Gary Bloom Interview: Big Data driving sales boom at MarkLogic

TOM FOREMSKI | May 18, 2016

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When I first met Gary Bloom more than 15 years ago he was second to Larry Ellison at Oracle, and when he jumped to run Veritas, I was one of the first reporters to interview him. Then came a short stint at Symantec, eMeter, then consulting with private equity firm TPG. With over 30 years in key positions he's become one of Silicon Valley's top business leaders...

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OTHER ARTICLES

How big data can help the homeless

Article | March 12, 2020

Homeless policy needs to join the big data revolution. A data tsunami is transforming our world. Ninety percent of existing data was created in the last two years, and Silicon Valley is leveraging it with powerful analytics to create self-driving cars and to revolutionize business decision-making in ways that drive innovation and efficiency.Unfortunately, this revolution has yet to help the homeless. It is not due to a lack of data. Sacramento alone maintains data on half a million service interactions with more than 65,000 homeless individuals. California is considering integrating the data from its 44 continuums of care to create a richer pool of data. Additionally, researchers are uncovering troves of relevant information in educational and social service databases.These data, however, are only useful if they are aggressively mined for insights, looking for problems to solve and successful practices to replicate. At that juncture California falls short.

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NEW TECHNOLOGY CAN IMPROVE STORAGE CONGESTION OF AI’S MEMORY

Article | February 12, 2020

The upsurge in data generation and its computing has raised the need for more power, storage and speed. What we call as big data is extremely memory-hungry and power-sapping and to fetch this requirement, engineers have put forward an innovative method. Recently, electrical engineers at Northwestern University and the University of Messina in Italy have developed a new magnetic memory device that could potentially support the surge of data-centric computing, which requires ever-increasing power, storage, and speed. Based on antiferromagnetic (AFM) materials, the device is the smallest of its kind ever demonstrated and operates with record-low electrical current to write data.

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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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Machine Learning and AI is Supercharging the Modern Technology

Article | April 6, 2020

Today when we look around, we see how technology has revolutionized our world. It has created amazing elements and resources, putting useful intelligence at our fingertips. With all of these revolutions, technology has also made our lives easier, faster, digital and fun. Perhaps at a point when we are talking about technology, Machine learning and artificial intelligence are increasingly popular buzzwords used in modern terms.Machine Learning has proven to be one of the game changer technological advancements of the past decade. In the increasingly competitive corporate world, Machine learning is enabling companies to fast-track digital transformation and move into an age of automation. Some might even argue that AI/ML is required to stay relevant in some verticals, such as digital payments and fraud detection in banking or product recommendations.To understand what machine learning is, it is important to know the concepts of artificial intelligence (AI). It is defined as a program that exhibits cognitive ability similar to that of a human being. Making computers think like humans and solve problems the way we do is one of the main tenets of artificial intelligence.

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Spotlight

Hortonworks

Hortonworks is an industry leading innovator that creates, distributes and supports enterprise-ready open data platforms and modern data applications that deliver actionable intelligence from all data: data-in-motion and data-at-rest. Hortonworks is focused on driving innovation in open source communities such as Apache Hadoop, NiFi and Spark. Along with its 1,600+ partners, Hortonworks provides the expertise, training and services that allow customers to unlock transformational value for their organizations across any line of business.

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