#Cognitive Computing

[unwelcome-ozian]

Beta intelligence military esque

Alice in wonderland

Alters, files, jpegs, bugs, closed systems, open networks

brain chip with memory / data

Infomation processing updates and reboots

'Uploading' / installing / creating a system of information that can behave as a central infomation processing unit accessible to large portions of the consciousness. Necessarily in order to function as so with sufficient data. The unit is bugged with instructions, "error correction", regarding infomation processing.

It can also behave like a guardian between sensory and extra physical experience.

"Was very buggy at first". Has potential to casues unwanted glitches or leaks, unpredictability and could malfunction entirely, especially during the initial accessing / updating. I think the large amount of information being synthesized can reroute experiences, motivations, feelings and knowledge to other areas of consciousness, which can cause a domino effect of "disobedience", and or reprogramming.

I think this volatility is most pronounced during the initial stages of operation because the error correcting and rerouting sequences have not been 'perfected' yet and are in their least effective states, trail and error learning as it operates, graded by whatever instructions or result seeking input that called for the "error correction".

I read the ask about programming people like a computer. Whoever wrote that is not alone. Walter Pitts and Warren McCulloch, do you have anymore information about them and what they did to people?

Here is some information for you. Walter Pitts and Warren McCulloch weren't directly involved in the programming of individuals. Their work was dual-use.

Exploring the Mind of Walter Pitts: The Father of Neural Networks

McCulloch-Pitts Neuron — Mankind’s First Mathematical Model Of A Biological Neuron

Introduction To Cognitive Computing And Its Various Applications

Cognitive Electronic Warfare: Conceptual Design and Architecture

Security and Military Implications of Neurotechnology and Artificial Intelligence

Oz

[aiinsight47]

How to use Chat AI to Practice the Socratic Method

Master critical thinking with the Socratic Method and AI. Explore applications, step-by-step guidance, and a ready-to-use starter prompt to analyze topics effectively.

The Socratic Method is a time-tested approach to learning. It uses open-ended questions to spark deep thinking. This method promotes critical reflection and logical analysis. Many educators use it to guide students toward clear insights. Lawyers and philosophers also rely on it to sharpen debate skills. Today, new tools like Chat AI make it simpler to try this method at home or at work. Table of…

[lifes-little-corner]

The AI Leadership Paradox: Balancing Human Intuition with Machine Intelligence

As technology speeds up, a big question comes up: how can leaders use AI without losing the value of human insight? This paradox asks us to find a balance between AI’s precision and human creativity1. In today’s fast-changing world1, leaders face a challenge. They must blend AI into their decisions without losing the human touch that makes leadership great. The solution is to see AI and human…

[mehmetyildizmelbourne-blog]

Creating an Innovation Storm from Mini-Brains in a Teacup: The Simplified Science of Organoids 

A simplified version of my conceptual and intuitive exploration of the mysteries behind organoid intelligence for a potential discovery merging artificial intelligence with biocomputing.  Combinatorial Innovation in Science and Technology I have been fascinated by various kinds of intelligence for combinatorial innovation, exploring ideas on how the human brain works—how it learns, remembers,…

[juliebowie]

What are the important Subsets of Artificial Intelligence (AI)?

Summary: Explore the crucial subsets of artificial intelligence, such as Machine Learning, Deep Learning, and Natural Language Processing. Each subset contributes uniquely to AI, driving innovation and improving technology across different fields.

Introduction

Artificial Intelligence (AI) revolutionizes technology by enabling machines to mimic human intelligence. Its significance lies in its ability to transform industries, from healthcare to finance, by automating complex tasks and providing advanced solutions. Understanding the subsets of artificial intelligence, such as Machine Learning, Deep Learning, and Natural Language Processing, is crucial. 

This blog aims to explore these subsets, highlighting their unique roles and applications. By examining each subset, readers will gain insight into how these components work together to drive innovation and enhance decision-making processes. Discover the intricate landscape of AI and its impact on modern technology.

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines designed to think and learn like humans. The term AI encompasses various techniques and technologies aimed at creating systems capable of performing tasks that typically require human intelligence. 

These tasks include problem-solving, understanding natural language, and recognizing patterns. AI systems can be programmed to perform specific tasks or learn from data and adapt their behavior over time.

Important Subsets of Artificial Intelligence (AI)

Artificial Intelligence (AI) encompasses a broad range of technologies and methodologies that aim to create systems capable of performing tasks that typically require human intelligence. 

To fully understand AI's potential, it’s essential to delve into its key subsets, each with its unique focus and applications. This section explores the most important subsets of AI, shedding light on their roles, advancements, and impact on various industries.

Machine Learning (ML)

Machine Learning (ML) is a core subset of AI that empowers systems to learn from data and improve their performance over time without being explicitly programmed. ML algorithms analyze patterns in data and use these patterns to make predictions or decisions. 

The importance of ML lies in its ability to handle vast amounts of data, adapt to new information, and improve accuracy through experience.

Types of Machine Learning

Supervised Learning: This type involves training algorithms on labeled data, where the outcome is known. The system learns to map input data to the correct output, making it ideal for classification and regression tasks. Examples include email spam filters and predictive analytics in finance.

Unsupervised Learning: Unlike supervised learning, unsupervised learning deals with unlabeled data. The system tries to identify hidden patterns or intrinsic structures within the data. Techniques like clustering and association are commonly used. Applications include customer segmentation in marketing and anomaly detection in network security.

Reinforcement Learning: This approach focuses on training models to make sequences of decisions by rewarding desired behaviors and penalizing undesired ones. It's widely used in robotics and game development, exemplified by AI systems that master games like Go or complex simulations.

Deep Learning (DL)

Deep Learning (DL) is a subset of ML that uses neural networks with many layers (hence "deep") to model complex patterns in data. Unlike traditional ML algorithms, deep learning models can automatically extract features from raw data, such as images or text, without needing manual feature extraction.

Neural networks are the backbone of deep learning. They consist of interconnected layers of nodes, each performing mathematical operations on the input data. The depth of these networks allows them to capture intricate relationships and hierarchical features in the data.

Deep learning has revolutionized fields like image and speech recognition. Notable breakthroughs include advanced image classification systems and voice assistants like Siri and Alexa, which rely on deep learning to understand and generate human language.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a subset of AI focused on the interaction between computers and human languages. NLP enables machines to understand, interpret, and generate human language in a way that is both meaningful and useful.

Key Techniques and Models

Tokenization and Parsing: Breaking down text into smaller units (tokens) and analyzing grammatical structures. This is fundamental for tasks like language translation and sentiment analysis.

Transformers and BERT: Advanced models like Transformers and BERT (Bidirectional Encoder Representations from Transformers) have significantly improved NLP capabilities. These models understand context and nuances in language, enhancing tasks such as question answering and text summarization.

NLP is widely used in chatbots, virtual assistants, and language translation services. It also plays a crucial role in content analysis, such as extracting insights from social media or customer feedback.

Robotics

Robotics involves the design, construction, and operation of robots—machines capable of carrying out a series of actions autonomously or semi-autonomously. AI enhances robotics by providing robots with the ability to perceive, reason, and act intelligently.

Types of Robots and Their Functions

Industrial Robots: These are used in manufacturing for tasks such as welding, painting, and assembly. They enhance productivity and precision in production lines.

Service Robots: Designed for tasks like cleaning or assisting in healthcare, these robots improve quality of life and operational efficiency.

AI enables robots to learn from their environment, make real-time decisions, and adapt to new situations. This integration is crucial for advancements in autonomous vehicles and sophisticated robotic systems used in various fields.

Computer Vision

Computer Vision is a field of AI that enables machines to interpret and understand visual information from the world. By processing and analyzing images and videos, computer vision systems can make sense of their surroundings and perform tasks based on visual input.

Key Techniques and Technologies

Image Classification: Identifying objects within an image and assigning them to predefined categories. Used in applications like facial recognition and object detection.

Object Detection: Locating and identifying objects within an image or video stream. Essential for applications in autonomous driving and surveillance systems.

Computer vision is integral to technologies such as self-driving cars, medical imaging, and augmented reality. It helps automate processes, enhance safety, and provide new ways to interact with digital content.

Expert Systems

Expert Systems are AI programs designed to emulate the decision-making abilities of human experts in specific domains. These systems use a knowledge base of human expertise and an inference engine to solve complex problems and provide recommendations.

Expert systems rely on predefined rules and logic to process data and make decisions. They are often used in fields such as medical diagnosis, financial forecasting, and technical support.

Expert systems assist professionals in making informed decisions by providing expert-level advice. Examples include diagnostic systems in healthcare and financial advisory tools.

AI in Cognitive Computing

Cognitive Computing aims to mimic human thought processes in analyzing and interpreting data. Unlike traditional AI, cognitive computing focuses on simulating human-like understanding and reasoning to solve complex problems.

Cognitive computing systems can understand context, handle ambiguous information, and learn from interactions in a way that mirrors human cognitive abilities. This approach is more flexible and adaptive compared to rule-based AI systems.

Cognitive computing enhances areas such as personalized medicine, customer service, and business analytics. It enables systems to interact with users more naturally and provide insights based on nuanced understanding.

Frequently Asked Questions

What are the main subsets of artificial intelligence?

The main subsets of artificial intelligence include Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Robotics, Computer Vision, Expert Systems, and Cognitive Computing. Each subset plays a unique role in advancing AI technology.

How does Machine Learning differ from Deep Learning?

Machine Learning involves algorithms that improve from data over time, while Deep Learning uses neural networks with many layers to automatically extract features from raw data. Deep Learning is more complex and handles unstructured data like images and text better.

What role does Natural Language Processing play in AI?

Natural Language Processing (NLP) allows machines to understand, interpret, and generate human language. It powers applications such as chatbots, virtual assistants, and language translation, enhancing communication between humans and machines.

Conclusion

Understanding the subsets of artificial intelligence—Machine Learning, Deep Learning, Natural Language Processing, Robotics, Computer Vision, Expert Systems, and Cognitive Computing—provides valuable insights into AI's capabilities. Each subset contributes uniquely to technology, transforming industries and advancing automation. Exploring these areas highlights their significance in driving innovation and improving decision-making processes.

[amazonqualityproducts01]

The Role of Artificial Intelligence in Cyber Security

As we step into the digital age, the landscape of cyber threats continues to evolve at an unprecedented pace. With the ever-growing sophistication of cyber-attacks, the need for robust defense mechanisms has become paramount. Fortunately, the emergence of Artificial Intelligence (AI) is revolutionizing the realm of cybersecurity, empowering organizations to stay one step ahead of malicious actors. As a technology expert deeply entrenched in the world of AI, I am thrilled to delve into the transformative role it plays in safeguarding our digital ecosystems.

For more reading click here

[thetechinsiderblogs]

Artificial Intelligence’s Adaptive Intelligence and Cognitive Computing: Revolutionizing Problem-Solving

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life.

In the ever-evolving landscape of technology, Artificial Intelligence (AI) has emerged as a force which has reshaped the way we approach problem solving. At the very front of this revolution is Cognitive computing.

Table of Contents

Understanding Cognitive Computing

Adaptive Intelligence

Examples of Real-World Use Cases of Cognitive Computing:

Difference between AI and Cognitive Computing

The Future of Cognitive Computing

Understanding Cognitive Computing

In the most basic terms Cognitive Computing is an attempt to have computers mimic the way human brain works. In order to accomplish this, cognitive computing uses artificial intelligence (AI) and other underlying technologies:

Expert systems.

Neural networks.

Machine learning.

Deep learning.

Natural language processing (NLP).

Speech recognition.

Object recognition.

Robotics.

Cognitive Computing Systems are adaptive and capable of evolving overtime. This helps machines to solve real life scenario problems.

Systems used in the cognitive sciences combine data from various sources while weighing context and conflicting evidence to suggest the best possible answers. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition and NLP to mimic human intelligence.

Adaptive Intelligence

At the very core of Cognitive Computing lies Adaptive Intelligence. This is the ability of AI systems to learn from experience and adapt to new information. Traditional algorithms follow predetermined rules, but Adaptive Intelligence allows machines to continuously evolve.

Examples of Real-World Use Cases of Cognitive Computing:

IBM Watson has been utilized in the healthcare industry to analyze medical records and find insights to improve diagnoses and treatment plans.

Contrary to the fear of machines replacing humans, Cognitive Computing emphasizes a collaborative approach. The goal is to provide humans with a powerful tool which as assist them with their decisions and help them improve their decision-making capabilities.

Imagine a data analyst working alongside a Cognitive Computing system that sifts through vast datasets, uncovering hidden patterns and trends. This partnership accelerates the problem-solving process, enabling humans to focus on higher-order tasks that require creativity and critical thinking.

Difference between AI and Cognitive Computing

The basic use case of Artificial Intelligence is to implement the best algorithm for solving a problem. However, cognitive computing goes further to mimic human wisdom and intelligence by studying a series of factors. Cognitive computing varies widely from Artificial Intelligence in terms of concept.

Cognitive Computing Learns and Imitates Human Thought Process

Unlike AI systems that only attend to a given problem, cognitive computing can learn from the data and patterns to suggest human relevant actions depending on their understanding. When it comes to AI, the system takes complete control and uses a pre-defined algorithm to avoid a certain scenario or take the necessary steps.

However, cognitive computing can be applied to different fields where it performs the role of an assistant rather than the one completing a task.

Cognitive Computing Does Not Throw Human Out of the Picture

Cognitive computing enables users to analyze data faster and more accurately without worrying about being wrong. Its main aim is to assist humans with their decision-making. Unlike AI, it doesn’t completely disregard humans.

Deloitte refers to cognitive computing as “more encompassing than the traditional, narrow view of AI, AI has been primarily used to describe technologies capable of performing tasks normally requiring human intelligence”

Whereas they define cognitive computing as “We see cognitive computing as being defined by machine intelligence which is a collection of algorithmic capabilities than can augment employee performance, automate increasingly complex workloads, and develop cognitive agents that simulate both human thinking and engagement.”

As we embark on this journey of Cognitive Computing, ethical considerations take center stage

The Future of Cognitive Computing

In conclusion, Cognitive Computing is not just a technological evolution, it’s the fusion of adaptive intelligence and problem-solving capabilities which opens the doors to possibilities we once deemed were fantastical.

The future promises a landscape where Cognitive Computing plays a leading role in unravelling complex challenges, pushing the boundaries of what we thought achievable. It’s a journey where humans and machines dance together. The symphony of Cognitive Computing is just beginning, and its crescendo will undoubtedly shape the destiny of problem-solving in ways yet to be fully realized.

[industrydesignservices]

Surprising AI data that show how these technologies will affect many facets of our lives in the coming years are presented. To know more about browse: https://teksun.com/ Contact us ID: marketing@teksun.com

[bindspace]

WHAT IS COGNITIVE COMPUTING AND ITS APPLICATIONS

🧠 "The brain is wider than the sky." - Emily Dickinson 🌌

Ever wondered how cognitive computing is revolutionizing the world of technology? 💭

It's all about AI systems that emulate the human brain! 🤯

Cognitive computing not only enhances decision-making but also has a multitude of applications across industries. 🏥🏦🏭

Discover the power of cognitive computing and its far-reaching applications in our latest article! 🚀🌐

👉 Click the link to explore this fascinating technology:

What is Cognitive Computing and Its Applications - Dataspace Insights

Get to know the world of cognitive computing in a non-technical, easy-to-understand manner. Learn about its applications, and how it impacts our everyday lives. 🙂

What is Cognitive Computing?

