logo

Top-5 NLP news of December by a CTO of Sciforce — Max Ved

Published: January 19, 2023
# NLP

We are excited to announce that we are launching a new section today at Sciforce — “Top AI news of the month.” We decided to create this column to keep our valued clients aware of the latest news and technologies in the AI world. So, here, we will share some interesting information with you about SciTech!

Well, today we will discuss Top-5 NLP news in December:

  1. Marty Elisco, CEO of Augintel, states that NLP will become mainstream in 2023 in the Healthcare industry. Thanks to applications like interpreting or summarizing EHRs, the ROI from NLP implementation is already relatively high, and we absolutely agree with it. The NLP would be definitely useful in Healthcare, hospitals, and elderly care services as it helps to predict patient outcomes and generate diagnostic models that can detect chronic diseases at the early stages.
  2. To understand the direction we move better, let us provide you with some statistics: The Business Wire says that the artificial intelligence market size will grow from USD 86.9 billion in 2022 to USD 407.0 billion by 2027 at a Compound Annual Growth Rate (CAGR) of 36.2%. In the opinion of our CTO- Max Ved, the statistics are not as accurate as we expect them to be. He believes that the market size will expand even more. Just look at the technologies and projects that are announced on a daily basis: sentiment analysis, entity recognition, etc.
  3. Rishi Rajpopat, a student at the University of Cambridge, has solved a 2,500 years old Sanskrit problem with NLP. Taking into account our long history and broad interest in language learning we’re very excited to see such solutions for the tribe of Maya and Trypillian Cultures. By using AI, we can easily overcome the language barriers in the past and in the future.
  4. The ChatGPT model can recognise its mistakes! Actually, not every person is brave enough to admit their fault. This is the quality that is peculiar to really strong individuals. So, when we see the news that the ChatGPT model can recognize its mistake, we understand that progress is not something that will be unattainable in the future, progress is what people do every day.
  5. NLP use is approved for rare disease screening. According to a novel study published in the Journal of the Peripheral Nervous System, It may be a viable way of diagnosing and treating these conditions in the early stages. The AI can detect the predetermined symptoms grouped into 8 red flag categories. Such categories include family history, early autonomic dysfunction, gastrointestinal complaints, unexplained weight loss, etc.

At Sciforce, we provide comprehensive sci-tech solutions that we will announce in our blog!

What do you think about such news?

RELATED BLOG ARTICLES

How to Scale AI in Your Organization

According to WEKA's 2023 Global Trends in AI Report, 69% of organizations now have AI projects up and running, and 28% are using AI across their whole business. This shows a big move from just trying out AI to making it a key part of how companies operate and succeed. However, this is just the beginning as the major point is not to have only AI but to have it work to your benefit. Organizations have to address various challenges such as the collection of data, hiring the right skills, and fittin

# AI / ML
# Computer Vision
# NLP
Text Preprocessing for NLP and Machine Learning Tasks

As soon as you start working on a data science task you realize the dependence of your results on the data quality. The initial step — data preparation — of any data science project sets the basis for the effective performance of any sophisticated algorithm. In textual data science tasks, this means that any raw text needs to be carefully preprocessed before the algorithm can digest it. In the most general terms, we take some predetermined body of text and perform upon it some basic analysis and

# NLP
Biggest Open Problems in Natural Language Processing

The NLP domain reports great advances to the extent that a number of problems, such as part-of-speech tagging, are considered to be fully solved. At the same time, such tasks as text summarization or machine dialog systems are notoriously hard to crack and remain open for the past decades. However, if we look deeper into such tasks we’ll see that the problems behind them are rather similar and fall into two groups:

# NLP
View all Articles