The Createch stage will offer an upbeat vision of how converging creativity and technology can improve how we connect, create, and consume.Discover the pioneers re-imagining TV, theatre, fashion, music, and community, and inventing new possibilities for audiences, creatives, and investors.Between sessions, short films will showcase innovative creativity to keep you stimulated and entertained. CogX is hosted by Charlie Muirhead Co-Founder and CEO, and Co-Founder Tabitha Goldstaub. Find out more at: https://cogx.co/ CogX is an award-winning Festival with its roots in artificial intelligence. The fourth edition, June 8th to 10th 2020, adds a Virtual first experience and Global Leadership Summit, and builds on the huge success of the 2019 event, which brought together over 20,000 visitors.

Humans and artificial intelligences (AI) will increasingly participate digitally and physically in conflicts, yet there is a lack of trusted communications across agents and platforms. For example, humans in disasters and conflict already use messaging and social media to share information, however, international humanitarian relief organisations treat this information as unverifiable and untrustworthy. AI may reduce the 'fog-of-war' and improve outcomes, however AI implementations are often brittle, have a narrow scope of application and wide ethical risks. Meanwhile, human error causes significant civilian harms even by combatants committed to complying with international humanitarian law. AI offers an opportunity to help reduce the tragedy of war and deliver humanitarian aid to those who need it. In this paper we consider the integration of a communications protocol (the 'Whiteflag protocol'), distributed ledger technology, and information fusion with artificial intelligence (AI), to improve conflict communications called 'Protected Assurance Understanding Situation and Entities' (PAUSE). Such a trusted human-AI communication network could provide accountable information exchange regarding protected entities, critical infrastructure; humanitarian signals and status updates for humans and machines in conflicts.

The multi-task learning (MTL) paradigm can be traced back to an early paper of Caruana (1997) in which it was argued that data from multiple tasks can be used with the aim to obtain a better performance over learning each task independently. A solution of MTL with conflicting objectives requires modelling the trade-off among them which is generally beyond what a straight linear combination can achieve. A theoretically principled and computationally effective strategy is finding solutions which are not dominated by others as it is addressed in the Pareto analysis. Multi-objective optimization problems arising in the multi-task learning context have specific features and require adhoc methods. The analysis of these features and the proposal of a new computational approach represent the focus of this work. Multi-objective evolutionary algorithms (MOEAs) can easily include the concept of dominance and therefore the Pareto analysis. The major drawback of MOEAs is a low sample efficiency with respect to function evaluations. The key reason for this drawback is that most of the evolutionary approaches do not use models for approximating the objective function. Bayesian Optimization takes a radically different approach based on a surrogate model, such as a Gaussian Process. In this thesis the solutions in the Input Space are represented as probability distributions encapsulating the knowledge contained in the function evaluations. In this space of probability distributions, endowed with the metric given by the Wasserstein distance, a new algorithm MOEA/WST can be designed in which the model is not directly on the objective function but in an intermediate Information Space where the objects from the input space are mapped into histograms. Computational results show that the sample efficiency and the quality of the Pareto set provided by MOEA/WST are significantly better than in the standard MOEA.

Weak supervision (WS) frameworks are a popular way to bypass hand-labeling large datasets for training data-hungry models. These approaches synthesize multiple noisy but cheaply-acquired estimates of labels into a set of high-quality pseudolabels for downstream training. However, the synthesis technique is specific to a particular kind of label, such as binary labels or sequences, and each new label type requires manually designing a new synthesis algorithm. Instead, we propose a universal technique that enables weak supervision over any label type while still offering desirable properties, including practical flexibility, computational efficiency, and theoretical guarantees. We apply this technique to important problems previously not tackled by WS frameworks including learning to rank, regression, and learning in hyperbolic manifolds. Theoretically, our synthesis approach produces a consistent estimator for learning a challenging but important generalization of the exponential family model. Experimentally, we validate our framework and show improvement over baselines in diverse settings including real-world learning-to-rank and regression problems along with learning on hyperbolic manifolds.

Probabilistic context-free grammars (PCFGs) and dynamic Bayesian networks (DBNs) are widely used sequence models with complementary strengths and limitations. While PCFGs allow for nested hierarchical dependencies (tree structures), their latent variables (non-terminal symbols) have to be discrete. In contrast, DBNs allow for continuous latent variables, but the dependencies are strictly sequential (chain structure). Therefore, neither can be applied if the latent variables are assumed to be continuous and also to have a nested hierarchical dependency structure. In this paper, we present Recursive Bayesian Networks (RBNs), which generalise and unify PCFGs and DBNs, combining their strengths and containing both as special cases. RBNs define a joint distribution over tree-structured Bayesian networks with discrete or continuous latent variables. The main challenge lies in performing joint inference over the exponential number of possible structures and the continuous variables. We provide two solutions: 1) For arbitrary RBNs, we generalise inside and outside probabilities from PCFGs to the mixed discrete-continuous case, which allows for maximum posterior estimates of the continuous latent variables via gradient descent, while marginalising over network structures. 2) For Gaussian RBNs, we additionally derive an analytic approximation, allowing for robust parameter optimisation and Bayesian inference. The capacity and diverse applications of RBNs are illustrated on two examples: In a quantitative evaluation on synthetic data, we demonstrate and discuss the advantage of RBNs for segmentation and tree induction from noisy sequences, compared to change point detection and hierarchical clustering. In an application to musical data, we approach the unsolved problem of hierarchical music analysis from the raw note level and compare our results to expert annotations.

Artificial intelligence and deep learning. The last 10 years have seen huge advancements in machine learning, a part of artificial intelligence where …

Artificial intelligence (AI), machine learning (ML) and other emerging technologies have potential to solve complex problems for organizations.

In media news today, a women's group says'good riddance' to Chris Cuomo after CNN firing, Brooke Baldwin calls on the liberal network to replace Cuomo with a woman, and an MSNBC anchor appears to downplay Bob Dole's accomplishments because of his support for Trump. Washington Post columnist Dana Milbank doubled down Monday on his recent op-ed that claimed "sentiment analysis" data proved the media is tougher on President Biden than they were on former President Trump. Milbank's piece, titled, "The media treats Biden as badly as - or worse than - Trump. Here's proof," cited research from Forge.ai, a data analytics unit of the information company FiscalNote. The study used algorithms focused on adjectives and their placement in articles - more than 200,000 of them - to rate the coverage Biden received in the first 11 months of 2021 and the coverage Trump got in the first 11 months of 2020.

Artificial intelligence is already pervasive in our digital life, from cell phones to chatbots. The popularity of AI is growing, thanks in part to the vast amounts of data that machines can collect about our interests, purchases, and activities on a daily basis. Artificial intelligence researchers utilise all of this information to teach machines how to understand and predict whatever we want or don't want. Let's take a look at where AI is headed in the future. In the future, you could relax on your couch and order a personalised movie with your favourite virtual actors.

If America has stood for anything, it's surely forward-looking optimism. In New York, Chicago, Detroit and other shining cities, its soaring skyscrapers pointed to the future. But has the bubble burst in the 21st century? "We don't see ourselves striding toward a better tomorrow," columnist Frank Bruni wrote in the New York Times last month, citing research that found 71% of Americans believe that this country is on the wrong track. "We see ourselves tiptoeing around catastrophe. That was true even before Covid. That was true even before Trump."