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Bard vs ChatGPT, and the concerns with both AI chatbots

GS-3: Awareness in the fields of IT, Space, Computers, robotics, nano-technology, bio-technology and issues relating to intellectual property rights.


Google is developing a chatbot called Bard to compete with OpenAI'sChatGPT


The announcement intensifies the competition in the generative AI race


Artificial Intelligence (AI)

  • It refers to the development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation
  • AI systems can be trained to perform these tasks by being fed large amounts of data and using that information to improve their accuracy over time. 
  • The goal of AI is to create systems that can mimic or surpass human intelligence in various ways.


Requirements for Generating Artificial Intelligence

  • Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. 
  • These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.
  • The system requirements for AI depend on the specific application and the complexity of the task it is being used for. 
  • For example, simple AI tasks such as image classification can be done using a standard personal computer with a GPU, while more complex tasks such as training large language models may require a high-performance computing system with multiple GPUs.
  • Other factors that influence the system requirements for AI include the size of the dataset, the desired accuracy, and the computational resources required for training and inference. 
  • In general, the more data, accuracy, and computational resources, the better the AI system will perform.


How AI Works?

  • AI works by enabling machines to perform tasks that typically require human intelligence, such as perception, decision making, and language understanding.
  • There are several approaches to building AI systems, including:
  • Machine Learning: This is the most common approach to building AI systems, where the machine is trained on large amounts of data to learn how to perform a task. The machine uses algorithms to identify patterns in the data and make predictions based on those patterns.
  • Deep Learning: This is a subfield of machine learning that uses artificial neuralnetworks, which are modeled after the human brain, to perform tasks such as image and speech recognition.
  • Rule-based Systems: In this approach, the AI system is programmed with a set of rules to follow when making decisions. The rules are based on expert knowledge and are used to make decisions in specific situations.
  • Evolutionary Algorithms: This approach uses the principles of natural selection to generate solutions to problems. The AI system generates a population of potential solutions, and the best ones are selected to form the next generation.
  • The specific AI system used will depend on the task it is being used for and the desired outcome. 
  • Regardless of the approach used, AI systems typically involve the use of algorithms and statistical models to make decisions based on data inputs.


Branches of Artificial Intelligence (AI)

  • Machine Learning: This is the process of teaching a machine to make predictions or decisions without being explicitly programmed. 
  • Machine learning algorithms learn from data and use statistical analysis to find patterns and make decisions
  • There are several types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
  • Deep Learning: This is a subfield of machine learning that uses deep neural networks, which are composed of many layers, to learn complex representations of data. 
  • Deep learning algorithms are particularly well-suited to tasks involving large amounts of unstructured data, such as image and speech recognition.
  • Natural Language Processing (NLP): Itdeals with the interactions between computers and humans using natural language. 
  • NLP techniques are used in a variety of applications, such as language translation, sentiment analysis, and chatbots.
  • Computer Vision:It deals with the ability of computers to interpret and understand visual information from the world. 
  • Computer vision techniques are used in applications such as image classification, object detection, and face recognition.
  • Robotics: It deals with the design and development of robots that can perform tasks autonomously or with minimal human supervision.
  • Robotics combines AI, engineering, and computer science to create machines that can perform physical tasks in the real world.


Application of AI

  • Healthcare: To improve patient care, diagnose diseases, and develop new treatments
  • For example, AI algorithms can be used to analyze medical images and make more accurate diagnoses, or to analyze electronic health records to identify patients at risk for certain conditions.
  • Finance: To improve risk management, detect fraud, and make better investment decisions
  • For example, AI algorithms can be used to analyze large amounts of financial data and make predictions about market trends.
  • Retail: To improve the shopping experience, personalize recommendations, and increase efficiency.
  • For example, AI algorithms can be used to analyze customer behavior and make personalized product recommendations, or to optimize supply chain management.
  • Manufacturing: To improve quality control, increase efficiency, and reduce waste
  • For example, AI algorithms can be used to monitor production processes and detect defects, or to optimize the scheduling of resources.
  • Transportation: To improve safety, reduce congestion, and increase efficiency
  • For example, AI algorithms can be used to optimize routing for delivery trucks, or to develop self-driving cars.



