Since the beginning of the year, artificial intelligence (AI) is no longer a topic of a few, but has become the talk of the masses. Just about all the media are reporting on it and the general public is talking about it. Hopes, fears and silliness are exchanged. No AI-powered platform has ever grown so fast. ChatGPT, however, is far from the end of the line – rather, it's the starting gun of a rapid evolution. It demonstrates that AI is already an integral part of the present.
In the future, we will be confronted with the realization of previously utopian visions of the future - with all the advantages and disadvantages. Society is facing a huge upheaval, rules have to be redefined and concessions made to innovations.
Since the beginning of the year, artificial intelligence (AI) is no longer a topic of a few, but has become the talk of the masses. Just about all the media are reporting on it and the general public is talking about it. Hopes, fears and silliness are exchanged. No AI-powered platform has ever grown so fast. ChatGPT, however, is far from the end of the line – rather, it's the starting gun of a rapid evolution. It demonstrates that AI is already an integral part of the present.
In the future, we will be confronted with the realization of previously utopian visions of the future - with all the advantages and disadvantages. Society is facing a huge upheaval, rules have to be redefined and concessions made to innovations.
Since the beginning of the year, artificial intelligence (AI) is no longer a topic of a few, but has become the talk of the masses. Just about all the media are reporting on it and the general public is talking about it. Hopes, fears and silliness are exchanged. No AI-powered platform has ever grown so fast. ChatGPT, however, is far from the end of the line – rather, it's the starting gun of a rapid evolution. It demonstrates that AI is already an integral part of the present.
In the future, we will be confronted with the realization of previously utopian visions of the future - with all the advantages and disadvantages. Society is facing a huge upheaval, rules have to be redefined and concessions made to innovations.
The original idea was always to give machines the capabilities to take tasks off our hands with the help of human-like intelligence. AI is supposed to assist humans with time-consuming, data-rich and also cumbersome processes, reducing time, resources and errors. Mankind's desire to make work easier for itself and thus create more resources for further development goes back a long way. Starting with the most primitive tools over slide rules up to machine-like inventions.The term "artificial intelligence" has been around since the 1950s, and in recent decades it has developed faster and more extensively than ever before, yet its origins date back to the 19th century.In 1837 Charles Babbages presented one of the first and probably the most famous attempt to simulate human intelligence. The Analytical Engine was a mechanical general-purpose computer that could perform complex mathematical operations.In the 1950s, the modern concept of AI was approached. Scientist Alan Turing created the so-called Turing Test, a benchmark for determining the intelligence of computers. The test consisted of a judge deciding what was computer and what was human based on a conversation presented to him. If the judge could not distinguish or misattributed, the computer was classified as intelligent.Beginning in the 1960s, there was an "AI winter"; a time when enthusiasm for research in this area waned and no progress worth mentioning was recorded. At that time, one was limited to using specified algorithms for individual functions; these were static, but reliable.Only thanks to remarkable and still popular successes through the development of computer chess programs, such as IBM Deep Blue, voice assistants, especially Siri and Alexa and autonomous driving, the topic gained popularity again. The technical breakthroughs and social interest also boosted research. Now AI is enjoying renewed attention due to ChatGPT, which is spurring the development of applications based on or inspired by it.
The original idea was always to give machines the capabilities to take tasks off our hands with the help of human-like intelligence. AI is supposed to assist humans with time-consuming, data-rich and also cumbersome processes, reducing time, resources and errors. Mankind's desire to make work easier for itself and thus create more resources for further development goes back a long way. Starting with the most primitive tools over slide rules up to machine-like inventions.The term "artificial intelligence" has been around since the 1950s, and in recent decades it has developed faster and more extensively than ever before, yet its origins date back to the 19th century.In 1837 Charles Babbages presented one of the first and probably the most famous attempt to simulate human intelligence. The Analytical Engine was a mechanical general-purpose computer that could perform complex mathematical operations.In the 1950s, the modern concept of AI was approached. Scientist Alan Turing created the so-called Turing Test, a benchmark for determining the intelligence of computers. The test consisted of a judge deciding what was computer and what was human based on a conversation presented to him. If the judge could not distinguish or misattributed, the computer was classified as intelligent.Beginning in the 1960s, there was an "AI winter"; a time when enthusiasm for research in this area waned and no progress worth mentioning was recorded. At that time, one was limited to using specified algorithms for individual functions; these were static, but reliable.Only thanks to remarkable and still popular successes through the development of computer chess programs, such as IBM Deep Blue, voice assistants, especially Siri and Alexa and autonomous driving, the topic gained popularity again. The technical breakthroughs and social interest also boosted research. Now AI is enjoying renewed attention due to ChatGPT, which is spurring the development of applications based on or inspired by it.
The original idea was always to give machines the capabilities to take tasks off our hands with the help of human-like intelligence. AI is supposed to assist humans with time-consuming, data-rich and also cumbersome processes, reducing time, resources and errors. Mankind's desire to make work easier for itself and thus create more resources for further development goes back a long way. Starting with the most primitive tools over slide rules up to machine-like inventions.The term "artificial intelligence" has been around since the 1950s, and in recent decades it has developed faster and more extensively than ever before, yet its origins date back to the 19th century.In 1837 Charles Babbages presented one of the first and probably the most famous attempt to simulate human intelligence. The Analytical Engine was a mechanical general-purpose computer that could perform complex mathematical operations.In the 1950s, the modern concept of AI was approached. Scientist Alan Turing created the so-called Turing Test, a benchmark for determining the intelligence of computers. The test consisted of a judge deciding what was computer and what was human based on a conversation presented to him. If the judge could not distinguish or misattributed, the computer was classified as intelligent.Beginning in the 1960s, there was an "AI winter"; a time when enthusiasm for research in this area waned and no progress worth mentioning was recorded. At that time, one was limited to using specified algorithms for individual functions; these were static, but reliable.Only thanks to remarkable and still popular successes through the development of computer chess programs, such as IBM Deep Blue, voice assistants, especially Siri and Alexa and autonomous driving, the topic gained popularity again. The technical breakthroughs and social interest also boosted research. Now AI is enjoying renewed attention due to ChatGPT, which is spurring the development of applications based on or inspired by it.
The original idea was always to give machines the capabilities to take tasks off our hands with the help of human-like intelligence. AI is supposed to assist humans with time-consuming, data-rich and also cumbersome processes, reducing time, resources and errors. Mankind's desire to make work easier for itself and thus create more resources for further development goes back a long way. Starting with the most primitive tools over slide rules up to machine-like inventions.The term "artificial intelligence" has been around since the 1950s, and in recent decades it has developed faster and more extensively than ever before, yet its origins date back to the 19th century.In 1837 Charles Babbages presented one of the first and probably the most famous attempt to simulate human intelligence. The Analytical Engine was a mechanical general-purpose computer that could perform complex mathematical operations.In the 1950s, the modern concept of AI was approached. Scientist Alan Turing created the so-called Turing Test, a benchmark for determining the intelligence of computers. The test consisted of a judge deciding what was computer and what was human based on a conversation presented to him. If the judge could not distinguish or misattributed, the computer was classified as intelligent.Beginning in the 1960s, there was an "AI winter"; a time when enthusiasm for research in this area waned and no progress worth mentioning was recorded. At that time, one was limited to using specified algorithms for individual functions; these were static, but reliable.Only thanks to remarkable and still popular successes through the development of computer chess programs, such as IBM Deep Blue, voice assistants, especially Siri and Alexa and autonomous driving, the topic gained popularity again. The technical breakthroughs and social interest also boosted research. Now AI is enjoying renewed attention due to ChatGPT, which is spurring the development of applications based on or inspired by it.
The original idea was always to give machines the capabilities to take tasks off our hands with the help of human-like intelligence. AI is supposed to assist humans with time-consuming, data-rich and also cumbersome processes, reducing time, resources and errors. Mankind's desire to make work easier for itself and thus create more resources for further development goes back a long way. Starting with the most primitive tools over slide rules up to machine-like inventions.The term "artificial intelligence" has been around since the 1950s, and in recent decades it has developed faster and more extensively than ever before, yet its origins date back to the 19th century.In 1837 Charles Babbages presented one of the first and probably the most famous attempt to simulate human intelligence. The Analytical Engine was a mechanical general-purpose computer that could perform complex mathematical operations.In the 1950s, the modern concept of AI was approached. Scientist Alan Turing created the so-called Turing Test, a benchmark for determining the intelligence of computers. The test consisted of a judge deciding what was computer and what was human based on a conversation presented to him. If the judge could not distinguish or misattributed, the computer was classified as intelligent.Beginning in the 1960s, there was an "AI winter"; a time when enthusiasm for research in this area waned and no progress worth mentioning was recorded. At that time, one was limited to using specified algorithms for individual functions; these were static, but reliable.Only thanks to remarkable and still popular successes through the development of computer chess programs, such as IBM Deep Blue, voice assistants, especially Siri and Alexa and autonomous driving, the topic gained popularity again. The technical breakthroughs and social interest also boosted research. Now AI is enjoying renewed attention due to ChatGPT, which is spurring the development of applications based on or inspired by it.
