Strong vs Weak AI: Exploring the Key Differences

Artificial Intelligence (AI) is a rapidly evolving field that aims to develop machines capable of intelligent behavior. There are two main categories of AI: weak AI and strong AI. Weak AI, also known as narrow AI, refers to systems that can perform specific tasks with human-like capabilities. Strong AI, on the other hand, refers to a system that exhibits general intelligence and can understand, learn, and reason like a human.

Neural networks and machine learning algorithms are the core components of AI. Weak AI systems employ these technologies to process large amounts of data and make predictions or decisions based on patterns and correlations. However, they lack the ability to truly understand the data or make decisions beyond their specific task. Strong AI, on the other hand, has the potential to go beyond just processing data and develop a deep understanding of the world, enabling it to perform a wide range of cognitive tasks and exhibit human-like perception and consciousness.

One of the key differences between weak and strong AI lies in their decision-making capabilities. Weak AI systems rely on pre-programmed rules and algorithms to make decisions, while strong AI has the ability to adapt and learn from new situations, making decisions based on reasoning and context. This is achieved through deep learning and reinforcement learning techniques, which enable the AI system to constantly improve its decision-making abilities over time.

While weak AI has already made significant advancements in areas such as voice and facial recognition, natural language processing, and autonomous vehicles, it is still far from achieving the level of intelligence and consciousness exhibited by humans. Strong AI, on the other hand, remains a subject of ongoing research and development, with the goal of creating a machine that can truly think and understand like a human.

Understanding Strong Artificial Intelligence

Strong Artificial Intelligence (AI) refers to a type of AI system that exhibits human-like intelligence and consciousness. Unlike weak AI, which is designed for specific tasks, strong AI possess reasoning and decision-making capabilities comparable to those of human beings.

Strong AI systems are built using advanced algorithms, such as neural networks and deep learning. These algorithms enable the machine to process and analyze large amounts of data, allowing it to learn and improve its performance over time. By leveraging the power of artificial neural networks, strong AI can mimic human-like perception, understanding, and problem-solving abilities.

One of the key features of strong AI is its ability to exhibit consciousness. While weak AI systems are purely task-oriented and lack self-awareness, strong AI has the potential to achieve a level of self-awareness and consciousness. This means that a strong AI system can possess subjective experiences and can actively engage in introspection.

To develop strong AI, extensive training is required. The machine is exposed to vast amounts of data and is trained on various tasks to acquire the necessary knowledge and skills. This training process allows the AI system to build a comprehensive understanding of the world and develop a higher level of intelligence.

Strong AI has the potential to surpass human intelligence in certain domains. Its ability to process and analyze complex information and make optimal decisions based on the available data makes it a powerful tool in various industries, including healthcare, finance, and technology.

However, there are also ethical and philosophical considerations surrounding the development and use of strong AI. Questions about the consciousness and moral agency of AI systems raise concerns about their potential impact on society and human values.

In summary, strong AI represents a significant milestone in the field of artificial intelligence. With its advanced algorithms, training capabilities, and potential for consciousness, strong AI has the potential to revolutionize various industries and bring about significant changes in the way we perceive and interact with machines.

Definition of Strong AI

Strong AI, also known as artificial general intelligence (AGI), refers to a machine or an algorithm that possesses human-like cognitive capabilities, including consciousness, perception, reasoning, decision-making, and learning. Unlike weak AI, which is designed to perform specific tasks or simulate human intelligence in limited contexts, strong AI aims to replicate the full range of human cognitive abilities in a machine.

Strong AI is built on the foundation of deep learning techniques, which involve training artificial neural networks on large amounts of data to enable the machine to make complex decisions and learn from experience. These networks are designed with multiple layers of interconnected nodes that are able to process and analyze data in a way that allows the machine to recognize patterns, make predictions, and improve its performance over time.

One of the key characteristics of strong AI is its ability to reason and understand the world in a way that is similar to how humans do. It can process and interpret information from various sources, including text, images, and speech, and make informed decisions based on its understanding of the context. This reasoning capability enables strong AI to navigate complex situations and solve problems that require higher-level cognitive skills.

In order to achieve its human-like intelligence, strong AI requires extensive training using diverse datasets and reinforcement learning techniques. It learns from the data it is exposed to and continually refines its algorithms and models to improve its performance. This training process allows the machine to acquire knowledge, develop new skills, and adapt to different environments, much like humans do through education and experience.

