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What is the most common issue when using ML?
The most common hurdle in Machine Learning is often the foundation: data. Data, like the ingredients in a recipe, needs to be high quality for the ML model to function well. The biggest culprit? Poor quality data. This can mean data that's inaccurate, incomplete, or biased. Imagine training a spam fRead more
The most common hurdle in Machine Learning is often the foundation: data. Data, like the ingredients in a recipe, needs to be high quality for the ML model to function well. The biggest culprit? Poor quality data.
This can mean data that’s inaccurate, incomplete, or biased. Imagine training a spam filter on emails that include a lot of false positives (not spam marked as spam). The filter will learn these mistakes and become worse at identifying real spam.
Another data issue is having too little or unrepresentative data. An ML model trained only on sunny day photos might struggle to recognize objects on rainy days. Data scientists spend a lot of time cleaning, organizing, and ensuring their data is well-suited for the task at hand. It’s like having the perfect ingredients for a delicious meal – garbage in, garbage out applies to ML too.
How Indian farmers can benefit from AI
Krishna, a smallholding farmer, diligently cultivates his half-hectare plot in Telangana, India, every day. For this, he earns $120 per month—just enough to meet his family's basic needs. But Krishna must also contend with unpredictable monsoons, frequent droughts, pest infestations, and diminishingRead more
Krishna, a smallholding farmer, diligently cultivates his half-hectare plot in Telangana, India, every day. For this, he earns $120 per month—just enough to meet his family’s basic needs.
But Krishna must also contend with unpredictable monsoons, frequent droughts, pest infestations, and diminishing yields. He must battle the impacts of changing climate patterns and soil health. With no access to a bank, Krishna is also forced to use local loan sharks for finance, paying crippling interest rates. Even then, the essential resources he buys with this money – such as seeds, fertilizers and pesticides – aren’t always available.
Post-harvest, Krishna faces another hurdle: 40% wastage in other parts of the supply chain. Logistics, warehousing and accessing a market at which to sell their produce also present significant challenges for many farmers like Krishna.
Strict quality requirements set by traders and processors are also very difficult to meet. These farmers are then trapped in a cycle of subsistence farming because low revenues leave them with less to invest in the next crop cycle. New technologies that make this work easier – precision farming, digital market access or drones, for example – remain out of reach for most farmers like Krishna. They can’t afford the equipment, have limited access to technology and may not have the time to spare to adjust their processes to adopt them properly.
The dynamics of market supply and fluctuating prices only add to these challenges because farmers like Krishna often find themselves losing out when prices fall or demand drops.
Like the other roughly 125 million smallholding farmers in India, Krishna faces these daunting challenges to support himself and his family. For these farmers, agriculture is a high-stakes gamble marked by big risks and minimal returns. Thousands of farmers in India have committed suicide, reflecting financial desperation and weather-induced challenges affect these people.
And Krishna’s story is not unique to India either. An estimated500 million smallholder farmsin the developing world support almost 2 billion people and produce about 80% of the food consumed in Asia and sub-Saharan Africa. Addressing the plight of Krishna and his counterparts around the world to create a more sustainable and equitable future for smallholding farmers will require a holistic, scalable approach that encompasses financial inclusion and climate resilience.
Using AI for agriculture innovation
This is why the World Economic Forum India’s Centre for the Fourth Industrial Revolution, in collaboration with India’s Union Ministry of Agriculture and the state of Telangana, launched the AI4AI initiative (AI for Agriculture Innovation). Reflecting the complexity of the challenge, organisations involved come from industry (agri-inputs, consumer, food processing, finance, insurance and technology firms), the startup ecosystem and farmer cooperatives.
Over eight months starting June 2020, this endeavour held more than 45 workshops, to discuss the challenges smallholder farmers face and how 4IR could help. These discussions lead to a AI4AI plan that helps smallholder farmers by harnessing the power of new technologies including AI, drones and blockchain.
From framework to impact
We tested the AI4AI framework in the Khammam district of Telangana, India, among 7,000 farmers. We involved industry and start-up partners and used state-government data management tools (the agriculture data exchange and the agriculture data management framework) to scale up the initiative among this large group of farmers.
Named Saagu Baagu locally, this initiative has transformed chili farming in Khammam district using bot advisory services, soil testing technology, AI-based quality testing and a digital platform to connect buyers and sellers.
The pilot took 18 months and three crop cycles to complete. During this time, farmers reported a remarkable surge in net income: $800 per acre in a single crop cycle (6 months), effectively double the average income. The digital advisory services contributed to a 21% increase in chili yield production per acre. Pesticide use fell by 9% and fertilizers dropped by 5%, while quality improvements boosted unit prices by 8%.
