Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations. Such techniques are critical in risk-based decision-making and planning under uncertainties. Existing approaches are primarily based on parametric or semi-parametric time-series models that are restrictive, difficult to validate, and challenging to adapt to varying conditions. This paper proposes a nonparametric method based on the classic notion of {\em innovations} pioneered by Norbert Wiener and Gopinath Kallianpur that causally transforms a nonparametric random process to an independent and identical uniformly distributed {\em innovations process}. We present a machine-learning architecture and a learning algorithm that circumvent two limitations of the original Wiener-Kallianpur innovations representation: (i) the need for known probability distributions of the time series and (ii) the existence of a causal decoder that reproduces the original time series from the innovations representation. We develop a deep-learning approach and a Monte Carlo sampling technique to obtain a generative model for the predicted conditional probability distribution of the time series based on a weak notion of Wiener-Kallianpur innovations representation. The efficacy of the proposed probabilistic forecasting technique is demonstrated on a variety of electricity price datasets, showing marked improvement over leading benchmarks of probabilistic forecasting techniques.

A long-standing legal dispute to determine whether generative AI technologies can be named as the legal creators of their innovations has reached the highest court in the land, with a hearing at the UK Supreme Court on 2nd March 2023. A similar appeal hearing is underway at the US Supreme Court too. These hearings have been brought by a group of academics and inventors who believe that a generative AI system, called Dabus AI, is solely responsible for its own innovative outputs, two of which are the subject of patent applications filed in the UK, Europe and the US. These innovations include a novel interlocking food container that is easy for robots to handle, and a warning light or beacon that flickers in a rhythm similar to neural activity, which makes it difficult to ignore. Developed by Stephen Thaler in 1994, Dabus AI, also known as'The Creativity Machine', is a computational paradigm that claims to replicate human cognition.

AI has the potential to transform innovation management practice by enabling a more effective and efficient innovation process. There are numerous opportunities to extend, complement, or even substitute human capabilities throughout the idea-to-launch innovation process. While organizations agree on the great potential of AI-based innovation management, clear differences can be observed in the configuration preferences of how organizations implement AI-based innovation management. Organizations seem to choose their own context-specific ways of how to explore and implement AI-based innovation management. The application of AI is expected to enable new opportunities for innovation management and reshape innovation practice in organizations.

Within the last decade, there has been growing awareness that causal inference can improve scientific research in many disciplines as interpretability and robustness become increasingly important (Doshi-Velez and Kim, 2017; Roscher et al., 2020; Marcinkevičs and Vogt, 2020; Moraffah et al., 2020). Causality is a crucial factor for gaining insights into the decision process of algorithms, which has many use cases such as avoiding bias and discrimination (Mehrabi et al., 2019), improving user experience (Zhou and Fu, 2007) and gathering biological insights (Angermueller et al., 2016). If the causal relation between variables is known, causality can be used to study the interaction between statistical units such as estimating the average effect of treatments (Imbens and Rubin, 2015; Holland, 1986), analyze their mediation (Berzuini et al., 2012), detect the root causes of anomalies (Janzing et al., 2019) or quantifying the causal influence of variables in a system (Janzing et al., 2013, 2020).

In this short article, we showcase the derivation of an optimal predictor, when one part of system's output is not measured but is able to be predicted from the rest of the system's output which is measured. According to author's knowledge, similar derivations have been done before but not in state-space representation.

Objective: Mixtures of temporally nonstationary signals are very common in biomedical applications. The nonstationarity of the source signals can be used as a discriminative property for signal separation. Herein, a semi-blind source separation algorithm is proposed for the extraction of temporally nonstationary components from linear multichannel mixtures of signals and noises. Methods: A hypothesis test is proposed for the detection and fusion of temporally nonstationary events, by using ad hoc indexes for monitoring the first and second order statistics of the innovation process. As proof of concept, the general framework is customized and tested over noninvasive fetal cardiac recordings acquired from the maternal abdomen, over publicly available datasets, using two types of nonstationarity detectors: 1) a local power variations detector, and 2) a model-deviations detector using the innovation process properties of an extended Kalman filter. Results: The performance of the proposed method is assessed in presence of white and colored noise, in different signal-to-noise ratios. Conclusion and Significance: The proposed scheme is general and it can be used for the extraction of nonstationary events and sample deviations from a presumed model in multivariate data, which is a recurrent problem in many machine learning applications.

If ever there was a time for companies to truly commit to digital transformation, this is it. Surviving the most challenging economic conditions since the Great Depression, and thriving once the pandemic eases, requires flexible and imaginative thinking and the ability to manage the innovation process. It may be tempting during this pandemic to pull back the reins on innovation or hold off on product development, but that may not be the best idea. Times of uncertainty are also times of opportunity. No one knows when the pandemic will end, but it does seem clear that the pace of digital transformation has only accelerated.

There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. Most of the work is limited to either independent and identically distributed setting, or time series with independent and/or (sub-)Gaussian innovations. We extend current literature to a broader set of innovation processes, by assuming that the error process is non-sub-Gaussian and conditionally heteroscedastic, and the generating process is not necessarily sparse. This setting covers fat tailed, conditionally dependent innovations which is of particular interest for financial risk modeling. It covers several multivariate-GARCH specifications, such as the BEKK model, and other factor stochastic volatility specifications.

At AIT he has implemented a digital safety and security research program, resulting in an international leadership role in important key areas, including cyber security, data science, large-scale sensor networks, intelligent vision systems, autonomous systems, highly reliable software and systems as well as optical broadband and 5G wireless networking technologies. He is currently focusing on cyber security, post quantum encryption, safety of complex IT systems, critical infrastructure protection, next generation wireless (5G) and Blockchain technologies. During his assignment with Telekom Austria, he was acting as CTO and thus responsible for IT and network infrastructure development as well as product management for the overall wireline product portfolio. As part of this role, he was the driving-force behind Telekom Austria s cultural change program, transitioning the organization from a telephone network operator to a multimedia service provider. He was mainly responsible for the all optical and broadband internet infrastructure roll-out ads well as the interaktive TV service portfolio (IPTV/aon.tv).

The Innovation is synonymous with risk-taking and organizations that create revolutionary products or technologies take on the greatest risk because they create new markets. Innovations create opportunities and are critical for the survival, economic growth, and success of a Business. Innovating helps in optimizing processes within an Organization. Companies that innovate set themselves in a different paradigm in terms of identifying opportunities or best methods to solve current problems. "Ideas are Dime a Dozen, People who implement them successfully are Priceless" To drive innovation and reap benefits, Leaders should be open-minded and collaborative. Feeling comfortable with uncertainty and manage changes are behavioural components enroute to innovate. Innovative leaders are curious and are optimistic since they dare to take risks. No one knows where innovation will bring the organization or individual. Innovation and Adaptability are the innate abilities of all Humans. From the Time of Birth, we innovate by learning, observing and doing.