Current genAI shows a contractionary picture. Consumer AI produces widely acceptable content and praised. Whereas Enterprise AI projects fail at the rate of 95%. Only area where genAI is useful in coding applications that too has issues when codebase size grows. We need to understand why Consumer AI works and Enterprise AI. With all the 100s of billions poured into Large Language Model (LLM) AI now called generative AI, why promised changes in businesses not seen even after 3

OpenSource search frameworks such as OpenSearch, Facebook AI Similarity Search (FAISS), Weaviate and Qdrant etc using vectors for similarity search. An improvement added via LLMs to create embeddings. Here vector means one dimensional numerical representation of data, embedding means multi-dimensional numerical representation of data. Though both are now interchangeably used, vectors are pre-LLM era usage. Let us see why vectors, embeddings are needed and why it still fails t

Context Graphs are needed for Enterprises to make better decisions and analyze data, that is being agreed upon by a lot of people. The question is what is context and how to create the context graphs from data. Context is generally understood as meaning or description which is not known explicitly but understood implicitly.  In Linguistics, there is a hypothesis called Distributional Hypothesis which states that the meaning of the word depends upon the surrounding words. This

​Before thinking about Context Graph, understanding what is a graph database, how it is different from relational databases is needed. Representing data in graphs with nodes and edges started in the 1960s. In the 2000s what we know as Graph databases came to life and started to be used in Enterprises. ​Let us see the difference between a relational database and graph database.  Relational databases define relationships in a structured way. A Person has name, job title, work l

Businesses still run on Spreadsheets. People find it easy to put the data and look at the data. Business executives spend time analyzing data manually or run queries to generate reports. That works for financial or numerical data.  What happens when the data is mixed or mostly text such as databases? Manual analysis was the only way. Yes it was the only way. AI aka LLMs tried to change that by generating summaries or answering questions. Does that solve the needs of the busin

NaturalText AI offers a natural way of solving Enterprise Search Problem. With the bonus of getting to know hidden info from human sight.  This is a continuation from the previous part. Take a look at the previous part .  Relevancy and Ranking are two metrics basis of the Enterprise Search solution. Given a user query/prompt, what information needs to be returned, how to sort the information. Simple as it seems to be, however, even arriving at a metric or benchmark for evalua

Since the time, Google's magic of finding relevant content in public data, the race for solving the ranking problem in private data aka Enterprise data is still going on.  There were various attempts to solve it. This article lists those failed attempts and how the latest RAG-e of trying the luck with LLMs won't solve the problem.  Search or Information Retrieval in the structured data is easy and enabled by softwares known as databases. There are various types of databases a

Diagnosis is a complex task. Before treatment can even begin, doctors spend hours on testing, reviewing reports, and consulting. Diagnosis, in fact, accounts for up to 90% of the cost and time spent on a given ailment. While the advent of advanced testing and imaging technologies has improved diagnostic accuracy and reduced time to diagnosis, making a definitive connection between a person’s symptom profile and a specific disease condition remains a complex task. Machine lear

Why does a Tech company get emails from Food experts? In this video founder Rajasankar Viswanathan talks about his experience with lead generation tools and the issues he faced. How do spam filters with basic Machine learning training can spot irrelevant mails for better? ​ Contact info at naturaltext dot com for a free trial

Building the list of potential le​ads, accounts and companies is the hardest task in the lead generation. NaturalText AI offers a new way to do that. Finding relevant information in a hosted database is hard. Enterprise search is hard because there is no way that information can be ranked. NaturalText AI offers a unique method to create connections between documents, rows of a database so that network graphs can be created, search results can be ranked. For example, in findin

genAI Pro subscription is just a scaffold as in the case of Web Search?   The low cost normal subscription produces hallucinations but if you pay a higher fee those hallucinations may be removed to get the valid links and references.   But what may happen in the background?  Is it actually a different AI for different prices or just the same AI but links and references are verified by automated scripting?  With the reference of "there is no self spelling wand", let us compare

Sentiment Analysis with NaturalText AI Sentiment Analysis as a way to understand customer feedback or gauging the market has three major issues  The issue with Results - Results show only numbers or scores i.e how many are happy or not happy etc But the businesses need why customers are not happy or happy. That can give ways to solve those issues.  The issue with Data - Data can come with various issues such as spam, unwanted feedback. Even though businesses need to understan

CEO Rajasankar Viswanathan spoke to Life Science Success with Don Davis, PhD about NaturalText and how it can help life sciences organizations.

Not too long ago, in 2015, a leading investor in Silicon Valley made headlines with the claim that AI would replace radiologists by 2020....

Can Twitter’s decision-making algorithm, which decides what tweets to show, really be open sourced? Many AI and other technical experts...