#cognitive computing

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

Single cortical neurons as deep artificial neural networks. Beniaguev, D., Segev, I., & London, M. (2021). Neuron, 109(17), 2727-2739.e3.

Cortical magnification in human visual cortex parallels task performance around the visual field. Benson, N. C., Kupers, E. R., Barbot, A., Carrasco, M., & Winawer, J. (2021). eLife, 10, e67685.

Separable pupillary signatures of perception and action during perceptual multistability. Brascamp, J. W., de Hollander, G., Wertheimer, M. D., DePew, A. N., & Knapen, T. (2021). eLife, 10, e66161.

Why grid cells function as a metric for space. Dang, S., Wu, Y., Yan, R., & Tang, H. (2021). Neural Networks, 142, 128–137.

Retinotopic organization of visual cortex in human infants. Ellis, C. T., Yates, T. S., Skalaban, L. J., Bejjanki, V. R., Arcaro, M. J., & Turk-Browne, N. B. (2021). Neuron, 109(16), 2616-2626.e6.

Oculo-retinal dynamics can explain the perception of minimal recognizable configurations. Gruber, L. Z., Ullman, S., & Ahissar, E. (2021). Proceedings of the National Academy of Sciences, 118(34), e2022792118.

Prior experience conditionally inhibits the expression of new learning in Drosophila. Jacob, P. F., Vargas-Gutierrez, P., Okray, Z., Vietti-Michelina, S., Felsenberg, J., & Waddell, S. (2021). Current Biology, 31(16), 3490-3503.e3.

The Human Brain Encodes a Chronicle of Visual Events at Each Instant of Time Through the Multiplexing of Traveling Waves. King, J.-R., & Wyart, V. (2021). Journal of Neuroscience, 41(34), 7224–7233.

Predictive encoding of motion begins in the primate retina. Liu, B., Hong, A., Rieke, F., & Manookin, M. B. (2021). Nature Neuroscience, 24(9), 1280–1291.

Optimal attention tuning in a neuro-computational model of the visual cortex–basal ganglia–prefrontal cortex loop. Maith, O., Schwarz, A., & Hamker, F. H. (2021). Neural Networks, 142, 534–547.

Inferences on a multidimensional social hierarchy use a grid-like code. Park, S. A., Miller, D. S., & Boorman, E. D. (2021). Nature Neuroscience, 24(9), 1292–1301.

Long-term stability of cortical ensembles. Pérez-Ortega, J., Alejandre-García, T., & Yuste, R. (2021). eLife, 10, e64449.

Linear reinforcement learning in planning, grid fields, and cognitive control. Piray, P., & Daw, N. D. (2021). Nature Communications, 12, 4942.

Developmental changes in natural scene viewing in infancy. Pomaranski, K. I., Hayes, T. R., Kwon, M.-K., Henderson, J. M., & Oakes, L. M. (2021). Developmental Psychology, 57(7), 1025–1041.

The physiological basis for contrast opponency in motion computation in Drosophila. Ramos-Traslosheros, G., & Silies, M. (2021). Nature Communications, 12, 4987.

Visual pursuit biases tactile velocity perception. Scotto, C. R., Moscatelli, A., Pfeiffer, T., & Ernst, M. O. (2021). Journal of Neurophysiology, 126(2), 540–549.

Mental representations distinguish value-based decisions from perceptual decisions. Smith, S. M., & Krajbich, I. (2021). Psychonomic Bulletin & Review, 28(4), 1413–1422.

Observing others’ joint attention increases 9-month-old infants’ object encoding. Thiele, M., Hepach, R., Michel, C., & Haun, D. B. M. (2021). Developmental Psychology, 57(6), 837–850.

By Arjuwan Lakkdawala

Ink in the Internet

I’m taking a radical look at future hypothetical robots. Sort of like a Frankenstein of modern science. A new era of tech is coming and have no doubt it’s going to turn the world upside down.

As a tech enthusiast and someone who is actually rather frightened of how far scientists are pushing the threshold of “progress.” I found myself thinking about that in the future which is happening before me, and that which one might say is impossible to happen.

I’m going to write about both prospects because I think each is a variable important to evaluate in order to deduce the mental and material outcome of the present state of things in this regard.

What my eyes see: Neuromorphic computing it appears is almost all set to eclipse deep learning, machine learning, take Artificial Intelligence to a whole new level.

Basics of it: a memristor sits in the heart of each chip, which has synapses that simulate the passing of information like in the human brain. Please note: the “simulation” is crude and in its infancy, because the human brain is not a solid electrical device.

In our brains and down to our spinal cord there are billions of neurons with chemical and electrical signal processing synapses. That send commends to our whole body.

The electrical synapses which our brain uses less often than the chemical ones, are responsible for our reflexes among perhaps other things. Like when you’re suddenly about to trip and fall but on reflex gain your balance. You didn’t think of balancing yourself, but that super instant decision made with lightning speed that saved you is the work of the electrical synapse.

Leading tech companies are trying to make the chips smaller.

The idea is that the processing and memory components on the memristor, work with the least energy consumption, and highest speed possible.

Presently in computers the processor and memory are located separately and the flow of electricity which sends signals to transistors in the binary language of 1s and 0s doesn’t permit for programming which could let Artificial Intelligence simulate any form of self-derived creativity or interpretation.

Deep learning which so far is considered the ultimate edge of AI is still based on logical computation but with layers of data from which the algorithm outputs the highest number of matches.

This logic based thinking which is an artificial simulation of how the left side of our brain operates, is what scientists want to surpass into the territory ruled by the right side of our brains – creativity.

It is believed the Neuromorphic chip is the first step in this direction.

However, don’t get confused by the difference in the Neuromorphic chip’s ability to process information and the programming needed for it to get “creative.” Artificial Intelligence is still hungry for human produced data and programming.

Neuromorphic computing is simply the beginning of a clever way of processing information. But it does seem soon enough it’s going to become a needed option.

Because according to Moore’s Law (the doubling of transistors every couple of years) means we are going to reach a point where the transistors produced won’t be enough to process rapidly growing new data in the world.

Suppose Neuromorphic computing improves to somewhat science fiction levels, in this case it would be a good summary of the brain of our hypothetical robot.

I’m certain it won’t actually go in the robot’s head space. Because it’s not necessary.

Cloud computing is so much more convenient.

Next for its eyes just imagine Google Glass. (You get the picture)

Hearing: Ultra-sensitive, can pick up frequencies humans can’t.

Muscles: depending for what the robot is used for, it could be soft plastic, or titanium metal.

What about the hands and touch? Here is where my fear lies. Over ambitious scientists can’t stop making robots that are “human-like” or animal-like” the purpose of these isn’t to replace humans or pets, but for people who prefer to be served by robots in restaurants, in sales, etc.

As for pets it’s thought “robotic pets are so much easier to care for.”

This is the biggest fault in the idea. There is no caring, there is no sense of guilt if it breaks, no pity if you haven’t fed it for days.

I recently saw a video on Youtube in which a man beats a humanoid looking robot, and kicks a dog-like robot. The footage meant to express that treat them as you want. This thing looks like a human but it’s not, and this looks like a dog but it’s not.

As someone who grew up taking care of actual pets, and cried rivers when they died. This footage doesn’t change a thing about how I feel about real humans and animals.

But imagine if you’re a kid born in this tech era. And you grow up with a mechanical pet dog. Won’t it distort your balance of touching with care, in comparison to rough touching?

Won’t it distort your sense of humanity of what hurts and what doesn’t?

The obsession to make robots human-like is so bad. I wouldn’t be surprised if future robots had human hearts grown in human tissue sacks, connected to its brain via prosthetics.

This too is serious bad news. Because if the robot is indeed given some form of artificial intelligence independence.

I wouldn’t expect it to have good feelings. Humans are the best example of how hard it is to fight evil temptations.

Copyright ©Arjuwan Lakkdawala 2020

Arjuwan Lakkdawala is an Author, Science Communicator, and Journalist. @Spellrainia Twitter Instagram

References:

Brain-Like (Neuromorphic) Computing - Computerphile

Neuroscience 2nd. Edition (book)

Exam Sam (website) Neurotransmitters, Reflexes, Synapses.

Cognitive computing is the simulation of human thought processes in a computerized model.

Cognitive computing involves data mining, pattern recognition, and natural language processing to mimic the way the human brain works

Noise correlations and perceptual inference. Bányai, M., & Orbán, G. (2019). Current Opinion in Neurobiology, 58, 209–217.

Bayesian Decision Models: A Primer. Ma, W. J. (2019). Neuron, 104(1), 164–175.

Premembering Experience: A Hierarchy of Time-Scales for Proactive Attention. Nobre, A. C., & Stokes, M. G. (2019). Neuron, 104(1), 132–146.

Object shape and surface properties are jointly encoded in mid-level ventral visual cortex. Pasupathy, A., Kim, T., & Popovkina, D. V. (2019). Current Opinion in Neurobiology, 58, 199–208.

Bonus Paper

Online Reinforcement Learning Using a Probability Density Estimation. Agostini, A., & Celaya, E. (2017). Neural Computation, 29(1), 220–246.

On the Mathematical Consequences of Binning Spike Trains. Cessac, B., Le Ny, A., & Löcherbach, E. (2017). Neural Computation, 29(1), 146–170.

Eye movements reveal a dissociation between memory encoding and retrieval in adults with autism. Cooper, R. A., Plaisted-Grant, K. C., Baron-Cohen, S., & Simons, J. S. (2017). Cognition, 159, 127–138.

Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance. Cortese, A., Amano, K., Koizumi, A., Kawato, M., & Lau, H. (2016). Nature Communications, 7, 13669.

Temporal pairwise spike correlations fully capture single-neuron information. Dettner, A., Münzberg, S., & Tchumatchenko, T. (2016). Nature Communications, 7, 13805.

Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality. Drugowitsch, J., Wyart, V., Devauchelle, A.-D., & Koechlin, E. (2016). Neuron, 92(6), 1398–1411.