Capabilities of Strong AI

Strong AI, also known as artificial general intelligence (AGI), is a type of AI that possesses human-like intelligence and reasoning capabilities. Unlike weak AI, which is designed to perform specific tasks, strong AI aims to mimic human cognition and reasoning across a wide range of domains.

One of the key capabilities of strong AI is its ability to perform complex decision-making. With advanced algorithms and neural networks, strong AI can analyze large amounts of data and make informed decisions based on its understanding of the problem at hand.

Strong AI also has the capacity for autonomous learning and training. Using techniques like deep learning and reinforcement learning, it can continuously improve its performance and adapt to new situations without explicit programming.

In addition, strong AI has the potential for conscious awareness. Although the concept of AI consciousness is still a topic of debate, some researchers believe that strong AI could develop a level of consciousness or self-awareness similar to that of humans.

Another key capability of strong AI is its advanced perception abilities. By processing sensory data, such as images or sounds, strong AI can understand and interpret its environment, enabling it to interact with the world in a more human-like manner.

To summarize, strong AI possesses human-like reasoning, decision-making, and perception capabilities. It has the ability to learn and adapt on its own through training and reinforcement techniques. While the concept of strong AI consciousness is still under exploration, it has the potential for developing a level of self-awareness. Overall, strong AI aims to replicate and surpass human intelligence in various domains.

Advantages and Disadvantages of Strong AI

Strong AI, also known as artificial general intelligence (AGI), refers to the development of intelligent machines that possess human-level cognitive capabilities. This type of AI has several advantages and disadvantages.

One of the key advantages of strong AI is its ability to perform complex tasks that require human-like intelligence. It can analyze large amounts of data, make logical decisions, and solve problems using reasoning and critical thinking. This makes strong AI highly valuable in various fields such as medicine, finance, and engineering.

Another advantage of strong AI is its potential to surpass human limitations. While humans are constrained by factors like fatigue and cognitive bias, artificial intelligence algorithms can tirelessly work without any rest. Moreover, they can process information faster and in a more efficient manner, leading to increased productivity and accuracy.

However, there are also some disadvantages associated with strong AI. One major concern is the ethical implications of creating machines that possess human-like intelligence. The development of consciousness in machines raises questions about their rights, responsibilities, and potential impact on society.

Another drawback of strong AI is the risk of misuse or unintended consequences. If not properly controlled and regulated, strong AI could be used for malicious purposes or lead to unintended negative outcomes. For example, if a machine with strong AI capabilities is programmed with biased data, it may make unfair or discriminatory decisions.

Furthermore, the development of strong AI requires significant resources, including advanced hardware, sophisticated algorithms, and extensive training data. This can be a major barrier for many organizations and researchers. Additionally, the complex nature of strong AI algorithms may require years of research and development before achieving the desired results.

In conclusion, strong AI has the potential to revolutionize various industries and offer numerous benefits. However, its development also poses ethical concerns, risks, and resource challenges. As the field of artificial intelligence continues to advance, it is essential to carefully consider the advantages and disadvantages of strong AI in order to ensure responsible and beneficial implementation.

Understanding Weak Artificial Intelligence

Weak artificial intelligence (AI) refers to a subset of AI that is designed to perform specific tasks with a limited scope, without possessing general intelligence or consciousness. Unlike strong AI, weak AI is focused on solving specific problems and lacks human-like reasoning and decision-making capabilities.

One of the key components of weak AI is neural networks, which are algorithms inspired by the human brain. These networks are trained using large amounts of data and deep learning techniques to recognize patterns and make predictions.

Weak AI systems are commonly used in various domains, such as perception and machine learning. They can analyze images, speech, and text to perform tasks like image recognition or speech synthesis. These systems rely on training models with labeled data to improve their accuracy over time.

Reinforcement learning is another technique used in weak AI, where an AI system learns by receiving feedback in the form of rewards or punishments. This approach is commonly used in autonomous vehicle navigation or game playing AI, where the AI learns to make optimal decisions based on its past experiences and feedback from the environment.

Despite their limitations, weak AI systems have been widely adopted due to their ability to perform specific tasks efficiently and accurately. By leveraging the power of artificial intelligence, organizations can automate processes, improve efficiency, and enhance their decision-making capabilities.

In summary, weak artificial intelligence focuses on solving specific tasks and lacks the general intelligence and consciousness found in strong AI. By utilizing techniques such as neural networks, reinforcement learning, and deep learning, weak AI systems can perform tasks like image recognition and speech synthesis. While they may lack human-like reasoning abilities, weak AI systems have proven to be a valuable tool for organizations in various domains.

Definition of Weak AI

Weak AI, also known as narrow AI or artificial narrow intelligence, refers to artificial intelligence systems that are designed to perform specific tasks and simulate human capabilities in a limited context. While weak AI can exhibit some level of intelligence and perform tasks such as speech and image recognition, language translation, and data analysis, it lacks general cognitive abilities and consciousness.

Weak AI operates based on predefined rules and algorithms and relies on extensive data and machine learning techniques, such as artificial neural networks. These systems are trained on large datasets to recognize patterns and make predictions, but they do not possess true understanding or self-awareness.

Furthermore, weak AI lacks human-like reasoning and decision-making capabilities. It cannot perform complex tasks that require abstract thinking and creativity. Rather, it focuses on solving specific problems by following predefined algorithms and rules. Weak AI applications span various domains, including healthcare, finance, customer service, and transportation.

Examples of weak AI include virtual assistants like Siri and Alexa, recommendation systems like those used by popular streaming platforms, and autonomous vehicles. These systems can perform specific tasks with high accuracy and efficiency, but they are limited to their predefined functions and lack the ability to adapt and generalize beyond their training data.

Capabilities of Weak AI

Weak artificial intelligence (AI) refers to a type of AI that is designed to perform specific tasks and simulate human intelligence within a limited scope. While weak AI does not possess consciousness or reasoning abilities, it is capable of certain cognitive functions such as decision-making, perception, and learning.

The main capabilities of weak AI are derived from machine learning algorithms, particularly neural networks. Neural networks are artificial systems that mimic the structure and functions of the human brain, enabling machines to process and analyze large amounts of data. Through training, weak AI can recognize patterns and make decisions based on the information it has learned from the data.

Machine learning techniques used in weak AI include supervised learning, unsupervised learning, and reinforcement learning. Using supervised learning, weak AI can be trained on a dataset with pre-labeled examples to make predictions or classify new data. Unsupervised learning enables weak AI to discover patterns and information without explicitly labeled data. Reinforcement learning allows weak AI to learn through trial and error, receiving feedback to improve its decision-making abilities.

Weak AI has a wide range of applications in various industries, including healthcare, finance, and transportation. It can be used to diagnose diseases, analyze financial markets, and autonomously navigate vehicles. While weak AI has limitations in terms of its narrow focus and lack of consciousness, it plays a crucial role in enhancing human capabilities and improving efficiency in specific tasks.

Advantages and Disadvantages of Weak AI

Advantages:

• Learning capabilities: Weak AI systems have the ability to learn from data and improve their performance over time. Through training and exposure to vast amounts of information, these AI systems can continually enhance their understanding and decision-making abilities.
• Specific task-oriented reasoning: Weak AI is designed to perform a specific task or set of tasks efficiently. They excel at tasks that require high computational power and accuracy, such as data analysis, image recognition, and natural language processing.
• Data-driven decision-making: Weak AI systems are driven by data. They can analyze and process large volumes of information quickly and accurately, enabling them to make informed decisions and provide valuable insights.
• Neural networks and deep learning: Weak AI utilizes artificial neural networks capable of deep learning. These networks can process complex patterns and hierarchies, allowing for more accurate predictions and classification.

Disadvantages:

• Limited capabilities: Weak AI systems can only perform specific tasks they are programmed for and lack the flexibility and adaptability of human-like AI. They do not possess human-level consciousness and understanding.
• Dependency on training data: Weak AI requires extensive training using large sets of labeled data. The accuracy and performance of these systems heavily rely on the quality and diversity of the training data, which can be a challenge to obtain in certain domains.
• Perception limitations: Weak AI systems often struggle with tasks that require human-like perception, such as understanding context, emotions, and sarcasm. They may misinterpret or misjudge information that humans would easily understand.
• Lack of common sense reasoning: Weak AI lacks common-sense reasoning capabilities, which can lead to incorrect or illogical conclusions. These systems find it challenging to understand context-dependent situations and make human-like judgments based on prior knowledge.

