Artificial intelligence could be used to help catch paedophiles operating on the dark web. The technology would target the most dangerous and sophisticated offenders in efforts to tackle child sexual abuse, the Home Office said. Earlier this month Chancellor Sajid Javid announced £30 million would be set aside to tackle online child sexual exploitation. The Government has pledged to spend more money on the Child Abuse Image Database (CAID), which since 2014 has allowed police and other law enforcement agencies to search seized computers and other devices for indecent images of children quickly against a record of 14 million images to help identify victims. The investment will be used to consider whether adding aspects of artificial intelligence (AI) to the system to analyse voices and estimate ages would help in tracking down child abusers.

Artificial intelligence could be used to help catch paedophiles operating on the dark web, the Home Office has announced. The government has pledged to spend more money on the child abuse image database, which since 2014 has allowed police and other law enforcement agencies to search seized computers and other devices for indecent images of children quickly, against a record of 14m images, to help identify victims. The investment will be used to trial aspects of AI including voice analysis and age estimation to see whether they would help track down child abusers. Earlier this month, the chancellor, Sajid Javid, announced £30m would be set aside to tackle online child sexual exploitation, with the Home Office releasing more information on how this would be spent on Tuesday. There has been debate over the use of machine learning algorithms, part of the broad field of AI, with the government's Centre for Data Ethics and Innovation developing a code of practice for the trialling of the predictive analytical technology in policing.

As the plumes of smoke settle over two of Saudi Arabia's critical oil production facilities – which came under crippling drone strikes over the weekend – both the U.S. and Saudi Arabia are deliberating options for retaliation, raising the possibility of much broader instability across the region, although President Trump was quick to point out Monday, "I don't want war with anybody." Intelligence officials from both countries have been quick to point fingers at Iran as the orchestrators of the attack, which analysts have deemed as one of the most disruptive in history. "This is perhaps one of the greatest examples of kinetic economic warfare we have seen in recent times. Iran is suffering from our sanctions but does not want to escalate into an active war with us," Andrew Lewis, a former Defense Department staffer and the president of a private intelligence firm, the Ulysses Group, told Fox News. "They can do a lot to manipulate the world economy, which will have a negative impact on the U.S. and our allies in Europe."

Event detection has long been the domain of physical sensors operating in a static dataset assumption. The prevalence of social media and web access has led to the emergence of social, or human sensors who report on events globally. This warrants development of event detectors that can take advantage of the truly dense and high spatial and temporal resolution data provided by more than 3 billion social users. The phenomenon of concept drift, which causes terms and signals associated with a topic to change over time, renders static machine learning ineffective. Towards this end, we present an application for physical event detection on social sensors that improves traditional physical event detection with concept drift adaptation. Our approach continuously updates its machine learning classifiers automatically, without the need for human intervention. It integrates data from heterogeneous sources and is designed to handle weak-signal events (landslides, wildfires) with around ten posts per event in addition to large-signal events (hurricanes, earthquakes) with hundreds of thousands of posts per event. We demonstrate a landslide detector on our application that detects almost 350% more land-slides compared to static approaches. Our application has high performance: using classifiers trained in 2014, achieving event detection accuracy of 0.988, compared to 0.762 for static approaches.

A new machine learning methodology, called successive subspace learning (SSL), is introduced in this work. SSL contains four key ingredients: 1) successive near-to-far neighborhood expansion; 2) unsupervised dimension reduction via subspace approximation; 3) supervised dimension reduction via label-assisted regression (LAG); and 4) feature concatenation and decision making. An image-based object classification method, called PixelHop, is proposed to illustrate the SSL design. It is shown by experimental results that the PixelHop method outperforms the classic CNN model of similar model complexity in three benchmarking datasets (MNIST, Fashion MNIST and CIFAR-10). Although SSL and deep learning (DL) have some high-level concept in common, they are fundamentally different in model formulation, the training process and training complexity. Extensive discussion on the comparison of SSL and DL is made to provide further insights into the potential of SSL.

Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. Existing methods for measuring attention, such as manual labeling according to established codebooks, are expensive and can be restrictive. We describe two computational models that automatically distinguish topics in politicians' social media content. Our models---one supervised classifier and one unsupervised topic model---provide different benefits. The supervised classifier reduces the labor required to classify content according to pre-determined topic list. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). These models are effective, inexpensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress.

The devastating effects of cyber-attacks, highlight the need for novel attack detection and prevention techniques. Over the last years, considerable work has been done in the areas of attack detection as well as in collaborative defense. However, an analysis of the state of the art suggests that many challenges exist in prioritizing alert data and in studying the relation between a recently discovered attack and the probability of it occurring again. In this article, we propose a system that is intended for characterizing network entities and the likelihood that they will behave maliciously in the future. Our system, namely Network Entity Reputation Database System (NERDS), takes into account all the available information regarding a network entity (e. g. IP address) to calculate the probability that it will act maliciously. The latter part is achieved via the utilization of machine learning. Our experimental results show that it is indeed possible to precisely estimate the probability of future attacks from each entity using information about its previous malicious behavior and other characteristics. Ranking the entities by this probability has practical applications in alert prioritization, assembly of highly effective blacklists of a limited length and other use cases.

We present an architecture for integrating real-time, multimodal input into a computational agent's contextual model. Using a human-avatar interaction in a virtual world, we treat aligned gesture and speech as an ensemble where content may be communicated by either modality. With a modified nondeterministic pushdown automaton architecture, the computer system: (1) consumes input incrementally using continuation-passing style until it achieves sufficient understanding the user's aim; (2) constructs and asks questions where necessary using established contextual information; and (3) maintains track of prior discourse items using multimodal cues. This type of architecture supports special cases of pushdown and finite state automata as well as integrating outputs from machine learning models. We present examples of this architecture's use in multimodal one-shot learning interactions of novel gestures and live action composition.

MOUNTAIN VIEW, Calif. and TALLINN, Estonia, September 16, 2019 -- Launching today, Pactum is an AI-based system that helps global companies to autonomously offer personalized, commercial negotiations on a massive scale. The Mountain View, California company, with engineering and operations in Estonia, has raised an initial $1.15 Million in pre-seed funding to augment negotiation and AI capabilities as well as scale operations. Pactum has also filed the patent this week related to its technology IP. Inefficient contracting has been estimated to cause firms to lose between 17% to 40% of the value on a given deal, depending on circumstances, according to research by KPMG. Pactum's AI helps companies improve their bottom line by implementing bespoke negotiation services for large volumes of incremental partners in every market, that might have previously been unmanaged.

This article is part of Fast Company's editorial series The New Rules of AI. More than 60 years into the era of artificial intelligence, the world's largest technology companies are just beginning to crack open what's possible with AI--and grapple with how it might change our future. Click here to read all the stories in the series. Artificial intelligence is still in its youth. But some very big things have already happened.