| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | ||
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1 | Step | 1 🗂️ | 2 📑 | 3 🤔 | 4 🗣️ | 5❓🤔 | 6 🗣️ | |||||||||||||||
2 | Commentary | Will the ycombinator startup be successful? | The AI is a computer. Computer's speak ones and zeroes. The AI perceives in binary true/false statements. We represent that with a YES/NO perception. The AI can perceive that data and come to meaningful conclusions. | Machine Readable Input Data - The AI can perceive in 1's and 0's. The human redable terms must be translated into a one or a zero. Then the AI can read the information and provide it's response. | Matrix with rows matching input data features. Three features = 3 rows. Number of columns is memory capacity. Must have enough columns to maintain memory. It can be too dense and take too much compute and not be efficient. I can be not dense enough and not have the capacity required to be successful. Fewer than 2x input features is okay. | The last layer needs to match the training data output dimentions with the density of the previos layer. 4 columns (from previous layer) x 1 output label. 4 x 1 | Test what the AI Matrix knows using the first input layer and a TANH function. | Test what the AI Matrix knows using the second layer (output) without a non-linear function. | ||||||||||||||
3 | Simplified Terminology | Yes/No Perceptions: AI requires true/false binary format to conclude an answer. | Questions and Answers used for Teaching the AI | AI Memory Matrix | AI Response Matrix | AI Rationalization / Thoughts about the Question | AI Answers | |||||||||||||||
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5 | ML Terminology | Features (unvectorized) | Vectorized Features | Hidden Layer Weights | Output Layer Weights | Hidden Layer Results | Output Layer Results | |||||||||||||||
6 | using AWS | 3+ team size | 50%+ Rev Growth | ▶ | one | two | three | |||||||||||||||
7 | Startup A | YES | YES | YES | ▶ | 1 | 1 | 1 | 0.51 | 0.00 | 0.94 | 0.98 | 0.07 | 0.72 | 0.86 | 0.94 | 0.99 | 2.14 | ||||
8 | Startup B | YES | NO | NO | 1 | 0 | 0 | 0.06 | 0.89 | 0.60 | 0.89 | 0.87 | 0.47 | 0.00 | 0.73 | 0.75 | 1.07 | |||||
9 | Startup C | YES | YES | NO | 1 | 1 | 0 | 0.34 | 0.38 | 0.16 | 0.60 | 0.47 | 0.51 | 0.71 | 0.91 | 0.95 | 1.96 | |||||
10 | Startup D | YES | NO | YES | 1 | 0 | 1 | 0.92 | 0.69 | 0.37 | 0.80 | 0.92 | 1.59 | |||||||||
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12 | YouTube: https://youtu.be/xY4iKQ8TfiQ | |||||||||||||||||||||
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