Abstract: This perspective calls for scholars across disciplines to address the challenge of audio deepfake detection and discernment through an interdisciplinary lens across Artificial Intelligence methods and linguistics. With an avalanche of tools for the generation of realistic-sounding fake speech on one side, the detection of deepfakes is lagging on the other. Particularly hindering audio deepfake detection is the fact that current AI models lack a full understanding of the inherent variability of language and the complexities and uniqueness of human speech. We see the promising potential in recent transdisciplinary work that incorporates linguistic knowledge into AI approaches to provide pathways for expert-in-the-loop and to move beyond expert agnostic AI-based methods for more robust and comprehensive deepfake detection. Introduction Fifteen years ago, The Fourth Paradigm (1) emphasized the significance of the data revolution that has now thoroughly permeated our global society.

In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.

The internet has enabled an unprecedented free flow and wide distribution of information on a global scale, which largely accelerated the democratization of information, fueling platforms like Wikipedia, YouTube, and StackOverflow. While this facilitated information democratization, it concurrently lowered barriers against unauthorized data use and piracy. The success of Deep Learning (DL) owes significantly to the availability of large-scale datasets available for training DL models [3], predominantly sourced from the internet [4].

Data is the new oil, but raw data is no good in and of itself. Like oil, data assets have to be gathered entirely and accurately and sent through different refining processes to create value for end users. This is the general data lifecycle -- an area where artificial intelligence (AI) is going to play a major role for enterprises. Initially, managing the data lifecycle was a task small enough to be handled manually by a team of experts. The volume of information was not that much, the sources were just a handful and the possible applications were also limited.

Geospatial data is a powerful tool that enables us to better understand our world with the power of location. In the geospatial field, we like to say that you don't truly understand your data until you can analyze or visualize it from a geographic perspective. This is why the geospatial industry is one of the fastest-growing and most exciting fields in data analytics, artificial intelligence, and machine learning engineers today. You probably already know that GIS stands for Geographic Information System. However, do you know what the connection between GIS and data engineering is?

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! A recent survey from McKinsey showed that 56% of respondents reported AI adoption in at least one function, up from 50% the year prior, with the three most common use cases focused on service-operations optimization, AI-based enhancement of products, and contact-center automation. Businesses are committing huge amounts of money to AI initiatives. According to Appen's 2021 State of AI report, AI budgets increased 55% year-over-year, reflecting a shift from an experimental project mindset to an expectation of business benefits and ROI.

Imagine that you are a digital map application. You collect live data from cell towers, GPS signals, and anonymous users. This includes information such as travel times, traffic speeds, and roadworks. Every data source is unique and each one has different ownership. Access, formats, accuracy,y, and access can all change depending on signal strength.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. A recent survey from McKinsey showed that 56% of respondents reported AI adoption in at least one function, up from 50% the year prior, with the three most common use cases focused on service-operations optimization, AI-based enhancement of products, and contact-center automation. Businesses are committing huge amounts of money to AI initiatives. According to Appen's 2021 State of AI report, AI budgets increased 55% year-over-year, reflecting a shift from an experimental project mindset to an expectation of business benefits and ROI. One reason this shift is happening now is that many businesses have built expert data science teams and matured their understanding of the discipline.

This article is sponsored by IBM. It is a new concept in which creators can apply passion and creativity to make money, instead of simply relying on likes and views. It focuses on bringing more life and meaning to the traditional media landscape in a way that empowers creative people worldwide to bring out the best in themselves, entirely driven by their passion. According to EMarketer, the creator economy is defined as follow: "We define creators as people or entities that develop original content for digital properties, and who consider creating that content to be either their full-time or parti-time career or livelihood. Of course, there is some overlap between many of these groups; for instance, celebrities can also be creators and vice versa. What's more, few successful influencers today are purely sales-oriented, and many of them are also creators, developing digital or, at times, physical products."

Enterprises have struggled to collaborate well around their data, which hinders their ability to adopt transformative applications like AI. The evolution of DataOps could fix that problem. The term DataOps emerged seven years ago to refer to best practices for getting proper analytics, and research firm Gartner calls it a major trend encompassing several steps in the data lifecycle. Just as the DevOps trend led to a better process for collaboration between developers and operations teams, DataOps refers to closer collaboration between various teams handling data and operations teams deploying data into applications. Getting DataOps right is a significant challenge because of the multiple stakeholders and processes involved in the data lifecycle.