Schlagwort: KI

Artificial Intelligence is rapidly gaining popularity and making advancements in industries, societies, and economies globally. As AI continues to evolve, countries are racing to develop policies and regulations that foster innovation while ensuring ethical use. Recently, the European Union has been seen at the forefront of the media for its stringent AI regulations. Comparing the European Union’s AI policies with those of China and the United States reveals disparate philosophies and strategies.

 

The EU Approach

The European Union AI Act was adopted in May of this year emphasizing trustworthiness, transparency, and human-centric values. The obligations under this act are hoped to be implemented within 36 months and the major components in 24 months. On the 12th of July the AI act was officially published, counting down to the first European AI law which will be enforced starting on the 1st of August 2024. The purpose of this act is to be a legal framework that ensures AI systems used in Europe are safe, respect fundamental rights, and comply with existing laws on data protection, privacy, and non-discrimination. 

Key Components of the EU AI Act:

Risk-Based Classification 

• There are four risk levels used (unacceptable, high, limited, and minimal) that will assist in identifying non-compliance with the regulations and determining the severity of the situations as well as the suitable penalties for said non-compliance.

Human Oversight

• This requires the presence of mechanisms for human oversight in AI systems (specifically high-risk systems) to ensure human intervention in decision-making to prevent automated systems from making critical decisions independently.

Data Governance

• This focuses on using high-quality, non-biased data sets to train AI systems and through that, minimize the risk of discrimination and ensure fairness.

Transparency and Accountability

• There must be clear information provided regarding the functioning and limitations of the AI systems. This includes logging and documentation to keep accountability and traceability of AI operations.

 

United States – The Innovative Approach

The U.S. lacks a centralized AI policy, however, there are various initiatives and frameworks to help guide AI development in the states. Voluntary standards and guidelines for AI have been developed to emphasize risk management and warrant trustworthy AI. The guidelines in the US encourage industries to self-regulate and adopt “best practices” to their liking. The U.S. also relies heavily on the private sector to drive AI innovation. Using companies like Google, Microsoft, and OpenAI to play pivotal roles in advancing AI technologies often leads to industry standards being set through their research and development efforts. In regards to ethical guidelines, they are often developed by individual companies or industry groups, rather than mandated by federal regulation in the United States.

 

China – The State-Driven Approach

China uses strong state involvement and ambitious national strategies for its AI development with ultimate goals aimed at global dominance. Their government heavily invests in AI research and development. They support both state-owned enterprises and private companies. They have even established “AI development zones” in 11 major cities to allow for rapid innovation in these technologies. They have begun to integrate AI into smart cities, healthcare, and military applications to ensure the alignment of AI with the Chinese economic and strategic objectives. Significant privacy and human rights concerns have been raised due to the government’s use of facial recognition and social credit systems in AI for social governance. Data sharing is widely used in China’s efforts to strengthen AI systems which has led to even more concerns regarding data privacy and security.

 

Concluding points

All three of these entities have varying focuses regarding the development of AI because they each have unique values, political systems, and strategic goals. There are clear contrasts between the three approaches, especially the EU’s major focus on ethical governance compared to the US and China. As AI continues to transform societies, these differing approaches will shape the development of AI technologies and also the global discourse on ethical standards, privacy, and human rights. International dialogue regarding this topic is essential for balancing innovation and ethical considerations in future developments of artificial intelligence systems.

The hidden cost of Artificial Intelligence (AI) is the water consumed to run ChatGPT. As AI capabilities grow, so does the amount of natural resources, such as water, needed. The consequences of not addressing this issue could be dire. When considering where the most freshwater is allocated, many people consider daily human consumption for appliances such as the toilet, shower, laundry, and cooking. For large industries requiring water to succeed, textiles, chemicals, paper, energy production (oil, gas), and agriculture are examples. 

Let’s understand why AI requires water

A computer is comparable to a radiator. Each unit of electricity consumed by a computer releases an equal amount of megawatts of heat. If the equipment runs at that heat level for long periods, it will damage or potentially ruin the electronics. Water has the thermal capacity to transfer heat 25 times higher than air, meaning that using water to cool artificial intelligence equipment consistently is the most efficient resource for maintaining AI’s capabilities. 

One might wonder, if water is so crucial, why don’t we use just any type of water to maintain AI equipment? The answer lies in the necessity of clean, drinkable water. A system of evaporative cooling uses fresh water to maintain the temperatures in buildings such as university campuses. In AI Equipment Cooling, four types of water are used: deionized, distilled, chilled, and specially treated water. Clean, drinkable water is not just a luxury but a necessity for AI equipment to run smoothly and avoid deterioration. The water must be free from algae, bacteria, conductive properties, mineral deposits, or corrosion, which are water components that are unsafe to drink. 

Artificial intelligence is now an industry that demands large amounts of natural water resources. At the astronomical pace at which AI is growing, the hidden amount of water used in industries and human consumption raises concerns about what the long-term outcome will mean for this natural resource. As AI technologies continue to be developed, advanced, and deployed in all industries, the challenge is not just about growth, but about ensuring that the implementation is ethically and strategically used to benefit humanity while recognizing the economic effect of maintaining these systems. 

The astonishing rate at which AI consumes water

For around 10-50 questions imputed into Chat-GPT, an estimated 500 milliliters of water—equal to a standard bottle of water—are required to provide the answers. The silent and often overlooked cost of Big Tech’s water footprint is visually understood when recognizing that hundreds of millions of users interact with Chat-GPT daily. Looking at the water consumption of Big Tech organizations in 2022, Microsoft’s consumption was 1.2 billion gallons, and Google’s was 5.6 billion gallons. Both firms hope to replenish more water than they have used by becoming water-positive by the end of 2030. Their water footprint was released in 2022 before their AI integrations, Bing Chat and Google Bard, were published. Data centers typically generate the highest percentage of Big Tech’s water footprint, using water in evaporation to expel building heat. The United States operates over 5,300 data centers, ten times more than Germany, which houses the second most data centers worldwide. As of January 2024, the computing power required for AI was estimated to double every 100 days.

Water scarcity is a primary issue across all continents during the current climate crisis. It is essential to govern AI within planetary boundaries. This is possible by providing less energy-intensive solutions such as the following:

• Energy-Efficient Data Centers: renewable energy, advanced cooling solutions, efficient server utilization
• Algorithmic Improvements: efficient neural networks, removing unnecessary parameters and quantization, knowledge distillation
• Hardware Optimization: specialized AI chips, application-specific integrated circuits
• Data Management: data augmentation and compression to reduce storage and transmission energy costs
• Green AI Initiatives: transparency, benchmarking, promoting research in energy-efficient AI, and collaboration between industries to develop best practices
• Global effort by individual users: avoid using generative AI in unnecessary situations and instead do a simple search to obtain information which is less resource-consuming

Lastly, if market forces or innovation do not decrease water consumption, limitations should be put in place to ensure the water footprint does not continue to increase exponentially through the use of Big Tech. Additionally, more information should be publicized about this hidden cost of Artificial Intelligence, and companies should be required to disclose their use.

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