I was reading yet another document about artificial intelligence (AI). The introduction was covering the basics and the history of the subject. The authors mentioned expert systems and the real flaws that tactic had. Then the authors said that, luckily, there was an alternative called "machine learning." Yet more people who think anything older than them couldn't be classified the same way as the things they know.

Localizing the root cause of network faults is crucial to network operation and maintenance. However, due to the complicated network architectures and wireless environments, as well as limited labeled data, accurately localizing the true root cause is challenging. In this paper, we propose a novel algorithm named NetRCA to deal with this problem. Firstly, we extract effective derived features from the original raw data by considering temporal, directional, attribution, and interaction characteristics. Secondly, we adopt multivariate time series similarity and label propagation to generate new training data from both labeled and unlabeled data to overcome the lack of labeled samples. Thirdly, we design an ensemble model which combines XGBoost, rule set learning, attribution model, and graph algorithm, to fully utilize all data information and enhance performance. Finally, experiments and analysis are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge to demonstrate the superiority and effectiveness of our approach.

IN the last part we discussed what is machine learning, the history of machine learning, how the data is used and the use cases of machine learning. Now in this part we are going to discuss the difference between AI vs ML vs DL. Most of the beginners when try to get into this field, they are curious to know about what actually is the difference between AI, ML and DL. When we google this term we got to see this picture. The outermost section represents AI, the middle section represents ML and the innermost represents DL.

You didn't think Netflix would leave its TikTok-style comedy feed on phones, did you? Sure enough, the company is launching a test that brings the Fast Laughs feature to TVs. Opt in and you'll get a flurry of hopefully funny clips from Netflix shows, movies and (of course) comedy specials. Find something you enjoy and you can watch the whole affair or add it to your watch list. The addition is "slowly" deploying to subscribers in English-speaking countries including the US, Canada, UK, Ireland, Australia and New Zealand.

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved; the deviation in dose-volume histogram (DVH) criterion; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P<0.05; one-sided Wilcoxon test) on 18 of 23 DVH criteria. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for a conventional planning model. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. In the interest of reproducibility, our data and code is freely available at https://github.com/ababier/open-kbp-opt.

Accurate electricity demand forecasts play a crucial role in sustainable power systems. To enable better decision-making especially for demand flexibility of the end-user, it is necessary to provide not only accurate but also understandable and actionable forecasts. To provide accurate forecasts Global Forecasting Models (GFM) trained across time series have shown superior results in many demand forecasting competitions and real-world applications recently, compared with univariate forecasting approaches. We aim to fill the gap between the accuracy and the interpretability in global forecasting approaches. In order to explain the global model forecasts, we propose Local Interpretable Model-agnostic Rule-based Explanations for Forecasting (LIMREF), a local explainer framework that produces k-optimal impact rules for a particular forecast, considering the global forecasting model as a black-box model, in a model-agnostic way. It provides different types of rules that explain the forecast of the global model and the counterfactual rules, which provide actionable insights for potential changes to obtain different outputs for given instances. We conduct experiments using a large-scale electricity demand dataset with exogenous features such as temperature and calendar effects. Here, we evaluate the quality of the explanations produced by the LIMREF framework in terms of both qualitative and quantitative aspects such as accuracy, fidelity, and comprehensibility and benchmark those against other local explainers.

Historically, the two encompassing streams of symbolic and sub-symbolic stances to AI evolved in a largely separate manner, with each camp focusing on selected narrow problems of their own. Originally, researchers favored the discrete, symbolic approaches towards AI, targeting problems ranging from knowledge representation, reasoning, and planning to automated theorem proving. While the particular techniques in symbolic AI varied greatly, the field was largely based on mathematical logic, which was seen as the proper ("neat") representation formalism for most of the underlying concepts of symbol manipulation. With this formalism in mind, people used to design large knowledge bases, expert and production rule systems, and specialized programming languages for AI. These symbolic logic representations have then also been commonly used in the machine learning (ML) sub-domain, particularly in the form of Inductive Logic Programming (discussed in the previous article), which introduced the powerful ability to incorporate background knowledge into learning models and algorithms. Amongst the main advantages of this logic-based approach towards ML have been the transparency to humans, deductive reasoning, inclusion of expert knowledge, and structured generalization from small data.

Defense in depth strategy has proven it's effectiveness in preventing cyber threats over the years. At the abstract level, most security controls are designed with two main components; 1) a knowledge base, and 2) a matching engine. Each security product has its own version of a growing knowledge base of feeds (whatever these feeds are). The content and how frequent these knowledge bases get updated are often the basis of competition between vendors. In this context, where these knowledge bases are complementary, defense in depth is meaningful.

A large and complex knowledge base that models some aspect of the real world can rarely be fully specified. Two examples of such underspecification are that (i) some of the cardinality constraints are omitted; (ii) some properties of all individual instances of a class are specialized across a class hierarchy, but specific references to which particular values are specialized are omitted. Such knowledge bases are of great practical interest as they are the basis of an empirically tested knowledge acquisition system that has been used to construct a knowledge base from a significant portion of a biology textbook. In this paper, we formalize an underspecified knowledge base using answer set programming, and give a set of rules called UMAP that support inheritance reasoning in such a knowledge base.

In this paper, we propose a general framework, both parameterized and parameter-free, for defining a family of fine-grained inconsistency measures for propositional knowledge bases. The parameterized approach allows to encompass several existing inconsistency mea- sures as specific cases, by properly setting its parameter. And the parameter-free approach is defined to avoid the difficulty in choosing a suitable parameter in practice but still keeps a desired ranking for knowledge bases by their inconsistency degrees. The fine granularity of our framework is based on the notion of MIS partition that considers the inner structure of all the minimal inconsistent subsets of a knowledge base. Moreover, MinCostSAT-based encodings are provided, which enable the use of efficient SAT solvers for the computation of the proposed measures. We implement these algo- rithms and test them on some real-world datasets. The preliminary experimental results for a variety of inputs show that the proposed framework gives a wide range of possibilities for evaluating large knowledge bases.