[{"id":1198,"title":"'Accuracy per FLOP': A Green AI Metric for Fair and Efficient AI Development","sha":"7ca347b740b1e3876e3987d1fc986df9","user_id":null,"state":"published","body":"Recent advancements in Artificial Intelligence (AI), particularly through Deep Learning and large-scale datasets, have led to the creation of powerful models like OpenAI's GPT-$n$. These models have significantly enhanced Natural Language Processing and driven innovation in the field. However, concerns are growing about the monopolization of AI by entities with the resources to develop such large models.\r\n\r\nThe current emphasis on accuracy favors well-funded projects, potentially stifling innovation and disadvantaging researchers with limited resources. An alternative metric, “accuracy per FLOP” (floating-point operations), could provide a fairer comparison by measuring model efficiency relative to computational cost. The concept of “accuracy per FLOP” aligns with \"Green AI,\" a term from [Schwartz et al., 2020](https://doi.org/10.1145/3381831), which advocates for considering computation cost in AI research.\r\n\r\nTo promote inclusivity and innovation, researchers should explore new techniques and models that improve efficiency rather than merely increasing model size. This shift is crucial to ensure AI research benefits everyone and avoids a potential decline in AI progress, known as an Artificial Intelligence Winter. Human-centered AI, focusing on human interaction, should also remain a primary research focus.","created_at":"2024-05-31T09:46:53.143Z","updated_at":"2024-09-30T07:53:42.999Z","zenodo_id":12660216,"doi":"https://doi.org/10.5281/zenodo.12660216","subject":null,"tags":["artificial intelligence","green ai","democratization","new metrics"],"vote_count":2,"view_count":1,"score":2.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":633,"title":"Dermatoglyphics to predict genetic disorders","sha":"b4ed72f1220f9d126a3b96f13b862820","user_id":null,"state":"published","body":"In recent times, genetic disorders have risen to be one of the significant causes of mortality. As we improve our understanding of the human genome, we see that nearly all diseases have a genetic component linked to them. Early diagnosis of these genetic diseases is crucial for successful treatment.\r\n\r\nThe dermatoglyphics or the fingerprints of a human individual are among the very few features that can uniquely identify an individual and are determined by various phenomena, including genetic and chromosomal factors. Thus the analysis of fingerprint patterns might reveal insights into the genetic makeup of an individual. Fingerprints are proven to act as reliable biological markers for several medical conditions such as hypertension and other coronary diseases (https://doi.org/10.1016/j.ihj.2018.07.007). Fingerprint patterns can be exploited using artificial intelligence and cutting-edge machine learning algorithms, which can be trained to detect markers that correspond to several genetic disorders. \r\n","created_at":"2019-12-26T19:17:31.082Z","updated_at":"2020-02-09T06:00:38.357Z","zenodo_id":3660060,"doi":"https://doi.org/10.5281/zenodo.3660060","subject":null,"tags":["fingerprints","genetic disorders","machine learning","artificial intelligence"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":541,"title":"AutoLab: Can an AI Replace Human Data Collectors?","sha":"0ef39e22b61491bdb64d4127d53668c4","user_id":null,"state":"published","body":"Can an autonomous AI conduct experiments on real humans in a lab safely and efficiently? With [Automated Laboratory (AutoLab)](https://devpost.com/software/autolab#updates), our open-source ubiquitous computing project, we will do just that. Imagine a Star Trek-like computer infused into a lab room. Participants enter the smart lab by activating an electronic lock, converse naturally with an AI during the study, and are compensated digitally. This will greatly speed up and standardize data collection in the health and social sciences. Currently, unpaid and overworked assistants must spend inordinate time and money to collect data. This results in human errors that waste tax dollars, lost time that could be spent on more creative tasks, and small datasets that limit generalizability to the larger population.\r\n\r\nTypical human experiments in the socio-behavioral and health sciences span 1-2 hours and need one more hour to prep and manage data. For sample sizes of 50-200, this requires at least 100-600 hours for one study (~ $2,000-12,000 tax dollars annually). Not long ago, statistical/visualization software, word processors, and communication tools advanced science by standardizing tasks, reducing errors, increasing speed, and fostering more creative pursuits. Ubiquitous computing can do the same by collecting large datasets from human subjects in social and health science labs 24/7/365.","created_at":"2018-12-08T00:07:27.531Z","updated_at":"2018-12-09T06:01:18.768Z","zenodo_id":2092676,"doi":"https://doi.org/10.5281/zenodo.2092676","subject":null,"tags":["ai","artificial intelligence","research cycle","internet of things","data collection","open source","productivity","software","ubiquitous computing","ubicomp"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":768,"title":"Genre films can be considered as extensions of default logics","sha":"726aaf29d77b07f65ff3cdb2e73a9978","user_id":null,"state":"published","body":"At the level of story, a genre is a proto-typical narrative (*T*) shared by a group of films. This leads viewers to form hypotheses about the future course of events based on their knowledge of what normally occurs in similar narrative situations. \r\n\r\nThis process can be described using Reiter’s default logic (https://doi.org/10.1016/0004-3702(80)90014-4), which deals with reasoning where a rule generally applies but which allows for exceptions. Such rules are of the order ‘if *A*, and normally *B*, assume *B*,’ allowing an agent to form a hypothesis about the future in the absence of contradictory information. For example, the sexually active characters in a slasher film are normally dispatched by the killer, which can be expressed as the default rule: *D* = {Sexually active(*X*) : Killed(*X*) / Killed(*X*)}. This rule will generally apply but, due to demands for some narrative variation between films in a genre, not always. \r\n\r\nA film genre comprises the viewers’ prior beliefs about a narrative and the set of default rules that apply to a specific type of film, with each film in a genre considered to be an extension of the proto-typical narrative (*T* ∪ {*B*}) realised in the active process of viewing. \r\n\r\n","created_at":"2020-06-08T13:40:48.554Z","updated_at":"2020-07-19T06:01:12.871Z","zenodo_id":3951199,"doi":"https://doi.org/10.5281/zenodo.3951199","subject":null,"tags":["film","logic","narrative","artificial intelligence","default logic","genre"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":722,"title":"Vein Dynamics to predict the immune response and susceptibility to disorders","sha":"665ce75e67bd2011f08c961a4f1978f4","user_id":null,"state":"published","body":"The immune system acts as a protective framework against the pathogenic microbes. Understanding the immune system can give us insights of how vulnerable someone is to foreign invaders. Amidst the recent events such as virus outbreaks it becomes highly advantageous if we pinpoint the individuals who are more prone to be affected.\r\n\r\nThe vein patterns such as finger veins or palm veins are amongst the most robust features which are [unique](https://www.mdpi.com/2078-2489/9/9/213) to individuals. These vascular networks which are determined by genetic and chromosomal patterns can give us more insights to a person’s response to fight foreign microbes. In order to understand how these vascular patterns can predict the immune response and susceptibility to foreign microbes we can make use of artificial intelligence and cutting-edge machine learning algorithms, which can be trained to correlate with immune markers such as C-reactive proteins, differential cell count, lymphocyte proliferation and subset distribution and red blood cell distribution width. A designated score sheet which categorizes the vein patterns with the immune markers can help us mark the groups that are more immunocompromised and thereby keep us prepared during pandemics. \r\n","created_at":"2020-03-25T03:00:11.873Z","updated_at":"2020-04-08T06:00:21.745Z","zenodo_id":3742668,"doi":"https://doi.org/10.5281/zenodo.3742668","subject":null,"tags":["vein patterns","immune response","machine learning","artificial intelligence"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]