Energy, Edge Computing, and Data Centers

Key Topics

AI-optimized vs Commercially Available Hot Sauce: Angelo and Petter perform a blind taste test with three different hot sauces, one of which is AI-optimized, to see if they can determine which one is created by AI.

Background of the AI Hot Sauce Creators: A brief insight into the story of Shekeib and Shohaib, the two brothers who combined their passion for data science and business to create an AI-optimized hot sauce.

Understanding Bayesian Optimization: A comprehensive discussion on Bayesian Optimization, a technique that uses previous knowledge to influence future decisions, perfect for creating unique hot sauce recipes.

Discussion on Other Optimization Techniques: Petter invites Angelo to delve into the different types of optimization algorithms and their pros and cons.

Understanding Gradient Descent: Angelo gives a brief introduction to the concept of Gradient Descent, a popular optimization algorithm, explaining it as akin to finding a valley when on a mountain.

Recommendations

• Check out the previous episode interviewing the creators of the AI-optimized hot sauce to understand their process better.
• For tech enthusiasts interested in AI and its applications, further exploration into optimization techniques like Bayesian Optimization and Gradient Descent can be insightful.

Episode Quotes

"Hot sauces are part of my favorite start of the day, so it'd be interesting to see what AI could come up with here." - Petter Graff

"Bayesian is an optimization technique that centers around using your previous knowledge to influence the future and that works really well." - Angelo Kastroulis

"Bayesian can kind of skip a bunch of steps because you've got a better second try." - Angelo Kastroulis

"The algorithm of gradient descent basically goes like this. If you're trying to find from where you are to where you should go, imagine that you're on a mountain trying to find the valley." - Angelo Kastroulis

Introduction:

• Angelo and Shohaib discuss the inclusion of new ingredients in hot sauce batches.
• Shohaib explains the process of introducing new ingredients and the excitement surrounding it.

Incorporating New Ingredients:

• Angelo asks about the approach to incorporating new ingredients: creating new models or expanding the feature space.
• Shohaib suggests keeping the base model and increasing the search space for new ingredients.
• Both options are considered, including transferring the optimal features to another model.

Metaphorical Understanding:

• Angelo highlights the advantage of using hot sauce as a metaphor for complex concepts.
• Shekeib acknowledges the clarity provided by the hot sauce analogy and the opportunity to learn more.

Engaging with Mathematics:

• Angelo expresses his enthusiasm for discussing the mathematical side of AI.
• Shekeib shares his brother's interest in math and how it goes beyond his own understanding.
• Shohaib emphasizes the subset of AI concepts being discussed and the value of conceptualizing them through hot sauce.

AI as an Expansive Field:

• Angelo mentions that AI encompasses various subfields, such as machine learning, Bayesian optimization, and active learning.
• Neural networks, deep learning, and reinforcement learning are discussed as additional branches of AI.
• Shohaib highlights the similarities between Bayesian optimization and reinforcement learning.

Reinforcement Learning:

• Angelo mentions the significance of reinforcement learning in solving video games and its applicability to different domains.
• Shohaib shares his experience with reinforcement learning in an AI class, specifically using it to make Pac-Man play autonomously.

Specialization and Continuous Learning:

• Angelo praises Shohaib's expertise in Bayesian optimization while acknowledging the vastness of AI knowledge.
• The discussion emphasizes the complexity of AI and the continuous learning required to stay up to date.

Generative Pre-trained Transformers:

• Angelo brings up the popularity of generative pre-trained transformers like ChatGPT.
• The ensemble nature of these models and their unique combination of techniques is highlighted.

Introduction

• Shekeib and Shohaib join the podcast as guests to talk about their experience with creating hot sauces using AI optimization.
• They created a special hot sauce, named "Counting Sauce," specifically for the podcast hosts.

The Making of Counting Sauce

• This is a unique hot sauce that includes pineapple and mango flavors.
• The sauce was created as a token of appreciation for being featured on the podcast.

Journey through Different Versions of the Sauce

• The hosts have tried versions 19, 20, 21, and they just received version 25.
• There will be a blind taste test to determine if they can tell the difference between the different iterations and compare them with other sauces to tell which is AI-created.

Optimization Process

• The process involves optimizing the amount of each ingredient.
• They use a Gaussian process regression model and an acquisition function called Expected Improvement for the optimization.

Choice of Ingredients

• The base hot sauce has five main ingredients: vinegar, pepper, jalapeno, and lime.
• After 25 iterations, the differences in taste become so minute it becomes hard to tell the difference.

