AI REGULATION AND APPLICATION

1. Artificial intelligence (AI) Regulation and Application 

Why in the news?

 Recently, the World’s first-ever AI Safety Summit was held at Bletchley Park in Buckinghamshire near London (United Kingdom). 





 ☆ 27 major countries including the United States, China, Japan, UK, France, and India, and the European Union agreed to sign a declaration, named the Bletchley Declaration.  

☆ The Declaration fulfils key summit objectives in establishing shared agreement and responsibility on the risks, opportunities and a forward process for international collaboration on frontier AI safety and research. 






Refer below articles for detailed understanding of AI in Healthcare, Agriculture and empowering other technologies

 What are the risks associated with AI development that necessitate its regulation?

 ■ Control of Big Tech:  Decisions about the development of AI are overwhelmingly in the hands of the big tech companies with access to vast stores of digital data and immense computing power. 

 ■ Misuse: Substantial risks may arise from potential intentional misuse or unintended issues of control relating to alignment with human intent. 

● Frontier AI systems may amplify risks such as disinformation through the use of algorithms. 

●Increasing instances of deepfakes, intentional sharing of harmful information and cyber frauds are examples of it. E.g., instances observed in elections across the world.  

To know more about application of Artificial Intelligence in elections, kindly refer to Article 1.3. Artificial Intelligence and elections in September 2023 edition of VisionIAS Current Affairs Magazine.

☆Model Collapse scenario: Over time, datasets may be poisoned by AI-generated content which changes the patterns in the dataset, incorporating mistakes of previous AI models. E.g., issues of racial discrimination experienced in previous AI models. 

 ☆ Model adoption  challenges:There are risks associated with different models for AI development. 

 ● Closed :  An ecosystem limited to a small number of closed models and private organizations can prevent misuse by malicious actors but has the potential for safety failures and undetected biases to propagate.  

● Open _ source : On the other hand, an open-source model can spot biases, risks or faults but increases the risk of misuse by malicious actors.  

 ● Cyber risks: Global tensions and the rise in cyber capabilities have led to escalating cyber crime or hacking incidents and consequent disruption of public services.  

● Economic  risks: The effects of AI in the economy, such as labour market displacement or the automation of financial markets, could cause social and geopolitical instability.



 

 What has been done to regulate AI?

  ☆ European  Union: EU’s AI Act intends to be the world’s first comprehensive AI law.  

●  It classifies AI systems into four tiers of risk and different tiers are subject to different regulations.

  ●  A new EU AI office would be created to monitor enforcement and penalties including fines of up to 6% of total worldwide revenue. 


☆ USA:   Regulation to set standards on security and privacy protections and builds on voluntary commitments adopted by more than a dozen companies.

 ☆ India :  Government of India is contemplating to bring out a comprehensive Digital India Act to regulate AI. 

  NITI Aayog released the National Strategy on Artificial Intelligence (NSAI) which focuses on Responsible AI for All (RAI) principles.

  ☆ China :  China’s regulations require an advanced review of algorithms by the state and should adhere to the core socialist values.

 ● AI-generated content must be properly labelled and respect rules on data privacy and intellectual property. 

What can be done to better regulate AI systems? 

☆☆ International  Cooperation:  Since many challenges posed by AI regulation cannot be addressed at a purely domestic level, international cooperation is urgently needed to establish basic global standards.

 ☆☆ Impact assessment:International efforts to examine and address the potential impact of AI systems is needed. 

 ☆☆ Proportionate Governance:Countries should consider the importance of a pro-innovation and proportionate governance and regulatory approach that maximises the benefits and takes into account the risks associated with AI. 

 ☆☆ Private sector accountability: Increased transparency by private actors developing frontier AI capabilities, appropriate evaluation metrics, tools for safety testing, and developing relevant public sector capability and scientific research. 

☆☆ Better Design:  To reduce degree and impact of bias and harmful responses, there is a need for curated, fine-tuned datasets with inclusion of more diverse groups and continuous feedback mechanism.  

  AI IN HEALTHCARE

Why is it important?  


