simMachines, Inc. is a Chicago-based company that offers an explainable AI (XAI)/machine learning technology. simMachines de-risks traditional …

What's more, the intelligent architecture uses machine learning algorithms to implement self-optimization of storage resource provisioning, …

Most AI and blackbox machine learning fraud detection solutions also keep the actual fundamentals of how they fight fraud a secret and how risk …

In my blog "The Importance of Determining Intent", I discussed the importance of determining user intent to create an "intelligent" user or stakeholder experience. Analytics-centric organizations specialize in determining and codifying a user's intent in order to provide a more engaging, relevant, hyper-personalized experience (Figure 1). Figure 1: Using "Intent Determination" to Create an Intelligent Customer Experience To create an "intelligent" user experience requires leveraging AI/ML to analyze a deep history of the user's interactions to determine the user's intentions, and then coupling those intentions with current trends, patterns, and relationships to match those intentions with a deep understanding of the available content to recommend the most relevant action. We reviewed how digital marketing companies, such as those featured in Figure 1, determine user intent. These companies accumulate a deep history of each individual user's interactions including what sites or content they visited or viewed, how long they spent with each site or piece of content, what they clicked on, what they did not click on, and their contextual search requests. They analyze the user's interaction history, and match that with current trends and behaviors of similar cohorts, to determine and codify (think propensity scores) the user's intentions (areas of interest) that drives real-time recommendation decisions.

Czech team among those using artificial intelligence and machine learning to enable fairer online exams.

LISBON, Nov 8 -- Tech evangelists habitually brim with enthusiasm over artificial intelligence's potential to transform our lives, and the crowds …

Higher-order tensor datasets are rising ubiquitously in modern data science applications, for instance, recommendation systems (Baltrunas et al., 2011; Bi et al., 2018), social networks (Bickel and Chen, 2009), genomics (Hore et al., 2016), and neuroimaging (Zhou et al., 2013). Tensor provides effective representation of data structure that classical vector-and matrix-based methods fail to capture. One example is music recommendation system (Baltrunas et al., 2011) that records ratings of songs from users on various contexts. This three-way tensor of user song context allows us to investigate interactions of users and songs in a context-specific manner. Another example is network dataset that records the connections among a set of nodes. Pairwise interactions are often insufficient to capture the complex relationships, whereas multi-way interactions improve the understanding of networks in molecular system (Young et al., 2018) and social networks (Han et al., 2020). In both examples, higher-order tensors represent multi-way interactions in an efficient way. Tensor estimation problem cannot be solved without imposing structures. An appropriate reordering of tensor entries often provides effective representation of the hidden salient structure.

In recent years, there has been a growing interest in using machine learning models for creative purposes. In most cases, this is with the use of large generative models which, as their name implies, can generate high-quality and realistic outputs in music [Huang et al., 2019], images [Esser et al., 2021], text [Brown et al., 2020], and others. The standard approach for artistic creation using these models is to take a pre-trained model (or set of models) and use them for producing output. The artist directs the model's generation by "navigating" the latent space [Castro, 2020], fine-tuning the trained parameters [Dinculescu et al., 2019], or using the model's output to steer another generative process [White, 2019, Castro, 2019]. At a high-level what all these approaches are doing is converting the numerical signal of a machine learning model's output into art, whether implicitly or explicitly. However, in most (if not all) cases they only do so after the initial model has been trained.

Duran Duran, the memorable hit band of the 1980s, returned in 2021 with the new album "Future Past" (BMG) and "Invisible" a video made with Artificial Intelligence. The video was directed by Huxley, an artificial intelligence whose "mind" was modeled after the cognitive and emotional processes of humans. In the introduction of the video, you can read that "It is the very first collaboration of its kind, between artists in different planes of existence. Although groundbreaking, this is not entirely new to Duran Duran's artistic career, during which the band has made music videos that strayed from prevailing trends. In that category can be cited "The Reflex", "Save a Prayer", "Hungry Like The Wolf" and "A View to a Kill", the latter a subject for the James Bond filmography. As for the use of artificial intelligence, it is the first time that it is used to "direct a video", although songs created with algorithms in the style of some authors have already been released. An example of this is the song "Drowned in the Sun" made in the style of Nirvana from the patterns of the music of Kurt Cobain. The song "Invisible" features Blur's Graham Coxon on guitar, David Bowie's pianist Mike Garson and indie-pop artist Lykke Li. The production of "Future Past" corresponded to the legendary Giorgio Moroder, the DJ and remixer Erol Alkan, and Mark Ronson. In a press release from the label, the singer of the band Simon Le Bon reveals that the band first went into the studio in 2018. "I was trying to persuade the guys that all we needed to do was write two or three tracks for an EP.

BAGHDAD – Iraqi Prime Minister Mustafa al-Kadhimi escaped unharmed in an assassination attempt by armed drone in Baghdad on Sunday, officials said, in an incident that dramatically raises tension in the country weeks after a general election disputed by Iran-backed militia groups. Six members of Kadhimi's personal protection force stationed outside his residence in the Green Zone were wounded, security sources said. Three drones were used in the attack, including two that were intercepted and downed by security forces while a third drone hit the residence, state news agency INA quoted a spokesman for the interior ministry as saying. A spokesman for the armed forces commander in chief said the security situation was stable inside the fortified Green Zone, which houses the residence, government buildings and foreign embassies, after the drone attack. No group immediately claimed responsibility for the attack.