What is Cognitive AI? Cognitive Computing vs Artificial Intelligence | AI Tutorial | Edureka

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14 Jan 202010:18

TLDRCognitive computing, a subset of artificial intelligence, focuses on mimicking human reasoning to solve complex problems, enhancing decision-making. It integrates technologies like machine learning, deep learning, and natural language processing to synthesize data and learn from interactions. Key applications span smart IoT, AI-enabled cybersecurity, content AI, healthcare analysis, and intent-based NLP, showcasing its potential to revolutionize various sectors.

Takeaways

  • 🧠 Cognitive computing refers to technologies that enhance human intelligence by performing specific tasks and simulating human reasoning and behavior.
  • 🌐 Cognitive systems learn and reason from interactions with humans and their environment, using better data and algorithms for improved analysis.
  • 🤖 Cognitive AI works by synthesizing data from various sources, considering context, and using self-learning technologies like data mining and NLP.
  • 🔄 Key attributes of cognitive computing include adaptability, interactivity, iterative problem-solving, and contextual understanding.
  • 📊 Cognitive computing and AI share similarities in technologies like machine learning and deep learning but differ in their focus and application.
  • 🤔 AI focuses on providing accurate results and making decisions, while cognitive computing simulates human thought processes to find solutions and supplements human decision-making.
  • 💡 Cognitive AI applications span across sectors like smart IoT, AI-enabled cybersecurity, content AI, cognitive analysis in healthcare, and intent-based NLP for business management.
  • 🏥 In healthcare, cognitive AI implements human-like reasoning for deductive, inductive, and abductive analysis in Life Sciences applications.
  • 🔗 Cognitive AI in IoT connects and optimizes devices through enhanced situational awareness and smart, distributed data security.
  • 📈 Content AI solutions learn and reason, integrating various personal attributes and contextual data for personalized content delivery.
  • 📊 Cognitive analysis aids businesses in becoming more analytical in their management and decision-making, acting as a logical reasoning step beyond machine learning.

Q & A

  • What is cognitive computing?

    -Cognitive computing refers to technologies that perform specific tasks to facilitate human intelligence. These are smart decision support systems that use better data and algorithms to analyze large amounts of information, simulating human reasoning and behavior to assist in making better human decisions at work.

  • How does cognitive AI work?

    -Cognitive AI works by synthesizing data from various sources, weighing context and conflicting evidence to suggest suitable answers. It uses self-learning technologies like data mining, pattern recognition, and natural language processing to understand the way the human brain works, refining its pattern identification and data processing capabilities over time.

  • What are the key attributes of cognitive computing systems?

    -Cognitive computing systems should be adaptive, interactive, iterative and stateful, and contextual. They must be flexible to understand changes in information, digest dynamic data in real time, interact with users and other systems, identify problems by asking questions or pulling in additional data, and understand, identify, and mind contextual data.

  • How does cognitive computing differ from artificial intelligence?

    -Cognitive computing focuses on mimicking human behavior and reasoning to solve complex problems, while AI augments human thinking and focuses on providing accurate results. Cognitive computing supplements information for humans to make decisions, whereas AI is responsible for making decisions on its own, minimizing the role of humans.

  • What are some applications of cognitive computing?

    -Applications of cognitive computing include speech recognition, sentiment analysis, face detection, and risk assessment. It is used in sectors like customer service, healthcare, and IoT to optimize devices and data, enhance cybersecurity, and perform advanced content analysis.

  • Can you provide an example to illustrate the difference between cognitive computing and artificial intelligence?

    -In the context of career change, an AI assistant might assess a job seeker's skills and find a relevant job, negotiating pay and benefits on their behalf. In contrast, cognitive computing would suggest potential career paths, provide additional insights like education requirements and salary comparisons, but leave the final decision to the job seeker.

  • How does cognitive AI enhance the field of healthcare?

    -Cognitive AI in healthcare uses human-like reasoning software functions to perform deductive, inductive, and abductive analysis for Life Sciences applications. It can help in diagnosing diseases, predicting patient outcomes, and personalizing treatment plans based on complex data analysis.

  • What is the role of cognitive computing in IoT?

    -In IoT, cognitive computing helps in connecting and optimizing devices and data. As more sensors and devices are integrated, cognitive computing plays a key role in analyzing and making decisions based on the data collected from these devices, leading to smarter and more efficient IoT systems.

  • How does AI-enabled cybersecurity work?

    -AI-enabled cybersecurity uses data security encryption and enhanced situational awareness powered by AI to provide robust defense against cyber threats. It involves smart, distributed data securing using an AI key, which helps in detecting and responding to security breaches more effectively.

  • What is intent-based NLP and how does it benefit businesses?

    -Intent-based NLP is a cognitive computing application that helps businesses become more analytical in their approach to management and decision-making. It uses cognitive intelligence to perform logical reasoning and analysis, moving beyond traditional machine learning to assist in strategic business planning and decision-making.

  • How do cognitive computing and artificial intelligence complement each other?

    -Cognitive computing and artificial intelligence complement each other by combining their strengths. While AI provides accurate results and makes decisions, cognitive computing enhances human decision-making by simulating human thought processes and providing supplementary information. Together, they can create more effective and intelligent solutions for various industries.

Outlines

00:00

🤖 Introduction to Cognitive Computing

This paragraph introduces the concept of cognitive computing, highlighting its ability to learn, reason, and interact with humans naturally. It emphasizes the technology's overlap with artificial intelligence and its application in various fields such as speech recognition, sentiment analysis, and risk assessment. The paragraph also outlines the session's agenda, which includes defining cognitive computing, exploring its workings, differentiating it from AI, examining a use case, and discussing its applications. The introduction sets the stage for a comprehensive understanding of cognitive AI and its significance in decision-making and problem-solving.

05:00

🧠 How Cognitive Computing Works

This section delves into the operational mechanics of cognitive computing systems. It explains how these systems synthesize data from multiple sources, weigh context and conflicting evidence, and utilize self-learning technologies like data mining, pattern recognition, and natural language processing to mimic the human brain's functioning. The paragraph also outlines the key attributes of cognitive systems, such as adaptability, interactivity, iterative and stateful problem-solving, and contextual understanding. It highlights the evolution of cognitive systems from structured and unstructured data analysis to anticipating new problems and modeling potential solutions.

10:00

🔍 Differences and Applications of Cognitive AI

This paragraph contrasts cognitive computing with artificial intelligence, highlighting their underlying technologies and their distinct roles in problem-solving. It explains that while AI focuses on providing accurate results, cognitive computing simulates human thought processes to find solutions. The paragraph also discusses the sector-specific applications of cognitive AI, such as customer service, healthcare, IoT, cybersecurity, content AI, and healthcare analysis. It concludes with a mention of intent-based NLP as the next step in AI evolution, emphasizing the growing importance of cognitive intelligence in business management and decision-making.

📚 Conclusion and Future Learning

The final paragraph wraps up the session by reiterating the significance of cognitive computing as a subset of artificial intelligence and its potential to transform technology. It encourages viewers to share their opinions and invites them to engage with more content on the channel for further learning. The paragraph ends with a call to action for viewers to like, comment, and subscribe for more informative content, reinforcing the educational value of the session.

Mindmap

Keywords

💡Cognitive Computing

Cognitive computing refers to technologies designed to simulate human thought processes and augment human decision-making. It involves the use of data mining, pattern recognition, and natural language processing to understand and interact with humans in a natural way. In the context of the video, cognitive computing is presented as a subset of artificial intelligence, focusing on mimicking human behavior and reasoning to solve complex problems, thereby enhancing human intelligence through smart decision support systems.

💡Artificial Intelligence (AI)

Artificial intelligence (AI) encompasses a broad range of technologies that enable machines to learn from experience, adapt to new inputs, and perform human-like tasks. AI is about providing accurate results and making decisions on its own, often minimizing the role of humans. In the video, AI is contrasted with cognitive computing, highlighting that while both aim to solve complex problems, AI is more about revealing hidden information and making decisions autonomously.

💡Machine Learning

Machine learning is a subset of AI that allows computers to learn from and make predictions or decisions based on data. It involves the development of algorithms that can learn from and adapt to data inputs, improving their accuracy over time without being explicitly programmed. In the context of the video, machine learning is one of the key technologies that power cognitive computing systems, enabling them to refine their pattern identification and data processing capabilities.

💡Deep Learning

Deep learning is a specialized subset of machine learning that uses artificial neural networks to model complex patterns in data, particularly those with many layers of abstraction. It is inspired by the structure and function of the human brain, allowing systems to learn and improve through experience. In the video, deep learning is mentioned as one of the technologies behind cognitive computing, contributing to the system's ability to understand and simulate human reasoning and behavior.

💡Natural Language Processing (NLP)

Natural Language Processing (NLP) is a field of AI that focuses on the interaction between computers and humans through natural language. It involves enabling computers to understand, interpret, and generate human language in a way that is both meaningful and useful. In the context of the video, NLP is a critical component of cognitive computing, allowing systems to communicate effectively with humans and understand the nuances of language, which is essential for simulating human thought processes.

💡Adaptive

Adaptability in the context of cognitive computing refers to the system's ability to adjust and learn from changes in information and the environment. An adaptive system is flexible enough to understand variations in data and can make real-time adjustments to provide the most relevant and accurate responses. This is crucial for cognitive systems to maintain their effectiveness and stay up-to-date with dynamic data inputs.

💡Interactive

Interactivity in cognitive computing systems is the ability for humans to communicate with machines and for machines to respond to human input effectively. This is a critical component as it allows users to define their needs and for the system to adjust its responses accordingly. Interactivity also extends to the system's capability to engage with other processors, devices, and cloud platforms, ensuring a seamless integration into various technological ecosystems.

💡Iterative and Stateful

Iterative and stateful refers to the capability of cognitive computing systems to maintain information about previous interactions or situations, which allows them to identify problems by asking questions or pulling in additional data when faced with incomplete information. This attribute enables systems to learn from past experiences and apply that knowledge to current and future problem-solving processes, enhancing their decision-making and analytical capabilities.

💡Contextual

Contextuality in cognitive computing systems is the ability to understand, identify, and consider contextual data such as syntax, time, location, domain requirements, user profiles, tasks, or goals. This allows systems to process both structured and unstructured data, including visual, auditory, or sensor data, from multiple sources in a way that is relevant and meaningful to the user's specific situation or query.

💡Smart IoT

Smart IoT (Internet of Things) refers to the integration of IoT devices with cognitive computing and AI capabilities, allowing these devices to not only collect and transmit data but also to analyze, learn from, and make decisions based on that data. This enhances the functionality of IoT devices, making them more efficient, secure, and responsive to user needs.

💡Cognitive Analysis in Healthcare

Cognitive analysis in healthcare involves the application of cognitive computing technologies to perform complex analysis and decision-making tasks within the healthcare industry. This can include tasks such as medical diagnosis, treatment planning, and patient care management, where cognitive systems use human-like reasoning to enhance the capabilities of healthcare professionals and improve patient outcomes.

💡Intent-based NLP

Intent-based NLP is a form of natural language processing that focuses on understanding the underlying intent or purpose behind a user's statement or query. This advanced form of NLP enables systems to not only comprehend the literal meaning of words but also the context, emotions, and goals associated with the communication, leading to more accurate and personalized responses.

Highlights

Cognitive computing is a system that learns, reasons, and interacts with humans naturally, without being explicitly programmed.

Cognitive systems learn and reason from their interactions with humans and their environment.

Cognitive computing overlaps with artificial intelligence and involves similar technologies.

Cognitive computing is about understanding and simulating human reasoning and behavior.

Applications of cognitive computing include speech recognition, sentiment analysis, and face detection.

Cognitive systems must be adaptive, interactive, iterative, stateful, and contextual.

Cognitive computing focuses on mimicking human behavior and reasoning to solve complex problems.

Artificial intelligence augments human thinking and focuses on providing accurate results.

Cognitive computing is used in customer service and healthcare, while AI is used in finance, security, retail, and manufacturing.

Cognitive computing helps humans make smart decisions by leveraging machines, while AI makes decisions on our behalf.

Cognitive AI is a combination of cognitive computing and AI that can perform logical reasoning and analysis.

Smart IoT is an application of cognitive AI that connects and optimizes devices, data, and IoT.

AI-enabled cybersecurity uses data security, encryption, and enhanced situational awareness to protect networks.

Content AI is a solution that learns and reasons, integrating various personal attributes and contextual awareness.

Cognitive analysis in healthcare uses human-like reasoning for Life Sciences applications.

Intent-based NLP helps businesses become more analytical in their approach to management and decision-making.

Cognitive AI is expected to change the world of technology by enhancing smart decision-making and problem-solving.