In breakthrough, neural network ‘explains’ how it found new antibiotic

In breakthrough, neural network ‘explains’ how it found new antibiotic

Subject: Science and tech

Section: Awareness in IT and computers

Context:

• Scientists proposed the first artificial neural network, a technology that later led to the birth of deep-learning and artificially intelligent systems like ChatGPT, discovered streptomycin, the world’s first aminoglycoside antibiotic. It would soon revolutionise the treatment of life-threatening diseases like tuberculosis.

More about News: 

• A groundbreaking study in Nature revealed a symbiotic relationship between deep learning and antibiotics.
• Scientists employed deep learning algorithms to unearth a novel class of antibiotics, revolutionizing drug discovery.
• The findings hold promise in combating antibiotic-resistant pathogens like methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE), offering a ray of hope amid a global health crisis. While experts applaud the study’s ingenuity, concerns linger regarding the transparency of deep learning models.
• Implicit integration of explainability could enhance trust and understanding, guiding future breakthroughs.

Significance: 

• As the nexus between artificial intelligence and antibiotics evolves, researchers navigate uncharted territories, driven by the shared quest for innovation and medical advancement.

About Deep Learning, Neural Networks and Machine Learning

• Deep learning, a subset of machine learning, has revolutionized the way machines process and interpret data. It teaches computers to do what comes naturally to humans; for example, in the case of Self-Driving cars, recognizing the signals to stop or go. Deep Learning involves the use of neural networks with multiple layers (three or more layers) to simulate human brain functions.
• All artificial neural networks are made of artificial ‘neurons’. These are algorithms that receive an input, perform a computation, and relay the output.

Machine Learning Vs Deep Learning 

• Machine learning employs a simple structure of neural networks with a limited number of layers, while deep learning uses a deep, hierarchical structure with multiple layers to capture complex relationships in the data.
• The Machine Learning though, is quick and easy to set up but may have limitations in effectiveness. Deep Learning takes more time to set up but gives immediate and effective results.
• Deep learning is well-suited for tasks involving image and speech recognition, while machine learning is used in tasks to recommend items on different shopping websites like Amazon, Flipkart, etc.

Applications of Deep Learning

• Image Recognition: It classifies images, clusters them by similarities, and performs object recognition within scenes.
• Law Enforcement: Deep learning algorithms are important in analyzing transactional data to detect patterns indicative of fraudulent or criminal activity. For example, speech recognition like Siri or Alexa and computer vision technologies
• Financial Services: Financial institutions leverage predictive analytics powered by deep learning to inform algorithmic trading, assess business risks for loan approvals, detect fraud, and manage credit and investment portfolios.
• Customer Service: Chatbots and virtual assistants like Slush, Maya, etc., employing deep learning technology, enhance customer service experiences. They utilize natural language processing and speech recognition to engage with users in a personalized manner, significantly improving the efficiency of customer support.
• Healthcare: Deep learning has found extensive applications in healthcare, particularly in medical imaging detecting disease from X-ray images and classifying them into several disease types in radiology. It aids specialists in interpreting a large volume of images in less time, thereby improving diagnostic accuracy.
• Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells. Teams at UCLA built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells.
• Education: It enables adaptive learning platforms that analyze individual student performance and edit content to suit their needs.
• Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops.