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Obviously AI Introduces Natural Language Predictive Analytics Service

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The advantage of natural language processing can be seen when considering the following two statements: "Cloud computing insurance should be part of every service-level agreement," and, "A good SLA ensures an easier night's sleep -- even in the cloud." If a user relies on natural language processing for search, the program will recognize that cloud computing is an entity, that cloud is an abbreviated form of cloud computing and that SLA is an industry acronym for service-level agreement.


The key is a combination of natural language processing and AutoML-like capabilities that let you write a query down in standard English (you can skip the SQL), choose the data set and column(s) you want to target. Then the service performs some data cleansing to normalize the data, internally runs a handful of competing algorithms to select the best one, and then spin out the results. You can have the system provide an additional assist in recommending the best columns of data to use. As part of the process, the service can recommend the best columns to use based on which ones have the most impact on the results. They have benchmarked performance to crunch 10 million records and produce a prediction report within 30 seconds.



Obviously AI introduces natural language predictive analytics service

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Clearly, Obviously AI is not the only provider that is offering natural language query. For instance, Tableau's Ask Data provides a similar natural language query service. So does Power BI. But neither of them go as far as Obviously AI and extend the process to optimizing and running predictive machine learning models.


With the emergence of natural language query, guided analytics, and AutoML services, it was bound to be a matter of time before someone put the pieces together to deliver an automated service that went from query to prediction.


We analyzed only use cases for which generative AI could deliver a significant improvement in the outputs that drive key value. In particular, our estimates of the primary value the technology could unlock do not include use cases for which the sole benefit would be its ability to use natural language. For example, natural-language capabilities would be the key driver of value in a customer service use case but not in a use case optimizing a logistics network, where value primarily arises from quantitative analysis.


Introducing Natural Language Processing is part one of the Text Analytics with Python professional certificate (or you can study it as a stand-alone course). This first course introduces the core techniques of natural language processing (NLP) and computational linguistics. But we introduce these techniques from data science alongside the cognitive science that makes them possible.


In the form of chatbots, natural language processing can take some of the weight off customer service teams, promptly responding to online queries and redirecting customers when needed. NLP can also analyze customer surveys and feedback, allowing teams to gather timely intel on how customers feel about a brand and steps they can take to improve customer sentiment.


While NLP-powered chatbots and callbots are most common in customer service contexts, companies have also relied on natural language processing to power virtual assistants. These assistants are a form of conversational AI that can carry on more sophisticated discussions. And if NLP is unable to resolve an issue, it can connect a customer with the appropriate personnel.


AI/ML capabilities are transforming the financial sector.3 AI/ML systems are reshaping client experiences, including communication with financial service providers (for example, chat bots), investing (for example, robo-advisor), borrowing (for example, automated mortgage underwriting), and identity verification (for example, image recognition). They are also transforming the operations of financial institutions, providing significant cost savings by automating processes, using predictive analytics for better product offerings, and providing more effective risk and fraud management processes and regulatory compliance. Finally, AI/ML systems provide central banks and prudential oversight authorities with new tools to improve systemic risk surveillance and strengthen prudential oversight.


Heterogeneity of data is another challenge in big data analysis. The huge size and highly heterogeneous nature of big data in healthcare renders it relatively less informative using the conventional technologies. The most common platforms for operating the software framework that assists big data analysis are high power computing clusters accessed via grid computing infrastructures. Cloud computing is such a system that has virtualized storage technologies and provides reliable services. It offers high reliability, scalability and autonomy along with ubiquitous access, dynamic resource discovery and composability. Such platforms can act as a receiver of data from the ubiquitous sensors, as a computer to analyze and interpret the data, as well as providing the user with easy to understand web-based visualization. In IoT, the big data processing and analytics can be performed closer to data source using the services of mobile edge computing cloudlets and fog computing. Advanced algorithms are required to implement ML and AI approaches for big data analysis on computing clusters. A programming language suitable for working on big data (e.g. Python, R or other languages) could be used to write such algorithms or software. Therefore, a good knowledge of biology and IT is required to handle the big data from biomedical research. Such a combination of both the trades usually fits for bioinformaticians. The most common among various platforms used for working with big data include Hadoop and Apache Spark. We briefly introduce these platforms below.






