Artificial Intelligence and Machine Learning
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Content
- Intro
- Applications of Artificial Intelligence and Machine Learning
- Neural Networks
- Deep Learning
- Criticism of AI
- India: Promotion of Artificial Intelligence and Machine Learning
Intro
Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs which can complete tasks that typically require human intelligence.
- With the explosion of available data and expansion of computing capacity, the world is witnessing rapid advancements in AI, ML, and deep learning.
Machine learning is a science that involves development of self-learning algorithms. Machine learning uses statistics (mostly inferential statistics) to develop self-learning algorithm. It is a type of artificial intelligence.
Note: All Machine Learning is AI, but not all AI is machine learning
- For e.g., symbolic logic (rules engines, expert systems, and knowledge graphs) as well as evolutionary algorithms and Bayesian statistics could all be described as AI, and none of them are machine learning.
In Machine Learning the computer program should learn from experience “i.e., given data” such that the overall performance on doing a certain task increase.
- Input data
- Model Training
- Output
Applications of Artificial Intelligence and Machine Learning
- Advertisements, Online shopping suggestions etc.
- Spam filtering
- Search engines
- Fighting Black Money (e.g., Project Insight of India)
- Space Exploration (e.g., identifying exoplanets from pictures)
- Health Sector (e.g., identifying cancerous lumps, development of new medicines/molecules etc, early detection and prevention of diseases.)
- E.g., a Bengaluru based startup has developed a non-invasive, AI-enabled technology to screen for early signs of breast cancer.
- For COVID-19, AI enabled chatbot was used by MyGov for ensuring communications.
- Education (e.g., Personalized learning through adaptive tools; customizing professional development courses)
- Agriculture: AI enabled solutions for water-management, crop insurance, and pest control are also being developed. Technologies like image recognition, drones, and automated intelligent monitoring of irrigation systems can help farmers kill weeds more effectively, harvest better crops, and ensure higher yields.
- ICRISAT has developed an AI-power sowing app, which utilises weather models and data on local crop yield and rainfall to more accurately predict and advise local farmers on when they should plant their seeds
- Disaster Management: An AI-based flood forecasting system has been deployed in Bihar and is now being deployed throughout the country. It gives warnings 48 hours earlier about impending floods.
- Improve Ease of Doing Business
- Natural Language Processing (NLP)
- Image Processing (Facial Recognition)
A) Neural Networks
Neural network, also known as Artificial Neural Network (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way biological neurons signal to each other.
A neural network can fine tune its output based on the feedback given to it during stages of training.
ANNs consist of node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neurons, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along the next layer of the network.
Note: ANN also rely on training data to learn and improve their accuracy over time.
Neural Networks vs. Deep Learning:
Terms are sometimes used interchangeably. ‘Deep’ in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers – which would be inclusive of the inputs and output – can be considered a deep learning algorithm. A neural network that only has two or three layers is just a basic neural network.
B) Deep Learning
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. It can achieve state of art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architecture that contain many layers.
Most deep learning methods use neural network architecture, which is why deep learning models are often referred as Deep Neural networks. The term deep usually refers to number of hidden layers in the neural network.
Where is it being used today?
- Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents
- Aerospace and Defence: Deep learning are used to identify objects from satellites that locate areas of interest and identify safe or unsafe zones for troops.
- Medical Research: To detect cancer
- Industrial Automation: Improve work safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines.
- Electronics: Used in automated hearing and speech translation. For e.g., home assistance devices that respond to your voice and know your preferences are powered by deep learning applications.
C) Some Criticism of AI
- Idea of intelligent machines is obscene anti human and immoral.
- Would make life more mechanical.
- A lot of investment has taken place -> many AI companies going bankrupt
- Taking away the human jobs
D) India: Promotion of Artificial Intelligence and Machine Learning
India has been ranked second on the Stanford AI Vibrancy Index primarily on account of its large AI-trained workforce.
In 2018, NITI Aayog launched National Strategy for Artificial Intelligence detailing core strategies and recommendations of promoting the use of AI in key areas of governance
Five Key Sectors identified by NITI Aayog to focus its efforts on implementation of artificial intelligence (AI) to serve societal needs.
- Healthcare: increased access and affordability of quality healthcare
- Agriculture: enhanced farmers income, increased farm productivity and reduction of wastage
- Education: Improved access and quality of education
- Smart Cities and infrastructure
- Transportation
CBSE has integrated AI in the school curriculum to ensure students passing out have basic knowledge and skills of data science, machine learning and Artificial intelligence.
Responsible AI for Youth – A National Program for the youth launched by MEITY – Launched in May 2020
- The program is designed to reach out to students from Government schools pan India and provide them with opportunity to become part of the skilled workforce in an inclusive manner.
- It is open to students of classes 8 – 12 from Central and State government-run schools (including KVS, NVS, JNV) from across the country – all 28 States and 8 Union Territories.
National Education Policy 2020 provides for setting up of the National Research Foundation, which should boost research in AI.
RAISE (Responsible AI for Social Empowerment) 2020
- It is a first of its kind, global meeting of minds on Artificial Intelligence to drive India’s vision and roadmap for social transformation, inclusion and empowerment through Responsible AI.
- It was organized by GoI through MEITY and NITI Aayog.
India joins Global Partnership on Artificial Intelligence (GPAI) as a founding member to support the responsible and human centric development and use of AI (July 2020)
- GPAI is an international and multi-stakeholder initiative to guide the responsible development and use of AI, grounded in human rights, inclusion, diversity, innovation, and economic growt
- This is also a first initiative of its type for evolving better understanding of the challenges and opportunities around AI using the experience and diversity of participating countries.
- In order to achieve this goal, the initiative will look to bridge the gap between theory and practice on AI by supporting cutting-edge research and applied activities on AI-related priorities
- GPAI will be supported by a Secretariat, to be hosted by Organization for Economic Cooperation and Development (OECD) in Paris, as well as by two Centers of Expertise- one each in Montreal and Paris.
Key Pain Points challenges involved in the implementation of Artificial Intelligence in India
- Human Resource Shortfall in terms of number of AI experts including PhDs.
- Lack of trained professionals: Only around 4% of Indian AI professionals are trained in emerging technologies such as deep learning.
Way Forward
Promote More R&D in AI
- Better facilities at HEIs
- More academia-Industry collaboration
Human Resource Development: Rejuvenate Higher Education Sector for AI
- Come up with a clear-cut action plan for rejuvenating Higher education system for development of AI
- Dealing with faculty shortage by increasing attractiveness of Indian HEIs for highly qualified PHDs and experienced faculties (salaries, infrastructure, recognitions etc.)
AI-Startups should be encouraged through tax breaks, reduced compliance burden and increased support from R&D institutions.
Institutional commitment to excellence, politically open environment and the motivation of individual researchers to unlock the potential of AI will, in long run success of AI in a country.
Strong, high tech regulatory framework to deal with problems which may be created by deep fakes
Conclusion: India, with its “AI for ALL” strategy, a vast pool of AI-trained workforce and an emerging startup ecosystem, has a unique opportunity to be a major contributor in AI-driven solutions that can revolutionize healthcare, agriculture, manufacturing, education and skilling.