical knowledge of their subjects and observation processes. Also, once trained, they are capable of rapidly generating a very large amount of mock data. §5 looks to the future and predicts that we will soon enter a fourth wave of astronomical connectionism. If astronomy follows in the footsteps of other applied deep learning fields we will see the removal of expertly crafted deep learning models, to be replaced with finetuned versions of an all-encompassing ‘foundation’ model. As part of this fourth wave I argue for a symbiosis between astronomy and connectionism. This symbiosis is predicated on astronomy’s relative data wealth, and contemporary deep learning’s enormous data appetite; many ultra-large datasets in machine learning are proprietary or of poor quality, and so astronomy as a whole could develop and provide a high quality multimodal public dataset. In turn, this dataset could be used to train an astronomical foundation model that can be used for state-of-the-art downstream tasks. Due to the foundation models’ hunger for data and compute, a single astronomical research group could not bring about such a model alone. Therefore, I conclude that astronomy as a whole has slim chance of keeping up with a research pace set by the Big Tech goliaths—that is, unless we follow the examples of EleutherAI and HuggingFace and pool our resources in a grassroots open source fashion

Contemporary Natural Philosophy and Philosophies - Part 1

This book is a printed edition of the Special Issue titled "Contemporary Natural Philosophy and Philosophies" - Part 1 that was published in the journal Philosophies

New Foundation in the Sciences: Physics without sweeping infinities under the rug

It is widely known among the Frontiers of physics, that “sweeping under the rug” practice has been quite the norm rather than exception. In other words, the leading paradigms have strong tendency to be hailed as the only game in town. For example, renormalization group theory was hailed as cure in order to solve infinity problem in QED theory. For instance, a quote from Richard Feynman goes as follows: “What the three Nobel Prize winners did, in the words of Feynman, was to get rid of the infinities in the calculations. The infinities are still there, but now they can be skirted around . . . We have designed a method for sweeping them under the rug. [1] And Paul Dirac himself also wrote with similar tune: “Hence most physicists are very satisfied with the situation. They say: Quantum electrodynamics is a good theory, and we do not have to worry about it any more. I must say that I am very dissatisfied with the situation, because this so-called good theory does involve neglecting infinities which appear in its equations, neglecting them in an arbitrary way. This is just not sensible mathematics. Sensible mathematics involves neglecting a quantity when it turns out to be small—not neglecting it just because it is infinitely great and you do not want it!”[2] Similarly, dark matter and dark energy were elevated as plausible way to solve the crisis in prevalent Big Bang cosmology. That is why we choose a theme here: New Foundations in the Sciences, in order to emphasize the necessity to introduce a new set of approaches in the Sciences, be it Physics, Cosmology, Consciousness etc