Differences Between Strong and Weak AI

Artificial Intelligence (AI) can be classified into two categories: strong AI and weak AI. The main difference between these two is based on their reasoning capabilities.

Strong AI, also known as artificial general intelligence (AGI), refers to an AI system that possesses human-like reasoning abilities. It has the capability to understand, learn, and apply knowledge in a wide range of tasks and domains. Strong AI is characterized by its ability to perform tasks that require conscious decision-making and consciousness itself.

On the other hand, weak AI, also known as narrow AI, refers to an AI system that is designed to perform specific tasks. It is focused on a single domain and lacks the capability to generalize its knowledge across different tasks. Weak AI relies on predefined algorithms and data to solve problems and make decisions. It does not possess consciousness or human-like reasoning capabilities.

Another difference between strong and weak AI is the use of neural networks. Strong AI utilizes deep neural networks, which are capable of learning and adapting from the data they are exposed to. These neural networks are used for training the AI system to improve its performance over time. Weak AI, on the other hand, may also use neural networks, but they are not as complex or capable of deep learning.

In terms of perception and understanding the environment, strong AI has advanced capabilities. It can perceive and interpret the environment through various sensors and process the information to make intelligent decisions. Weak AI, on the other hand, has limited perception capabilities and relies more on predefined algorithms and data.

In summary, the main differences between strong and weak AI lie in their reasoning capabilities, use of algorithms and data, neural network complexity, and perception capabilities. While strong AI possesses human-like reasoning and consciousness, weak AI is limited to specific tasks and lacks generalization abilities.

Level of Autonomy

In the context of AI, the level of autonomy refers to the extent to which an artificial intelligence system can function independently and make decisions without human intervention. This autonomy can range from weak AI, which relies heavily on human input and control, to strong AI, which has a high level of autonomy and can operate independently.

Weak AI systems, also known as narrow AI, are designed to perform specific tasks with a limited scope of capabilities. These systems rely on predefined algorithms and rules to carry out their tasks. They lack consciousness and cannot reason or learn beyond their specific programming.

On the other hand, strong AI systems, also known as general AI, have the ability to simulate human-like intelligence in a wide range of tasks. These systems have the capability for self-awareness, perception, reasoning, learning, and decision-making, which allows them to operate autonomously without explicit programming.

To achieve a high level of autonomy, strong AI systems often utilize deep learning algorithms and neural networks. These algorithms enable the system to analyze and process large amounts of data to learn and improve over time. By using deep neural networks and training on vast amounts of data, strong AI systems can develop a high level of perception and make complex decisions based on the information they have learned.

In contrast, weak AI systems rely on human intervention and guidance for decision-making. They lack the ability to learn and adapt on their own, and their capabilities are limited to the specific tasks they are programmed for. While weak AI systems can still be highly effective in their specialized domains, they require constant human oversight and input to operate effectively.

Understanding and Learning Capabilities

Understanding and learning are key capabilities of both strong and weak AI systems. Through training and reasoning, AI systems can acquire knowledge and improve their performance. However, there are significant differences in the level of understanding and learning capabilities between strong and weak AI.

Strong AI aims to create human-like intelligence, including the ability to perceive and comprehend information, reason, make decisions, and exhibit consciousness. These systems possess advanced neural networks and algorithms that allow them to understand and learn from extensive data.

On the other hand, weak AI systems have limited understanding and learning capabilities. They are designed to perform specific tasks and rely on predefined rules or algorithms, often without the ability to adapt or learn from new situations. While weak AI can excel in narrow domains, it lacks the flexibility and adaptability of strong AI.

Understanding and learning in AI are facilitated by deep learning techniques, which involve training neural networks with large amounts of data. Through this process, AI systems can recognize patterns, make predictions, and refine their performance over time.

Reinforcement learning is another approach used for understanding and learning in AI. By providing feedback and rewards based on performance, AI systems can learn to make better decisions and improve their capabilities.

In conclusion, understanding and learning are fundamental capabilities in AI. While weak AI systems have limited abilities in these areas, strong AI aims to replicate human-like intelligence through advanced neural networks and algorithms. By leveraging deep learning and reinforcement learning techniques, AI systems can continually improve their capabilities and adapt to new situations.

