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
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.
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.
Obviously AI, founded in 2018 by Tapojit Debnath and Nirman Dave, helps non-technical business users in doing fast predictive analytics using data science. It claims to enable users to run complex data predictions and analytics by asking questions in natural language with no code, no specialised training, within a minute. The company has designed the platform to help SMBs as well as the small departments working in larger companies in sectors like banking, insurance, marketing, and gaming.
DataRobot is a platform that enables business analysts to build predictive analytics without knowledge of machine learning or programming. The platform uses automated machine learning (AutoML) to generate accurate predictive models in a short amount of time. DataRobot provides a user-friendly user interface for creating machine learning models. In just a few steps, a company can deploy a real-time predictive analytics service.
At the intersection of computational linguistics and artificial intelligence is where we find natural language processing. Very broadly, natural language processing (NLP) is a discipline which is interested in how human languages, and, to some extent, the humans who speak them, interact with technology. NLP is an interdisciplinary topic which has historically been the equal domain of artificial intelligence researchers and linguistics alike; perhaps obviously, those approaching the discipline from the linguistics side must get up to speed on technology, while those entering the discipline from the technology realm need to learn the linguistic concepts.
Narrow AI has a broader application in customer service. It supports customers by guiding them and answering any questions or requests throughout their journey. Additionally, it manifests in the form of customer support chatbots, customer self-service, machine learning to analyze customer data, natural language processing for voice recognition and support, and many other potential use cases.
One example of prebuilt AI might be a pretrained model that can be incorporated as is or used to provide a baseline for further custom training. Another example would be a cloud-based API service that can be called at will to process natural language in a desired fashion.
The ability to clearly communicate, both verbally and in writing, is essential in excellent customer service, especially if you are speaking to someone who has a different native language. Answers to your questions should be clear, concise, and in your natural tone of voice. 2ff7e9595c
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