Over the past decade, AWS has witnessed a digital transformation in health, with organizations capturing huge volumes of patient information every day. But this data is often unstructured and the process to extract this information is labor-intensive and error-prone. Amazon Comprehend Medical is a HIPAA-eligible natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text, such as prescriptions, procedures, or diagnoses. Amazon Comprehend Medical can help you extract information from unstructured medical text accurately and quickly with medical ontologies like ICD-10-CM, RxNorm, and SNOMED CT and in turn accelerate insurance claim processing, improve population health, and accelerate pharmacovigilance.


TensorFlow is one of many deep learning frameworks available to researchers and developers to enhance their applications with machine learning. AWS provides broad support for TensorFlow, enabling customers to develop and serve their own models across computer vision, natural language processing, speech translation, and more. You can get started with TensorFlow on AWS using Amazon SageMaker, a fully managed ML service that makes it easy and cost-effective to build, train, and deploy TensorFlow models at scale. If you prefer to manage the infrastructure yourself, you can use the AWS Deep Learning AMIs or the AWS Deep Learning Containers, which come built from source and optimized for performance with the latest version of TensorFlow to quickly deploy custom ML environments.


Amazon Transcribe Call Analytics is an AI-powered API that provides rich call transcripts and actionable conversation insights that you can add into their call applications to improve customer experience and agent productivity. It combines powerful speech-to-text and custom natural language processing (NLP) models that are trained specifically to understand customer care and outbound sales calls. As a part of AWS Contact Center Intelligence (CCI) solutions, this API is contact center agnostic and makes it easy for customers and ISVs to add call analytics capabilities into their applications.


Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Neural machine translation is a form of language translation automation that uses deep learning models to deliver more accurate and more natural sounding translation than traditional statistical and rule-based translation algorithms. Amazon Translate allows you to localize content such as websites and applications for your diverse users, easily translate large volumes of text for analysis, and efficiently enable cross-lingual communication between users.


Obviously AI's no-code tool allows users to upload their datasets from CSV, databases, or CRMs to summon a Google-like search bar in which to ask a question in natural language. For predictive questions, such as "Which customers are likely to cancel their subscriptions?" the platform will understand what the user is asking, find the right data, and build a machine learning algorithm on the fly, the company said. It also shows users what factors drove the results. The platform was also designed to answer analytical questions that look for existing patterns in data, such as "What is the average daily foot traffic for my retail stores?" Users do not need any familiarity with writing SQL queries or working with programming languages to code regressions, neural networks, and other ML algorithms, the company said.


Obviously AI was founded on the belief that business users should be able to get insights from their data, without waiting on an engineer. Our mission is to make data science effortless by enabling anyone to run complex data predictions and analytics, simply by asking questions in natural language with no code, no hassle and no waiting. It is the only platform to use simple Natural Language questions for data prediction, delivering predictions and analytics in less than a minute. For more information, visit obviously.ai.


Natural language capabilities are being integrated into data analysis workflows as more BI vendors offer a natural language interface to data visualizations. One example is smarter visual encodings, offering up the best visualization for the right task based on the semantics of the data. This opens up more opportunities for people to explore their data using natural language statements or question fragments made up of several keywords that can be interpreted and assigned a meaning. Applying language to investigate data not only enhances the level of accessibility, but lowers the barrier to analytics across organizations, beyond the expected community of analysts and software developers. To learn more about how natural language can help you better visualize and explore your data, check out this webinar.

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Obviously AI Introduces Natural Language Predictive Analytics Service Sherilyn Akim <akimsherilyn@gmail.com> - 2023-12-21 21:03 -0800

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