Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights.
Google has had an eventful couple of weeks, announcing enhancements to its search and map capabilities at its virtual "Search On" event on Oct. 15, and on Oct. 20 being accused by the US Justice Department of engaging in anti-competitive practices in order to preserve its search engine business. At the Search On event, Google detailed how it has tapped AI and machine learning techniques to make improvements to Google Maps as well as Search. In an expansion of its search "busyness metrics," users will be able to see how busy locations are without identifying the specific beach, grocery store, pharmacy or other location. COVID-19 safety information will also be added to business profiles across Search and Maps, indicating whether the business is using safety precautions such as temperature checks or plexiglass shields, according to an account in VentureBeat. An improvement to the algorithm beneath the "Did you mean?" features of search, will enable more accurate and precise spelling suggestions.
Artificial intelligence, the latest facet of information technology, has gained increasing momentum and been widely applied in various sectors with tremendous potential, thus becoming a driving force of scientific and technological development during China's 13th Five-Year Plan (2016-20) period. It has also injected new impetus into the digital economy and played a key role in bolstering high-quality development and accelerating the nation's push for industrial upgrading, experts said. According to the 13th Five-Year Plan, the country called for developing AI, with a focus on fostering the industrial ecology of AI and promoting the integration and application of AI into key industries and fields. In July 2017, the State Council, China's Cabinet, issued a plan that set benchmarks for the country's AI sector, with the value of core AI industries predicted to exceed 1 trillion yuan ($150 billion) and making the country one of the global leaders in AI innovation by 2030. China has made tremendous strides in AI over the past five years as it has outpaced the United States in the number of worldwide AI-related patent applications, said a report from a Ministry of Industry and Information Technology research unit. The report also pointed out that AI is considered an important direction for industrial upgrading, and the country's strategic plan for AI offers a broad space for the research and development of AI technologies and related industries.
Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinformation and hate speech, selling illegal drugs, and coordinating sex trafficking or child exploitation. This paper surveys the state of the art in keeping online platforms and their users safe from such harm, also known as the problem of preserving integrity. This survey comes from the perspective of having to combat a broad spectrum of integrity violations at Facebook. We highlight the techniques that have been proven useful in practice and that deserve additional attention from the academic community. Instead of discussing the many individual violation types, we identify key aspects of the social-media eco-system, each of which is common to a wide variety violation types. Furthermore, each of these components represents an area for research and development, and the innovations that are found can be applied widely.
JOHANNESBURG - Artificial Intelligence (AI) is one of the important building blocks of the Fourth Industrial Revolution (4IR) or the age of "intelligentisation." The past few years have seen tremendous advances in machine learning and the building of algorithms from data; deep learning simulating the human brain and the processing power and decreasing costs of powerful and fast computers. Intelligent devices are, therefore, increasingly finding their way into our lives, whether it is a personal assistant such as the Amazon Alexa, Google Home, Apple Siri or Samsung Bixby; satellite navigation; real-time language translation; biometric identification such as fingerprint, iris or facial recognition; or industrial process management and decision-making. Unfortunately, noble AI technology also has the possibility to be misused and exploited for criminal purposes. In 2016 two computational social scientists by the name of Seymour and Tully used AI to convince social media users to click on a phishing link within a mass-produced message.
As part of a perennial project, our team is actively engaged in developing new synthetic assistant (SA) technologies to assist in training combat medics and medical first responders. It is critical that medical first responders are well trained to deal with emergencies more effectively. This would require real-time monitoring and feedback for each trainee. Therefore, we introduced a voice-based SA to augment the training process of medical first responders and enhance their performance in the field. The potential benefits of SAs include a reduction in training costs and enhanced monitoring mechanisms. Despite the increased usage of voice-based personal assistants (PAs) in day-to-day life, the associated effects are commonly neglected for a study of human factors. Therefore, this paper focuses on performance analysis of the developed voice-based SA in emergency care provider training for a selected emergency treatment scenario. The research discussed in this paper follows design science in developing proposed technology; at length, we discussed architecture and development and presented working results of voice-based SA. The empirical testing was conducted on two groups as user studies using statistical analysis tools, one trained with conventional methods and the other with the help of SA. The statistical results demonstrated the amplification in training efficacy and performance of medical responders powered by SA. Furthermore, the paper also discusses the accuracy and time of task execution (t) and concludes with the guidelines for resolving the identified problems.
Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes.
Artificial Neural Networks have reached Grandmaster and even super-human performance across a variety of games: from those involving perfect-information (such as Go) to those involving imperfect-information (such as Starcraft). Such technological developments from AI-labs have ushered concomitant applications across the world of business - where an AI brand tag is fast becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong - an autonomous vehicle crashes; a chatbot exhibits racist behaviour; automated credit scoring processes discriminate on gender etc. - there are often significant financial, legal and brand consequences and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that, 'all the impressive achievements of deep learning amount to just curve fitting'. The key, Judea Pearl suggests, is to replace reasoning by association with causal-reasoning - the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the New York Times: 'we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets - often using an approach known as Deep Learning - and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality'. In this paper, foregrounding what in 1949 Gilbert Ryle termed a category mistake, I will offer an alternative explanation for AI errors: it is not so much that AI machinery cannot grasp causality, but that AI machinery - qua computation - cannot understand anything at all.
This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. It is constructed such that unimodal models struggle and only multimodal models can succeed: difficult examples ("benign confounders") are added to the dataset to make it hard to rely on unimodal signals. The task requires subtle reasoning, yet is straightforward to evaluate as a binary classification problem. We provide baseline performance numbers for unimodal models, as well as for multimodal models with various degrees of sophistication. We find that state-of-the-art methods perform poorly compared to humans (64.73% vs. 84.7%
A Google search for AI use cases turns up millions of results, an indication of the many ways in which AI is applied in the enterprise -- or at least can be applied (see section "Adoption in the enterprise"). AI use cases span industries from financial services -- an early adopter -- to healthcare, education, marketing and retail. AI has made its way into every business department, from marketing, finance and HR to IT and business operations. Additionally, the use cases incorporate a range of AI applications. Among them: natural language generation tools used in customer service, deep learning platforms used in automated driving, and biometric identifiers used by law enforcement. Here is a sampling of current AI use cases in multiple industries and business departments with links to the TechTarget articles that explain each one in depth.