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Now in AI: How we got here
Ashwin Rao
Co-Founder, CX Score +
Adjunct Professor, Applied
Mathematics (ICME),
Stanford University
All rights reserved. © 2023 Aerospike, Inc.
2
Taking Stock:
Is AI Magically
Solving
Everything
Now?
All rights reserved. © 2023 Aerospike, Inc.
3
Taking stock:
Is AI Magically
Solving
Everything
Now?
Write a python script to write a record to an Aerospike database.
To write a record to an Aerospike database using Python, you can make use of the
aerospike package. However, please note that this package needs to be installed
before running the script. You can install it by running pip install aerospike.
Here's an example Python script that demonstrates how to write a record to an
Aerospike database:
Make sure to replace the 'localhost' and 3000 with the appropriate hostname and
port of your Aerospike server. You can also modify the namespace, set_name, key,
and bins to match your specific data model.
All rights reserved. © 2023 Aerospike, Inc.
4
Taking Stock: Is AI Magically Solving Everything Now?
Speed of Tasks!
Enormous help
Where Used
• Software
• Marketing
• Education
• Creative content-
generation
Risk
Inconsistent or
unreliable results
Need
Human
involvement
Generative AI
Machine-Human
à huge benefit!
• Gen AI’s strength:
speed + broad
knowledge
• Human’s strength:
domain
knowledge +
nuanced judgment
à“Chat
All rights reserved. © 2023 Aerospike, Inc.
Providing
Personalized
Health Counsel
Deciding What
Groceries to Order
Helping Us With
Parenting
Helping Us With
Management
Decisions In The
Enterprise ….
Predicting The
Stock Market
5
What’s Not Working Yet?
Here are some examples:
All rights reserved. © 2023 Aerospike, Inc.
6
To answer this, we must learn
about how we got here ….
Do We Expect
AI to Help Us
with These
Things Soon?
All rights reserved. © 2023 Aerospike, Inc.
7
Recent History of AI
1980 1990 2000 2010
1980s: Expert Systems and
Revival of Neural Networks
1987-1993: Second AI
Winter
Expert Systems, AI
companies failed
1994-2010: Low-hype practical
progress, Moore’s Law
Compute Power
2011-Present: Big Data, Deep
Learning, Shades of AGI
2020
2023: ChatGPT-4
Launched
2012: Hinton builds
Neural network
All rights reserved. © 2023 Aerospike, Inc.
8
Demystifying the AI Jargon in Simple Terms
Levels of AI
Assistive
Augmented
Autonomous
Narrow AI
General AI
(AGI)
vs
Generative AI
(GenAI)
Discriminative
AI
vs Planning AI Learning AI
vs
All rights reserved. © 2023 Aerospike, Inc.
9
Assistive AI:
Basic Assistance
Like Cruise
Control to
Improve Human
Productivity
All rights reserved. © 2023 Aerospike, Inc.
10
Augmented AI:
Machine and AI
Take Turns and
Collaborate to
Create Better
Outcomes
All rights reserved. © 2023 Aerospike, Inc.
11
Autonomous AI:
Machine
Performs
Tasks/Solves
Problems
Without Human
Help
All rights reserved. © 2023 Aerospike, Inc.
Types of Machine Learning
12
Machine
Learning
Supervised Unsupervised Reinforcement
Task Driven
(Predict next value)
Data Driven
(Identify Clusters)
Learn
from Mistakes
All rights reserved. © 2023 Aerospike, Inc.
13
Supervised Learning
All rights reserved. © 2023 Aerospike, Inc.
14
Supervised Learning Unsupervised Learning
All rights reserved. © 2023 Aerospike, Inc.
Reinforcement Learning: Sequential Optimization
15
Environment
Agent
Interpreter Reward
State
Action
All rights reserved. © 2023 Aerospike, Inc.
How RL Works: Learning from Samples of Data
16
Incrementally from
state/reward transitions
data
Trial-and-error linking
actions to rewards
In a Simulated
Environment
Streaming, real-time data
architecture
All rights reserved. © 2023 Aerospike, Inc.
Fitting the RL Framework
17
Controlling ChatGPT
Real-world problem Optimized for
Autonomous vehicle
Game of chess
Manage an investment portfolio
Inventory Replenishment
Speed/steering to optimize safety/time
Win / lose
Maximize risk-adjusted gains
Ensure high availability at low cost
Produce highly valuable responses
All rights reserved. © 2023 Aerospike, Inc.
What Were the Game-changers in the
Making of ChatGPT?
18
Very deep networks
with large number of
parameters
Very large data
sets
Data
Engineering
Hyper-parameter
tuning
Embeddings Transformers RL
All rights reserved. © 2023 Aerospike, Inc.
What Problems Suit AI?
19
Errors tolerated in
the solution
Interactions with
skilled humans
Ample time for
learning and tuning
All rights reserved. © 2023 Aerospike, Inc.
Daring to Predict the Future of AI
20
AI will help AI get better
Software engineering speed
continued rise
Massive upgrades in
Information Retrieval,
Education, Advisors/Counselors
Data collection bottlenecking
(advanced problems)
Data engineering hyper-
focus (real-time/streaming
data systems)
Physical tasks automation
continued lag
Enormous focus on privacy,
bias, safety, ethics
Human skills will benefit
from AI
Legislative interventions
All rights reserved. © 2023 Aerospike, Inc.