AI should not be understood as a singular technology; rather, it is composed of several that, in combination, enable machines to learn, analyze, and act with human-like intelligence in the end result.
Human-like intelligence means that a machine is not just a rule-based system, but can make decisions and act on its own based on "sensory perceptions." A machine that understands natural language can receive commands differently. It no longer needs "translation." A machine that can see understands images or objects and can react to them. A machine that interprets errors and learns from them evolves itself and becomes more accurate. Basically, the end product AI consists of the following technologies.
In the Beginning Was the Word
Natural language processing is designed to enable human-machine communication using natural language. This requires understanding and correctly interpreting the language and syntax of the person speaking. Instead of having to master tools or program commands, we explain to the machine in our language what the objective is - the AI then takes over the processing and implementation.
Large Language Models (LLM) are deep neural networks trained specifically for natural language processing. They can understand and respond to natural language and are capable of performing tasks such as translation, text generation, and text summarization.
Everything in View
Computer vision is the recognition of objects, patterns, and general features from digitized images and videos.
This is also what computer vision is built on - the ability to understand and interpret images and videos. This technology is used to develop systems capable of recognizing faces, identifying objects and tracking movement. This process involves image analysis and related techniques. It involves trying to determine a specific meaning or structure from the extensive visual information in an image.
Another related technology that has undergone massive development in recent years is Generative Adversarial Networks (GANs). GANs consist of two neural networks, a generator and a discriminator, which are trained against each other to generate realistic images, text, and other media.
From Student to Teacher
Machine learning is the ability to learn from the results of previous data inputs. It is the basis for autonomous development and increasingly accurate software. Unlike humans, machines do not forget mistakes made; in fact, they make mistakes only once. With the ability to automatically respond to changes in data, the machine is consistently adaptive.
Another important area of AI development is the use of Reinforcement Learning (RL), which is a learning approach in which an agent receives rewards by performing actions in an environment to optimize its behavior. RL is used to develop autonomous systems such as self-driving cars and drones, and in games where the goal is to find the best strategies.
Neural Networks
Neural networks make it possible to solve complex problems with programs that are already known. Unlike conventional programming languages, in which a specific problem and its given solution must first be described, neural networks are used to analyze information, create new information, and create their own solution.
This can then serve as the basis for further development. A neural network is an artificial network consisting of several layers of neurons (in the form of mathematical models) that are interconnected. Each neuron holds a task based on which it can be put to use.
Overall, machines are being trained in increasingly human tasks, increasing the performance and applicability of AI systems, leading to machines being able to take over more and more of the human tasks.
AI should not be understood as a singular technology; rather, it is composed of several that, in combination, enable machines to learn, analyze, and act with human-like intelligence in the end result.
Human-like intelligence means that a machine is not just a rule-based system, but can make decisions and act on its own based on "sensory perceptions." A machine that understands natural language can receive commands differently. It no longer needs "translation." A machine that can see understands images or objects and can react to them. A machine that interprets errors and learns from them evolves itself and becomes more accurate. Basically, the end product AI consists of the following technologies.
In the Beginning Was the Word
Natural language processing is designed to enable human-machine communication using natural language. This requires understanding and correctly interpreting the language and syntax of the person speaking. Instead of having to master tools or program commands, we explain to the machine in our language what the objective is - the AI then takes over the processing and implementation.
Large Language Models (LLM) are deep neural networks trained specifically for natural language processing. They can understand and respond to natural language and are capable of performing tasks such as translation, text generation, and text summarization.
Everything in View
Computer vision is the recognition of objects, patterns, and general features from digitized images and videos.
This is also what computer vision is built on - the ability to understand and interpret images and videos. This technology is used to develop systems capable of recognizing faces, identifying objects and tracking movement. This process involves image analysis and related techniques. It involves trying to determine a specific meaning or structure from the extensive visual information in an image.
Another related technology that has undergone massive development in recent years is Generative Adversarial Networks (GANs). GANs consist of two neural networks, a generator and a discriminator, which are trained against each other to generate realistic images, text, and other media.
From Student to Teacher
Machine learning is the ability to learn from the results of previous data inputs. It is the basis for autonomous development and increasingly accurate software. Unlike humans, machines do not forget mistakes made; in fact, they make mistakes only once. With the ability to automatically respond to changes in data, the machine is consistently adaptive.
Another important area of AI development is the use of Reinforcement Learning (RL), which is a learning approach in which an agent receives rewards by performing actions in an environment to optimize its behavior. RL is used to develop autonomous systems such as self-driving cars and drones, and in games where the goal is to find the best strategies.
Neural Networks
Neural networks make it possible to solve complex problems with programs that are already known. Unlike conventional programming languages, in which a specific problem and its given solution must first be described, neural networks are used to analyze information, create new information, and create their own solution.
This can then serve as the basis for further development. A neural network is an artificial network consisting of several layers of neurons (in the form of mathematical models) that are interconnected. Each neuron holds a task based on which it can be put to use.
Overall, machines are being trained in increasingly human tasks, increasing the performance and applicability of AI systems, leading to machines being able to take over more and more of the human tasks.
AI should not be understood as a singular technology; rather, it is composed of several that, in combination, enable machines to learn, analyze, and act with human-like intelligence in the end result.
Human-like intelligence means that a machine is not just a rule-based system, but can make decisions and act on its own based on "sensory perceptions." A machine that understands natural language can receive commands differently. It no longer needs "translation." A machine that can see understands images or objects and can react to them. A machine that interprets errors and learns from them evolves itself and becomes more accurate. Basically, the end product AI consists of the following technologies.
In the Beginning Was the Word
Natural language processing is designed to enable human-machine communication using natural language. This requires understanding and correctly interpreting the language and syntax of the person speaking. Instead of having to master tools or program commands, we explain to the machine in our language what the objective is - the AI then takes over the processing and implementation.
Large Language Models (LLM) are deep neural networks trained specifically for natural language processing. They can understand and respond to natural language and are capable of performing tasks such as translation, text generation, and text summarization.
Everything in View
Computer vision is the recognition of objects, patterns, and general features from digitized images and videos.
This is also what computer vision is built on - the ability to understand and interpret images and videos. This technology is used to develop systems capable of recognizing faces, identifying objects and tracking movement. This process involves image analysis and related techniques. It involves trying to determine a specific meaning or structure from the extensive visual information in an image.
Another related technology that has undergone massive development in recent years is Generative Adversarial Networks (GANs). GANs consist of two neural networks, a generator and a discriminator, which are trained against each other to generate realistic images, text, and other media.
From Student to Teacher
Machine learning is the ability to learn from the results of previous data inputs. It is the basis for autonomous development and increasingly accurate software. Unlike humans, machines do not forget mistakes made; in fact, they make mistakes only once. With the ability to automatically respond to changes in data, the machine is consistently adaptive.
Another important area of AI development is the use of Reinforcement Learning (RL), which is a learning approach in which an agent receives rewards by performing actions in an environment to optimize its behavior. RL is used to develop autonomous systems such as self-driving cars and drones, and in games where the goal is to find the best strategies.
Neural Networks
Neural networks make it possible to solve complex problems with programs that are already known. Unlike conventional programming languages, in which a specific problem and its given solution must first be described, neural networks are used to analyze information, create new information, and create their own solution.
This can then serve as the basis for further development. A neural network is an artificial network consisting of several layers of neurons (in the form of mathematical models) that are interconnected. Each neuron holds a task based on which it can be put to use.
Overall, machines are being trained in increasingly human tasks, increasing the performance and applicability of AI systems, leading to machines being able to take over more and more of the human tasks.
AI should not be understood as a singular technology; rather, it is composed of several that, in combination, enable machines to learn, analyze, and act with human-like intelligence in the end result.
