Working with Big Data on Alibaba Cloud

| February 15, 2019

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Big Data, as well as Cloud Computing, are the hottest topics, trending nowadays which makes an impactful experience in most of our lives. Although both the term isn’t the same but somehow different from each other and working independently.  Big Data works on large volumes of data where it is incapable of handling one machine. If we look in Cloud Computing then it is more than an application. It stores the data with the help of remote servers on the internet and also provides services like Saas, Paas, and Iaas. In the following sections, we will come to know about how much impact will it have on the IT industry as well as how Alibaba Big Data competes with the pre-established network of Big data companies. A decade ago any company had to build its own data center to keep all the office information safe and secure by renting a place in one of the physical data centers. But as the commencement of Big Data, it became easier for them to just store their data on the cloud and keep their work going. As the data increased, the storage also increased and financial burden decreased on the company.

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H2O.ai

H2O.ai brings AI to business through software. H2O is the leading open source machine learning platform for smarter applications and data products. H2O is operationalizing data science by developing & deploying algorithms, models and code for R, Python and Sparkling Water for Apache Spark communities. Some of H2O's mission critical applications include predictive maintenance, operational intelligence, security, fraud, auditing, churn, credit scoring, user based insurance, sepsis, ICU transfers and others in over 5,000 organizations. Some of H2O.ai's customers include Capital One, Progressive, Zurich, TransAmerica, PWC, Comcast, Nielsen, Neustar, Macy's, Walgreens, Kaiser, United Healthcare and Aetna. H2O is brewing a grassroots culture of data transformation in its customer communities. ML is the new SQL and all software will use AI...

OTHER ARTICLES

Self-supervised learning The plan to make deep learning data-efficient

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Despite the huge contributions of deep learning to the field of artificial intelligence, there’s something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn’t emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data.Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers.

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The terms data science and data analytics are not unfamiliar with individuals who function within the technology field. Indeed, these two terms seem the same and most people use them as synonyms for each other. However, a large proportion of individuals are not aware that there is actually a difference between data science and data analytics.It is pertinent that individuals whose work revolves around these terms or the information and technology industries, should know how to use these terms in the appropriate contexts. The reason for this is quite simple: the right usage of these terms has significant impacts on the management and productivity of a business, especially in today’s rapidly data-dependent world.