Evidence and Counterevidence in Motion Perception. Duijnhouwer, J., & Krekelberg, B. (2016). Cerebral Cortex, 26(12), 4602–4612.

Neural Encoding of Self and Another Agent’s Goal in the Primate Prefrontal Cortex: Human–Monkey Interactions. Falcone, R., Brunamonti, E., Ferraina, S., & Genovesio, A. (2016). Cerebral Cortex, 26(12), 4613–4622.

Active Inference: A Process Theory. Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017). Neural Computation, 29(1), 1–49.

A probabilistic approach to demixing odors. Grabska-Barwińska, A., Barthelmé, S., Beck, J., Mainen, Z. F., Pouget, A., & Latham, P. E. (2016). Nature Neuroscience, 20(1), 98–106.

Oscillatory integration windows in neurons. Gupta, N., Singh, S. S., & Stopfer, M. (2016). Nature Communications, 7, 13808.

Dendritic excitation–inhibition balance shapes cerebellar output during motor behaviour. Jelitai, M., Puggioni, P., Ishikawa, T., Rinaldi, A., & Duguid, I. (2016). Nature Communications, 7, 13722.

Visual sensitivity of frontal eye field neurons during the preparation of saccadic eye movements. Krock, R. M., & Moore, T. (2016). Journal of Neurophysiology, 116(6), 2882–2891.

Parallel processing by cortical inhibition enables context-dependent behavior. Kuchibhotla, K. V, Gill, J. V, Lindsay, G. W., Papadoyannis, E. S., Field, R. E., Sten, T. A. H., … Froemke, R. C. (2016). Nature Neuroscience, 20(1), 62–71.

A feedback neural circuit for calibrating aversive memory strength. Ozawa, T., Ycu, E. A., Kumar, A., Yeh, L.-F., Ahmed, T., Koivumaa, J., & Johansen, J. P. (2016). Nature Neuroscience, 20(1), 90–97.

The spatial structure of correlated neuronal variability. Rosenbaum, R., Smith, M. A., Kohn, A., Rubin, J. E., & Doiron, B. (2016). Nature Neuroscience, 20(1), 107–114.

Active cortical dendrites modulate perception. Takahashi, N., Oertner, T. G., Hegemann, P., & Larkum, M. E. (2016). Science, 354(6319), 1587–1590.

Astrocytes gate Hebbian synaptic plasticity in the striatum. Valtcheva, S., & Venance, L. (2016). Nature Communications, 7, 13845.

Linking Electrical Stimulation of Human Primary Visual Cortex, Size of Affected Cortical Area, Neuronal Responses, and Subjective Experience. Winawer, J., & Parvizi, J. (2016). Neuron, 92(6), 1213–1219.

An short, clear introduction cognitive computing

Congintive computing is a problem-solving approach that uses hardware or software to approximate the form or function of natural cognitive process.

Natural Cognitive Processes

Learning

Perception

Motivation

Learning

Improved performance through experience/training

Perception

How we get data

Motivation

Enterprise organizations today face an unprecedented challenge: how to unlock actionable insights from exponentially growing data repositories while maintaining speed, accuracy, and relevance. Traditional search and retrieval systems struggle to keep pace with the dynamic needs of modern AI-driven decision support systems. The next generation of intelligent information access is here, and it's fundamentally changing how enterprises approach knowledge management and cognitive computing integration.

At the core of this transformation are Adaptive Retrieval Agents, intelligent systems that dynamically adjust their search strategies based on context, user intent, and real-time feedback. Unlike static retrieval mechanisms, these agents employ reinforcement learning and neural architecture search to optimize how they locate, rank, and present information across distributed data lakes architecture. For enterprises managing petabytes of structured and unstructured data, this adaptive capability represents a fundamental shift from reactive search to proactive intelligence delivery.

Core Architecture and Capabilities

Adaptive retrieval agents leverage multi-layered neural network design combined with natural language processing services to interpret complex queries and navigate heterogeneous data sources. The architecture typically includes a query understanding module, a dynamic retrieval strategy selector, and a context-aware ranking system. These components work in concert to handle everything from simple fact retrieval to complex reasoning tasks that require synthesizing information across multiple enterprise systems.

What distinguishes these agents is their ability to learn from interaction patterns. Through continuous hyperparameter tuning and model training and optimization, they refine their understanding of what constitutes relevant information for specific use cases. This learning loop is essential for enterprises deploying enterprise AI solutions that must adapt to evolving business contexts and user needs.

Integration with Enterprise Ecosystems

One of the most significant challenges enterprises face is the integration complexity when deploying new AI capabilities across existing technology stacks. Adaptive retrieval agents address this through flexible API layers and multi-cloud AI integration support. They can interface with legacy databases, modern data lakes, edge computing deployments, and real-time data processing pipelines without requiring wholesale system replacements.

This integration capability is particularly valuable in Industry 4.0 environments where data governance requirements demand strict controls over information access while still enabling agile AI model lifecycle management. Organizations like IBM and Microsoft have demonstrated how properly architected retrieval systems can bridge operational silos while maintaining compliance and security standards.

Conclusion