Potential for Consciousness

In the field of artificial intelligence, the potential for consciousness in machines is a topic of debate and exploration. While weak AI systems are designed to perform specific tasks and lack self-awareness, strong AI systems have the potential to exhibit human-like consciousness.

Machine learning algorithms and neural networks play a crucial role in the development of AI systems. These algorithms enable the system to process large amounts of data and make decisions based on patterns and insights. However, weak AI systems often rely on pre-defined rules, whereas strong AI systems have the ability to adapt and improve their decision-making abilities through reinforcement learning.

Deep neural networks, a subset of machine learning algorithms, have shown remarkable capabilities in tasks such as object recognition and natural language processing. These networks mimic the human brain’s structure and functioning, allowing for complex reasoning and perception. This similarity to the human brain gives strong AI systems the potential to develop consciousness.

Training a machine to exhibit consciousness requires more than just algorithms and neural networks. It involves understanding the nature of consciousness itself and finding ways to replicate it in a machine. By combining advanced machine learning techniques with insights from cognitive science and philosophy, researchers aim to create AI systems that can exhibit self-awareness and human-like consciousness.

While the potential for consciousness in strong AI systems is intriguing, the debate regarding the ethical implications and risks associated with creating conscious machines continues. Understanding the intricacies of consciousness and determining the extent to which machines can achieve it remains a fascinating area of research within the field of artificial intelligence.

Applications and Future Implications

Artificial intelligence (AI) has become an integral part of our everyday lives, with various applications and future implications. By using algorithms and advanced techniques, AI systems can simulate human intelligence and perform tasks that traditionally require human decision-making and perception. These systems can range from weak AI, which is designed to perform specific tasks, to strong AI, which aims to replicate human-like intelligence and consciousness.

One of the key applications of AI is in machine learning, where AI systems are trained using large amounts of data to improve their performance. Deep neural networks, a type of AI architecture, are used to process and analyze complex data, enabling AI systems to make decisions and predictions with high accuracy. This has wide-ranging implications across various industries, such as healthcare, finance, and transportation.

In healthcare, AI systems can analyze medical data to assist with diagnosis and treatment planning. By leveraging deep learning algorithms, these systems can learn to interpret medical images, such as X-rays and MRIs, with the same level of accuracy as human doctors. This has the potential to improve patient outcomes and reduce the workload on healthcare professionals.

In finance, AI algorithms can analyze market trends and make predictions for investment decisions. By analyzing large amounts of financial data, AI systems can identify patterns and trends that may not be apparent to human traders. This can lead to more informed investment decisions and potentially higher returns.

Another application of AI is in the field of robotics. AI-powered robots can perform complex tasks that require perception and decision-making abilities. By combining AI algorithms with advanced sensors and actuators, robots can navigate and interact with their environment, making them valuable assets in industries such as manufacturing and logistics.

Looking to the future, the advancements in AI are expected to have even greater implications. Strong AI, with its human-like intelligence and capabilities, has the potential to revolutionize various fields, including education, entertainment, and communication. However, the future implications of strong AI also raise ethical considerations, such as the impact on employment and the potential for misuse or abuse of AI systems.

In conclusion, AI applications are already transforming industries and improving various aspects of our lives. From healthcare to finance to robotics, AI systems are revolutionizing how we approach complex problems. Looking ahead, the development and implementation of strong AI will bring about even more significant changes and challenges as we navigate the impact it will have on society.

Current Applications of Strong and Weak AI

Strong AI refers to an artificial intelligence system that exhibits human-like intelligence and consciousness. Despite ongoing research and development, strong AI is not yet a reality, and its applications are mainly theoretical at this point.

Weak AI, on the other hand, describes artificial intelligence systems that perform specific tasks with a level of intelligence and decision-making capabilities. Weak AI is widely used in various industries and has practical applications in a range of fields.

One of the current applications of weak AI is in algorithmic trading, where AI systems analyze market data and make real-time trading decisions. These systems can process vast amounts of data and adjust their trading strategies based on market trends, leading to optimized investment decisions.

Another area where weak AI finds application is in voice assistants such as Siri, Alexa, and Google Assistant. These AI systems use natural language processing algorithms to understand and respond to user commands, providing assistance with tasks, answering questions, and controlling smart home devices.