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THEORY AND STRATEGIES

Rethinking and Recontextualizing Context(s) in Natural Language Processing

Article | June 10, 2021

We discursive creatures are construed within a meaningful, bounded communicative environment, namely context(s) and not in a vacuum. Context(s) co-occur in different scenarios, that is, in mundane talk as well as in academic discourse where the goal of natural language communication is mutual intelligibility, hence the negotiation of meaning. Discursive research focuses on the context-sensitive use of the linguistic code and its social practice in particular settings, such as medical talk, courtroom interactions, financial/economic and political discourse which may restrict its validity when ascribing to a theoretical framework and its propositions regarding its application. This is also reflected in the case of artificial intelligence approaches to context(s) such as the development of context-sensitive parsers, context-sensitive translation machines and context-sensitive information systems where the validity of an argument and its propositions is at stake. Context is at the heart of pragmatics or even better said context is the anchor of any pragmatic theory: sociopragmatics, discourse analysis and ethnomethodological conversation analysis. Academic disciplines, such as linguistics, philosophy, anthropology, psychology and literary theory have also studied various aspects of the context phenomena. Yet, the concept of context has remained fuzzy or is generally undefined. It seems that the denotation of the word [context] has become murkier as its uses have been extended in many directions. Context or/ and contexts? Now in order to be “felicitous” integrated into the pragmatic construct, the definition of context needs some delimitations. Depending on the frame of research, context is delimitated to the global surroundings of the phenomenon to be investigated, for instance if its surrounding is of extra-linguistic nature it is called the socio-cultural context, if it comprises features of a speech situation, it is called the linguistic context and if it refers to the cognitive material, that is a mental representation, it is called the cognitive context. Context is a transcendental notion which plays a key role in interpretation. Language is no longer considered as decontextualized sentences. Instead language is seen as embedded in larger activities, through which they become meaningful. In a dynamic outlook on communication, the acts of speaking (which generates a form discourse, for instance, conversational discourse, lecture or speech) and interpreting build contexts and at the same time constrain the building of such contexts. In Heritage’s terminology, “the production of talk is doubly contextual” (Heritage 1984: 242). An utterance relies upon the existing context for its production and interpretation, and it is, in its own right, an event that shapes a new context for the action that will follow. A linguistic context can be decontextualized at a local level, and it can be recontextualized at a global level. There is intra-discursive recontextualization anchored to local decontextualization, and there is interdiscursive recontextualization anchored to global recontextualization. “A given context not only 'legislates' the interpretation of indexical elements; indexical elements can also mold the background of the context” (Ochs, 1990). In the case of recontextualization, in a particular scenario, it is valid to ask what do you mean or how do you mean. Making a reference to context and a reference to meaning helps to clarify when there is a controversy about the communicative status and at the same time provides a frame for the recontextualization. A linguistic context is intrinsically linked to a social context and a subcategory of the latter, the socio-cultural context. The social context can be considered as unmarked, hence a default context, whereas a socio-cultural context can be conceived as a marked type of context in which specific variables are interpreted in a particular mode. Culture provides us, the participants, with a filter mechanism which allows us to interpret a social context in accordance with particular socio-cultural context constraints and requirements. Besides, socially constitutive qualities of context are unavoidable since each interaction updates the existing context and prepares new ground for subsequent interaction. Now, how these aforementioned conceptualizations and views are reflected in NLP? Most of the research work has focused in the linguistic context, that is, in the word level surroundings and the lexical meaning. An approach to producing sense embeddings for the lexical meanings within a lexical knowledge base which lie in a space that is comparable to that of contextualized word vectors. Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. The task of associating a word in context with the most suitable meaning from a predefined sense inventory is better known as Word Sense Disambiguation (Navigli, 2009). Linguistically speaking, “context encompasses the total linguistic and non-linguistic background of a text” (Crystal, 1991). Notice that the nature of context(s) is clearly crucial when reconstructing the meaning of a text. Therefore, “meaning-in-context should be regarded as a probabilistic weighting, of the list of potential meanings available to the user of the language.” The so-called disambiguating role of context should be taken with a pinch of salt. The main reason for language models such as BERT (Devlin et al., 2019), RoBERTA (Liu et al., 2019) and SBERT (Reimers, 2019) proved to be beneficial in most NLP task is that contextualized embeddings of words encode the semantics defined by their input context. In the same vein, a novel method for contextualized sense representations has recently been employed: SensEmBERT (Scarlini et al., 2020) which computes sense representations that can be applied directly to disambiguation. Still, there is a long way to go regarding context(s) research. The linguistic context is just one of the necessary conditions for sentence embeddedness in “a” context. For interpretation to take place, well-formed sentences and well-formed constructions, that is, linguistic strings which must be grammatical but may be constrained by cognitive sentence-processability and pragmatic relevance, particular linguistic-context and social-context configurations, which make their production and interpretation meaningful, will be needed.

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Data Analytics: Five use cases in telecom industry

Article | May 27, 2021

The telecom industry has witnessed spectacular growth since its establishment in the 1830s. Enabling distant communications, collaborations, and transactions globally, telecommunication plays a significant role in making our lives more convenient and easier. With enhanced flexibility and advanced communication methods, the telecom industry gains more customers and creates new revenue streams. According to Grand View Research, the global telecom market size would expand at a compound annual growth rate (CAGR) of 5.4% between 2021-2028. With the rapidly growing digital connectivity, the communication service providers (CSPs) have to deal with large datasets. Datasets that can allow them better to understand their customers, competitors, industry trends and derive valuable insights for decision making.

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Spotlight

H2O.ai

H2O.ai brings AI to business through software. H2O is the leading open source machine learning platform for smarter applications and data products. H2O is operationalizing data science by developing & deploying algorithms, models and code for R, Python and Sparkling Water for Apache Spark communities. Some of H2O's mission critical applications include predictive maintenance, operational intelligence, security, fraud, auditing, churn, credit scoring, user based insurance, sepsis, ICU transfers and others in over 5,000 organizations. Some of H2O.ai's customers include Capital One, Progressive, Zurich, TransAmerica, PWC, Comcast, Nielsen, Neustar, Macy's, Walgreens, Kaiser, United Healthcare and Aetna. H2O is brewing a grassroots culture of data transformation in its customer communities. ML is the new SQL and all software will use AI...

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