  • Increased Efficiency: AI algorithms can process vast amounts of data much faster than humans and make decisions based on that data with a high degree of accuracy.
  • Improved Accuracy: AI systems can perform repetitive tasks with a high degree of accuracy and consistency, reducing the risk of human error.
  • Enhanced Customer Experience: AI systems can provide personalized experiences and recommendations to customers, improving the customer experience and increasing customer satisfaction.
  • New Insights and DiscoveIncreased Efficiency: AI algorithms can process vast amounts of data much faster than humans and make decisions based on that ries: AI systems can analyze large amounts of data and identify patterns that may not be immediately obvious to humans, leading to new insights and discoveries in various fields.
  • Cost Savings: AI systems can automate repetitive and time-consuming tasks, freeing up human workers to focus on more value-added activities and reducing labor costs.
  • 24/7 Availability: AI systems can work around the clock, without breaks or downtime, improving response times and increasing overall productivity.



  • Job Loss: AI systems can automate many tasks that were previously performed by humans, leading to job losses and increased unemployment.
  • Bias and Discrimination: AI systems can perpetuate and even amplify existing biases and discrimination if the data used to train the algorithms contains such biases.
  • Lack of Human Touch: AI systems can lack empathy and the human touch, which is essential in certain jobs and industries, such as healthcare and customer service.
  • Security and Privacy Concerns: AI systems can be vulnerable to cyber-attacks and may pose a risk to privacy if personal data is collected and stored by these systems.
  • High Implementation Costs: Implementing AI systems can be expensive, particularly for small and medium-sized businesses.
  • Lack of Regulation: There is currently a lack of regulation surrounding the development and deployment of AI systems, which can lead to ethical concerns and unintended consequences.
  • Dependence on Technology: Society may become too dependent on AI systems, leading to a loss of critical thinking skills and a decline in creativity.


What are the concerns with the increasing competition to build AI-based generative chatbots?

  • Text generation software from Google and OpenAI, while impressive, can contain inaccuracies and perpetuate hate speech, racial and gender biases, and stereotypes, due to the software's ability to search the Internet. 
  • OpenAI builds ChatGPT in public, while Google has taken a more cautious approach for Bard, as more is at stake for the corporation. 
  • In 2020, Google AI researchers raised concerns about text generation technology, leading to tension and the firing of two prominent researchers.


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Fact File

NASA-ISRO partnership’s satellite all set to arrive in India: What is NISAR and its mission?

  • NASA and ISRO jointly developed the NISAREarth-observation satellite. It was sent off from Jet Propulsion Laboratory (JPL) in California on February 3and will be shipped to India for a possible launch in 2024 from Satish Dhawan Space Centre in Andhra Pradesh.


  • NISAR is a joint project of NASA and ISRO. 
  • It weighs 2,800 kg and features both L-band and S-band Synthetic Aperture Radar (SAR) instruments. 
  • NASA provided L-band radar, GPS, data recorder, and data subsystem, while ISRO contributed S-band radar, GSLV launch system, and spacecraft. 
  • The satellite is equipped with a large 39-foot gold-plated wire mesh antenna reflector, which helps focus the radar signals.

Significance of NISAR Mission

  • Natural Disaster Monitoring:Todetect warning signs of volcanic eruptions, earthquakes, and landslides for improved response and preparedness.
  • Groundwater Level Tracking: For better water management and resource planning.
  • Glacier and Ice Sheet Monitoring:Measures the flow rates of glaciers and ice sheets to track polar region changes and their impact on sea levels.
  • Agricultural Mapping:To provide valuable data for farmers and policymakers.
  • Forest Monitoring:To track changes in carbon exchange and the effects of deforestation.
  • Coastal Monitoring:Monitors changes in the coastline for data on erosion and other coastal hazards.
  • High-Resolution Imaging:Uses SAR to produce detailed high-resolution images of Earth's surfaces.
  • Global Coverage:Images the entire Earth in just 12 days with a 150-mile (240 km) imaging swath.
  • All-Weather Operation:Collects data day and night, through clouds and regardless of weather conditions.
  • Joint Collaboration: It is a joint effort between NASA and ISRO, with NASA using the L-band radar and ISRO using the S-band radar for a minimum of three and five years respectively.


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