Neuro-Symbolic AI Research

Digital Organism Controller

Simultaneously control Artificial Intelligence and Machine Learning components on distributed local and cloud resources. Seamlessly combine best-of-breed platform tools. Asynchronous, multi-threaded, objected-oriented, building-block architecture. Evolve and replicate function and behavior.

Organism Learning

  • Habituation, Sensitization
    • Single-feature Stimuli Selection
    • Non-associative (Binary)
    • Stimuli Enhancement or Attenuation
    • Adaptive Threshold
    • Feature Pruning
    • Signal Amplification or Reduction
  • Associative
    • Multi-feature Stimuli Selection
    • Classical Conditioning
    • Observational Learning
    • Operant Conditioning
    • Reinforcement Learning
    • Deep Learning
    • Continuous Learning
    • Neuron Growth
Organism Function

  • Asynchronous Nervous System
    • Neuromodulator
    • Neuro-signal
  • Tectum Realtime Sensory Processor
    • Overt Attention
    • Concrete Modeling
    • Feature Well
    • Predictions
    • Planning
  • Cerebral Cortex
    • Covert Attention
    • Abstract Modeling
    • Deep Learning
Organism Behavior

  • Homeostatic Process Equilibrium
  • Multi-threaded Behavior
  • Innate (hardcoded) Behavior
  • Instinctual Behavior
  • Learned Behavior
  • Stimuli Response
  • Environment Interaction
  • Learning Evolution
  • Organism Communication
More on Neurocomputing (37)
More on Cognitive Computing (480)

Top weighted terms correlating to ‘cognitive computing’. (480)
cognitive computing266.5.5.
enterprise cognitive system76.5.5.12.
cognitive computer56.21.26.168.
neuromorphic chip56.
intel loihi56.
glossary of artificial intelligence46.18.
natural computing32.3.7.2.
cellular computing32.3.7.76.
industrial automation33.6.98.
natural computation36.14.3.18.
automatic machine36.28.7.2.
automatic control36.8.113.
fourth industrial revolution22.
industrial internet of things26.8.104.
gradient descent11.2.1.
anomaly detection11.2.1.10.
association rule learning11.2.1.12.
structured prediction11.2.1.14.
learning to rank11.2.1.15.
grammar induction11.2.1.16.
ontology learning11.2.1.17.
loss functions for classification11.2.1.172.
batch normalization11.2.1.173.
training, validation, and test sets11.2.1.176.
data augmentation11.2.1.177.
bootstrap aggregating11.2.1.19.
optical character recognition11.2.1.197.
deep learning speech synthesis11.2.1.198.
batch learning11.2.1.2.
relevance vector machine11.2.1.23.
variational autoencoder11.2.1.233.
graph neural network11.2.1.234.
cure algorithm11.2.1.25.
hierarchical clustering11.2.1.26.
k means clustering11.2.1.27.
fuzzy clustering11.2.1.28.
optics algorithm11.2.1.31.
mean shift11.2.1.32.
canonical correlation11.2.1.34.
independent component analysis11.2.1.35.
non negative matrix factorization11.2.1.37.
principal component analysis11.2.1.38.
proper generalized decomposition11.2.1.39.
sparse dictionary learning11.2.1.41.
graphical model11.2.1.42.
conditional random field11.2.1.44.
random sample consensus11.2.1.46.
local outlier factor11.2.1.47.
rule based machine learning11.2.1.5.
restricted boltzmann machine11.2.1.53.
self organizing map11.2.1.54.
u net11.2.1.55.
q learning11.2.1.60.
state action reward state action11.2.1.61.
temporal difference learning11.2.1.62.
kernel machines11.2.1.68.
bias variance tradeoff11.2.1.69.
cluster analysis11.2.1.7.
computational learning theory11.2.1.70.
empirical risk minimization11.2.1.71.
occam learning11.2.1.72.
probably approximately correct learning11.2.1.73.
statistical learning theory11.2.1.74.
vapnik chervonenkis theory11.2.1.75.
conference on neural information processing systems11.2.1.76.
international conference on machine learning11.2.1.77.
international conference on learning representations11.2.1.78.
list of datasets for machine learning research11.2.1.81.
learning rate11.2.1.93.
data clustering11.2.5.480.
k means++
list of machine learning algorithms11.2.5.609.
eclat algorithm11.2.5.612.
one attribute rule11.2.5.613.
softmax activation function11.2.6.51.
test set11.2.7.20.
training set11.2.7.21.
softplus function11.2.9.35.
bayesian model averaging11.9.1.19.
artificial neural networks11.9.2.56.
class membership probabilities11.9.3.103.
regression analysis11.9.4.1.
cognitive sciences12.10.62.
cognitive process12.
outline of thought12.
mental function12.
Terms correlating to ‘cognitive computing’ exceed 100 (480).
Results limited to top 100 weighted terms.

I have analyzed 44 news headlines for the given date (2024-07-14). Market sentiment analysis: The overall market sentiment is cautious, with a slight bias towards optimism. The news headlines indicate a mixed bag of economic indicators, with some positive signs such as a decrease in mortgage rates and a strong stock market, while others suggest continued challenges like inflationary pressures and a soft housing market. Hidden trends, patterns, or relationships: * There appears to be a correlation between the performance of big banks and the overall market sentiment. * The news headlines highlight the importance of interest rates in shaping the economy, with both rate cuts and increases having significant impacts. Short-term prediction: The stock market is likely to continue its upward trend, driven by strong corporate earnings and positive economic indicators. However, caution should be exercised due to potential volatility caused by geopolitical tensions and inflationary concerns. Long-term prediction: The US economy is expected to experience a moderate recovery in the coming years, with growth driven by technological advancements, infrastructure spending, and increased consumer confidence. Recommendation: * Investors should consider diversifying their portfolios to minimize risk. * Businesses should focus on innovation and adaptability to stay competitive in an evolving economic landscape. Number of words: 106 (2024-07-14)

Published 2021-12-11 19:15.17

Updated 2024-07-22 21:10:33