If you're worried about the malevolent potential of deepfake video, you're not alone – so is Facebook. The company has launched a project to sniff out deepfake videos, and it's pledging more than $10m to the cause. It has pulled in a range of partners including Microsoft for help. Deepfakes are videos that use AI to superimpose one person's face on another. They work using generative adversarial networks (GANs), which are battling neural networks.

Gordon's wine bar is reached through a discreet side-door, a few paces from the slipstream of London theatregoers and suited professionals powering towards their evening train. A steep staircase plunges visitors into a dimly lit cavern, lined with dusty champagne bottles and faded newspaper clippings, which appears to have had only minor refurbishment since it opened in 1890. "If Miss Havisham was in the licensing trade," an Evening Standard review once suggested, "this could have been the result." The bar's Dickensian gloom is a selling point for people embarking on affairs, and actors or politicians wanting a quiet drink – but also for pickpockets. When Simon Gordon took over the family business in the early 2000s, he would spend hours scrutinising the faces of the people who haunted his CCTV footage. "There was one guy who I almost felt I knew," he says. "He used to come down here the whole time and steal." The man vanished for a six-month stretch, but then reappeared, chubbier, apparently after a stint in jail.

At more than seven decades old, the United Nations has often been criticized for being too slow to respond to crises. But behind the scenes, a high-tech team is harnessing the power of big data and artificial intelligence to predict, monitor and respond to emergencies. CGTN's U.N. correspondent Liling Tan has an inside look at how U.N. Global Pulse is keeping the organization up to speed in the 21st century. Three blocks from the United Nations headquarters in New York, a veritable geek squad of data scientists, analysts and engineers are using big data and artificial intelligence for global good. Or, as U.N. Global Pulse's Director Robert Kirkpatrick puts it, "Our job is to help superheroes find out where people are in trouble, so they can rescue them."

Social media sources can provide crucial information in crisis situations, but discovering relevant messages is not trivial. Methods have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods). Event-specific models could implement a more focused search area, but collecting data and training new models for a crisis that is already in progress is costly and may take too much time for a prompt response. As a compromise, manually collecting a small amount of example messages is feasible. Few-shot models can generalize to unseen classes with such a small handful of examples, and do not need be trained anew for each event. We compare how few-shot approaches (matching networks and prototypical networks) perform for this task. Since this is essentially a one-class problem, we also demonstrate how a modified one-class version of prototypical models can be used for this application.

The graph represents a network of 2,109 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 18 August 2019 at 11:32 UTC. The requested start date was Sunday, 18 August 2019 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 8-day, 13-hour, 30-minute period from Friday, 09 August 2019 at 10:25 UTC to Saturday, 17 August 2019 at 23:56 UTC.

Fior Markets offers a latest published report on Global Machine Learning in Finance Market Growth (Status and Outlook) 2019-2024, providing key insights and giving a competitive advantage to consumers through a detailed report. The researchers have included essential figures associated with the production and consumption forecast for the major regions that the market is separated into consumption forecast by application and production forecast by type. The research study is a source of methodical information rich in both quantity and quality. It shows upcoming as well as future opportunities, revenue growth, pricing, and profitability, focusing on both global and the regional market. The report identifies the key trends related to the different sectors of the market. Various important players have mentioned in the report are: Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance A top-to-bottom research wraps the market dynamics such as growth drivers, threats, opportunities, and challenges.

According to a new market study entitled "Artificial Intelligence in Aviation Market to 2027 – Global Analysis and Forecasts by Deployment Type (On-Premise and Cloud) and Industry Vertical (BFSI, Healthcare & Life Sciences, Retail & Consumer Goods, Manufacturing, Travel & Hospitality, IT & Telecommunication, Media & Entertainment, and Others) and Geography, "explains the report, explaining the key drivers of this growth and highlighting key market players and their evolution. The report factors this growth and also highlights the major players in the market and their developments. Growing urbanization has resulted in advent of several disruptive technologies including the artificial intelligence. The AI has become integrated fragment of almost the sectors and recently the technology has also taken a plunge into aviation sector. Autopilot and flight management system are some of the key areas of implementation of the AI in aviation industry.

For those who are new to AI, Machine Learning, and Deep Learning, I recommend taking a look at the following article entitled "An Introduction to AI." I will refer to Machine Learning and Deep Learning as being subsets of AI. Furthermore, this article is non-exhaustive in relation to potential applications of AI to healthcare and Quantum Computing to various sectors of the economy. The reason for the focus on AI in healthcare is in light of recent articles by a few senior medical practitioners in the US expressing concern about the role of AI in healthcare. Some of the concerns expressed, such as the need for improved sharing of data by healthcare participants including hospitals and ensuring the highest quality in the preparation of data, are entirely valid and I take the view that the need for access to data and sharing of data by hospitals may need to become a matter of political and regulatory concern.

Automatic analysis of teacher and student interactions could be very important to improve the quality of teaching and student engagement. However, despite some recent progress in utilizing multimodal data for teaching and learning analytics, a thorough analysis of a rich multimodal dataset coming for a complex real learning environment has yet to be done. To bridge this gap, we present a large-scale MUlti-modal Teaching and Learning Analytics (MUTLA) dataset. This dataset includes time-synchronized multimodal data records of students (learning logs, videos, EEG brainwaves) as they work in various subjects from Squirrel AI Learning System (SAIL) to solve problems of varying difficulty levels. The dataset resources include user records from the learner records store of SAIL, brainwave data collected by EEG headset devices, and video data captured by web cameras while students worked in the SAIL products. Our hope is that by analyzing real-world student learning activities, facial expressions, and brainwave patterns, researchers can better predict engagement, which can then be used to improve adaptive learning selection and student learning outcomes. An additional goal is to provide a dataset gathered from the real-world educational activities versus those from controlled lab environments to benefit educational learning community.

In recent years, machine learning has made leaps and bounds enabling applications with high recognition accuracy for speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. In particular, it can be relatively challenging to accurately classify single or multi-model, real-time sensor data. Labelling is an indispensable stage of data pre-processing that can be even more challenging in real-time sensor data collection. Currently, real-time sensor data labelling is an unwieldly process with limited tools available and poor performance characteristics that can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a systematic performance comparison of two popular types of Deep Neural Networks running on five custom built edge devices. These state-of-the-art edge devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This research provides results and insights that can help researchers utilising edge devices for real-time data collection select appropriate labelling techniques. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist developers building adaptive, high performance edge solutions.