Thinking in the FUTURE of AI

Popular AI

Since 2012

Popular AI

TEXT GENERATION

  1. ChatGPT: The Leading AI model
  2. Gemini: Google's competetor to ChatGPT
  3. Llama 3: Best AI open source model from Meta
  4. Claude 3: GPT competitor by Anthropic
  5. Perplexity: ChatGPT for research
  6. MM1: Apple's Large Language Model
  7. Titan: Amazon's LLM - Comes in Express & Premiere
  8. Grok: Elon Musk's OpenAI competitor
  9. CoPilot: Microsoft's Coding Assistant
  10. CodeWhisperer: Amazon's CoPilot Competetor
  11. Tabnine: Copilot competitor company
  12. Jasper: AI copilot for marketing
  13. Mistral AI: Open source text gen models

Popular AI

Generative Images

  1. Midjourney: Easy AI Image Generator
  2. Dall-E: OpenAI Image Generator
  3. Adobe Firefly: AI tool from Adobe
  4. Stable Diffusion: Best Image Generation
  5. Getty iStock: "Commercially Safe" generation
  6. Synthesia: AI text to video
  7. Sora: OpenAI text to video
  8. Runway: AI text-to-video
  9. Pika: AI Video Generator

Popular AI

OTHER POPULAR AI

  1. ElevenLabs: AI text-to-speech & voice clone
  2. Suno: AI music generator
  3. Voice.ai: AI voice changer
  4. AIMastering.com: AI Audio Mastering
  5. Lalal.AI: Remove instrument or voices from music
  6. Text to Speech and Speech to Text (Too many!)

ON Device

Object Detection

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NSFW JS

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Tracking

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Person Detection

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Face Detection

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Enjoy the Show

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Facial Recognition - For Nic Cage

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Face Mesh

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Makeup Try on

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Pose Detection

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Counting Exercises

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Scan Document

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Scan Document

- FIX ANGLES

- MAGIC ERASE

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LANGUAGE OPTIONS

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LANGUAGE OPTIONS

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LANGUAGE OPTIONS

THE FUTURE

A combination of AI models working in concert to give excellent customer experience.

Anything you could do if you were given enough instruction and examples, is something an AI could do for you.

Let's Do The Creativity Exercise!

WHY NEURAL?

Neural Synapses Are

  • Densely connected
  • Have varying thickness in connections (weights)
  • Vary synapse size (bias)
  • Need a certain amount of electricity to fire, or activation voltage.

Example Problem

XOR operation

A Simple Network

b = -5

a = σ

b = 15

a = σ

b = -15

 

w=10

w=-10

w=10

w=-10

w=10

w=10

A Simple Network

b = -5

a = σ

b = 15

a = σ

b = -15

a = σ

w=10

w=-10

w=10

w=-10

w=10

w=10

0

1

b = -5

a = σ

0.9933

(0 * 10)
+ (1 * 10)
+ -5
= 5
σ(5) = 0.9933

A Simple Network

b = -5

a = σ

b = 15

a = σ

b = -15

a = σ

w=10

w=-10

w=10

w=-10

w=10

w=10

0

1

0.9933

(0 * -10)
+ (1 * -10)
+ 15
= 5
σ(5) = 0.9933

b = 15

 

a = σ

0.9933

A Simple Network

b = -5

a = σ

b = 15

a = σ

b = -15

a = σ

w=10

w=-10

w=10

w=-10

w=10

w=10

0

1

0.9933

(0.9933 * 10)
+ (0.9933 * 10)
+ -15
= 4.866
σ(4.866) ≈ 0.9924

0.9933

b = -15

a = σ

0.9924

DONE!

GPU Speed

Is this the same as code?

Matrix Math

Linear Algebra

\sigma\left(\begin{bmatrix} \begin{bmatrix} 10 & 10 \\ -10 & -10 \end{bmatrix} & \begin{bmatrix} -5 \\ 15 \end{bmatrix} \\ \begin{bmatrix} 10 \\ 10 \end{bmatrix}^T & \begin{bmatrix} -15 \end{bmatrix} \end{bmatrix} \cdot \begin{bmatrix} 0 \\ 1 \\ 1 \end{bmatrix} \right)
\sigma\left(\begin{bmatrix} \begin{bmatrix} 10 & 10 \\ -10 & -10 \end{bmatrix} & \begin{bmatrix} -5 \\ 15 \end{bmatrix} \\ \begin{bmatrix} 10 \\ 10 \end{bmatrix}^T & \begin{bmatrix} -15 \end{bmatrix} \end{bmatrix} \cdot \begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix} \right)
\sigma\left(\begin{bmatrix} \begin{bmatrix} 10 & 10 \\ -10 & -10 \end{bmatrix} & \begin{bmatrix} -5 \\ 15 \end{bmatrix} \\ \begin{bmatrix} 10 \\ 10 \end{bmatrix}^T & \begin{bmatrix} -15 \end{bmatrix} \end{bmatrix} \cdot \begin{bmatrix} 1 \\ 0 \\ 1 \end{bmatrix} \right)
\sigma\left(\begin{bmatrix} \begin{bmatrix} 10 & 10 \\ -10 & -10 \end{bmatrix} & \begin{bmatrix} -5 \\ 15 \end{bmatrix} \\ \begin{bmatrix} 10 \\ 10 \end{bmatrix}^T & \begin{bmatrix} -15 \end{bmatrix} \end{bmatrix} \cdot \begin{bmatrix} 1 \\ 1 \\ 1 \end{bmatrix} \right)

Congratulations

You Understand Inference of a Basic Neural Network

A Simple Network

b = -5

a = σ

b = 15

a = σ

b = -15

 

w=10

w=-10

w=10

w=-10

w=10

w=10

1

2

3

4

5

6

7

8

9

Parameters

HOW?

MAGIC PARAMETER NUMBERS!?

HOW?

MAGIC PARAMETER NUMBERS!?

Math From Scratch?

NO!

Let's See Some AI Training

https://playground.tensorflow.org/

Let's Build a Model

https://teachablemachine.withgoogle.com/

WHY SHOULD YOU NOT USE THIS IN PRODUCTION?

Let's Code!

https://github.com/tensorflow/tfjs-models

Let's Explore Data / Training

https://aisortinghat.com/

6. Let's LLM

Understand LLMs deeper

  1. Understand Generative AI
  2. Learn LLM Lingo

THE QUESTIONS

Gant Laborde

Thanks!

GantLaborde.com

 

Company: Infinite Red

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