When working with their clients Accenture under Tricarico's guidance focuses on "on guiding (their) clients to more safely scale their use of AI, and build a culture of confidence within their organizations." Not all companies have an established north star of AI use. Companies and partners like Accenture are vital to these companies and their proper and ethical use of the technology.

This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data. Given a set of relations defined over a collection of domains, the model first infers multiple non-overlapping clusters of relations using a top-level Chinese restaurant process. Within each cluster of relations, a Dirichlet process mixture is then used to partition the domain entities and model the probability distribution of relation values. The HIRM generalizes the standard infinite relational model and can be used for a variety of data analysis tasks including dependence detection, clustering, and density estimation. We present new algorithms for fully Bayesian posterior inference via Gibbs sampling. We illustrate the efficacy of the method on a density estimation benchmark of twenty object-attribute datasets with up to 18 million cells and use it to discover relational structure in real-world datasets from politics and genomics.

Responsible usage of AI is growing extensively since 2017 and 2021 will see expansion fully into the operationalization of AI ethical principles, frameworks, and policies. Operationalization defined as taking principles into useful practice and thus requiring prioritization for businesses. The challenge is focusing on the top initiatives which I will identify in this article. In my pro bono contributions across 100 global programs with non-profits, I am seeing businesses are still challenged in moving from proof-of-concept responsible AI applications, within one business unit, to scaling across the enterprise. With more than 300 AI principles, frameworks, policy, and regulatory initiatives--businesses must keep current of the top contenders as AI usage grows.

Generative neural network architectures such as GANs, may be used to generate synthetic instances to compensate for the lack of real data. However, they may be employed to create media that may cause social, political or economical upheaval. One emerging media is "Deepfake". Techniques that can discriminate between such media is indispensable. In this paper, we propose a modified multilinear (tensor) method, a combination of linear and multilinear regressions for representing fake and real data. We test our approach Figure 1: Deepfake technique replaces a person's appearance in by representing Deepfakes with our modified multilinear (tensor) an existing image or video with someone else's appearance [20].

We introduce a noisy channel approach for language model prompting in few-shot text classification. Instead of computing the likelihood of the label given the input (referred as direct models), channel models compute the conditional probability of the input given the label, and are thereby required to explain every word in the input. We use channel models for recently proposed few-shot learning methods with no or very limited updates to the language model parameters, via either in-context demonstration or prompt tuning. Our experiments show that, for both methods, channel models significantly outperform their direct counterparts, which we attribute to their stability, i.e., lower variance and higher worst-case accuracy. We also present extensive ablations that provide recommendations for when to use channel prompt tuning instead of other competitive models (e.g., direct head tuning): channel prompt tuning is preferred when the number of training examples is small, labels in the training data are imbalanced, or generalization to unseen labels is required.

In logic-based knowledge representation, query answering has essentially replaced mere satisfiability checking as the inferencing problem of primary interest. For knowledge bases in the basic description logic ALC, the computational complexity of conjunctive query (CQ) answering is well known to be ExpTime-complete and hence not harder than satisfiability. This does not change when the logic is extended by certain features (such as counting or role hierarchies), whereas adding others (inverses, nominals or transitivity together with role-hierarchies) turns CQ answering exponentially harder. We contribute to this line of results by showing the surprising fact that even extending ALC by just the Self operator - which proved innocuous in many other contexts - increases the complexity of CQ entailment to 2ExpTime. As common for this type of problem, our proof establishes a reduction from alternating Turing machines running in exponential space, but several novel ideas and encoding tricks are required to make the approach work in that specific, restricted setting.

It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".

Alexa, Siri and Cortana may sound like the top three hipster baby names in 2021, but they are actually Amazon, Apple and Microsoft's virtual assistants. In recent years, we have experienced a boom in speech recognition tools that understand what we are saying. And soon they could also understand how we are feeling. The list of companies working on the development of emotion recognition technology is growing exponentially, and investors appear to be excited when it comes to emotionally intelligent tech. The industry is undoubtedly booming, with estimates predicting that the global emotional intelligence market will grow to $64m by 2027. The most common form of emotion detection software uses cameras to record and analyse facial expressions, body movements and gestures to detect how people are feeling.

Microsoft: A new Artificial Intelligence (AI) platform was launched this Wednesday (4) to facilitate actions to prevent and combat deforestation in the Amazon rainforest. The PrevisIA tool, developed by Microsoft, the Amazon Institute of Man and Environment (Imazon) and the Vale Fund, anticipates information on regions susceptible to deforestation and fires. The algorithm analyzes data on topography, land cover and legal and illegal roads in satellite images, to find risks of felling trees or fire and inform public agencies to carry out prevention and combat actions. Alerts generated by the platform are also open to the public on an initiative's dashboard. Microsoft Azure cloud capabilities and Imazon's AI algorithm to detect roads helped improve the deforestation risk model to identify territories most threatened by deforestation in the Amazon, such as Indigenous Lands and Conservation Units.

The world of technology, which often breaks down barriers, can significantly promote more integration of people with disabilities into social and work contexts. In particular, artificial intelligence solutions may allow the removal of accessibility barriers. For those who develop technology, it is essential not only to think about usability but increasingly about accessibility. Especially those who deal with AI have the opportunity to create systems and solutions that can really break down barriers for people with disabilities of various kinds. This opens up an important debate that must involve both the world of technology and all those involved in ethical issues.