Behind the Scenes : The Brain by Rithwik Ramesh

 The human brain is a fascinating thing indeed. It determines our thoughts, actions, behavior and emotions. The brain helps us analyse and interact with the external world in the form of thoughts and feelings. So, the question has to be asked. How exactly is the brain, an organ that takes up only 2% of our body weight, able to comprehend human-environment interactions? A clear and concise understanding about the functioning of the human brain seems to be one of the most demanding tasks faced by humanity.

Fortunately, the field of information and technology has progressed at such a rate that nothing seems out of reach anymore. Computational neuroscience is the field of study in which mathematical tools and theories are used to determine the functioning of the Human brain. Theory and computational modeling play a vital role in tackling this challenge. The challenges being- First, massive data from neurons demand out of the box thinking to analyse. Second, the brain systems are too complex to comprehend by experiments and intuition alone. Neuroscience is an incredibly vast domain, but remains, without question, the best approach to understand the inner workings of the brain.

Machine learning has been increasingly used in data analysis and computational modeling in brain research. The current framework of artificial intelligence has been largely limited to input–output mappings such as object recognition or language translation. Discovering the working mechanism of cognitive functions such as multi-tasking and creativity, when translated into mathematical algorithms, will influence the coming generation of Information exchange and technology.

I seek answers by Namrata Kaul

 Hello, my name is John. I am a robot. The creator created me to serve mankind in a way which is humanly impossible. I can do surgery, lift weights, rescue people stuck in floods, ice, fire, sea and many more. But there are some answers that I seek. I am not a human, I’m supposed to do inhuman task which others can’t do, but what if I don’t know how to do a task that is assigned to me? What if because of me innocent people die? I don’t know what is grief, joy, pain or hope. I am deprived of human sentiments and instincts but does it make my judgement better or worse? I am here to share a story; you tell me what you would have done or what you wouldn’t have if you were me. When India was hit by the second wave of Covid-19, thousands and thousands of people were getting affected and dying every day. There was scarcity of clinical oxygen, ventilators, doctors, medical stuffs and hospital beds all over the country. During this time, I was sent to a remote village of Karnataka. In that village, there was only one medical center and in that medical center there was only one doctor, one nurse and 50 hospital beds. The population of the village was more than 500. And people coming to the medical center with breathing issues are 20, 25 or 30 per day. By the time I reached there with oxygen all other medical supplies, almost 150 patients were sitting or laying in the floors or beds of the medical center, and the village had witnessed more than 50 casualties of covid. I built single handedly a medical camp with 50 beds and was assigned the task to shift critical patients to that camp and connect them with oxygen, while the doctor and nurse were attending newly arrived villagers with covid symptoms. I started at the end of the ward and were carrying patients to the medical camp who have SpO2 below 70 and who immediately need oxygen or ventilator. I transported 49 patients like that. But when I came back to take the last one, I saw, there were three critical patients with SpO2 below 70 and immediate need of oxygen. But only one spot was left at the medical center and only one person could have oxygen. Three patients were 

Ruma, 10-year-old girl, malnourished, SpO2 68-69, fever 102, left lung inflammation, SpO2 decreasing from yesterday. 

Manoj, 65-year-old man, diabetic, SpO2 65-67, fever 101, both lungs affected, breathing hysterically, needs immediate oxygen or he will die. 

Ramalingam, 40-year-old man, only son of village head (it was written in his medical record I didn’t understand why), heavy smoker, SpO2 65-67, fever 100, lying unconscious, both lungs affected, came to the center just this morning. 

I was told to shift the critical patients to the medical camp and only one spot was left and I had to choose one among these three. I chose Manoj, because according to my judgement he was the most critical patient. I shifted him to the medical camp and gave him oxygen. By end of that day, all three died. Ruma died within 1 and half hours, Ramalingam died after her, and at night Manoj also took his last breath. I couldn’t save any of them, I was told my judgement was wrong. I was told I chose the wrong patient. 

Can you tell me who would have been the right and why? I seek answers

WHAT WILL THE WORLD BE LIKE IN THE YEAR 3000?

by Shridevi Angadi, Mainak Chandra 

 We certainly cannot predict what is going to happen but we can make educated predictions based on our understanding of how evolution works. A lot of scientists and top researchers have focused much on the future. Their predictions give insights of what people should expect in the coming years. The world is dynamic and things are changing from old ways to new perspectives of life. Technology plays a major role in advancing human life making things easier. The world will be very different from what we all see now. Many scientists have predicted that the climate will worsen in most parts of the world where global warming seems to be caused by human activities. Most of their estimations are that the average temperature may rise above normal. The temperature may rise within the range of 1 to 5 degrees celsius, which may pose deadly consequences for humans. Other natural resources such as oil are expected to reduce in quantity. Humanity will run even out of renewable sources of energy. Technology will be more advanced and is visible in various ways having brilliant ideas that are due for implementation in the future. Most of the scientists tried to create life over decades. There is a prediction that some humans will possess more artificial characteristics rather than biological ones. Human robots will be used to enforce security and would have the ability to feel and coordinate with real people. Their programs are meant to be much help during a crisis or in war-torn areas. Let's just enter an imaginary world of the year 3000 - “Oh wait, What am I seeing? Is this even real? I can see humans adapted to this new life and just leading their life perfectly. They are so perfect, so accurate in their work. Wait! I doubt whether they are humans? Aren’t they humans? Damn! These are robots! Human invention! They look and function like humans. Now it's difficult for me to differentiate between humans and robots. I am wondering, where has this technology brought us today? What an excellent invention AI has given us. It’s beautiful”. Now talking about the future again, there will be more roads to accommodate the growing number of people with cars. The humans will have an average height of 200cm. The life span will increase to 120 years allowing humans to live beyond the current lifespan. Humanity will be advancing towards a brown skin tone and their nutrition will be improved by having a well understanding of the human body. The most important thing is that the year 3000 is predicted to attain the expected level of gender equality. Every gender will be treated equally regardless of age or race. The world will be evolving to a more comfortable life as everyone will receive fair treatment with respect and dignity. The next generation should expect the worst regarding climate and renewable energy. The year 3000 is seen as a year of preference. Technology will also rise to a greater extent making the world a better place. -

