In this paper, we consider the setting where the learner has its own preferences that it additionally takesintoconsideration.

Artificial intelligence has hit the mainstream. Across industries, companies have rolled out successful proofs-of-concept and have even been successful in deploying AI in production. Some organizations have even operationalized their AI and machine learning strategies, with projects proliferating across the enterprise, complete with best practices and pipelines. Today, companies at the leading edge of the AI maturity curve are making use of AI at scale. This overall maturation of how AI is deployed in enterprises is shifting how companies view the strategic value of AI -- and where they hope to see its benefits realized. Here is a look at 10 AI enterprise strategy trends that industry experts are seeing unfolding today.

Artificial intelligence has hit the mainstream. Across industries, companies have rolled out successful proofs-of-concept and have even been successful in deploying AI in production. Some organizations have even operationalized their AI and machine learning strategies, with projects proliferating across the enterprise, complete with best practices and pipelines. Today, companies at the leading edge of the AI maturity curve are making use of AI at scale. This overall maturation of how AI is deployed in enterprises is shifting how companies view the strategic value of AI -- and where they hope to see its benefits realized. Here is a look at 10 AI enterprise strategy trends that industry experts are seeing unfolding today.

How should we gather information in a network, where each node's visibility is limited to its local neighborhood? This problem arises in numerous real-world applications, such as surveying and task routing in social networks, team formation in collaborative networks and experimental design with dependency constraints. Often the informativeness of a set of nodes can be quantified via a submodular utility function. Existing approaches for submodular optimization, however, require that the set of all nodes that can be selected is known ahead of time, which is often unrealistic. In contrast, we propose a novel model where we start our exploration from an initial node, and new nodes become visible and available for selection only once one of their neighbors has been chosen. We then present a general algorithm \elgreedy for this problem, and provide theoretical bounds on its performance dependent on structural properties of the underlying network. We evaluate our methodology on various simulated problem instances as well as on data collected from social question answering system deployed within a large enterprise.

Lately, Zomato has been taking the internet by storm with intriguing posts like the independence day'not accepting orders anymore' or super friendly notifications reminding you to order food from its platform like never before. That is just one side of the coin. On the backdrop, Zomato has been experimenting with various machine learning models to provide personalised experiences to its customers, driver-partners and restaurants. Today, many consumer-focused brands are trying to understand customer preferences in real-time and offer personalised experiences. It requires surfing through the data lake, creating useful machine learning models, and deploying them in production.

In this article, you can explore AutoML, can AutoML replace Data Scientists, is AutoML close to Strong AI and, what is the difference between AutoML and Neural Architecture Search? Automated Machine Learning (AutoML) is tied in with producing Machine Learning solutions for the data scientist without doing unlimited inquiries on data preparation, model selection, model hyperparameters, and model compression parameters. On top of that AutoML frameworks help the data scientist in data visualization, model intelligibility, and model deployment. AutoML is viewed as about algorithm selection, hyperparameter tuning of models, iterative modeling, and model evaluation. It is about making Machine Learning tasks easier to use less code and avoid hyper tuning manually.

The amount of attention paid to Kubernetes has increased substantially over the past couple of years. What started out as a relatively obscure container management system open sourced by Google has turned into the must-have technology for running machine learning and advanced analytics applications, among other workloads. But is Kubernetes the real deal? Will K8s deliver on the hype, or turn into just another once-shiny thing that lost its luster? Kubernetes certainly seems to be the right technology for the right time.

When Sahil Singla joined the social impact startup Farmguide, he was shocked to discover that thousands of rural farmers in India commit suicide every year. When harvests go awry, desperate farmers are forced to borrow from microfinance loan sharks at crippling rates. Unable to pay back these predatory loans, victims kill themselves – often by grisly methods like swallowing pesticides – to escape the threats and violence of their ruthless debt collectors. Singla and his team are tackling this social injustice with one unexpected but powerful tool: deep learning. Recent growth of computational power and structured data sets has allowed deep learning algorithms to achieve extraordinary results.

Symmetry-breaking is a technique for efficiently solving SAT instances that contain high degrees of symmetry among the variables of the instance. When satisfiability problems are represented as a relational schema, symmetries between objects in the domain can be detected directly from evidence, that is, variables known to have a particular setting prior to solving. These symmetries between domain objects are called term symmetries. In this work, we present two novel extensions to the technique of term equivalent symmetry breaking which allow the detection and exploitation of conditional or hidden symmetries, those relationships between domain objects that are obscured until the instance is partially solved. We give promising preliminary experimental results for this technique, and discuss how the techniques could be extended for use in probabilistic domains.

Markov Logic Networks (MLNs) use a few weighted first-order logic formulas to represent large probabilistic graphical models and are ideally suited for representing both relational and probabilistic knowledge in a wide variety of application domains such as, NLP, computer vision, and robotics. However, inference in them is hard because the graphical models can be extremely large, having millions of variables and features (potentials). Therefore, several lifted inference algorithms that exploit relational structure and operate at the compact first-order level, have been developed in recent years. However, the focus of much of existing research on lifted inference is on marginal inference while algorithms for MAP and marginal MAP inference are far less advanced. The aim of the proposed thesis is to fill this void, by developing next generation inference algorithms for MAP and marginal MAP inference.