Saagu Baagu was not only a success for its farmers, it achieved the sustainability and efficiency goals set by AI4AI. As a result, in October 2023, the state government expanded Saagu Baagu to include 500,000 farmers, covering five crops across 10 districts.
Unlocking digital agriculture’s potential
As much of the global south grapples with the challenges of ensuring food security, mitigating climate change impacts and protecting livelihoods, this Indian agtech initiative shows promising results when using AI for agriculture. Collaboration between governments, industry, philanthropists, innovators and farmers can create national frameworks for implementing digital agriculture programmes that ensure food security, sustainability, and alignment with sustainable development goals.
Sharing lessons learned and success stories via these digital platforms gives farmers valuable insights and evidence-based strategies for using AI for agriculture. This can help accelerate innovation and guide global efforts in digital farming, promoting sustainability, inclusivity, efficiency and improved nutrition worldwide.
Ethical Implications of AI in Governance
Ethical issues arising from the application of artificial intelligence in the governance of the public sector are AI systems can yield unfair results if the bases used during their development contain bias as seen in cases such as law enforcement or social services. Transparency is another issue whiRead more
Ethical issues arising from the application of artificial intelligence in the governance of the public sector are AI systems can yield unfair results if the bases used during their development contain bias as seen in cases such as law enforcement or social services. Transparency is another issue which is a problem because AI systems often make decisions without providing a clear and easily understandable explanation behind them.
They are crucial to attend to these issues. In the developed countries, the European Union has set definite guidelines by its regulation of General Data Protection. This regulation guarantees citizens the right to know how their data is being processed and also the right to object against automated decision-making.
On the other hand, countries in the developing world such as India are creating their approaches to AI ethics to ensure that it does not widen gaps in the society. However, here is the challenge that needs to be addressed, i.e., sometimes the government sets too many rules that can hinder innovation and growth of new technologies.
Both contexts show that it is high time to strengthen the legal framework to implement AI-based solutions responsibly and take advantage of this technology for everyone.
See lessHow will be AI taking over humans in future.
The concept of AI taking over humans is a popular topic of discussion and speculation. While AI has made significant advancements in recent years, it's essential to separate fact from fiction and understand the actual possibilities and risks. AI has made tremendous progress in various domains, suchRead more
The concept of AI taking over humans is a popular topic of discussion and speculation. While AI has made significant advancements in recent years, it’s essential to separate fact from fiction and understand the actual possibilities and risks.
AI has made tremendous progress in various domains, such as:
However, AI still faces limitations and challenges:
Potential Risks and Concerns:
- Job displacement: AI could potentially displace human workers in certain industries, leading to unemployment and social unrest.
- Biased decision-making: AI systems can perpetuate biases present in their training data, leading to unfair outcomes.
- Security risks: AI-powered systems can be vulnerable to cyber attacks and hacking.
- Dependence on infrastructure: AI relies on complex infrastructure, which could be compromised or disrupted.
See lessFuture of Work against AI
The increasing automation and integration of Artificial Intelligence (AI) will significantly reshape the job market, leading to both opportunities and challenges. Here's a breakdown of the expected changes and the types of education and training that will be needed to prepare the workforce for the fRead more
The increasing automation and integration of Artificial Intelligence (AI) will significantly reshape the job market, leading to both opportunities and challenges. Here’s a breakdown of the expected changes and the types of education and training that will be needed to prepare the workforce for the future:
Job displacement:
New job creation:
Education and training needed:
- Lifelong learning: The pace of technological change will require workers to continuously update their skills throughout their careers.
- STEM education: Emphasis on Science, Technology, Engineering, and Math (STEM) education will be crucial for jobs in AI development and maintenance.
- Data analysis and interpretation: Workers will need to understand how to collect, analyze, and interpret large data sets to make informed decisions.
- Soft skills: Developing strong soft skills like communication, teamwork, adaptability, and emotional intelligence will be essential for success in a rapidly changing job market.
- Digital literacy: Workers should have basic digital literacy skills to effectively interact with AI systems and technology.
- Upskilling and reskilling: Governments, educational institutions, and employers will need to invest in upskilling and reskilling programs to help workers transition to new roles.
- Vocational training: Focus on vocational training programs that provide hands-on experience in emerging technologies like AI, robotics, and automation.
- Entrepreneurship: Encouraging entrepreneurship and innovation will be essential for creating new job opportunities in emerging industries.
See lessPossibility of controlling mind through technology
The development of technology that can manipulate human thought or behavior is an active area of research in various fields, including neuroscience, psychology, and computer science. While we have made significant progress in understanding the neural mechanisms underlying human cognition and behavioRead more
The development of technology that can manipulate human thought or behavior is an active area of research in various fields, including neuroscience, psychology, and computer science. While we have made significant progress in understanding the neural mechanisms underlying human cognition and behavior, we are still far from developing technology that can directly manipulate human thought or behavior with high accuracy and reliability.