AI IN PRODUCT OPTIMIZATION by Akshay C Gopal

 Product optimization is a common problem in many industries. In our context, optimization is any act, process, or methodology that makes something — such as a design, system, or decision — as good, functional, or effective as possible. Decision processes for minimal cost, best quality, performance, and energy consumption are examples of such optimization. Currently, the industry focuses primarily on digitalization and analytics. This focus is fueled by the vast amounts of data that are accumulated from up to thousands of sensors every day, even on a single production facility. Until recently, the utilization of these data was limited due to limitations in competence and the lack of necessary technology and data pipelines for collecting data from sensors and systems for further analysis. Within the context of the oil and gas industry, production optimization is essentially “production control”: You minimize, maximize, or target the production of oil, gas, and perhaps water. Your goal might be to maximize the production of oil while minimizing the water production. Or it might be to run oil production and gas-oilratio (GOR) to specified set-points to maintain the desired reservoir conditions. In most cases today, the daily production optimization is performed by the operators controlling the production facility offshore. This optimization is a highly complex task where a large number of controllable parameters all affect the production in some way or other. Somewhere in the order of 100 different control parameters must be adjusted to find the best combination of all the variables. Consider the very simplified optimization problem illustrated in the figure below. The fact that the algorithms learn from experience, in principle resembles the way operators learn to control the process. However, unlike a human operator, the machine learning algorithms have no problems analyzing the full historical datasets for hundreds of sensors over a period of several years. They can accumulate unlimited experience compared to a human brain. A machine learning-based optimization algorithm can run on real-time data streaming from the production facility, providing recommendations to the operators when it identifies a potential for improved production. A typical actionable output from the algorithm is indicated in the figure above: recommendations to adjust some controller set-points and valve openings. It also estimates the potential increase in production rate, which in this case was approximately 2 %. This machine learning-based optimization algorithm can serve as a support tool for the operators controlling the process, helping them make more informed decisions in order to maximize production.Fully autonomous operation of production facilities is still some way into the future. Until then, machine learning-based support tools can provide a substantial impact on how production optimization is performed

A TALE OF NLP MIX-UP by Anjali Ann Joseph

Grammarly has been my savior tool as a content writer, and it’s become hard to sometimes proofread content without it. It does not just help me write confident pieces but makes sure my content has clarity and engagement. A few days back, while proofreading an article me and my co-worker came across a misunderstanding. For sentences that were formed in write grammatical form, our dear Grammarly kept giving us errors and issues, and it went on to an extent where we ended up taking help from our English professors. As a Data Science student, it was easy for me to understand the misinterpretation of the tool, but my co-worker found it challenging. After dismissing many recommendations from Grammarly and proofreading the article ourselves, we realized that AI does make errors.

AI processing might be smart, easy, fast, and intelligent, but at the end of the day, it’s a creation of human intelligence, and a human is no God. Natural Language processing has made machines capable of understanding the human language and building systems that can make sense of the text and automatically perform tasks like translation, spell check, or topic classification. Still, the authentic interpretation which happens during a human to human communication can never be genuinely imitated by a machine. It’s not the backlog of NLP, but put Artificial Intelligence is artificial never real.

AI Artwork by MSc Data Science

 

Artwork by Bandi Jaswanth, Oct 2021

Artword By Preeti Sharma

Evolution

Artwork by Rumal Ragsania ,  Oct 2021

Artwork by Shilpa Thomas, Oct 2021

Orientation for Masters in Comp Sc and Masters in Statistics by Jibrael Jos

Leadership
Data Science
Mayuri, Rupal, Alex, Thejus, Ashrita, Reshma
Akhilandeshwari, Angshuman, Karunya (and many more)
MCA
Karen, Udit, Rohan, Anwesha, Lynford
MSc Comp Sc
Paridhi, Dev, Nayanika, Dhirendra, Mansi, Mansoor
MSc Statistics
Ronit,Ruchika,Tanya,Garima,Jayashree (and many more)

Talent/Enthusiasm
MSc Data Science
Yosha, Gopika, Roshini, Chaitra, Viola, Losel, Abhijith, Badri, Sandra, Andrea
MSc Statistics
Garima, Bhagya, Utkarsh, Prakhar
MCA
Adriel, Ashly, Ishita
MSc Comp Sc
Laxmy, Swarnava, Vaishnavi, Yashaswani, Bishal, Paridhi,Shravya

Change they would like to bring

 Areas to Excel

Artwork

Class Goals

Collages