Human-like intelligence means that a machine is not just a rule-based system, but can make decisions and act on its own based on "sensory perceptions." A machine that understands natural language can receive commands differently. It no longer needs "translation." A machine that can see understands images or objects and can react to them. A machine that interprets errors and learns from them evolves itself and becomes more accurate. Basically, the end product AI consists of the following technologies.
In the Beginning Was the Word
Natural language processing is designed to enable human-machine communication using natural language. This requires understanding and correctly interpreting the language and syntax of the person speaking. Instead of having to master tools or program commands, we explain to the machine in our language what the objective is - the AI then takes over the processing and implementation.
Large Language Models (LLM) are deep neural networks trained specifically for natural language processing. They can understand and respond to natural language and are capable of performing tasks such as translation, text generation, and text summarization.
Everything in View
Computer vision is the recognition of objects, patterns, and general features from digitized images and videos.
This is also what computer vision is built on - the ability to understand and interpret images and videos. This technology is used to develop systems capable of recognizing faces, identifying objects and tracking movement. This process involves image analysis and related techniques. It involves trying to determine a specific meaning or structure from the extensive visual information in an image.
Another related technology that has undergone massive development in recent years is Generative Adversarial Networks (GANs). GANs consist of two neural networks, a generator and a discriminator, which are trained against each other to generate realistic images, text, and other media.
From Student to Teacher
Machine learning is the ability to learn from the results of previous data inputs. It is the basis for autonomous development and increasingly accurate software. Unlike humans, machines do not forget mistakes made; in fact, they make mistakes only once. With the ability to automatically respond to changes in data, the machine is consistently adaptive.
Another important area of AI development is the use of Reinforcement Learning (RL), which is a learning approach in which an agent receives rewards by performing actions in an environment to optimize its behavior. RL is used to develop autonomous systems such as self-driving cars and drones, and in games where the goal is to find the best strategies.
Neural Networks
Neural networks make it possible to solve complex problems with programs that are already known. Unlike conventional programming languages, in which a specific problem and its given solution must first be described, neural networks are used to analyze information, create new information, and create their own solution.
This can then serve as the basis for further development. A neural network is an artificial network consisting of several layers of neurons (in the form of mathematical models) that are interconnected. Each neuron holds a task based on which it can be put to use.
Overall, machines are being trained in increasingly human tasks, increasing the performance and applicability of AI systems, leading to machines being able to take over more and more of the human tasks.
AI should not be understood as a singular technology; rather, it is composed of several that, in combination, enable machines to learn, analyze, and act with human-like intelligence in the end result.
Human-like intelligence means that a machine is not just a rule-based system, but can make decisions and act on its own based on "sensory perceptions." A machine that understands natural language can receive commands differently. It no longer needs "translation." A machine that can see understands images or objects and can react to them. A machine that interprets errors and learns from them evolves itself and becomes more accurate. Basically, the end product AI consists of the following technologies.
In the Beginning Was the Word
Natural language processing is designed to enable human-machine communication using natural language. This requires understanding and correctly interpreting the language and syntax of the person speaking. Instead of having to master tools or program commands, we explain to the machine in our language what the objective is - the AI then takes over the processing and implementation.
Large Language Models (LLM) are deep neural networks trained specifically for natural language processing. They can understand and respond to natural language and are capable of performing tasks such as translation, text generation, and text summarization.
Everything in View
Computer vision is the recognition of objects, patterns, and general features from digitized images and videos.
This is also what computer vision is built on - the ability to understand and interpret images and videos. This technology is used to develop systems capable of recognizing faces, identifying objects and tracking movement. This process involves image analysis and related techniques. It involves trying to determine a specific meaning or structure from the extensive visual information in an image.
Another related technology that has undergone massive development in recent years is Generative Adversarial Networks (GANs). GANs consist of two neural networks, a generator and a discriminator, which are trained against each other to generate realistic images, text, and other media.
From Student to Teacher
Machine learning is the ability to learn from the results of previous data inputs. It is the basis for autonomous development and increasingly accurate software. Unlike humans, machines do not forget mistakes made; in fact, they make mistakes only once. With the ability to automatically respond to changes in data, the machine is consistently adaptive.
Another important area of AI development is the use of Reinforcement Learning (RL), which is a learning approach in which an agent receives rewards by performing actions in an environment to optimize its behavior. RL is used to develop autonomous systems such as self-driving cars and drones, and in games where the goal is to find the best strategies.
Neural Networks
Neural networks make it possible to solve complex problems with programs that are already known. Unlike conventional programming languages, in which a specific problem and its given solution must first be described, neural networks are used to analyze information, create new information, and create their own solution.
This can then serve as the basis for further development. A neural network is an artificial network consisting of several layers of neurons (in the form of mathematical models) that are interconnected. Each neuron holds a task based on which it can be put to use.
Overall, machines are being trained in increasingly human tasks, increasing the performance and applicability of AI systems, leading to machines being able to take over more and more of the human tasks.
AI should not be understood as a singular technology; rather, it is composed of several that, in combination, enable machines to learn, analyze, and act with human-like intelligence in the end result.
Human-like intelligence means that a machine is not just a rule-based system, but can make decisions and act on its own based on "sensory perceptions." A machine that understands natural language can receive commands differently. It no longer needs "translation." A machine that can see understands images or objects and can react to them. A machine that interprets errors and learns from them evolves itself and becomes more accurate. Basically, the end product AI consists of the following technologies.
A well-known example of an LLM is GPT-3, developed by OpenAI. GPT-3 has shown impressive performance in tasks such as text generation and text summarization, and in some cases can even do tasks that previously could only be done by humans.
AI systems are being used to automate the recruitment of job applicants by scanning resumes, conducting interviews, and recommending suitable candidates.
AI systems are used to automate production processes, monitor product quality, and reduce failure rates.
AI systems are used to analyze customer behavior, generate personalized offers, and measure the effectiveness of marketing campaigns.
AI systems are used to automatically answer customer inquiries, solve problems, and provide personalized recommendations.
AI systems are used to analyze medical images, make diagnoses, and create personalized treatment plans.
AI systems are used to detect potential threats, control access to protected areas, and automatically trigger alarms.
AI systems are used to optimize the performance of power plants and networks, increase energy efficiency, and forecast energy production.
AI systems are used to manage inventory, make product recommendations, and automatically place orders.
AI systems are used to control self-driving vehicles, plan routes, and optimize traffic flows.
AI systems are used to analyze financial data, make forecasts, and automatically make trading decisions.
There are many more areas where AI has made and will continue to make great strides. These examples show how AI is being used in various industries to automate processes, analyze data, and make decisions to improve people's lives. Most importantly, it's important to remember that AI developments and advancements are growing exponentially. Moreover, 92% of all German AI companies founded since 1995 are still economically active; in 2021, there were 150,000 companies and individuals working in this field in Germany as a whole. The output generated by this multitude of companies is enormous. One of Tesla's software developers recently announced that 90% of the code is now written by AI.
So it remains exciting to see what else will be made possible in the near future and what innovations will come onto the market.
AI should not be understood as a singular technology; rather, it is composed of several that, in combination, enable machines to learn, analyze, and act with human-like intelligence in the end result.
Human-like intelligence means that a machine is not just a rule-based system, but can make decisions and act on its own based on "sensory perceptions." A machine that understands natural language can receive commands differently. It no longer needs "translation." A machine that can see understands images or objects and can react to them. A machine that interprets errors and learns from them evolves itself and becomes more accurate. Basically, the end product AI consists of the following technologies.
A well-known example of an LLM is GPT-3, developed by OpenAI. GPT-3 has shown impressive performance in tasks such as text generation and text summarization, and in some cases can even do tasks that previously could only be done by humans.
AI systems are being used to automate the recruitment of job applicants by scanning resumes, conducting interviews, and recommending suitable candidates.
AI systems are used to automate production processes, monitor product quality, and reduce failure rates.
AI systems are used to analyze customer behavior, generate personalized offers, and measure the effectiveness of marketing campaigns.
AI systems are used to automatically answer customer inquiries, solve problems, and provide personalized recommendations.
AI systems are used to analyze medical images, make diagnoses, and create personalized treatment plans.
AI systems are used to detect potential threats, control access to protected areas, and automatically trigger alarms.
AI systems are used to optimize the performance of power plants and networks, increase energy efficiency, and forecast energy production.
AI systems are used to manage inventory, make product recommendations, and automatically place orders.
AI systems are used to control self-driving vehicles, plan routes, and optimize traffic flows.
AI systems are used to analyze financial data, make forecasts, and automatically make trading decisions.
There are many more areas where AI has made and will continue to make great strides. These examples show how AI is being used in various industries to automate processes, analyze data, and make decisions to improve people's lives. Most importantly, it's important to remember that AI developments and advancements are growing exponentially. Moreover, 92% of all German AI companies founded since 1995 are still economically active; in 2021, there were 150,000 companies and individuals working in this field in Germany as a whole. The output generated by this multitude of companies is enormous. One of Tesla's software developers recently announced that 90% of the code is now written by AI.
So it remains exciting to see what else will be made possible in the near future and what innovations will come onto the market.
AI should not be understood as a singular technology; rather, it is composed of several that, in combination, enable machines to learn, analyze, and act with human-like intelligence in the end result.
Human-like intelligence means that a machine is not just a rule-based system, but can make decisions and act on its own based on "sensory perceptions." A machine that understands natural language can receive commands differently. It no longer needs "translation." A machine that can see understands images or objects and can react to them. A machine that interprets errors and learns from them evolves itself and becomes more accurate. Basically, the end product AI consists of the following technologies.
A well-known example of an LLM is GPT-3, developed by OpenAI. GPT-3 has shown impressive performance in tasks such as text generation and text summarization, and in some cases can even do tasks that previously could only be done by humans.
AI systems are being used to automate the recruitment of job applicants by scanning resumes, conducting interviews, and recommending suitable candidates.
AI systems are used to automate production processes, monitor product quality, and reduce failure rates.
AI systems are used to analyze customer behavior, generate personalized offers, and measure the effectiveness of marketing campaigns.
AI systems are used to automatically answer customer inquiries, solve problems, and provide personalized recommendations.
AI systems are used to analyze medical images, make diagnoses, and create personalized treatment plans.
AI systems are used to detect potential threats, control access to protected areas, and automatically trigger alarms.
AI systems are used to optimize the performance of power plants and networks, increase energy efficiency, and forecast energy production.
AI systems are used to manage inventory, make product recommendations, and automatically place orders.
AI systems are used to control self-driving vehicles, plan routes, and optimize traffic flows.
AI systems are used to analyze financial data, make forecasts, and automatically make trading decisions.
There are many more areas where AI has made and will continue to make great strides. These examples show how AI is being used in various industries to automate processes, analyze data, and make decisions to improve people's lives. Most importantly, it's important to remember that AI developments and advancements are growing exponentially. Moreover, 92% of all German AI companies founded since 1995 are still economically active; in 2021, there were 150,000 companies and individuals working in this field in Germany as a whole. The output generated by this multitude of companies is enormous. One of Tesla's software developers recently announced that 90% of the code is now written by AI.
So it remains exciting to see what else will be made possible in the near future and what innovations will come onto the market.
AI should not be understood as a singular technology; rather, it is composed of several that, in combination, enable machines to learn, analyze, and act with human-like intelligence in the end result.
Human-like intelligence means that a machine is not just a rule-based system, but can make decisions and act on its own based on "sensory perceptions." A machine that understands natural language can receive commands differently. It no longer needs "translation." A machine that can see understands images or objects and can react to them. A machine that interprets errors and learns from them evolves itself and becomes more accurate. Basically, the end product AI consists of the following technologies.
A well-known example of an LLM is GPT-3, developed by OpenAI. GPT-3 has shown impressive performance in tasks such as text generation and text summarization, and in some cases can even do tasks that previously could only be done by humans.
AI systems are being used to automate the recruitment of job applicants by scanning resumes, conducting interviews, and recommending suitable candidates.
AI systems are used to automate production processes, monitor product quality, and reduce failure rates.
AI systems are used to analyze customer behavior, generate personalized offers, and measure the effectiveness of marketing campaigns.
AI systems are used to automatically answer customer inquiries, solve problems, and provide personalized recommendations.
AI systems are used to analyze medical images, make diagnoses, and create personalized treatment plans.
AI systems are used to detect potential threats, control access to protected areas, and automatically trigger alarms.
AI systems are used to optimize the performance of power plants and networks, increase energy efficiency, and forecast energy production.
AI systems are used to manage inventory, make product recommendations, and automatically place orders.
AI systems are used to control self-driving vehicles, plan routes, and optimize traffic flows.
AI systems are used to analyze financial data, make forecasts, and automatically make trading decisions.
There are many more areas where AI has made and will continue to make great strides. These examples show how AI is being used in various industries to automate processes, analyze data, and make decisions to improve people's lives. Most importantly, it's important to remember that AI developments and advancements are growing exponentially. Moreover, 92% of all German AI companies founded since 1995 are still economically active; in 2021, there were 150,000 companies and individuals working in this field in Germany as a whole. The output generated by this multitude of companies is enormous. One of Tesla's software developers recently announced that 90% of the code is now written by AI.
So it remains exciting to see what else will be made possible in the near future and what innovations will come onto the market.
AI should not be understood as a singular technology; rather, it is composed of several that, in combination, enable machines to learn, analyze, and act with human-like intelligence in the end result.
Human-like intelligence means that a machine is not just a rule-based system, but can make decisions and act on its own based on "sensory perceptions." A machine that understands natural language can receive commands differently. It no longer needs "translation." A machine that can see understands images or objects and can react to them. A machine that interprets errors and learns from them evolves itself and becomes more accurate. Basically, the end product AI consists of the following technologies.
A well-known example of an LLM is GPT-3, developed by OpenAI. GPT-3 has shown impressive performance in tasks such as text generation and text summarization, and in some cases can even do tasks that previously could only be done by humans.
AI systems are being used to automate the recruitment of job applicants by scanning resumes, conducting interviews, and recommending suitable candidates.
AI systems are used to automate production processes, monitor product quality, and reduce failure rates.
AI systems are used to analyze customer behavior, generate personalized offers, and measure the effectiveness of marketing campaigns.
AI systems are used to automatically answer customer inquiries, solve problems, and provide personalized recommendations.
AI systems are used to analyze medical images, make diagnoses, and create personalized treatment plans.
AI systems are used to detect potential threats, control access to protected areas, and automatically trigger alarms.
AI systems are used to optimize the performance of power plants and networks, increase energy efficiency, and forecast energy production.
AI systems are used to manage inventory, make product recommendations, and automatically place orders.
AI systems are used to control self-driving vehicles, plan routes, and optimize traffic flows.
AI systems are used to analyze financial data, make forecasts, and automatically make trading decisions.
There are many more areas where AI has made and will continue to make great strides. These examples show how AI is being used in various industries to automate processes, analyze data, and make decisions to improve people's lives. Most importantly, it's important to remember that AI developments and advancements are growing exponentially. Moreover, 92% of all German AI companies founded since 1995 are still economically active; in 2021, there were 150,000 companies and individuals working in this field in Germany as a whole. The output generated by this multitude of companies is enormous. One of Tesla's software developers recently announced that 90% of the code is now written by AI.
So it remains exciting to see what else will be made possible in the near future and what innovations will come onto the market.
The added value and speed of development in the present inspires us to think about a bright future - made possible by AI.A world where AI is used in many areas to simplify processes, reduce pollution, improve quality of life, and enhance human prosperity.
A world where AI solves our problems instead of amplifying them. A world where AI enhances our knowledge, skills and creativity, rather than replacing them. A world where AI helps us achieve our goals instead of monitoring us. Such a utopia may be difficult to achieve, but thanks to the ongoing development of AI, it is no longer just a dream. With a variety of technologies and tools constantly evolving, AI is also driving hope. Ultimately, AI can help us reach our full potential by assisting us with complex tasks and giving us more time and energy for what really matters.
It's hard to predict what breakthrough inventions will be possible in the coming decades thanks to AI. However, we can venture a prediction based on the biggest achievements so far:
We are storing more data than ever before and yet only manage to actually process 20% of the data we store. Every day, 2.5 million TB of data are stored. Every two years, the amount of available data is currently doubling, and the trend is rising. We are sitting on a treasure trove of data and are hardly able to use it comprehensively yet. With the support of AI, we will be empowered to master this flood of data. While data volumes were previously almost impossible to process, we can now analyze more and more data and optimize what was previously possible for us. Insights gained from this will give us cause and ideas to drive further innovations.
Artificial intelligence and machine learning offer the possibility of developing autonomous vehicles so that people and goods are transported reliably and safely, optimizing traffic flows and avoiding accidents. According to a study by US auto insurers, 33% of accidents could be avoided. Everyday life in our cities can also be transformed by intelligent, optimized urban planning. The widespread use of AI could reduce our energy consumption by up to 30%, so we could slow the advancing climate change and better protect ourselves from disasters caused by it, because alarm systems would detect such earlier. AI City as Utopia can become reality. Humans as the center of a modern reimagined city.
