Artificial Intelligence Vibrations – Rebellion Research

Artificial Intelligence Vibrations

Human Brain and Universe = Unbiased AI

Artificial Intelligence Vibrations Let me introduce the concept of vibration in artificial intelligence (AI). Einstein said, “Every thing in life is vibration”. We use electromagnetism in computer science and are aware of the other forces of physics (gravity, weak force and strong force). I think that the issue of bias in artificial intelligence should be address through the fifth law of physics, vibration.

Human beings are biased and hence present AIs are biased too. The laws of nature (also know as the laws of physics) are precisely construct to allow this universe, our planet and very own existence. Hence, my first assumption is that using the laws of physics, correctly, would mitigate any bias that comes from human knowledge.

There are some entertainment media documentaries that question our reliance and trust in the development of AI by technology companies like Facebook, Microsoft, Google, or even governments that will use those AI advances to wage war. 

The Social Dilemma, Ex Machina, Coded Bias, and Superinteligence are movies about AI, which rightfully question the trust that we should put in these technologies and the way that they are design and implemented by companies and governments. Also, we have multiple academic, government, and technology examples on the bias of AI as presently designed and used.

We are in incredible and disturbing times concurrently when it comes to our trust in science. Advances in AI and nanotechnology are reflected all over industry and science, but we hesitate to use the law of vibration to correct human bias. We are the proud sub-atomic particle, refusing to integrate to a universe that laws of nature have specifically imposed upon all. Lastly, the other alternative is that somehow physics do not apply to the digital world.

In the AI and Machine Learning (ML) world we have made tremendous advances in computer science. One of the most significant discovery, in my opinion, is the similarities structures development and connections in the universe to the human brain, and hence to AI neurons. The second assumption is that there is universal interconnection patterns that are a combinations of frequencies, vibrations, oscillations, and spectrums. Although we cannot see those patterns using our senses, AI has the capacity to find those patterns because the development of microchip sensors and deep learning techniques has increased our human ability to “see”, “smell”, “taste”, and “feel”.

We know that the output of facial and linguistic AIs are biased and this affect all of us. If output is bias, I propose that we go to the initial data node , the very beginning and use the law of vibration before you put any other data. We should follow the roadmap design that physics have taught us. Start with hydrogen atom vibrations when they collide, continue adding databases of vibrations, oscillations, frequencies, and spectrum that are naturally occurring. Later on add the data that is going to support your experiment.

In conclusion, for those that are interested, I have put together a sequence/methodology of physics vibrations that you can start with to reduce bias in your project data at the end of this paper. Furthermore, these vibrations databases contain vibrations physics patterns. Remember to run your experiment at least three times so you can compare outputs and metrics. After a while, you should obtain the profile of vibrations that does not have the human bias, even if the other data that you input to solve your problem does not seem to be related to the law of vibrations. Trust the science. The specific order of the vibrations data that I recommend is from the Big Bang to quantum physics. These are your first ingredients (i.e., independent variables) for your experiment; you supply the rest according to the specific experience.

Alberto’s Anti-Bias Artificial Intelligence Compendium 

Methodology by Alberto Roldan

“Everything in Life is Vibration” – Albert Einstein

The law of nature that states everything has a vibration

1. Free Hydrogen Atom Collision Cross Sections of Interest in Controlled Thermonuclear Research,

2. The Leiden Atomic and Molecular Database (LAMDA): Current Status, Recent Updates, and Future Plans,

3. MinimumBias primary dataset in RECO format from the 0.9 TeV Commissioning run of 2010 (/MinimumBias/Commissioning10-07JunReReco_900GeV/RECO),

4. Consciousness Frequencies

5. Neurons Frequencies

6. This Is Your Brain on Charitable Giving

7. Social Rewards Enhance Offline Improvements in Motor Skill (we need data set),

8. Hippies,

9. iPhone Vibration Measure

10. Accelerometers 

Artificial Intelligence Vibrations

11. Sound Wave

12. Spectrum Measurement

13. Nanosensors

14. Medical Diagnoses Sensors

15. Intermediate states of molecular self-assembly from liquid-cell electron microscopy

16. Gut feelings: the emerging biology of gut–brain communication

17. Earth vibration

18. Cell vibrations

19. Inorganic compounds vibrations (spectrum)

20. Vibrational Coupling between Organic and Inorganic

21. The EM (light) spectrum

22. Image Recognition

23. Introduction Pattern Recognition

24. Text to Spectrum methodology

25. Cell to Cell Communication pathways

26. Teaching peace thru music

27. Art and Music showing Kindness

28. Franl Litz Charity

29. Music that depicts charity

30. The love frequency

528 Hz – The Love Frequency

31. Human body frequency

32. Frequency in Electronics

33. Other Data Sets (project specific) – these dataset contain bias

a. Computer Vision DataSets

b. Fake and Real News dataset

c. Emotions dataset

d. Classify emotions in text data set

e. EEG Brainwaves data set

f. Amazon Music reviews data set

g. Beehive Metrics Data Set

h. Air quality sensor data set

i. Human Activity Recognition data set

j. Human Activity Recognition Smart Devices data set

k. Appliances Energy Prediction data set

l. Cardiovascular Data set

m. National health data set (multiple measures includes smell)

n. Lexicon for 81 Languages data set

o. Computational Imaging Data set

p. Touch Sensors data set

q. Circadian Rhythm in the Brain

r. Classify gestures by muscle activity

s. Natural Images data set

t. 3D Surface data set (microscopic world)

u. Similar Sequence Data set

v. Google books data set

w. Kensho Derived Wikimedia Dataset

x. Brain Signals Data Sets

y. Gastro internal data set

z. Signal data sets resources

aa. List of datasets for machine-learning research (to be added after experiment)

Written by Alberto Roldan : Founder and Owner at Artificial Intelligence and Business Analytics Group

Deep Reinforcement Learning vs Deep Learning : Which is best for you?

Artificial Intelligence Vibrations

Read more here: Source link