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The 2024 Nobel Prize in Physics has been awarded to John J. Hopfield and Geoffrey E. Hinton for their groundbreaking work on artificial neural networks (ANNs) and machine learning. Their discoveries have played a crucial role in the development of modern artificial intelligence (AI), which draws from various branches of science such as statistical physics, neurobiology, and cognitive psychology. Artificial neural networks form the foundation of modern machine learning.
Hopfield and Hinton’s innovations provided essential methods for artificial neural networks. John Hopfield developed an associative memory network capable of storing and reconstructing patterns like images. Geoffrey Hinton, building on Hopfield’s work, invented a method that enables machines to autonomously identify features in data, such as specific elements in images.
What is Artificial Neural Network (ANN)?
Artificial neural networks are Computer algorithms that are designed to mimic the human brain’s ability to perform tasks. Hopfield pioneered the work on Artificial neural network (Hopfield Network), which was further built upon by Hinton (Boltzmann Machine). The two models of Artificial Neural Networks are mentioned below-
| Hopfield Network (Developed by Hopfield) | Hopfield network is a form of ANN that resembles a human brain’s nerve cells. In this network, each neuron is connected to all others. It allowed computers to ‘learn’ and ‘remember’ by processing information through the entire network, not just individual parts. This leap allowed machines to recognize patterns, and serves as a precursor to modern facial recognition and image enhancement technologies. |
| Boltzmann Machine (Developed by Hinton) | Boltzmann machine is another significant ANN model which function by minimizing an energy function, which is a concept rooted in physics. These models performed far more complex and cognitive tasks such as data classification, pattern generation and voice and picture recognition. Hinton also developed backpropagation, which allowed neural networks to learn from mistakes and improve through training on large data sets. This has given rise to deep learning (a system with multiple layers of networks that continually refine their accuracy). |
ANN models have led to development and advancement in the field of Artificial Intelligence, Machine Learning, Deep Learning and Artificial Generative Intelligence.
Artificial Intelligence is being applied in numerous fields, including astronomy, where it helps scientists analyse massive data sets to discover new information. Machine learning focuses efforts on data with the highest potential for groundbreaking discoveries. Deep learning is now central to technologies such as voice recognition, image identification, translation, and self-driving cars. The field of AI has further advanced to Generative AI, where AI is generating content.
Relation between AI, Machine Learning, Deep Learning & Generative AI
| Artificial Intelligence (AI) | AI is a discipline which focuses on formulating theories and methodologies for constructing machines that emulate human thought processes and behaviours. |
| Machine Learning (ML) | Machine learning is a subfield of Artificial Intelligence. ML involves the development of programs that train models using accessible data from sources such as webpages, articles, books, etc. These trained models are then used to make useful predictions for new and never-seen before data. The most common ML method to train the models is the supervised learning method. |
| Deep Learning | Deep learning is a subset of Machine Learning. Deep learning is a type of machine learning that uses artificial neural networks. These multilayered and interconnected neurons (inspired by the human brain), are used to process complex data and make predictions. |
| Generative AI | Generative AI is a subset of deep learning. It uses artificial neural networks to process data using supervised learning methods. This large-scale supervised learning technology is termed the Large Language Model (LLM). |
What are the advantages of AI which are built upon the Artificial Neural Networks (ANN)?
1. Writing and advertising- AI is being used as a brainstorming companion by the writers. For ex- Drafting press releases, language translation, creating new advertisements based on existing ones.
2. Reading- Apart from writing, AI technology is used as a reading tool. For ex- Auto Reading customer mails and segregating them based on complaints.
3. Chatting- AI is also being used for many special-purpose chatbot tasks. For ex- Government chatbots to help citizens get access to the right information on various schemes and policies.
4. Security Services- AI technology built on the advanced Neural Networks can create front-on photos from photos taken at different angles and vice versa. This is being used in face identification systems to secure the airports, international border check-points etc.
5. Enhanced capability of Search Engine Services- Advanced Artificial Neural Networks have the capability to take search engine services to the next level. For ex- Text to Image translation to provide search results.
6. Improving Healthcare System- AI technology has the potential to revolutionise the healthcare sector by improving the accuracy of diagnosis. For ex- Conversion of X-ray or any CT scan images to real images can improve the accuracy of diagnosis.
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What are the Challenges with the Artificial Neural Networks?
1. Increased Biases- Artificial Neural Networks can perpetuate and amplify existing biases. These systems can generate biased outputs like offensive language, demeaning imagery, and prejudicial content, if they are trained on biased, non-inclusive data. For ex- US rights group observation about an AI-based generative imagery programme showing images of only white men for the prompt ‘CEO’.
2. Threat of Job Losses- There are fears of job losses as AI can prove to be more cost-efficient and productive to firms as compared to human capital. For ex- Customer service jobs are under threat from the AI chatboxes (Zomato’s Zia).
3. Use for Malicious Purposes- Generative AI systems based on advanced artificial neural networks can be used to create content for malicious purposes, such as deepfakes, disinformation, and propaganda. Nefarious actors may use AI-generated media to manipulate people and influence public opinion, like use for Post Truth Doctrine.
4. Concern over Data Privacy- There are emerging concerns in regard to data privacy in using AI technology. For ex- Use of AI in healthcare involves collecting private information about individuals, which raises concerns about data privacy.
5. Issues Related to Copyright and plagiarised contents- AI technology has been associated with copyright violations and production of plagiarised content. For ex- Getty Images has sued Stable Diffusion (Generative AI Company), accusing them of copyright violations.
6. Limitations in Creativity- AI systems lack creativity, originality and human ingenuity as they use past data as a template for future work.
7. Environmental Concerns- AI systems require a lot of computing power, which have grave implications for the environment. For ex- According to analysts, training a transformer model just once with 213 million parameters can emit carbon emissions equivalent to 125 flights between New York and Beijing.
What should be the way Forward?
1. De-biasing while training the AI- We must ensure fairness of the information which is being fed into the system, to ensure that AI doesn’t perpetuate or amplify social biases, like gender and racial biases.
2. Transparency of information- Users should have transparent information about the limitation and risks of AI.
3. Privacy protection- The user data and confidentiality must be protected to ensure user privacy. For ex- Strict implementation of data protection laws.
4. Ethical use of AI- We must ensure that AI is used only for beneficial purposes. The push must be made towards universal adoption of the Bletchley Declaration by all the countries.
Conclusion
India’s progress in AI and scientific research has been hindered by decades of low funding, inefficient governance, and inadequate support for blue-sky research. Many Indian researchers face challenges like resource constraints and administrative burdens, limiting their ability to focus on cutting-edge research. The 2024 Nobel Prize in Physics serves as a reminder of the importance of supporting fundamental research, which often leads to technological breakthroughs. Dismissing such research risks missing out on future opportunities in AI and other emerging fields.
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