AI systems can also help detect more diseases early and make therapies more effective. Our research will become more potent through more efficient data processing. Our care will become more humane as repetitive, administrative tasks are taken over by AI. Humans will have more time for social, and AI support will create air for empathy and caring.
What's more, AI will make everyday life easier overall: by automating certain tasks, minimizing errors, and simplifying collaboration. Administration will be redeemed from being an end in itself. Government processes will become more digital and faster. Employees are no longer used as administrators but as designers. Time saved will create air for innovation and change.
AI systems will also open up new opportunities, such as the creation of improved diagnostic tools that provide fast and efficient insights into complex data sets, and the development of wearable technologies that allow information to be processed quickly and effectively. The previously common way of operating a computer and the tools it contains is being revolutionized by advances in human-machine communication. Clerks no longer have to create tables and lists in agonizingly lengthy processes, but instead formulate a goal - and AI does the rest. Direct communication with the machines simplifies operation and creates space for thinking about the result, not studying the way to get there. 94% of all companies that have worked with AI-based solutions so far are convinced of its innovative power and consider it an integral part of corporate developments in the coming years.
AI-based efficiency gains allow us to reallocate our limited resources and have more power and time to drive innovation and generally make our lives safer, more qualitative and more meaningful.
The added value and speed of development in the present inspires us to think about a bright future - made possible by AI.A world where AI is used in many areas to simplify processes, reduce pollution, improve quality of life, and enhance human prosperity.
A world where AI solves our problems instead of amplifying them. A world where AI enhances our knowledge, skills and creativity, rather than replacing them. A world where AI helps us achieve our goals instead of monitoring us. Such a utopia may be difficult to achieve, but thanks to the ongoing development of AI, it is no longer just a dream. With a variety of technologies and tools constantly evolving, AI is also driving hope. Ultimately, AI can help us reach our full potential by assisting us with complex tasks and giving us more time and energy for what really matters.
It's hard to predict what breakthrough inventions will be possible in the coming decades thanks to AI. However, we can venture a prediction based on the biggest achievements so far:
We are storing more data than ever before and yet only manage to actually process 20% of the data we store. Every day, 2.5 million TB of data are stored. Every two years, the amount of available data is currently doubling, and the trend is rising. We are sitting on a treasure trove of data and are hardly able to use it comprehensively yet. With the support of AI, we will be empowered to master this flood of data. While data volumes were previously almost impossible to process, we can now analyze more and more data and optimize what was previously possible for us. Insights gained from this will give us cause and ideas to drive further innovations.
Artificial intelligence and machine learning offer the possibility of developing autonomous vehicles so that people and goods are transported reliably and safely, optimizing traffic flows and avoiding accidents. According to a study by US auto insurers, 33% of accidents could be avoided. Everyday life in our cities can also be transformed by intelligent, optimized urban planning. The widespread use of AI could reduce our energy consumption by up to 30%, so we could slow the advancing climate change and better protect ourselves from disasters caused by it, because alarm systems would detect such earlier. AI City as Utopia can become reality. Humans as the center of a modern reimagined city.
AI systems can also help detect more diseases early and make therapies more effective. Our research will become more potent through more efficient data processing. Our care will become more humane as repetitive, administrative tasks are taken over by AI. Humans will have more time for social, and AI support will create air for empathy and caring.
What's more, AI will make everyday life easier overall: by automating certain tasks, minimizing errors, and simplifying collaboration. Administration will be redeemed from being an end in itself. Government processes will become more digital and faster. Employees are no longer used as administrators but as designers. Time saved will create air for innovation and change.
AI systems will also open up new opportunities, such as the creation of improved diagnostic tools that provide fast and efficient insights into complex data sets, and the development of wearable technologies that allow information to be processed quickly and effectively. The previously common way of operating a computer and the tools it contains is being revolutionized by advances in human-machine communication. Clerks no longer have to create tables and lists in agonizingly lengthy processes, but instead formulate a goal - and AI does the rest. Direct communication with the machines simplifies operation and creates space for thinking about the result, not studying the way to get there. 94% of all companies that have worked with AI-based solutions so far are convinced of its innovative power and consider it an integral part of corporate developments in the coming years.
AI-based efficiency gains allow us to reallocate our limited resources and have more power and time to drive innovation and generally make our lives safer, more qualitative and more meaningful.
The added value and speed of development in the present inspires us to think about a bright future - made possible by AI.A world where AI is used in many areas to simplify processes, reduce pollution, improve quality of life, and enhance human prosperity.
A world where AI solves our problems instead of amplifying them. A world where AI enhances our knowledge, skills and creativity, rather than replacing them. A world where AI helps us achieve our goals instead of monitoring us. Such a utopia may be difficult to achieve, but thanks to the ongoing development of AI, it is no longer just a dream. With a variety of technologies and tools constantly evolving, AI is also driving hope. Ultimately, AI can help us reach our full potential by assisting us with complex tasks and giving us more time and energy for what really matters.
It's hard to predict what breakthrough inventions will be possible in the coming decades thanks to AI. However, we can venture a prediction based on the biggest achievements so far:
We are storing more data than ever before and yet only manage to actually process 20% of the data we store. Every day, 2.5 million TB of data are stored. Every two years, the amount of available data is currently doubling, and the trend is rising. We are sitting on a treasure trove of data and are hardly able to use it comprehensively yet. With the support of AI, we will be empowered to master this flood of data. While data volumes were previously almost impossible to process, we can now analyze more and more data and optimize what was previously possible for us. Insights gained from this will give us cause and ideas to drive further innovations.
Artificial intelligence and machine learning offer the possibility of developing autonomous vehicles so that people and goods are transported reliably and safely, optimizing traffic flows and avoiding accidents. According to a study by US auto insurers, 33% of accidents could be avoided. Everyday life in our cities can also be transformed by intelligent, optimized urban planning. The widespread use of AI could reduce our energy consumption by up to 30%, so we could slow the advancing climate change and better protect ourselves from disasters caused by it, because alarm systems would detect such earlier. AI City as Utopia can become reality. Humans as the center of a modern reimagined city.
AI systems can also help detect more diseases early and make therapies more effective. Our research will become more potent through more efficient data processing. Our care will become more humane as repetitive, administrative tasks are taken over by AI. Humans will have more time for social, and AI support will create air for empathy and caring.
What's more, AI will make everyday life easier overall: by automating certain tasks, minimizing errors, and simplifying collaboration. Administration will be redeemed from being an end in itself. Government processes will become more digital and faster. Employees are no longer used as administrators but as designers. Time saved will create air for innovation and change.
AI systems will also open up new opportunities, such as the creation of improved diagnostic tools that provide fast and efficient insights into complex data sets, and the development of wearable technologies that allow information to be processed quickly and effectively. The previously common way of operating a computer and the tools it contains is being revolutionized by advances in human-machine communication. Clerks no longer have to create tables and lists in agonizingly lengthy processes, but instead formulate a goal - and AI does the rest. Direct communication with the machines simplifies operation and creates space for thinking about the result, not studying the way to get there. 94% of all companies that have worked with AI-based solutions so far are convinced of its innovative power and consider it an integral part of corporate developments in the coming years.
AI-based efficiency gains allow us to reallocate our limited resources and have more power and time to drive innovation and generally make our lives safer, more qualitative and more meaningful.
The added value and speed of development in the present inspires us to think about a bright future - made possible by AI.A world where AI is used in many areas to simplify processes, reduce pollution, improve quality of life, and enhance human prosperity.
A world where AI solves our problems instead of amplifying them. A world where AI enhances our knowledge, skills and creativity, rather than replacing them. A world where AI helps us achieve our goals instead of monitoring us. Such a utopia may be difficult to achieve, but thanks to the ongoing development of AI, it is no longer just a dream. With a variety of technologies and tools constantly evolving, AI is also driving hope. Ultimately, AI can help us reach our full potential by assisting us with complex tasks and giving us more time and energy for what really matters.
It's hard to predict what breakthrough inventions will be possible in the coming decades thanks to AI. However, we can venture a prediction based on the biggest achievements so far:
We are storing more data than ever before and yet only manage to actually process 20% of the data we store. Every day, 2.5 million TB of data are stored. Every two years, the amount of available data is currently doubling, and the trend is rising. We are sitting on a treasure trove of data and are hardly able to use it comprehensively yet. With the support of AI, we will be empowered to master this flood of data. While data volumes were previously almost impossible to process, we can now analyze more and more data and optimize what was previously possible for us. Insights gained from this will give us cause and ideas to drive further innovations.
