Artificial intelligence (AI) is becoming a buzz word nowadays, used in all context. AI encompasses machine learning, deep learning, and other subdivisions, it is a branch of computer science that aims to replicate human cognitive functions in computer systems, in other words, it is intelligence encoded into machines or computers. To those who are not familiar with computer science, this may appear to be a threat, as though human intelligence could be replaced by computers.
AI can be powered by deep learning or machine learning, or by simpler rule-based processes. The most fundamental artificial intelligence isn’t as difficult as it sounds. It can be a computer that has been programmed to behave in a certain way or to produce certain results based on certain rules.
Mr. Harsha Kanuri a Data Scientist for financial institutions such as Bank of America and Wells Fargo, who develops Computer Vision and Natural Language Processing (NLP) tasks to create Enterprise Document Search Engines, as well as Scalable Distributed Machine Learning Systems.
While Speaking with NSH, Harsha articulated the difference between AI and ML. Artificial intelligence (AI) is a technology that allows a computer to mimic human behaviour. Machine learning is a subset of artificial intelligence along with deep learning. This allows a machine to learn from prior data without having to design it explicitly. The goal of AI is to create a clever computer system that can solve complicated problems in the same way that people can. The purpose of machine learning is to allow machines to learn from data and produce reliable results. In AI, we create intelligent computers that can execute any task in the same way that a human can. In machine learning, we use data to train machines on how to do a task and produce reliable results. The main subset of machine learning is deep learning. The scope of AI is extremely broad. The application of machine learning is limited. AI is attempting to develop an intelligent system capable of doing a variety of difficult jobs. Machine learning aims to develop machines that can only accomplish the tasks for which they have been specifically educated. The goal of the AI system is to increase the odds of success. The accuracy and patterns are the fundamental concerns of machine learning. Siri, customer service via chatbots, expert systems, online gaming, intelligent humanoid robots, and other AI-based applications are among the most common. The online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, and other implementations of machine learning are some of the most common.
Are Medical students aware of the potential applications and implications of AI in radiology and medicine in general?
The fear that artificial intelligence (AI) may one day substitute radiologists had a negative impact on medical students’ interest in radiography as a profession. When students are contemplating radiology as a lifetime career option, academic radiologists are encouraged to teach their pupils about AI and its potential influence. The fear of AI’s impact on radiology has a detrimental impact on our participants, hence engaging in AI education is highly suggested.
Do you think artificial intelligence should be included in medical training?
In the health-care field, AI is gaining traction. AI is now being tested in health care for faster and more accurate diagnosis, augmenting imaging, reducing errors due to human weariness, lowering medical expenses, assisting and replacing mundane, repetitive, and labor-intensive activities, minimally invasive surgery, and lowering death rates. By combining enormous volumes of data and augmenting their decision-making process, AI could aid clinicians in identifying diagnoses and prescribing treatments. Physicians, on the other hand, must be able to analyse the findings and express a recommendation to the patient. Furthermore, AI may have an impact by relieving physicians of the strain of daily chores. Speech recognition could aid in the replacement of keyboards for information entry and retrieval. Decision management can assist physicians in filtering through massive volumes of data and enabling them to make informed and relevant decisions. Finally, in order to alleviate the acute shortage of health-care professionals, virtual agents may be able to assist with some parts of patient care in the future, as well as serve as a trustworthy source of information for patients.
Most of the Developing and underdeveloped countries face a challenge on two fronts: First, AI’s social and economic benefits are accruing to a few countries, and second, most of the current efforts and narratives on the relationship between AI and climate impact are being driven by the developed West. What is your opinion on this?
Let me share my thoughts on this. Many of the challenges that developing countries confront can be solved by artificial intelligence (AI). It can not only outperform humans, but it can also learn and adapt as it goes. AI takes the information it gets and processes it as instructed, while also looking for ways to improve the process. Artificial intelligence advances as a result of more people using it. It can assist developing countries with Disaster Relief, Tutoring, Improving Crop Production, Improving Health Care, and Accelerating Economic Development, among other things.
But one thing to be concerned about and consider as a serious issue is that AI may widen the gap between rich and poor.
What is AGIi and how close are we to achieving it? So do you think it’s possible for us to replicate a human brain?
Simply put, Artificial General Intelligence (AGI) is a machine’s ability to perform any task that a human can. While an AI application may be as effective as a hundred trained humans in one task, it may lose to a five-year-old child in another. While an AI must be trained with massive amounts of training data in order to perform any function, humans can learn with far fewer learning experiences. Furthermore, humans — and, perhaps one day, agents with artificial general intelligence — can generalise better, allowing them to apply what they’ve learned from one experience to others. Artificial general intelligence agents will not only learn with less training material, but they will also transfer information from one domain to another. An AGI agent that has been trained to process one language via NLP, for example, may be able to acquire languages with comparable origins and grammar. Artificially intelligent systems will be able to learn in the same way that people do, greatly lowering training time while allowing the machine to acquire many areas of proficiency. Although it is theoretically possible to duplicate the operation of a human brain, this is currently not feasible. As a result, we are still a long way from reaching artificial general intelligence in terms of capacity. However, given the quick rate at which AI develops new capabilities, we may be approaching an inflection point where the AI research community surprises us by achieving artificial general intelligence. Experts anticipate that artificial intelligence will be fully developed by 2030.