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What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
This article is a follow-up to my list of best data science undergraduate courses. While AI and data science make up part of most computer science undergrad degrees, it's at a post-grad level where students can really start to develop expertise. Some of the world's best tech-led colleges and universities offer specialized Master's Degree courses in these subjects, and often they are also world leaders in research, partnered with Silicon Valley businesses on cutting-edge projects. Studying data science and AI at this level will mark you out to employers as someone whose technical expertise is likely to exceed that of candidates who only have a computer science bachelor's degree. It might also be a step towards a Ph.D.
Artificial Intelligence is the hottest topic in technology and commerce today, and the field of data science is fundamental to how it works. Courses in data science all now contain a strong AI presence, and a few institutions are already offering specialized undergraduate degrees in AI. The increasing number of colleges and universities offering courses in these subjects indicates industry-wide expectations that there will be a world of rewarding opportunities for those with formal training and accreditation. Well, according to Glassdoor.com the average salary last year for a data scientist stood at $107,000. So, it's certainly a career worth considering if earning a good starting wage is on your list of priorities!
The authors of the Harrisburg University study make explicit their desire to provide "a significant advantage for law enforcement agencies and other intelligence agencies to prevent crime" as a co-author and former NYPD police officer outlined in the original press release. At a time when the legitimacy of the carceral state, and policing in particular, is being challenged on fundamental grounds in the United States, there is high demand in law enforcement for research of this nature, research which erases historical violence and manufactures fear through the so-called prediction of criminality. Publishers and funding agencies serve a crucial role in feeding this ravenous maw by providing platforms and incentives for such research. The circulation of this work by a major publisher like Springer would represent a significant step towards the legitimation and application of repeatedly debunked, socially harmful research in the real world. To reiterate our demands, the review committee must publicly rescind the offer for publication of this specific study, along with an explanation of the criteria used to evaluate it. Springer must issue a statement condemning the use of criminal justice statistics to predict criminality and acknowledging their role in incentivizing such harmful scholarship in the past. Finally, all publishers must refrain from publishing similar studies in the future.
Hello guys, I have been sharing some online degree programs you can take online from last a couple of weeks as more and more people are looking for online technical degree programs. Earlier, I have shared Top 5 Computer Science degree you can earn online, and today, I will share the top 5 Data Science and Machine learning degrees you can earn online from the world's reputed universities. Data Science is the way or the process of extracting insights and useful information from your data to understand different things and turn that data into a story in the shape of graphs and a dashboard that anyone can understand and by using many different programming languages like Python and R. But imagine if you can earn an online degree in this topic, that's what we are covering in this article. The field of Data Science is one of the standard in-demand fields in today's world, and some of the people called the future career or job since the world demands people who can obtain valuable insight from data to produce a better application or for a better understanding of the world and here comes the job for a data scientist.
When ranking big data observations such as colleges in the United States, diverse consumers reveal heterogeneous preferences. The objective of this paper is to sort out a linear ordering for these observations and to recommend strategies to improve their relative positions in the ranking. A properly sorted solution could help consumers make the right choices, and governments make wise policy decisions. Previous researchers have applied exogenous weighting or multivariate regression approaches to sort big data objects, ignoring their variety and variability. By recognizing the diversity and heterogeneity among both the observations and the consumers, we instead apply endogenous weighting to these contradictory revealed preferences. The outcome is a consistent steady-state solution to the counterbalance equilibrium within these contradictions. The solution takes into consideration the spillover effects of multiple-step interactions among the observations. When information from data is efficiently revealed in preferences, the revealed preferences greatly reduce the volume of the required data in the sorting process. The employed approach can be applied in many other areas, such as sports team ranking, academic journal ranking, voting, and real effective exchange rates.
An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.
Efficient human resource management needs accurate assessment and representation of available competences as well as effective mapping of required competences for specific jobs and positions. In this regard, appropriate definition and identification of competence gaps express differences between acquired and required competences. Using a detailed quantification scheme together with a mathematical approach is a way to support accurate competence analytics, which can be applied in a wide variety of sectors and fields. This article describes the combined use of software technologies and mathematical and statistical methods for assessing and analyzing competences in human resource information systems. Based on a standard competence model, which is called a Professional, Innovative and Social competence tree, the proposed framework offers flexible tools to experts in real enterprise environments, either for evaluation of employees towards an optimal job assignment and vocational training or for recruitment processes. The system has been tested with real human resource data sets in the frame of the European project called ComProFITS.