BUILDING A DATA SCIENCE PORTFOLIO

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Kaggle is a crowd-sourcing platform for data science. Real companies post their data science problems and Kaggle hosts competitions to the public in an effort to solve the problem and rank the participates against one another. The famous $1 million dollar Netflix challenge was originally a Kaggle competition. When large companies need help with their data science challenges, they turn to Kaggle and its community for help. Companies such as: Allstate, Bosh, State Farm, Red Hat,  Facebook,  Expedia, Home Depot, Yelp, Airbnb, Walmart, and Liberty Mutual.

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MSys Technologies LLC

MSys Technologies is an innovator in offering technology services and domain-specific test automation software (for both desktop and mobile apps). MSys’s unparalleled focus and deep technical expertise ensures the best value in the areas of storage, virtualization, telecommunications, servers, DR/BC, test automation, mobile test automation, etc…

OTHER ARTICLES

KAIST Develops Technology for AI-based High-resolution Image Creation

Article | April 7, 2020

The Korea Advanced Institute of Science and Technology (KAIST) announced on April 6 that professor Yoo Hoi-jun and his research team have succeeded in developing a generative adversarial networks processing unit (GANPU) as an AI chip processing GAN with low power and high efficiency. The AI chip is capable of quickly processing arithmetic operations required for image synthesis and restoration on a mobile basis. The single chip is capable of realizing image recognition, inference, learning and determination with low power and high efficiency and is expected to contribute to the use of AI in mobile devices for more purposes.

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Forward-thinking Business And The Implications Of Big Data

Article | April 7, 2020

Big data is a modern phenomenon transforming businesses of today. Organisations hold vast swathes of data, from historic and current orders to detailed insights about supply chain operations. This information, combined with external data such as market intelligence and even weather patterns, can provide businesses with a foundation on which to base their planning and decision-making. Business intelligence and analytical solutions pull valuable insights from huge datasets. From workforce optimisation to cost management, access to big data and the tools that manage and evaluate it allows firms to streamline key parts of their business. Adopters of modern solutions are seeing vast improvements in all areas of the company.

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DEEP THOMAS EMBEDDING DATA-DRIVEN CULTURE ACROSS BUSINESS WITH CUTTING EDGE INNOVATION

Article | April 7, 2020

A US$ 48.3 billion-corporation, the Aditya Birla Group is in the league of Fortune 500. Anchored by an extraordinary force of over 120,000 employees belonging to 42 nationalities, the Group is built on a strong foundation of stakeholder value creation. With over 7 decades of responsible business practices, Aditya Birla Group’s businesses have grown into global powerhouses in a wide range of sectors metals, chemicals, pulp & fibre, textiles, carbon black, cement and telecom. Today, over 50% of its revenues flow from overseas operations that span 36 countries in North and South America, Africa and Asia.The Group Data ‘n’ Analytics Cell (GDNA) is the Big Data and Analytics arm of the Aditya Birla Group created at its centre to strategize and partner with 18+ Group businesses across B2B and B2C domains to deliver on its strategic priorities through the power of AI. The company represents strong analytics and domain expertise drawn from the best-in-class talent from leading global and Indian businesses that leverage cutting edge tools and advanced AI algorithms built on a highly scalable and robust big data infrastructure to mine and act upon petabytes of structured and unstructured data.

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Natural Language Desiderata: Understanding, explaining and interpreting a model.

