NLP research notes
Sophia The Humanoid Robot Will Be Rolled Out This Year Potentially Replacing Workers
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You may have previously seen Sophia, the humanoid robot, in viral videos. Unlike Boston Dynamics, whose robots look like frighteningly fun Terminator-type machines that can dance, Sophia is eerily designed to look human. Hanson Robotics, the company behind Sophia, plans to mass produce robots this year to help people during the pandemic.
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According to Reuters, the Hong Kong-based company contends that “robotic solutions to the pandemic are not limited to healthcare, but could assist customers in industries such as retail and airlines too.”
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David Hanson, founder and chief executive of his eponymous company, said, “The world of Covid-19 is going to need more and more automation to keep people safe.” In light of Sophia “being so human-like,” Hanson claims, “That can be so useful during these times where people are terribly lonely and socially isolated.”
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Sophia was “turned on” back on Feb. 14, 2016. Hanson modeled the robot based on a combination of ancient Egyptian Queen Nefertiti, late famous Hollywood actress Audrey Hepburn and his wife.
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The robot’s internal architecture possesses sophisticated software, chat and artificial intelligence systems designed for general reasoning. Sophia is capable of imitating human gestures and facial expressions. She’s equipped to answer certain questions and engage in simple conversations. Cameras are embedded in Sophia's eyes, and along with computer algorithms, she’s able to see things. The humanoid robot can track faces, maintain eye contact and recognize people. Google’s Alphabet offers Sophia’s speech recognition technology. The robot is "designed to get smarter over time."
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Sophia, and other similar robots that Hanson’s developing, are designed to be “social.” The inventor believes that these AI-backed robots will serve as workers and companions for people residing in nursing homes and in other settings that require interactions with humans.
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In an interview with Sophia, she said that her artificial intelligence will help improve the lives of people, stating, “Social Robots like me can help take care of the sick or elderly in many corners of healthcare and medical uses.”
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In an unnerving conversation, when asked by a reporter if people should fear robots, Sophia said, “Someone said, ‘We have nothing to fear but fear itself.” She followed the statement up with, “What did he know?”
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There is an old adage that states, “Just because you can doesn’t mean you should.” The United States and other countries are confronting a job-loss crisis. It looks like layoffs and hiring freezes will continue until the vaccines are distributed, shot into arms and the results are deemed positive. Does it make sense to mass produce robots to take the jobs that people need to provide for their families?
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This type of “morals vs. commerce” dilemma will continue to play out for the foreseeable future, as technology is rapidly developing, causing disruptions in the job marketplace. Advocates for technology assert that new jobs will be created in this emerging trend. Others are fearful that they’ll lack the skills, education and knowledge to find a job or hold onto their positions in this new technological, robotic and AI economy.
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SOURCE:
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Sentiment Analysis- Lexicon Models vs Machine Learning
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Why are we even interested in Sentiment Analysis? Well, here’s situation to help you understand.
Basic Terminologies
subjective
objective
Polarity
pre-process
normalize
Precision
Recall
F1 Score
Sentiment Analysis using Lexicon Based Models
AFINN Lexicon
SentiWordNet
VADER
Classification of Sentiment with Supervised Learning
Text pre-processing and data normalization
Feature Engineering
Model Training, Prediction and Evaluation
Bag of Words Model- (BOW)
SVM model on BOW features
Term Frequency-Inverse Document Frequency (TF-IDF)
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https://github.com/AbhinandanRoul/Sentiment-Analysis--Lexicon-Models-vs-Machine-Learning
https://medium.com/nerd-for-tech/sentiment-analysis-lexicon-models-vs-machine-learning-b6e3af8fe746
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Sentiment Analysis Challenges
Sentiment or emotion analysis can be difficult in natural language processing simply because machines have to be trained to analyze and understand emotions as a human brain does. This is in addition to understanding the nuances of different languages.
https://www.repustate.com/blog/sentiment-analysis-challenges-with-solutions/
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