Artificial intelligence and machine learning offer the possibility of developing autonomous vehicles so that people and goods are transported reliably and safely, optimizing traffic flows and avoiding accidents. According to a study by US auto insurers, 33% of accidents could be avoided. Everyday life in our cities can also be transformed by intelligent, optimized urban planning. The widespread use of AI could reduce our energy consumption by up to 30%, so we could slow the advancing climate change and better protect ourselves from disasters caused by it, because alarm systems would detect such earlier. AI City as Utopia can become reality. Humans as the center of a modern reimagined city.
AI systems can also help detect more diseases early and make therapies more effective. Our research will become more potent through more efficient data processing. Our care will become more humane as repetitive, administrative tasks are taken over by AI. Humans will have more time for social, and AI support will create air for empathy and caring.
What's more, AI will make everyday life easier overall: by automating certain tasks, minimizing errors, and simplifying collaboration. Administration will be redeemed from being an end in itself. Government processes will become more digital and faster. Employees are no longer used as administrators but as designers. Time saved will create air for innovation and change.
AI systems will also open up new opportunities, such as the creation of improved diagnostic tools that provide fast and efficient insights into complex data sets, and the development of wearable technologies that allow information to be processed quickly and effectively. The previously common way of operating a computer and the tools it contains is being revolutionized by advances in human-machine communication. Clerks no longer have to create tables and lists in agonizingly lengthy processes, but instead formulate a goal - and AI does the rest. Direct communication with the machines simplifies operation and creates space for thinking about the result, not studying the way to get there. 94% of all companies that have worked with AI-based solutions so far are convinced of its innovative power and consider it an integral part of corporate developments in the coming years.
AI-based efficiency gains allow us to reallocate our limited resources and have more power and time to drive innovation and generally make our lives safer, more qualitative and more meaningful.
The added value and speed of development in the present inspires us to think about a bright future - made possible by AI.A world where AI is used in many areas to simplify processes, reduce pollution, improve quality of life, and enhance human prosperity.
A world where AI solves our problems instead of amplifying them. A world where AI enhances our knowledge, skills and creativity, rather than replacing them. A world where AI helps us achieve our goals instead of monitoring us. Such a utopia may be difficult to achieve, but thanks to the ongoing development of AI, it is no longer just a dream. With a variety of technologies and tools constantly evolving, AI is also driving hope. Ultimately, AI can help us reach our full potential by assisting us with complex tasks and giving us more time and energy for what really matters.
It's hard to predict what breakthrough inventions will be possible in the coming decades thanks to AI. However, we can venture a prediction based on the biggest achievements so far:
We are storing more data than ever before and yet only manage to actually process 20% of the data we store. Every day, 2.5 million TB of data are stored. Every two years, the amount of available data is currently doubling, and the trend is rising. We are sitting on a treasure trove of data and are hardly able to use it comprehensively yet. With the support of AI, we will be empowered to master this flood of data. While data volumes were previously almost impossible to process, we can now analyze more and more data and optimize what was previously possible for us. Insights gained from this will give us cause and ideas to drive further innovations.
Artificial intelligence and machine learning offer the possibility of developing autonomous vehicles so that people and goods are transported reliably and safely, optimizing traffic flows and avoiding accidents. According to a study by US auto insurers, 33% of accidents could be avoided. Everyday life in our cities can also be transformed by intelligent, optimized urban planning. The widespread use of AI could reduce our energy consumption by up to 30%, so we could slow the advancing climate change and better protect ourselves from disasters caused by it, because alarm systems would detect such earlier. AI City as Utopia can become reality. Humans as the center of a modern reimagined city.
AI systems can also help detect more diseases early and make therapies more effective. Our research will become more potent through more efficient data processing. Our care will become more humane as repetitive, administrative tasks are taken over by AI. Humans will have more time for social, and AI support will create air for empathy and caring.
What's more, AI will make everyday life easier overall: by automating certain tasks, minimizing errors, and simplifying collaboration. Administration will be redeemed from being an end in itself. Government processes will become more digital and faster. Employees are no longer used as administrators but as designers. Time saved will create air for innovation and change.
AI systems will also open up new opportunities, such as the creation of improved diagnostic tools that provide fast and efficient insights into complex data sets, and the development of wearable technologies that allow information to be processed quickly and effectively. The previously common way of operating a computer and the tools it contains is being revolutionized by advances in human-machine communication. Clerks no longer have to create tables and lists in agonizingly lengthy processes, but instead formulate a goal - and AI does the rest. Direct communication with the machines simplifies operation and creates space for thinking about the result, not studying the way to get there. 94% of all companies that have worked with AI-based solutions so far are convinced of its innovative power and consider it an integral part of corporate developments in the coming years.
AI-based efficiency gains allow us to reallocate our limited resources and have more power and time to drive innovation and generally make our lives safer, more qualitative and more meaningful.
While research celebrates numerous successes in the development of AI and inspires us to imagine what may yet come, however, there are also at least as many challenges to consider that may not stand in the way of AI's development, but may stand in the way of its application. These challenges, fears, and limitations run through all areas of society and technology:
The current arguably most popular AI, GPT-3, and the ChatGPT program based on it are used by millions - playfully, skeptically, and devoutly. But if you ask the AI who is currently ruling a country, for example, you quickly discover that GPT-3 doesn't quite have the latest political developments on its screen and doesn't have any other current information either. The AI currently draws on knowledge up to 2021 and is therefore not able to take current events into account.
Journalists are breathing a sigh of relief, of course, because this is no way to write a serious article, and this industry is also safe for the time being, because investigative research is still based on real interpersonal contacts that are built on trust. The AI will hardly be able to convince a whistleblower to reveal secrets.
In general, it is difficult for the AI to distinguish truth from lies. Finally, the AI produces texts based on probability calculations: Which word is most likely to follow another. Thus, misinformation on the Internet not only poses a threat to humans, but is also the cause of miscalculations, or hallucinations, by AI. One can influence AI by flooding the data sources it uses with certain information. The AI does not have an abstract and reflected sense of truth. For it, propositions are pure mathematics.
While the ever-expanding capabilities impress research and development, the dystopia of evil AI usurping world domination is simultaneously fueled. Regardless of the level of technological development, we must also be prepared as a society to embrace and allow innovation. As recently as 2014, Elon Musk called AI "our greatest existential threat." With Bill Gates, the founder of Microsoft, Tim Bernes-Lee, the inventor of the World Wide Web, the greats of the tech scene also point to potential AI dangers. Apple co-founder Steve Wozniak, for example, said, "If we build these devices to take care of everything for us, eventually they will want to get rid of slow humans faster than we think and run businesses more efficiently.
Anthropomorphizing AI, by making its capabilities increasingly human-like, also runs the risk of interpreting too much into AI's intentions and imputing negative (human) things to it.
AI, however, has come under constant criticism, especially on social media. The discussion was reignited after supporters of Trump stormed the Capitol, and the center of the discussion became the algorithms that decide and specify what is displayed to users of the respective platforms. The idea of displaying ever more appropriate content took on a life of its own to the point where users were driven into a kind of opinion spiral. AI is meant to radicalize in this way. At the time, Mark Zuckerberg, the CEO of Meta, himself called for greater regulation of AI. Companies like GPT-3 are already working on forward-looking restrictions here, and are being quite open about replacing their current control mechanisms with better ones coming to market. So there is an awareness in the industry and they don't want to repeat past mistakes. Legislators worldwide are also becoming increasingly involved in this discussion.
Precisely because AI is on the rise to reach and enrich every aspect of our lives, our society needs to define rules that apply to this newly created decision-making tool.
The EU has created a group of experts. This is pushing to establish ethical guidelines for AI. Central values and principles for AI are intended not only to build trust, but also to create a roadmap for implementing those very same. The 29-page draft identifies three stages: At the beginning, fundamental rights, principles and values are defined, from which the basic concept and concrete requirements of implementation - technical and non-technical methods - are derived. The third describes a control procedure including a questionnaire.
These AI ethics guidelines are to apply to everyone involved in the development, introduction or use of AI, i.e. companies, researchers, organizations, public services, institutions, individuals and other bodies. It shall be the cornerstone of the discourse on "trustworthy AI made in Europe" and shall always be adapted to progress.
We will need to sit down as a society with our brightest minds from the technical, legal and ethical fields and reconcile progress with regulation. The added value is undeniable to take potential threats seriously. With the right dose of limits, we will enjoy a perfectly balanced future.