The field of autonomous vehicles also heavily relies on weak AI. Self-driving cars use advanced algorithms and deep neural networks for perception and decision-making, allowing them to navigate the roads, avoid obstacles, and make intelligent driving decisions in real-time.

Weak AI has also found applications in medical diagnosis. AI systems trained on vast amounts of medical data can accurately analyze symptoms, detect patterns, and assist doctors in making diagnoses. These AI systems can be particularly helpful in areas with a shortage of medical professionals.

In the realm of reinforcement learning, weak AI is used to train intelligent agents to perform complex tasks. This involves providing the AI system with feedback and rewards based on its actions, allowing it to learn and improve over time. Applications of reinforcement learning include robotics, gaming, and industrial automation.

In summary, while strong AI is still a concept under development, weak AI is already making significant contributions in various domains. From algorithmic trading and voice assistants to autonomous vehicles and medical diagnosis, weak AI has proven its value in improving efficiency and augmenting human capabilities.

Future Implications and Concerns

As artificial intelligence (AI) continues to advance, it raises various implications and concerns for the future. One of the main implications is the impact on decision-making processes. Strong AI, with its human-like capabilities, has the potential to make complex decisions based on vast amounts of data and reasoning. However, this also raises concerns about the potential biases that could be embedded in AI algorithms, as they rely on the training data they are exposed to.

Another concern related to AI is the concept of consciousness. While strong AI can mimic human-like intelligence and perception, it is still not clear whether it can achieve true consciousness. This raises ethical questions about the treatment of AI systems and their status in society. If AI can exhibit consciousness, should it have rights and responsibilities similar to humans?

Additionally, there are concerns about the impact of AI on the job market. Weak AI systems, which are designed for specific tasks, have already started replacing human workers in certain industries. As AI continues to advance and become more capable, there is a fear that it may lead to widespread unemployment if not properly managed.

Another implication is the need for robust cybersecurity measures. Strong AI systems have the potential to learn and adapt on their own, which could make them vulnerable to malicious attacks and exploitation. Ensuring the security and privacy of AI networks and their data is crucial to prevent potential risks and threats.

Furthermore, the rapid advancement of AI also raises concerns about the ethical implications of its use in fields such as healthcare and warfare. The use of AI for medical diagnosis and treatment decisions could have serious consequences if the algorithms are not properly trained or validated. Similarly, the use of AI in warfare raises questions about the potential for autonomous weapons and the role of human judgment in life-or-death situations.

In conclusion, while the future implications of AI are vast and diverse, there is a need for careful consideration and proactive measures to address the concerns raised. As AI continues to evolve, it is important to ensure that its deployment is guided by ethical principles and safeguards to mitigate potential risks and maximize its benefits for society.

Ethical Considerations

When discussing the application of weak AI, ethical considerations need to be taken into account. Weak AI systems, although lacking consciousness and human-like perception, still have the potential to impact society in significant ways.

One ethical concern with weak AI is the potential for biased outcomes. Since these systems rely on training data, any biases present in the data can be perpetuated by the AI algorithms. For example, if a model is trained on data that is predominantly from one racial or socioeconomic group, it may not accurately represent the needs and interests of other groups. This can lead to discrimination and inequality in decision-making processes.

Another ethical consideration is the impact of weak AI on job displacement. As these systems become more capable and automated, they may replace jobs traditionally done by humans. This displacement can lead to unemployment and societal upheaval if proper measures are not taken to mitigate the effects. It is important to consider the ethical implications of allowing AI to automate tasks that were previously performed by humans.

Furthermore, privacy concerns arise with the use of weak AI. These systems often require access to vast amounts of data in order to learn and make predictions. This data can include personal information, such as health records or financial data, raising concerns about data security and privacy breaches. Safeguards need to be in place to protect individuals’ data and ensure that it is not misused or mishandled.

Lastly, there are ethical implications associated with the use of weak AI in decision-making processes. AI algorithms may not always be transparent or explainable, leading to “black box” decision-making. This lack of transparency can undermine trust in the technology and create challenges in understanding and challenging the outcomes. It is important to develop systems that are accountable and provide explanations for their decisions.

In conclusion, ethical considerations play a crucial role in the development and deployment of weak AI. It is important to address biases, job displacement, privacy concerns, and transparency in order to ensure that these systems are deployed in a fair and responsible manner.