Complexity of Networks

Network or graph structures are ubiquitous in the study of complex systems. Often, we are interested in complexity trends of these system as it evolves under some dynamic. An example might be looking at the complexity of a food web as species enter an ecosystem via migration or speciation, and leave via extinction. In this paper, a complexity measure of networks is proposed based on the {\em complexity is information content} paradigm. To apply this paradigm to any object, one must fix two things: a representation language, in which strings of symbols from some alphabet describe, or stand for the objects being considered; and a means of determining when two such descriptions refer to the same object. With these two things set, the information content of an object can be computed in principle from the number of equivalent descriptions describing a particular object. I propose a simple representation language for undirected graphs that can be encoded as a bitstring, and equivalence is a topological equivalence. I also present an algorithm for computing the complexity of an arbitrary undirected network.Comment: Accepted for Australian Conference on Artificial Life (ACAL05). To appear in Advances in Natural Computation (World Scientific

Language evolution: Sound meets gesture? [Review of the book From signal to symbol: The evolution of language by By R. Planer and K. Sterelny]

A molecular biologist, a historical linguist, and a developmental psychologist walk into a bar. This is not a joke and could instead well describe a social evening at a language evolution conference. Over the last 30 years and more, a plethora of disciplines has tried to find out how language originated and developed in our species. Scholarly contributions come from the humanities, social sciences, engineering and natural sciences. In particular, the many disciplines involved, to name just a few, are: philology, archeology, psychology, artificial life, computer science, physics, paleontology, and genetics. I imagine how granting agencies may dread funding proposals in language evolution: how can one assemble an evaluation panel with such diverse backgrounds

Pathfinding in Games

Commercial games can be an excellent testbed to artificial intelligence (AI) research, being a middle ground between synthetic, highly abstracted academic benchmarks, and more intricate problems from real life. Among the many AI techniques and problems relevant to games, such as learning, planning, and natural language processing, pathfinding stands out as one of the most common applications of AI research to games. In this document we survey recent work in pathfinding in games. Then we identify some challenges and potential directions for future work. This chapter summarizes the discussions held in the pathfinding workgroup

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To this end, we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multiscale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and improving state-of-the-art features for long-range contact prediction

Many bioinformatics programming tasks can be automated with ChatGPT

Computer programming is a fundamental tool for life scientists, allowing them to carry out many essential research tasks. However, despite a variety of educational efforts, learning to write code can be a challenging endeavor for both researchers and students in life science disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists' efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such model -- OpenAI's ChatGPT -- can successfully complete basic- to moderate-level programming tasks. On its first attempt, ChatGPT solved 139 (75.5%) of the exercises. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have important implications for life-sciences research and education. For many programming tasks, researchers no longer need to write code from scratch. Instead, machine-learning models may produce usable solutions. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public.Comment: 13 pages, 4 figures, to be submitted for publicatio

Extracellular Vesicles: Living Prototypal Communication System

Communication is an ever-present part of our world. Such transfer of information occurs on many levels from the spoken natural languages, to artificial languages, to the cellular exchanges that govern the molecular world. Cells interact using various coded and non-coded molecules, which although not natural languages, could be considered types of biological language. These molecules are packaged into extracellular vesicles by cells from all three domains of life. Vesicles may then participate in intracellular trafficking of their cargo molecules. Or cells may secrete vesicles into the extracellular world, from where they are transported to, and taken up by, target recipient cells. Once delivered, extracellular vesicles exert a plethora of physiological and pathological effects, as well as an influence on recipient cell evolution. In executing their functions, both vesicles and their molecular cargo face evolutionary pressures over time and across habitats, forcing them to adapt to meet changing needs. This chapter will present extracellular vesicles as a highly conserved prototypal communication system

User stories collection via interactive chatbot to support requirements gathering

Nowadays, software products have become an essential part of human life. To build software, developers must have a good understanding of the requirements of the software. However, software developers tend to jumpstart system construction without having a clear and detailed understanding of the requirements. The user story concept is one of the practices of the requirements elicitation. This paper aims to present the work conducted to develop an Android chatbot application to support the requirements elicitation activity in software engineering, making the work less time-consuming and structured even for users not accustomed to requirements engineering. The chatbot uses Nazief & Adriani stemming algorithm to pre-process the natural language it receives from the users and artificial mark-up language (AIML) as the knowledge base to process the bot’s responses. A preliminary acceptance test based on the technology acceptance model results in an 83.03% score for users’ behavioral intention to use

Ralph Waldo Emerson as Nature Poet

Since nature inherently contains moral truth, knowledge and wisdom, the artist should rely on it, rather than convention, in shaping, formulating and appraising his work. Emerson maintains that literary works should not be only be evaluated according to artificial standards of tradition, but should rather be judged by nature since art is based organically on it. He shares with Coleridge the belief that literary forms should innately stem from nature instead of following mechanical laws of decorum. Emerson affirms that if we succeed in having a direct relation with the "basic forces" of nature, by retreating to a primitive, simple life, we will be able to reinvent genuine, organic forms (Matthiessen 133-6). In "Nature", Emerson further argues that nature provides us with language as well as with an explanation of the use of language. Every word in language is a symbol of a natural fact; for example, "right" is a sign for "straight" while "wrong" means "twisted". Similarly, we borrow the word "heart" to express emotion and the word "head" as analogous to thought. Both the abstract and the concrete find their roots in the visible forms of nature. Moreover, every natural fact corresponds to some spiritual fact. We symbolically use "light" and "darkness" to express knowledge and ignorance