An essential problem space employed by all AI products, this is a very simple introduction to knowledge representation and their applications. In artificial intelligence (AI), knowledge representation is the process of encoding information about the world into a form that computers can use to solve problems. Usually, this means creating formal models of concepts and how they relate to each other. The goal is to make it possible for a computer to draw logical conclusions from a set of facts or hypotheses. No ideal form of knowledge representation exists that applies in all contexts.

We describe a novel approach to explainable prediction of a continuous variable based on learning fuzzy weighted rules. Our model trains a set of weighted rules to maximise prediction accuracy and minimise an ontology-based 'semantic loss' function including user-specified constraints on the rules that should be learned in order to maximise the explainability of the resulting rule set from a user perspective. This system fuses quantitative sub-symbolic learning with symbolic learning and constraints based on domain knowledge. We illustrate our system on a case study in predicting the outcomes of behavioural interventions for smoking cessation, and show that it outperforms other interpretable approaches, achieving performance close to that of a deep learning model, while offering transparent explainability that is an essential requirement for decision-makers in the health domain.

In this article, we will talk in-depth about an interactive, human-in-the-loop tool called SEER. SEER helps users who work with such text datasets extract relevant data from them. A user in SEER would highlight examples of text they wish to extract. Positive examples are texts they wish to extract. Negative examples are texts they do not wish to extract.

Te reo M\=aori (referred to as M\=aori), New Zealand's indigenous language, is under-resourced in language technology. M\=aori speakers are bilingual, where M\=aori is code-switched with English. Unfortunately, there are minimal resources available for M\=aori language technology, language detection and code-switch detection between M\=aori-English pair. Both English and M\=aori use Roman-derived orthography making rule-based systems for detecting language and code-switching restrictive. Most M\=aori language detection is done manually by language experts. This research builds a M\=aori-English bilingual database of 66,016,807 words with word-level language annotation. The New Zealand Parliament Hansard debates reports were used to build the database. The language labels are assigned using language-specific rules and expert manual annotations. Words with the same spelling, but different meanings, exist for M\=aori and English. These words could not be categorised as M\=aori or English based on word-level language rules. Hence, manual annotations were necessary. An analysis reporting the various aspects of the database such as metadata, year-wise analysis, frequently occurring words, sentence length and N-grams is also reported. The database developed here is a valuable tool for future language and speech technology development for Aotearoa New Zealand. The methodology followed to label the database can also be followed by other low-resourced language pairs.

Shared control can help in teleoperated object manipulation by assisting with the execution of the user's intention. To this end, robust and prompt intention estimation is needed, which relies on behavioral observations. Here, an intention estimation framework is presented, which uses natural gaze and motion features to predict the current action and the target object. The system is trained and tested in a simulated environment with pick and place sequences produced in a relatively cluttered scene and with both hands, with possible hand-over to the other hand. Validation is conducted across different users and hands, achieving good accuracy and earliness of prediction. An analysis of the predictive power of single features shows the predominance of the grasping trigger and the gaze features in the early identification of the current action. In the current framework, the same probabilistic model can be used for the two hands working in parallel and independently, while a rule-based model is proposed to identify the resulting bimanual action. Finally, limitations and perspectives of this approach to more complex, full-bimanual manipulations are discussed.

PLUSSES • MAJOR PLUS – Background in streaming/media/video broadcasting • Experience with CDN concepts or working with streaming video • Docker and Kubernetes (as a developer) • Experience working with other metric vendors: Mux, Cedexis, Conviva, etc. • An understanding of TCP/IP concepts and how the internet works (ASNs) As a Sr. Software Engineer, you'll be working across the VDE organization to create new features, APIs, tooling, and processes that innovate in the video delivery and experience space and play a large role in enabling internal users and partners to use and extend our core systems. You will start off focusing on our video delivery observability suite to supplement data engineering efforts to ingest, query, and action on billions of daily rows of CDN and streaming workflow data. This could be working on methods to ingest new data sources into our analytics pipeline, exposing operational tooling via HTTP API, or develop rule driven systems for root-cause-analysis and rule-based alerting. This role is great for someone looking to bounce around to different systems and play a visible role in both carrying out development efforts, but also collaborating and planning with our team and stake-holders to push the platform in innovative directions. Paramount also wants to stay on the cutting edge and integrate with new technology in the space like CMCD, CMSD, and CDN-level request tracing.

Risk scoring systems have been widely deployed in many applications, which assign risk scores to users according to their behavior sequences. Though many deep learning methods with sophisticated designs have achieved promising results, the black-box nature hinders their applications due to fairness, explainability, and compliance consideration. Rule-based systems are considered reliable in these sensitive scenarios. However, building a rule system is labor-intensive. Experts need to find informative statistics from user behavior sequences, design rules based on statistics and assign weights to each rule. In this paper, we bridge the gap between effective but black-box models and transparent rule models. We propose a two-stage method, RuDi, that distills the knowledge of black-box teacher models into rule-based student models. We design a Monte Carlo tree search-based statistics generation method that can provide a set of informative statistics in the first stage. Then statistics are composed into logical rules with our proposed neural logical networks by mimicking the outputs of teacher models. We evaluate RuDi on three real-world public datasets and an industrial dataset to demonstrate its effectiveness.

Financial regulators across Europe continue to levy steep enforcement fines against banks for failures to comply with know-your-customer (KYC) and anti-money laundering (AML) regulations. At the end of 2021, the Financial Conduct Authority (FCA) fined two of the UK's largest banks, HSBC and NatWest, a total of £328.95 million ($436.1 million) for failings in their money laundering processes. Meanwhile, members of the European Parliament are calling for cryptocurrencies to be governed by the European Commission's Anti-Money Laundering Authority, as illicit organisations continue to find new methods for laundering money through the financial system. Money laundering is a process that criminals use to hide the illegal source of their funds. By passing money through multiple, sometimes complex, transfers and transactions, the money is "cleaned" of its illegitimate origin and made to appear as legitimate business profits.

Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There's an endless supply of industries and applications that machine learning can make more efficient and intelligent. This course introduces you to one of the prominent modelling families of Unsupervised Machine Learning called Association Rule Learning. Association rule mining helps find exciting connections and linkages among large data items. The association rule learning is employed in Market Basket analysis, Web usage mining, Continuous production, Customer analytics, Catalogue design, Shop layout, Recommender systems etc. Association rules are critical in data mining for analyzing and forecasting consumer behaviour.

The trajectory of AI has been marked by several winters since its inception in 1955 in a formal proposal made by computer scientist and AI researcher Marvin Minksy and several others. Between 1956 and 1974, the U.S. Defense Advanced Research Projects Agency (DARPA) funded AI research with few requirements for developing functional projects. After the initial hype generated by these AI projects, a quiet decade followed where interest and support gradually tapered off. In 1969, Minsky and another AI researcher, Seymour Papert, published a book called Perceptrons, which pointed out the flaws and limitations of neural networks. This publication influenced DARPA to withdraw its previous funding of AI projects.