While many methods for learning vector space embeddings have been proposed in the field of Natural Language Processing, these methods typically do not distinguish between categories and individuals. Intuitively, if individuals are represented as vectors, we can think of categories as (soft) regions in the embedding space. Unfortunately, meaningful regions can be difficult to estimate, especially since we often have few examples of individuals that belong to a given category. To address this issue, we rely on the fact that different categories are often highly interdependent. In particular, categories often have conceptual neighbors, which are disjoint from but closely related to the given category (e.g.\ fruit and vegetable). Our hypothesis is that more accurate category representations can be learned by relying on the assumption that the regions representing such conceptual neighbors should be adjacent in the embedding space. We propose a simple method for identifying conceptual neighbors and then show that incorporating these conceptual neighbors indeed leads to more accurate region based representations.

To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services. To guide technology developments, this paper provides a systematic approach to relate social science concepts of trust with the technologies used in AI-based services and products. We conceive trust as discussed in the ABI (Ability, Benevolence, Integrity) framework and use a recently proposed mapping of ABI on qualities of technologies. We consider four categories of machine learning technologies, namely these for Fairness, Explainability, Auditability and Safety (FEAS) and discuss if and how these possess the required qualities. Trust can be impacted throughout the life cycle of AI-based systems, and we introduce the concept of Chain of Trust to discuss technological needs for trust in different stages of the life cycle. FEAS has obvious relations with known frameworks and therefore we relate FEAS to a variety of international Principled AI policy and technology frameworks that have emerged in recent years.

In my previous blog post, I discussed our endeavor to benefit from unsupervised learning on CyberPoint's malware dataset. One of the more intriguing tools I played with during that effort was the normalized compression distance (NCD). It achieves this by approximating the normalized Kolmogorov distance. The Kolmogorov distance between two objects is actually pretty easy to conceptualize -- it is the length of the shortest program that can transform one object into the other. Unlike many popular similarity measures, this provides a universal notion of similarity by quantifying the difference between two objects without restricting the type of difference.

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Sign in to report inappropriate content. Linear Regression in AI - video will help you understand the basics Linear Regression in a supervised learning algorithm, Types of linear regression.

Bibliometric analysis and systematic review of AI applied to wastewater treatment. Wastewater treatment technology, economy, management, and reuse were discussed. Prediction accuracy of AI technologies on pollutant removal ranged 0.64–1.00. Application of AI technology could reduce operational costs by up to 30 %. Combined AI methods could provide higher accuracy and lower error. Wastewater treatment is an important step for pollutant reduction and the promotion of water environment quality.

Landing a job at tech giant Google is like a dream come true for any engineer. One can get the opportunity of working with talented professionals as well as learn and share plenty of knowledge. However, cracking an interview for Google is a difficult task and one has to have in-depth knowledge and hands-on experience with projects. The interview comprises brain teasers like problem-solving questions, technical queries, and coding, among others. In this article, we listed down the top 10 machine learning questions which have been asked at Google Data Science interview.

Landing a job at tech giant Google is like a dream come true for any engineer. One can get the opportunity of working with talented professionals as well as learn and share plenty of knowledge. However, cracking an interview for Google is a difficult task and one has to have in-depth knowledge and hands-on experience with projects. The interview comprises brain teasers like problem-solving questions, technical queries, and coding, among others. In this article, we listed down the top 10 machine learning questions which have been asked at Google Data Science interview.

Attribute Oriented Induction (AOI) is a data mining algorithm used for extracting knowledge of relational data, taking into account expert knowledge. It is a clustering algorithm that works by transforming the values of the attributes and converting an instance into others that are more generic or ambiguous. In this way, it seeks similarities between elements to generate data groupings. AOI was initially conceived as an algorithm for knowledge discovery in databases, but over the years it has been applied to other areas such as spatial patterns, intrusion detection or strategy making. In this paper, AOI has been extended to the field of Predictive Maintenance. The objective is to demonstrate that combining expert knowledge and data collected from the machine can provide good results in the Predictive Maintenance of industrial assets. To this end we adapted the algorithm and used an LSTM approach to perform both the Anomaly Detection (AD) and the Remaining Useful Life (RUL). The results obtained confirm the validity of the proposal, as the methodology was able to detect anomalies, and calculate the RUL until breakage with considerable degree of accuracy.

This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial value function. This joint training method has the following traits. (1) The update of the energy-based model is based on noise contrastive estimation, with the flow model serving as a strong noise distribution. (2) The update of the flow model approximately minimizes the Jensen-Shannon divergence between the flow model and the data distribution. (3) Unlike generative adversarial networks (GAN) which estimates an implicit probability distribution defined by a generator model, our method estimates two explicit probabilistic distributions on the data. Using the proposed method we demonstrate a significant improvement on the synthesis quality of the flow model, and show the effectiveness of unsupervised feature learning by the learned energy-based model. Furthermore, the proposed training method can be easily adapted to semi-supervised learning. We achieve competitive results to the state-of-the-art semi-supervised learning methods.