The emergence of AI in healthcare has been ground-breaking, reshaping the way we diagnose, treat and monitor patients. This technology is drastically improving healthcare research and outcomes by producing more accurate diagnoses and enabling more personalized treatments. 



Application Areas 


●♧♧ Higher _ quality  patient care :AI-powered clinical decision support (CDS) tools can aid in developing accurate, appropriate and actionable diagnostic or treatment recommendations. 
Apollo hospitals launched Apollo Clinical Intelligence Engine, a CDS, open to use by all Indian doctors. 
 ●♧♧ Clinical research  and  discovery:  AI is improving clinical trials – supporting diversity in recruitment and innovation in operations. Also, AI is helping to advance early disease identification and intervention. 
●♧♧ Healthcare supply chain resilience: Predictive models driven by data provide longitudinal visibility of supply with real time information regarding shortages and surpluses.  

●♧♧ Workforce optimization  :Workflows automated with AI capabilities can help extend scarce labor resources, reduce work fatigue and burnout, and enable operational and cost efficiencies. 





Potential Challenges       Future  prospect 

Some of the roadblo_ :    All in Healthcare 

cks to more wide_    :  hold immense potent_

spread  adoption     : ial  and promise  for all 

still include             : of us, ushering in a new

                                    era  filed  with.


blind spots    :     ● advancement  in dia_

in data access    :    gnostics and treatment

and collection,  

☆ Privacy issues : ● therapeutic discovery                                      and clinical  research. 

data misuse,:    ● supply chain resiliency

☆ regulatory      : ●  a host operational and
ambiguity.            administrative  efficen_                                    cies .



 

AI IN AGRICULTURE

Why is it important? 


 The application of AI in agriculture has been widely considered as one of the most viable solutions to address food inadequacy and to adapt to the need of a growing population.  








Application Areas


    ●♤♤ Intelligent  crop  planning: It includes AI model based planning for micro and macro cropping, credit and extension, irrigation and sowing windows.  
 AI systems are helping to improve the overall harvest quality and accuracy – known as precision agriculture. 

●♤♤ Smart farming : AI frameworks help in nutrition management, promotion of one health, mechanization of farms, soil analysis, pest and weather predictions. 
 
■♤  World Economic Forum is implementing AI for Agriculture Innovation (AI4AI) initiative to transform the agriculture sector in India by promoting the use of AI. Under it, ‘Saagu-Baagu’ initiative was launched to promote innovation in agriculture in Telangana.  
●♤♤ Farmgate_ to _ fork :Market-based intelligence, traceability and quality of logistics, supply chain optimization, emergence of fintech, and demand and price production improves efficiency. 
 ●♤♤ Data _ driven agriculture:  Data driven AI can enhance agricultural productivity and help in creation of a national market through analysis. 

Potential Challenges     Future prospect 

There are continued       The future of AI in     challenges such as:        agriculture  in India ●The need for infra_     holds great promise
structure develop_       for improving    ment                              productivity, and 
●  access to techn_        making farming    ology                               practices more susti_   in remote areas         nable with following                                            steps _ 
● the  necessary        ●  continued involv_         for farmer               ement,
education and      ● Research, and collab_    awareness.            oration  between the                                           government, tech develo_                                  pers, and farmers. 



MULTIMODAL AI 


What is it and why is it important? 


 Multimodal AI combines the power of multiple inputs to solve complex tasks. In order to solve tasks, a multimodal AI system needs to associate the same object or concept across different facets of a given media. A multimodal AI system can piece together data from multiple data sources such as text, images, audio and video, creating applications across sectors.  




Application areas


 Business Analytics:  It can make the best use of machine learning algorithms because it can recognize different types of information and give better and more informed insights. 
By combining information from various streams, it can make predictions about a company’s financial results, and even predict maintenance needs. 

Data processing  :  It can help in generating textual descriptions, transcription of videos, text-to-speech conversion, analysis of facial expressions and development of sensors for autonomous vehicles or machines.  

Accessibility: Such systems can assist individuals with disabilities by providing environmental awareness.