While research celebrates numerous successes in the development of AI and inspires us to imagine what may yet come, however, there are also at least as many challenges to consider that may not stand in the way of AI's development, but may stand in the way of its application. These challenges, fears, and limitations run through all areas of society and technology:
The current arguably most popular AI, GPT-3, and the ChatGPT program based on it are used by millions - playfully, skeptically, and devoutly. But if you ask the AI who is currently ruling a country, for example, you quickly discover that GPT-3 doesn't quite have the latest political developments on its screen and doesn't have any other current information either. The AI currently draws on knowledge up to 2021 and is therefore not able to take current events into account.
Journalists are breathing a sigh of relief, of course, because this is no way to write a serious article, and this industry is also safe for the time being, because investigative research is still based on real interpersonal contacts that are built on trust. The AI will hardly be able to convince a whistleblower to reveal secrets.
In general, it is difficult for the AI to distinguish truth from lies. Finally, the AI produces texts based on probability calculations: Which word is most likely to follow another. Thus, misinformation on the Internet not only poses a threat to humans, but is also the cause of miscalculations, or hallucinations, by AI. One can influence AI by flooding the data sources it uses with certain information. The AI does not have an abstract and reflected sense of truth. For it, propositions are pure mathematics.
While the ever-expanding capabilities impress research and development, the dystopia of evil AI usurping world domination is simultaneously fueled. Regardless of the level of technological development, we must also be prepared as a society to embrace and allow innovation. As recently as 2014, Elon Musk called AI "our greatest existential threat." With Bill Gates, the founder of Microsoft, Tim Bernes-Lee, the inventor of the World Wide Web, the greats of the tech scene also point to potential AI dangers. Apple co-founder Steve Wozniak, for example, said, "If we build these devices to take care of everything for us, eventually they will want to get rid of slow humans faster than we think and run businesses more efficiently.
Anthropomorphizing AI, by making its capabilities increasingly human-like, also runs the risk of interpreting too much into AI's intentions and imputing negative (human) things to it.
AI, however, has come under constant criticism, especially on social media. The discussion was reignited after supporters of Trump stormed the Capitol, and the center of the discussion became the algorithms that decide and specify what is displayed to users of the respective platforms. The idea of displaying ever more appropriate content took on a life of its own to the point where users were driven into a kind of opinion spiral. AI is meant to radicalize in this way. At the time, Mark Zuckerberg, the CEO of Meta, himself called for greater regulation of AI. Companies like GPT-3 are already working on forward-looking restrictions here, and are being quite open about replacing their current control mechanisms with better ones coming to market. So there is an awareness in the industry and they don't want to repeat past mistakes. Legislators worldwide are also becoming increasingly involved in this discussion.
Precisely because AI is on the rise to reach and enrich every aspect of our lives, our society needs to define rules that apply to this newly created decision-making tool.
The EU has created a group of experts. This is pushing to establish ethical guidelines for AI. Central values and principles for AI are intended not only to build trust, but also to create a roadmap for implementing those very same. The 29-page draft identifies three stages: At the beginning, fundamental rights, principles and values are defined, from which the basic concept and concrete requirements of implementation - technical and non-technical methods - are derived. The third describes a control procedure including a questionnaire.
These AI ethics guidelines are to apply to everyone involved in the development, introduction or use of AI, i.e. companies, researchers, organizations, public services, institutions, individuals and other bodies. It shall be the cornerstone of the discourse on "trustworthy AI made in Europe" and shall always be adapted to progress.
We will need to sit down as a society with our brightest minds from the technical, legal and ethical fields and reconcile progress with regulation. The added value is undeniable to take potential threats seriously. With the right dose of limits, we will enjoy a perfectly balanced future.
While research celebrates numerous successes in the development of AI and inspires us to imagine what may yet come, however, there are also at least as many challenges to consider that may not stand in the way of AI's development, but may stand in the way of its application. These challenges, fears, and limitations run through all areas of society and technology:
The current arguably most popular AI, GPT-3, and the ChatGPT program based on it are used by millions - playfully, skeptically, and devoutly. But if you ask the AI who is currently ruling a country, for example, you quickly discover that GPT-3 doesn't quite have the latest political developments on its screen and doesn't have any other current information either. The AI currently draws on knowledge up to 2021 and is therefore not able to take current events into account.
Journalists are breathing a sigh of relief, of course, because this is no way to write a serious article, and this industry is also safe for the time being, because investigative research is still based on real interpersonal contacts that are built on trust. The AI will hardly be able to convince a whistleblower to reveal secrets.
In general, it is difficult for the AI to distinguish truth from lies. Finally, the AI produces texts based on probability calculations: Which word is most likely to follow another. Thus, misinformation on the Internet not only poses a threat to humans, but is also the cause of miscalculations, or hallucinations, by AI. One can influence AI by flooding the data sources it uses with certain information. The AI does not have an abstract and reflected sense of truth. For it, propositions are pure mathematics.
While the ever-expanding capabilities impress research and development, the dystopia of evil AI usurping world domination is simultaneously fueled. Regardless of the level of technological development, we must also be prepared as a society to embrace and allow innovation. As recently as 2014, Elon Musk called AI "our greatest existential threat." With Bill Gates, the founder of Microsoft, Tim Bernes-Lee, the inventor of the World Wide Web, the greats of the tech scene also point to potential AI dangers. Apple co-founder Steve Wozniak, for example, said, "If we build these devices to take care of everything for us, eventually they will want to get rid of slow humans faster than we think and run businesses more efficiently.
Anthropomorphizing AI, by making its capabilities increasingly human-like, also runs the risk of interpreting too much into AI's intentions and imputing negative (human) things to it.
AI, however, has come under constant criticism, especially on social media. The discussion was reignited after supporters of Trump stormed the Capitol, and the center of the discussion became the algorithms that decide and specify what is displayed to users of the respective platforms. The idea of displaying ever more appropriate content took on a life of its own to the point where users were driven into a kind of opinion spiral. AI is meant to radicalize in this way. At the time, Mark Zuckerberg, the CEO of Meta, himself called for greater regulation of AI. Companies like GPT-3 are already working on forward-looking restrictions here, and are being quite open about replacing their current control mechanisms with better ones coming to market. So there is an awareness in the industry and they don't want to repeat past mistakes. Legislators worldwide are also becoming increasingly involved in this discussion.
Precisely because AI is on the rise to reach and enrich every aspect of our lives, our society needs to define rules that apply to this newly created decision-making tool.
The EU has created a group of experts. This is pushing to establish ethical guidelines for AI. Central values and principles for AI are intended not only to build trust, but also to create a roadmap for implementing those very same. The 29-page draft identifies three stages: At the beginning, fundamental rights, principles and values are defined, from which the basic concept and concrete requirements of implementation - technical and non-technical methods - are derived. The third describes a control procedure including a questionnaire.
These AI ethics guidelines are to apply to everyone involved in the development, introduction or use of AI, i.e. companies, researchers, organizations, public services, institutions, individuals and other bodies. It shall be the cornerstone of the discourse on "trustworthy AI made in Europe" and shall always be adapted to progress.
We will need to sit down as a society with our brightest minds from the technical, legal and ethical fields and reconcile progress with regulation. The added value is undeniable to take potential threats seriously. With the right dose of limits, we will enjoy a perfectly balanced future.
While research celebrates numerous successes in the development of AI and inspires us to imagine what may yet come, however, there are also at least as many challenges to consider that may not stand in the way of AI's development, but may stand in the way of its application. These challenges, fears, and limitations run through all areas of society and technology:
The current arguably most popular AI, GPT-3, and the ChatGPT program based on it are used by millions - playfully, skeptically, and devoutly. But if you ask the AI who is currently ruling a country, for example, you quickly discover that GPT-3 doesn't quite have the latest political developments on its screen and doesn't have any other current information either. The AI currently draws on knowledge up to 2021 and is therefore not able to take current events into account.
Journalists are breathing a sigh of relief, of course, because this is no way to write a serious article, and this industry is also safe for the time being, because investigative research is still based on real interpersonal contacts that are built on trust. The AI will hardly be able to convince a whistleblower to reveal secrets.
In general, it is difficult for the AI to distinguish truth from lies. Finally, the AI produces texts based on probability calculations: Which word is most likely to follow another. Thus, misinformation on the Internet not only poses a threat to humans, but is also the cause of miscalculations, or hallucinations, by AI. One can influence AI by flooding the data sources it uses with certain information. The AI does not have an abstract and reflected sense of truth. For it, propositions are pure mathematics.