Current state of the art:
Ethical implications:
Future prospects:
While we are not yet close to developing technology that can manipulate human thought or behavior with high accuracy and reliability, researchers continue to make progress in understanding the neural mechanisms underlying human cognition and behavior.
In the near term (10-20 years), we may see advancements in BCIs and neuromorphic computing that enable more sophisticated interaction between humans and machines. However, these technologies are likely to be limited to specific applications, such as medical treatments or assistive technologies.
In the long term (20-50 years), we may see the development of more advanced technologies that can manipulate human thought or behavior. However, these technologies will require significant advancements in our understanding of the human brain and the development of sophisticated algorithms that can accurately predict and influence human behavior.
See lessHow can India leverage advancements in artificial intelligence to boost its economic growth and address social challenges?
India can leverage advancements in artificial intelligence (AI) to boost its economic growth and address social challenges in several ways: 1. **Agriculture**: AI can help optimize crop yields through predictive analytics, pest detection, and weather forecasting. Drones and AI-powered machinery caRead more
India can leverage advancements in artificial intelligence (AI) to boost its economic growth and address social challenges in several ways:
1. **Agriculture**: AI can help optimize crop yields through predictive analytics, pest detection, and weather forecasting. Drones and AI-powered machinery can improve precision farming, reducing waste and increasing productivity.
2. **Healthcare**: AI can enhance diagnostics, personalized medicine, and patient management. Telemedicine platforms powered by AI can extend healthcare services to rural and remote areas, improving access to quality care.
3. **Education**: AI-driven personalized learning systems can cater to individual student needs, improving educational outcomes. AI can also support teacher training and curriculum development, ensuring that education is inclusive and effective.
4. **Manufacturing**: AI can streamline supply chains, enhance quality control, and automate repetitive tasks, leading to increased efficiency and reduced costs. Smart factories can adapt to changing demands and optimize production processes.
5. **Finance**: AI can enhance fraud detection, risk management, and customer service in the financial sector. Automated financial planning and advisory services can make financial management more accessible to a broader population.
6. **Public Services**: AI can improve governance by enabling data-driven decision-making, enhancing public service delivery, and detecting corruption. Smart city initiatives can use AI to manage resources efficiently and improve urban living conditions.
7. **Transportation**: AI can optimize traffic management, reduce congestion, and improve public transportation systems. Autonomous vehicles and AI-powered logistics can enhance supply chain efficiency and reduce transportation costs.
8. **Environment**: AI can help monitor environmental changes, predict natural disasters, and manage natural resources sustainably. AI-driven solutions can aid in conservation efforts and combat climate change.
9. **Social Welfare**: AI can identify and address social issues such as poverty, unemployment, and inequality. AI-powered platforms can connect individuals with job opportunities, social services, and welfare programs more effectively.
10. **Research and Development**: Investing in AI research can spur innovation and create new industries. Collaboration between academia, industry, and government can drive technological advancements and ensure that AI development aligns with national priorities.
To maximize these benefits, India needs to focus on:
– **Policy and Regulation**: Establishing a robust regulatory framework to ensure ethical AI use, data privacy, and security.
– **Skill Development**: Investing in education and training to build a skilled workforce capable of developing and deploying AI technologies.
– **Infrastructure**: Enhancing digital infrastructure to support AI initiatives, including high-speed internet, data centers, and cloud computing capabilities.
– **Public-Private Partnerships**: Encouraging collaboration between the government, private sector, and academia to drive AI innovation and application.
– **Inclusive Growth**: Ensuring that AI advancements benefit all sections of society and do not exacerbate existing inequalities.
By strategically integrating AI into various sectors, India can drive economic growth, improve public services, and address pressing social challenges.
See lessEthical Implications and threat of AI on marginalization
Deploying AI in decision-making, particularly in healthcare and criminal justice, raises significant ethical concerns. In healthcare, AI can enhance diagnostics and treatment but may also lead to decisions that lack human empathy. In criminal justice, AI could perpetuate existing biases, leading toRead more
Deploying AI in decision-making, particularly in healthcare and criminal justice, raises significant ethical concerns. In healthcare, AI can enhance diagnostics and treatment but may also lead to decisions that lack human empathy. In criminal justice, AI could perpetuate existing biases, leading to unjust outcomes.
Ensuring accountability and transparency is crucial. This can be achieved through stringent regulatory frameworks, regular audits, and clear documentation of AI systems’ decision-making processes. Transparent algorithms and open data practices allow stakeholders to understand how decisions are made.