Potential Challenges     Future prospect

There are continued      Multimodel AI syst_    challenges  such as:     ems are versatile  and    ● Privacy concerns  continues to advance,   ● ethical consider     expanding their hori_     ations  and                zon and potential  use                                         cases by creating  multi

● the need  for       _ stakeholder  frame_        standardized       works addressing priva_    frameworks         cy, security and ethical                                       concerns. 

Conclusion 


 Striking the right regulatory framework is crucial to harness the full potential of AI while ensuring responsible and ethical deployment. As we navigate this evolving landscape, collaborative efforts between policymakers, industry stakeholders, and researchers are imperative to shape a future where AI contributes positively to society. 

AI regulation and application examples 


AI regulation refers to the laws, policies, and guidelines put in place to govern the development, deployment, and use of artificial intelligence technologies. The aim of AI regulation is to ensure ethical, responsible, and safe use of AI, addressing potential risks and concerns.





There are several aspects of AI regulation that are being considered by governments, organizations, and international bodies:

 1. Privacy  and data protection  : AI systems often rely on vast amounts of data, and there is a need to protect personal information and ensure individuals have control over their data.

 2. Bias and fairness  : AI systems can be influenced by biases in training data, leading to unfair outcomes or discrimination. Regulations aim to address biases and establish frameworks for fairness in AI systems.

 3. Transparency  and  explainability  : AI algorithms can be complex and difficult to interpret. Regulations may require AI systems to be transparent and provide explanations for their decisions.

4. Accountability  and liability  : It is important to establish responsibility and accountability when AI systems cause harm or generate unintended outcomes.

 5. Safety and  reliability  : Regulations may focus on ensuring that AI systems are safe, reliable, and adhere to certain standards.

 6. Ethical considerations: AI regulations may incorporate ethical guidelines to ensure AI is developed and used in accordance with ethical principles.








Examples of AI regulation and application:


  1. General  Data Protection  Regulation ( GDPR): Implemented in the European Union, the GDPR provides regulations on the collection, storage, and use of personal data, including AI systems that process such data.

2. Algorithmic  Transparency  :Some jurisdictions are considering regulations that require companies to disclose how their AI algorithms make decisions, ensuring transparency and accountability.

3. Ethical AI Guidelines  : Organizations like the European Commission and IEEE have developed guidelines for AI development and use, promoting ethical considerations, fairness, and safety.

 4. AI in  Healthcare  : Regulations are being developed to govern the use of AI in healthcare, including applications like diagnostic systems, personalized medicine, and telemedicine.



 5. Autonomous  Vehicles  : Governments are creating regulations to address the deployment of autonomous vehicles, including safety standards, liability frameworks, and ethical considerations.

6. Facial Recognition: Some jurisdictions are implementing regulations and restrictions on the use of facial recognition technology to protect privacy rights and prevent misuse.






It is worth noting that AI regulation is an evolving field, and different countries and regions may have varying approaches and levels of regulation. The aim is to strike a balance between fostering AI innovation and addressing potential risks and societal concerns.

Al  _ Driven Start _ Up for water  purification  launched:

Union Minister Dr. Jitendra Singj launched  Artificial  intelligence  ( AI) driven  Start _ Up by Indian  Institute  Technology  ( IIT) alumni  for  water purification  through  innovative  technology  with financial  support from  Technology  Development  Borad ( TDB) on January   15, 2022.

   An MoU was also  signed  between  Technology  Development  Board ( TDB), a statutory  body of the Department  of  Science  & Technology  and  Swajal  Water  Private  Limited, a tech  Start Up company founded  by ex_ IITians   based in Gurugram.  


Meta Development  World's  Fastest  AI  Supercomputer  

☆ Facebook  parent  company, Meta has introduced  the AI Research  super Cluster  ( RSC) on January  24, 2022.

☆ According  to Meta, it's  new artificial  intelligence  supercomputer'  will be the fastest  across the world, by the middle  of year 2022. 

☆  RSC  will work  across hundreds  of  different  languages , analyse text,  images  and video together,  which will   help in building  better  AI models. 

☆ Meta  has collaborated  with NVIDIA to build  the AI Research  supercomputer  .









































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