Thank You
21

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Now in AI- How we got here_Ashwin Rao.pdf

  • 1. Now in AI: How we got here Ashwin Rao Co-Founder, CX Score + Adjunct Professor, Applied Mathematics (ICME), Stanford University
  • 2. All rights reserved. © 2023 Aerospike, Inc. 2 Taking Stock: Is AI Magically Solving Everything Now?
  • 3. All rights reserved. © 2023 Aerospike, Inc. 3 Taking stock: Is AI Magically Solving Everything Now? Write a python script to write a record to an Aerospike database. To write a record to an Aerospike database using Python, you can make use of the aerospike package. However, please note that this package needs to be installed before running the script. You can install it by running pip install aerospike. Here's an example Python script that demonstrates how to write a record to an Aerospike database: Make sure to replace the 'localhost' and 3000 with the appropriate hostname and port of your Aerospike server. You can also modify the namespace, set_name, key, and bins to match your specific data model.
  • 4. All rights reserved. © 2023 Aerospike, Inc. 4 Taking Stock: Is AI Magically Solving Everything Now? Speed of Tasks! Enormous help Where Used • Software • Marketing • Education • Creative content- generation Risk Inconsistent or unreliable results Need Human involvement Generative AI Machine-Human à huge benefit! • Gen AI’s strength: speed + broad knowledge • Human’s strength: domain knowledge + nuanced judgment à“Chat
  • 5. All rights reserved. © 2023 Aerospike, Inc. Providing Personalized Health Counsel Deciding What Groceries to Order Helping Us With Parenting Helping Us With Management Decisions In The Enterprise …. Predicting The Stock Market 5 What’s Not Working Yet? Here are some examples:
  • 6. All rights reserved. © 2023 Aerospike, Inc. 6 To answer this, we must learn about how we got here …. Do We Expect AI to Help Us with These Things Soon?
  • 7. All rights reserved. © 2023 Aerospike, Inc. 7 Recent History of AI 1980 1990 2000 2010 1980s: Expert Systems and Revival of Neural Networks 1987-1993: Second AI Winter Expert Systems, AI companies failed 1994-2010: Low-hype practical progress, Moore’s Law Compute Power 2011-Present: Big Data, Deep Learning, Shades of AGI 2020 2023: ChatGPT-4 Launched 2012: Hinton builds Neural network
  • 8. All rights reserved. © 2023 Aerospike, Inc. 8 Demystifying the AI Jargon in Simple Terms Levels of AI Assistive Augmented Autonomous Narrow AI General AI (AGI) vs Generative AI (GenAI) Discriminative AI vs Planning AI Learning AI vs
  • 9. All rights reserved. © 2023 Aerospike, Inc. 9 Assistive AI: Basic Assistance Like Cruise Control to Improve Human Productivity
  • 10. All rights reserved. © 2023 Aerospike, Inc. 10 Augmented AI: Machine and AI Take Turns and Collaborate to Create Better Outcomes
  • 11. All rights reserved. © 2023 Aerospike, Inc. 11 Autonomous AI: Machine Performs Tasks/Solves Problems Without Human Help
  • 12. All rights reserved. © 2023 Aerospike, Inc. Types of Machine Learning 12 Machine Learning Supervised Unsupervised Reinforcement Task Driven (Predict next value) Data Driven (Identify Clusters) Learn from Mistakes
  • 13. All rights reserved. © 2023 Aerospike, Inc. 13 Supervised Learning
  • 14. All rights reserved. © 2023 Aerospike, Inc. 14 Supervised Learning Unsupervised Learning
  • 15. All rights reserved. © 2023 Aerospike, Inc. Reinforcement Learning: Sequential Optimization 15 Environment Agent Interpreter Reward State Action
  • 16. All rights reserved. © 2023 Aerospike, Inc. How RL Works: Learning from Samples of Data 16 Incrementally from state/reward transitions data Trial-and-error linking actions to rewards In a Simulated Environment Streaming, real-time data architecture
  • 17. All rights reserved. © 2023 Aerospike, Inc. Fitting the RL Framework 17 Controlling ChatGPT Real-world problem Optimized for Autonomous vehicle Game of chess Manage an investment portfolio Inventory Replenishment Speed/steering to optimize safety/time Win / lose Maximize risk-adjusted gains Ensure high availability at low cost Produce highly valuable responses
  • 18. All rights reserved. © 2023 Aerospike, Inc. What Were the Game-changers in the Making of ChatGPT? 18 Very deep networks with large number of parameters Very large data sets Data Engineering Hyper-parameter tuning Embeddings Transformers RL
  • 19. All rights reserved. © 2023 Aerospike, Inc. What Problems Suit AI? 19 Errors tolerated in the solution Interactions with skilled humans Ample time for learning and tuning
  • 20. All rights reserved. © 2023 Aerospike, Inc. Daring to Predict the Future of AI 20 AI will help AI get better Software engineering speed continued rise Massive upgrades in Information Retrieval, Education, Advisors/Counselors Data collection bottlenecking (advanced problems) Data engineering hyper- focus (real-time/streaming data systems) Physical tasks automation continued lag Enormous focus on privacy, bias, safety, ethics Human skills will benefit from AI Legislative interventions
  • 21. All rights reserved. © 2023 Aerospike, Inc. Thank You 21