ZVEI – ZVEI calls for quick clarification on the interpretation of the AI ​​Act

EMR Analysis

More information on ZVEI: See the full profile on EMR Executive Services

More information on Wolfgang Weber (Chairman of the Executive Board, ZVEI): See the full profile on EMR Executive Services

More information on Dr. Gunther Kegel (President, ZVEI): See the full profile on EMR Executive Services

 

More information on The European Union: https://european-union.europa.eu/index_en + The European Union’s institutional set-up is unique and its decision-making system is constantly evolving. The 7 European institutions, 7 EU bodies and over 30 decentralised agencies are spread across the EU. They work together to address the common interests of the EU and European people. 

In terms of administration, there are a further 20 EU agencies and organisations which carry out specific legal functions and 4 interinstitutional services which support the institutions.

All of these establishments have specific roles – from developing EU laws and policy-making to implementing policies and working on specialist areas, such as health, medicine, transport and the environment.

There are 4 main decision-making institutions which lead the EU’s administration. These institutions collectively provide the EU with policy direction and play different roles in the law-making process: 

• the European Parliament (Brussels/Strasbourg/Luxembourg)
• the European Council (Brussels)
• the Council of the European Union (Brussels/Luxembourg)
• the European Commission (Brussels/Luxembourg/Representations across the EU)

Their work is complemented by other institutions and bodies, which include:

• the Court of Justice of the European Union (Luxembourg)
• the European Central Bank (Frankfurt)
• the European Court of Auditors (Luxembourg)

The EU institutions and bodies cooperate extensively with the network of EU agencies and organisations across the European Union. The primary function of these bodies and agencies is to translate policies into realities on the ground.

Around 60,000 EU civil servants and other staff serve the 450 million Europeans (and countless others around the world).

Currently, 27 countries are part of the EU: https://european-union.europa.eu/principles-countries-history/country-profiles_en 

More information on The European Commission: https://ec.europa.eu/info/index_en + The Commission helps to shape the EU’s overall strategy, proposes new EU laws and policies, monitors their implementation and manages the EU budget. It also plays a significant role in supporting international development and delivering aid.

The Commission is steered by a group of 27 Commissioners, known as ‘the college’. Together they take decisions on the Commission’s political and strategic direction.

A new college of Commissioners is appointed every 5 years.

The Commission is organised into policy departments, known as Directorates-General (DGs), which are responsible for different policy areas. DGs develop, implement and manage EU policy, law, and funding programmes. In addition, service departments deal with particular administrative issues. Executive agencies manage programmes set up by the Commission.

Principal roles in law: The Commission proposes and implements laws which are in keeping with the objectives of the EU treaties. It encourages input from business and citizens in the law-making process and ensures laws are correctly implemented, evaluated and updated when needed.

More information on Ursula von der Leyen (President, The European Commission): https://ec.europa.eu/commission/commissioners/2019-2024/president_en + https://www.linkedin.com/in/ursula-von-der-leyen/ 

 

More information on the EU AI Act: https://www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence + The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law.

As part of its digital strategy, the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits, such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy.

In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. The different risk levels will mean more or less regulation. Once approved, these will be the world’s first rules on AI.

AI Act: different rules for different risk levels: The new rules establish obligations for providers and users depending on the level of risk from artificial intelligence. While many AI systems pose minimal risk, they need to be assessed.

• Unacceptable risk: Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include:
  • Cognitive behavioural manipulation of people or specific vulnerable groups: for example voice-activated toys that encourage dangerous behaviour in children
  • Social scoring: classifying people based on behaviour, socio-economic status or personal characteristics
  • Biometric identification and categorisation of people
  • Real-time and remote biometric identification systems, such as facial recognition
  • Some exceptions may be allowed for law enforcement purposes. “Real-time” remote biometric identification systems will be allowed in a limited number of serious cases, while “post” remote biometric identification systems, where identification occurs after a significant delay, will be allowed to prosecute serious crimes and only after court approval.
• High risk: AI systems that negatively affect safety or fundamental rights will be considered high risk and will be divided into two categories:
  • 1) AI systems that are used in products falling under the EU’s product safety legislation. This includes toys, aviation, cars, medical devices and lifts.
  • 2) AI systems falling into specific areas that will have to be registered in an EU database:
    • Management and operation of critical infrastructure
    • Education and vocational training
    • Employment, worker management and access to self-employment
    • Access to and enjoyment of essential private services and public services and benefits
    • Law enforcement
    • Migration, asylum and border control management
    • Assistance in legal interpretation and application of the law.
  • All high-risk AI systems will be assessed before being put on the market and also throughout their lifecycle.
•  General purpose and generative AI: Generative AI, like ChatGPT, would have to comply with transparency requirements:
  • Disclosing that the content was generated by AI
  • Designing the model to prevent it from generating illegal content
  • Publishing summaries of copyrighted data used for training
  • High-impact general-purpose AI models that might pose systemic risk, such as the more advanced AI model GPT-4, would have to undergo thorough evaluations and any serious incidents would have to be reported to the European Commission.
• Limited risk: Limited risk AI systems should comply with minimal transparency requirements that would allow users to make informed decisions. After interacting with the applications, the user can then decide whether they want to continue using it. Users should be made aware when they are interacting with AI. This includes AI systems that generate or manipulate image, audio or video content, for example deepfakes.
• Next steps: On December 9 2023, Parliament reached a provisional agreement with the Council on the AI act. The agreed text will now have to be formally adopted by both Parliament and Council to become EU law.

 

 

 

EMR Additional Notes:

•  AI – Artificial Intelligence:
  • https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence  +
    • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
    • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but a few, including Python, R and Java, are popular.
    • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
    • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
    • What are the 4 types of artificial intelligence?
      • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
      • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
      • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
      • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
    • Machine Learning:
      • Developed to mimic human intelligence. It lets the machines learn independently by ingesting vast amounts of data and detecting patterns. Many ML algorithms use statistics formulas and big data to function.
      • Type of artificial intelligence allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
      • Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and Predictive maintenance.
      • Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.
    • Deep Learning:
      • Subset of machine learning. Deep learning enabled much smarter results than were originally possible with machine learning. Consider the face recognition example.
      • Deep learning makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones learn about shapes, the following about combinations of those shapes, and finally actual objects. Deep learning demonstrated a breakthrough in object recognition.
      • Deep learning is currently the most sophisticated AI architecture we have developed.
    • Computer Vision:
      • Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information.
      • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision:
      • Machine vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine vision is similar in complexity to voice recognition.
      • Machine vision uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
      • Computer vision systems can gain valuable information from images, videos, and other visuals, whereas machine vision systems rely on the image captured by the system’s camera. Another difference is that computer vision systems are commonly used to extract and use as much data as possible about an object.
• Generative AI:
  • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.

 

• Edge AI Technology:
  • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
  • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
  • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.