Addressing bias in AI models is vital to prevent harm to marginalized communities. Bias can arise from unrepresentative training data or flawed algorithmic design. Identifying bias requires diverse datasets and continuous monitoring. Techniques like fairness-aware machine learning and adversarial testing can help.
Effective mitigation strategies include:
1. Diverse and inclusive data collection to ensure representation.
2. Bias auditing tools to detect and measure bias.
3. Algorithmic transparency to allow for external review.
4. Involving ethicists and community representatives in the development process.
Regularly updating models and incorporating feedback loops can also help in adapting to changing societal norms and reducing bias over time.
See lessWhy ASI(artificial super intelligence) is in talks?
Artificial Super Intelligence (ASI) is a topic of significant interest and concern in discussions surrounding artificial intelligence (AI) for several reasons: Potential Impact on Society: ASI refers to AI systems that surpass human intelligence across all domains and activities. The prospect of sucRead more
Artificial Super Intelligence (ASI) is a topic of significant interest and concern in discussions surrounding artificial intelligence (AI) for several reasons:
Potential Impact on Society: ASI refers to AI systems that surpass human intelligence across all domains and activities. The prospect of such advanced AI raises profound questions about its impact on society, jobs, ethics, and governance.
Technological Feasibility: While ASI remains theoretical and speculative at present, advancements in AI research, particularly in machine learning, neural networks, and computational power, have led some experts to consider the possibility of achieving ASI in the future.
Ethical and Safety Concerns: The development of ASI prompts ethical concerns regarding its control, governance, and potential consequences for humanity. Issues such as AI alignment (ensuring AI goals align with human values), unintended consequences, and the potential for misuse or accidents are critical areas of concern.
Existential Risks: Some experts, including prominent figures like Elon Musk and Stephen Hawking, have raised alarms about the existential risks associated with ASI. Concerns include scenarios where ASI could autonomously make decisions that threaten human existence or radically alter society in unpredictable ways.
Policy and Governance Challenges: ASI raises complex challenges for policymakers and regulators in terms of developing frameworks for its ethical use, ensuring safety and security, and addressing potential socioeconomic disruptions.
Research and Development: Despite the speculative nature of ASI, research efforts in AI ethics, safety, and governance are actively addressing these concerns. Initiatives like the Future of Life Institute and partnerships among AI researchers, policymakers, and ethicists aim to promote responsible AI development.
Public Awareness and Debate: Discussions about ASI contribute to raising public awareness about the implications of AI technology. Debates encompass diverse perspectives, including scientific, philosophical, ethical, and policy-related viewpoints.
In summary, ASI is in the spotlight because it represents the theoretical pinnacle of AI development, posing profound societal, ethical, and existential questions. While ASI remains a long-term goal for AI researchers, its potential impacts and challenges warrant ongoing dialogue, research, and thoughtful consideration to navigate its development responsibly.
See lessWhat ethical considerations arise from the development of artificial intelligence?
There are several significant ethical considerations that arise from the development of artificial intelligence (AI): Algorithmic Bias and Fairness: AI systems can perpetuate and amplify societal biases present in the data used to train them, leading to unfair and discriminatory decisions. EnsuringRead more
There are several significant ethical considerations that arise from the development of artificial intelligence (AI):
- Algorithmic Bias and Fairness:
- AI systems can perpetuate and amplify societal biases present in the data used to train them, leading to unfair and discriminatory decisions.
- Ensuring AI algorithms are designed to be fair and unbiased, and thoroughly testing for and mitigating potential biases, is crucial.
- Transparency and Explainability:
- Many advanced AI models, such as deep learning systems, can be “black boxes” where the decision-making process is not easily interpretable.
- There is an ethical imperative for AI systems to be as transparent and explainable as possible, particularly when used for high-stakes decisions that impact people’s lives.
- Privacy and Data Rights:
- The vast amounts of data required to train AI systems raise significant concerns around data privacy, ownership, and the rights of individuals.
- Robust data governance frameworks and strong privacy protections are essential to safeguard individual privacy.
- Accountability and Liability:
- As AI systems become more autonomous and capable of making consequential decisions, questions of moral and legal responsibility become critical.
- Clearly defining accountability mechanisms and liability frameworks for AI-related harms is necessary.
- AI Safety and Alignment:
- There are concerns about the potential for advanced AI systems to become misaligned with human values and goals, potentially leading to unintended and harmful consequences.
- Ensuring AI systems are designed to be safe and reliably aligned with human interests is a crucial ethical challenge.
- AI Existential Risk:
- Some experts worry that the development of superintelligent AI systems could pose an existential threat to humanity if not managed carefully.
- Addressing the potential long-term risks of advanced AI is an important ethical consideration.
See less