While the ever-expanding capabilities impress research and development, the dystopia of evil AI usurping world domination is simultaneously fueled. Regardless of the level of technological development, we must also be prepared as a society to embrace and allow innovation. As recently as 2014, Elon Musk called AI "our greatest existential threat." With Bill Gates, the founder of Microsoft, Tim Bernes-Lee, the inventor of the World Wide Web, the greats of the tech scene also point to potential AI dangers. Apple co-founder Steve Wozniak, for example, said, "If we build these devices to take care of everything for us, eventually they will want to get rid of slow humans faster than we think and run businesses more efficiently.
Anthropomorphizing AI, by making its capabilities increasingly human-like, also runs the risk of interpreting too much into AI's intentions and imputing negative (human) things to it.
AI, however, has come under constant criticism, especially on social media. The discussion was reignited after supporters of Trump stormed the Capitol, and the center of the discussion became the algorithms that decide and specify what is displayed to users of the respective platforms. The idea of displaying ever more appropriate content took on a life of its own to the point where users were driven into a kind of opinion spiral. AI is meant to radicalize in this way. At the time, Mark Zuckerberg, the CEO of Meta, himself called for greater regulation of AI. Companies like GPT-3 are already working on forward-looking restrictions here, and are being quite open about replacing their current control mechanisms with better ones coming to market. So there is an awareness in the industry and they don't want to repeat past mistakes. Legislators worldwide are also becoming increasingly involved in this discussion.
Precisely because AI is on the rise to reach and enrich every aspect of our lives, our society needs to define rules that apply to this newly created decision-making tool.
The EU has created a group of experts. This is pushing to establish ethical guidelines for AI. Central values and principles for AI are intended not only to build trust, but also to create a roadmap for implementing those very same. The 29-page draft identifies three stages: At the beginning, fundamental rights, principles and values are defined, from which the basic concept and concrete requirements of implementation - technical and non-technical methods - are derived. The third describes a control procedure including a questionnaire.
These AI ethics guidelines are to apply to everyone involved in the development, introduction or use of AI, i.e. companies, researchers, organizations, public services, institutions, individuals and other bodies. It shall be the cornerstone of the discourse on "trustworthy AI made in Europe" and shall always be adapted to progress.
We will need to sit down as a society with our brightest minds from the technical, legal and ethical fields and reconcile progress with regulation. The added value is undeniable to take potential threats seriously. With the right dose of limits, we will enjoy a perfectly balanced future.
While research celebrates numerous successes in the development of AI and inspires us to imagine what may yet come, however, there are also at least as many challenges to consider that may not stand in the way of AI's development, but may stand in the way of its application. These challenges, fears, and limitations run through all areas of society and technology:
The current arguably most popular AI, GPT-3, and the ChatGPT program based on it are used by millions - playfully, skeptically, and devoutly. But if you ask the AI who is currently ruling a country, for example, you quickly discover that GPT-3 doesn't quite have the latest political developments on its screen and doesn't have any other current information either. The AI currently draws on knowledge up to 2021 and is therefore not able to take current events into account.
Journalists are breathing a sigh of relief, of course, because this is no way to write a serious article, and this industry is also safe for the time being, because investigative research is still based on real interpersonal contacts that are built on trust. The AI will hardly be able to convince a whistleblower to reveal secrets.
In general, it is difficult for the AI to distinguish truth from lies. Finally, the AI produces texts based on probability calculations: Which word is most likely to follow another. Thus, misinformation on the Internet not only poses a threat to humans, but is also the cause of miscalculations, or hallucinations, by AI. One can influence AI by flooding the data sources it uses with certain information. The AI does not have an abstract and reflected sense of truth. For it, propositions are pure mathematics.
While the ever-expanding capabilities impress research and development, the dystopia of evil AI usurping world domination is simultaneously fueled. Regardless of the level of technological development, we must also be prepared as a society to embrace and allow innovation. As recently as 2014, Elon Musk called AI "our greatest existential threat." With Bill Gates, the founder of Microsoft, Tim Bernes-Lee, the inventor of the World Wide Web, the greats of the tech scene also point to potential AI dangers. Apple co-founder Steve Wozniak, for example, said, "If we build these devices to take care of everything for us, eventually they will want to get rid of slow humans faster than we think and run businesses more efficiently.
Anthropomorphizing AI, by making its capabilities increasingly human-like, also runs the risk of interpreting too much into AI's intentions and imputing negative (human) things to it.
AI, however, has come under constant criticism, especially on social media. The discussion was reignited after supporters of Trump stormed the Capitol, and the center of the discussion became the algorithms that decide and specify what is displayed to users of the respective platforms. The idea of displaying ever more appropriate content took on a life of its own to the point where users were driven into a kind of opinion spiral. AI is meant to radicalize in this way. At the time, Mark Zuckerberg, the CEO of Meta, himself called for greater regulation of AI. Companies like GPT-3 are already working on forward-looking restrictions here, and are being quite open about replacing their current control mechanisms with better ones coming to market. So there is an awareness in the industry and they don't want to repeat past mistakes. Legislators worldwide are also becoming increasingly involved in this discussion.
Precisely because AI is on the rise to reach and enrich every aspect of our lives, our society needs to define rules that apply to this newly created decision-making tool.
The EU has created a group of experts. This is pushing to establish ethical guidelines for AI. Central values and principles for AI are intended not only to build trust, but also to create a roadmap for implementing those very same. The 29-page draft identifies three stages: At the beginning, fundamental rights, principles and values are defined, from which the basic concept and concrete requirements of implementation - technical and non-technical methods - are derived. The third describes a control procedure including a questionnaire.
These AI ethics guidelines are to apply to everyone involved in the development, introduction or use of AI, i.e. companies, researchers, organizations, public services, institutions, individuals and other bodies. It shall be the cornerstone of the discourse on "trustworthy AI made in Europe" and shall always be adapted to progress.
We will need to sit down as a society with our brightest minds from the technical, legal and ethical fields and reconcile progress with regulation. The added value is undeniable to take potential threats seriously. With the right dose of limits, we will enjoy a perfectly balanced future.
ChatGPT is the starting signal of an enormous AI wave that is ready to revolutionize all areas known to us and create explicit added value for us humans. Nevertheless, we should not fall into blind euphoria, but rather use a cool head to steer the upcoming changes in the right direction so that social values and rules are not submerged. There are groundbreaking decisions to be made in order to bring major innovations into line with society's sometimes tenacious willingness to change.
In summary, we can all look forward to seeing how artificial intelligence enriches private life, business and our environment. The future has begun.
ChatGPT is the starting signal of an enormous AI wave that is ready to revolutionize all areas known to us and create explicit added value for us humans. Nevertheless, we should not fall into blind euphoria, but rather use a cool head to steer the upcoming changes in the right direction so that social values and rules are not submerged. There are groundbreaking decisions to be made in order to bring major innovations into line with society's sometimes tenacious willingness to change.
In summary, we can all look forward to seeing how artificial intelligence enriches private life, business and our environment. The future has begun.
ChatGPT is the starting signal of an enormous AI wave that is ready to revolutionize all areas known to us and create explicit added value for us humans. Nevertheless, we should not fall into blind euphoria, but rather use a cool head to steer the upcoming changes in the right direction so that social values and rules are not submerged. There are groundbreaking decisions to be made in order to bring major innovations into line with society's sometimes tenacious willingness to change.
In summary, we can all look forward to seeing how artificial intelligence enriches private life, business and our environment. The future has begun.
ChatGPT is the starting signal of an enormous AI wave that is ready to revolutionize all areas known to us and create explicit added value for us humans. Nevertheless, we should not fall into blind euphoria, but rather use a cool head to steer the upcoming changes in the right direction so that social values and rules are not submerged. There are groundbreaking decisions to be made in order to bring major innovations into line with society's sometimes tenacious willingness to change.
In summary, we can all look forward to seeing how artificial intelligence enriches private life, business and our environment. The future has begun.
ChatGPT is the starting signal of an enormous AI wave that is ready to revolutionize all areas known to us and create explicit added value for us humans. Nevertheless, we should not fall into blind euphoria, but rather use a cool head to steer the upcoming changes in the right direction so that social values and rules are not submerged. There are groundbreaking decisions to be made in order to bring major innovations into line with society's sometimes tenacious willingness to change.
In summary, we can all look forward to seeing how artificial intelligence enriches private life, business and our environment. The future has begun.
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