Cognitive computing is a field of artificial intelligence (AI) that focuses on creating systems that can learn, reason, and interact with humans in a natural way.

By mimicking human thought processes, cognitive computing systems can understand, analyze, and interpret complex information to solve problems and make decisions.

#CognitiveComputing #AI #Technology #Innovation #ArtificialIntelligence #linkedin #data #job #interview

Don't miss out on this exciting journey into the future! 🔮💡

Please follow our Quora Space - Bindspace Technologies Bindspace Technologies

Follow us on - Dataspace Insights (Digital Content Platform| Latest Technology | Dataspace Insights)

Company Page - Bind Space Tech (Bindspace Technologies)

[stemgirlchic]

why neuroscience is cool

space & the brain are like the two final frontiers

we know just enough to know we know nothing

there are radically new theories all. the. time. and even just in my research assistant work i've been able to meet with, talk to, and work with the people making them

it's such a philosophical science

potential to do a lot of good in fighting neurological diseases

things like BCI (brain computer interface) and OI (organoid intelligence) are soooooo new and anyone's game - motivation to study hard and be successful so i can take back my field from elon musk

machine learning is going to rapidly increase neuroscience progress i promise you. we get so caught up in AI stealing jobs but yes please steal my job of manually analyzing fMRI scans please i would much prefer to work on the science PLUS computational simulations will soon >>> animal testing to make all drug testing safer and more ethical !! we love ethical AI <3

collab with...everyone under the sun - psychologists, philosophers, ethicists, physicists, molecular biologists, chemists, drug development, machine learning, traditional computing, business, history, education, literally try to name a field we don't work with

it's the brain eeeeee

[archiveofaffinities]

William J. Mitchell, The Logic of Architecture Design, Computation and Cognition, A Vocabulary of Stair Motifs (After Thiis Evensen, 1988)

[aiinsight47]

Build AI Tools for Money: Simple Developers Guide

Learn how developers can create and sell AI tools or APIs for profit. Discover steps to identify problems, build solutions, monetize effectively, and grow your AI business.

The world of artificial intelligence (AI) is booming, and developers are uniquely positioned to take advantage of this trend. Creating and selling AI tools or APIs is one of the most lucrative ways to turn your coding skills into a business. Whether you’re solving a niche problem or building tools for broader use, there are endless opportunities in this space. Table of Contents What Are AI Tools…

[compneuropapers]

Interesting Reviews for Week 13, 2025

Neural circuits for goal-directed navigation across species. Basu, J., & Nagel, K. (2024). Trends in Neurosciences, 47(11), 904–917.

Neural Network Excitation/Inhibition: A Key to Empathy and Empathy Impairment. Tang, Y., Wang, C., Li, Q., Liu, G., Song, D., Quan, Z., Yan, Y., & Qing, H. (2024). The Neuroscientist, 30(6), 644–665.

Event perception and event memory in real-world experience. Bailey, H., & Smith, M. E. (2024). Nature Reviews Psychology, 3(11), 754–766.

Plasticity of Dendritic Spines Underlies Fear Memory. Choi, J. E., & Kaang, B.-K. (2024). The Neuroscientist, 30(6), 690–703.

[idontmindifuforgetme]

Shadowed a very pissed off neurologist today. It was so hot

[bme-girl]

17.Dec.2024

Honestly treating my PhD like my engineering undergrad is the most fun thing ever. I’m not working as hard as I did as a baby engineer, but learning stuff I find incredibly fascinating is so much FUN! You’re telling me I get to code, debug, play with new tools, collect and play with data, then just keep learning whatever I want?? This is a treat. It’s stuff like this that makes me feel lucky. Always the student, sometimes the teacher, never the expert 😉