Article | April 7, 2020

Clear conceptualization, taxonomies, categories, criteria, properties when solving complex real-life contextualized problems is non-negotiable, a “must” to unveil the hidden potential of NPL impacting on the transparency of a model. It is common knowledge that many authors and researchers in the field of natural language processing (NLP) and machine learning (ML) are prone to use explainability and interpretability interchangeably, which from the start constitutes a fallacy. They do not mean the same, even when looking for a definition from different perspectives. A formal definition of what explanation, explainable, explainability mean can be traced to social science, psychology, hermeneutics, philosophy, physics and biology. In The Nature of Explanation, Craik (1967:7) states that “explanations are not purely subjective things; they win general approval or have to be withdrawn in the face of evidence or criticism.” Moreover, the power of explanation means the power of insight and anticipation and why one explanation is satisfactory involves a prior question why any explanation at all should be satisfactory or in machine learning terminology how a model is performant in different contextual situations. Besides its utilitarian value, that impulse to resolve a problem whether or not (in the end) there is a practical application and which will be verified or disapproved in the course of time, explanations should be “meaningful”. We come across explanations every day. Perhaps the most common are reason-giving ones. Before advancing in the realm of ExNLP, it is crucial to conceptualize what constitutes an explanation. Miller (2017) considered explanations as “social interactions between the explainer and explainee”, therefore the social context has a significant impact in the actual content of an explanation. Explanations in general terms, seek to answer the why type of question. There is a need for justification. According to Bengtsson (2003) “we will accept an explanation when we feel satisfied that the explanans reaches what we already hold to be true of the explanandum”, (being the explanandum a statement that describes the phenomenon to be explained (it is a description, not the phenomenon itself) and the explanan at least two sets of statements, used for the purpose of elucidating the phenomenon). In discourse theory (my approach), it is important to highlight that there is a correlation between understanding and explanation, first and foremost. Both are articulated although they belong to different paradigmatic fields. This dichotomous pair is perceived as a duality, which represents an irreducible form of intelligibility. When there are observable external facts subject to empirical validation, systematicity, subordination to hypothetic procedures then we can say that we explain. An explanation is inscribed in the analytical domain, the realm of rules, laws and structures. When we explain we display propositions and meaning. But we do not explain in a vacuum. The contextual situation permeates the content of an explanation, in other words, explanation is an epistemic activity: it can only relate things described or conceptualized in a certain way. Explanations are answers to questions in the form: why fact, which most authors agree upon. Understanding can mean a number of things in different contexts. According to Ricoeur “understanding precedes, accompanies and swathes an explanation, and an explanation analytically develops understanding.” Following this line of thought, when we understand we grasp or perceive the chain of partial senses as a whole in a single act of synthesis. Originally, belonging to the field of the so-called human science, then, understanding refers to a circular process and it is directed to the intentional unit of discourse whereas an explanation is oriented to the analytical structure of a discourse. Now, to ground any discussion on what interpretation is, it is crucial to highlight that the concept of interpretation opposes the concept of explanation. They cannot be used interchangeably. If considered as a unit, they composed what is called une combinaison éprouvé (a contrasted dichotomy). Besides, in dissecting both definitions we will see that the agent that performs the explanation differs from the one that produce the interpretation. At present there is a challenge of defining—and evaluating—what constitutes a quality interpretation. Linguistically speaking, “interpretation” is the complete process that encompasses understanding and explanation. It is true that there is more than one way to interprete an explanation (and then, an explanation of a prediction) but it is also true that there is a limited number of possible explanations if not a unique one since they are contextualized. And it is also true that an interpretation must not only be plausible, but more plausible than another interpretation. Of course there are certain criteria to solve this conflict. And to prove that an interpretation is more plausible based on an explanation or the knowledge could be related to the logic of validation rather than to the logic of subjective probability. Narrowing it down How are these concepts transferred from theory to praxis? What is the importance of the "interpretability" of an explainable model? What do we call a "good" explainable model? What constitutes a "good explanation"? These are some of the many questions that researchers from both academia and industry are still trying to answer. In the realm on machine learning current approaches conceptualize interpretation in a rather ad-hoc manner, motivated by practical use cases and applications. Some suggest model interpretability as a remedy, but only a few are able to articulate precisely what interpretability means or why it is important. Hence more, most in the research community and industry use this term as synonym of explainability, which is certainly not. They are not overlapping terms. Needless to say, in most cases technical descriptions of interpretable models are diverse and occasionally discordant. A model is better interpretable than another model if its decisions are easier for a human to comprehend than decisions from the other model (Molnar, 2021). For a model to be interpretable (being interpretable the quality of the model), the information conferred by an interpretation may be useful. Thus, one purpose of interpretations may be to convey useful information of any kind. In Molnar’s words the higher the interpretability of a machine learning model, the easier it is for someone to comprehend why certain decisions or predictions have been made.” I will make an observation here and add “the higher the interpretability of an explainable machine learning model”. Luo et. al. (2021) defines “interpretability as ‘the ability [of a model] to explain or to present [its predictions] in understandable terms to a human.” Notice that in this definition the author includes “understanding” as part of the definition, giving the idea of completeness. Thus, the triadic closure explanation-understanding-interpretation is fulfilled, in which the explainer and interpretant (the agents) belong to different instances and where interpretation allows the extraction and formation of additional knowledge captured by the explainable model. Now are the models inherently interpretable? Well, it is more a matter of selecting the methods of achieving interpretability: by (a) interpreting existing models via post-hoc techniques, or (b) designing inherently interpretable models, which claim to provide more faithful interpretations than post-hoc interpretation of blackbox models. The difference also lies in the agency –like I said before– , and how in one case interpretation may affect the explanation process, that is model’s inner working or just include natural language explanations of learned representations or models.

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MSys Technologies LLC

MSys Technologies is an innovator in offering technology services and domain-specific test automation software (for both desktop and mobile apps). MSys’s unparalleled focus and deep technical expertise ensures the best value in the areas of storage, virtualization, telecommunications, servers, DR/BC, test automation, mobile test automation, etc…

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