Toward Sustainability: Trade-Off Between Data Quality and Quantity in Crop Pest Recognition

The crop pest recognition based on the convolutional neural networks is meaningful and important for the development of intelligent plant protection. However, the current main implementation method is deep learning, which relies heavily on large amounts of data. As known, current big data-driven deep learning is a non-sustainable learning mode with the high cost of data collection, high cost of high-end hardware, and high consumption of power resources. Thus, toward sustainability, we should seriously consider the trade-off between data quality and quantity. In this study, we proposed an embedding range judgment (ERJ) method in the feature space and carried out many comparative experiments. The results showed that, in some recognition tasks, the selected good data with less quantity can reach the same performance with all training data. Furthermore, the limited good data can beat a lot of bad data, and their contrasts are remarkable. Overall, this study lays a foundation for data information analysis in smart agriculture, inspires the subsequent works in the related areas of pattern recognition, and calls for the community to pay more attention to the essential issue of data quality and quantity.

AI Ethics Needs Good Data

Arguing that discourses on AI must engage with power and political economy this chapter, in particular, makes the case that we must move beyond the depoliticised language of ‘ethics’ currently deployed in determining whether AI is ‘good’ given the limitations of ethics as a frame through which AI issues can be viewed. In order to circumvent these limits, we use instead the language and conceptualisation of ‘Good Data’ which we view as a more expansive term to elucidate the values, rights and interests at stake when it comes to AI’s development and deployment. These considerations include, but go beyond privacy, as well as fairness, transparency and accountability to include explicit political economy critiques of power.

DEEP NEURAL NETWORKS FOR SOCIAL VISUALS: STUDYING CLIMATE COMMUNICATION ON YOUTUBE

Visual politics is becoming increasingly salient online. The qualitative methods of the research tradition do not expand to complex media ecologies, but advances in deep neural networks open an unprecedented path to large-scale analysis on the basis of actual visual content. However, the analysis of social visuals is challenging, since social and political scenes are semantically rich and convey complex narratives and ideas. This paper examines validity conditions for integrating deep neural network tools in the study of digitally augmented social visuals. It argues that the complexity of social visuals needs to be reflected in the validation process and its communication: It is necessary to move beyond the conventionally dichotomous approach to neural network validation which focuses on data and neural network respectively, to instead acknowledge the interdependency between data and tool. The final definition of good data is not available until the end of the process, which itself relies on a tool that needs good data to be trained. Themes change during the process not just because of our interaction with the data, but also because of our interactions with the tool and the specific way in which it mediates our analysis. An upshot is that the conventional approach of performance assessment – i.e., counting errors – is potentially misleading in this context. We explore our argument experimentally in the context of a study that addresses climate communication on YouTube. Climate themes such as polar bear in arctic landscapes and elite people/events present tough cases of social visuals.

Website-Based Sales Information System at One Coffe Point Cafe

. Currently the development of technology in the marketing of a product is growing and easy, The amount of competition in the business world requires business actors to develop an innovation and strategy in marketing their products so that they can increase sales results. One of them is by using website media in their marketing process and data processing. With good data processing and marketing, it can be an evaluation for entrepreneurs in their product development and marketing. So that it can reduce unwanted things in the future. The purpose of this research is to facilitate entrepreneurs in marketing and processing data from their business by creating a website-based sales information system. While the method used is the waterfall method where this method has several stages of manufacture from start to finish and in making it is made as attractive as possible. The results of this study are the website can provide all information about the point one coffee cafe to customers and can be accessed anywhere from the device available by the customer.

The Most Important Thing That Ever Happened: Big, Bad Data and the Doubling of Human Life Expectancy

The global doubling of human life expectancy between 1850 and 1950 is arguably the most important thing that ever happened, undergirding massive improvements in human life and lifestyles while also contributing to insectageddons, septic oceans, and collapsing ecosystems. The story of that global doubling is typically told as a series of medical breakthroughs—Jenner and vaccination, Lister and antisepsis, Snow and germ theory, and Fleming and penicillin—but the lion’s share of the credit belongs to urban planning based upon good data. Until we had sophisticated systems of death registration, we could not conceive of the health problems we were facing, much less solve them. Today, the greatest threat we face is not disease but data denial.

U.S. AI Workforce: Labor Market Dynamics

A lack of good data on the U.S. artificial intelligence workforce limits the potential effectiveness of policies meant to increase and cultivate this cadre of talent. In this issue brief, the authors bridge that information gap with new analysis on the state of the U.S. AI workforce, along with insight into the ongoing concern over AI talent shortages. Their findings suggest some segments of the AI workforce are more likely than others to be experiencing a supply-demand gap.