Subjective Taste Testing

• Shekeib talks about how his taste tolerance changes after tasting hot sauces all day.
• They involved family and friends in the tasting process and asked for ratings on a scale of one to ten.

The Learning Curve of the AI

• Early on, the AI would try extreme variations like too much or too little salt.
• It learned quickly from feedback and adjusted accordingly.

Strength of Bayesian Optimization

• The AI can learn mathematically from feedback and apply the learnings, making the optimization process quicker and more efficient.
• It was also able to tweak multiple ingredients simultaneously, unlike a human who might focus on one ingredient at a time.

No Prior Experience in Hot Sauce Making

• Both brothers had no prior experience or generational knowledge in hot sauce making.
• The AI managed to create a decent hot sauce in just five iterations.

Power of Bayesian Optimization with Human Expertise

• The brothers emphasize the importance of having a human expert in the loop of Bayesian optimization.
• The AI simulates the intuition and experience of a human expert, but having a real human guide the process further enhances the results.

1. Application Beyond Hot Sauce

• They discuss the potential of their Bayesian optimization process in other areas such as drug discovery.
• The process can be guided by human experts in the respective fields for even better results.

In a time crunch? Check out the time stamps below:

[00:45] - Moore's Law, where do we go from here?

[03:00] - How do we improve data system efficiency?

[10:30] - Purpose-built systems (FPGAs)

[11:13] - Insights on (FPGAs)

[13:32] - Event Streaming

[17:50] - Data storage

[18:34] - Google’s approach to data storage

[19:00] - Downtime

[21:06] - Serves impact on environment and solutions to optimize

[23:00] - Improving data systems, machine learning, artificial intelligence

[24:06] - How do we regulate AI?

[26:10] - Benefits of simulations through machine learning

[28:38] - The impact computer science has on astrophysics

[31:09] - How do we defy Moore's Law, the future of quantum computing

Our Team:

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

The last time we had Manos on the program, we talked about Moore's Law coming to an end. It's important to note that we can't rely on just sheer computing power doubling to be able to meet our ever-increasing demand for data. We must find new and exciting ways to collect and compute large amounts of data. In this episode of Counting Sand, we will dive deep into what does a database actually do? What is at the core of a data system? Most importantly, how can we use new and exciting techniques to free up the CPU's load by algorithmic trickery.

In a time crunch? Check out the time stamps below:

[00:53] - Guest Intro 

[01:30] - Intro to data systems

[03:00] - Hardware types

[05:00] - Why is it important to choose the right format

[10:15] - What is column storage, and what the benefits

[16:30] - Injecting the CPU, The hierarchy of memory

[20:00] - Why not just duplicate data

[22:55] - Acid properties

Notable references:

Relational Memory: Native In-Memory Accesses on Rows and Columns

Our Team:

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

The cycle between research and application is often too long and can take decades to complete. It is often asked what bit of research or technology is the most important? Before we can answer that question, I think it's important to take a step back and share the story of why we believe The Dynamo Paper is so essential to our modern world and how we encountered it.

Citations:

DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., ... & Vogels, W. (2007). Dynamo: Amazon’s highly available key-value store. ACM SIGOPS operating systems review, 41(6), 205-220.Karger, D., Lehman, E., Leighton, T., Panigrahy, R., Levine, M., & Lewin, D. (1997, May). Consistent hashing and random trees: Distributed caching protocols for relieving hot spots on the world wide web. In Proceedings of the twenty-ninth annual ACM symposium on Theory of computing (pp. 654-663).Lamport, L. (2019). Time, clocks, and the ordering of events in a distributed system. In Concurrency: the Works of Leslie Lamport (pp. 179-196).Merkle, R. C. (1987). A digital signature based on conventional encryption. In Proceedings of the USENIX Secur. Symp (pp. 369-378).

Our Team:

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

This show often discusses artificial intelligence and ideas to consider as technology progresses. We have discussed the deep tech of how it works and its implications on privacy. In this episode, we'll talk about the complex and controversial topic of AI policy and speak about some of the things we should be worried about regarding its future.

In a time crunch? Check out the time stamps below:

[01:15] - Guest Intro 

[03:38] - Western technology leadership

[04:50] - Regulating AI

[11:00] - The promise of self-driving cars

[13:05] - AI data audition 

[17:50] - Neural networks to train AI

[19:00] - Reducing mathematical knowledge, AI bottleneck 

[20:35] - What is in the way of the promise of AI

[24:20] - Eric Daimler book

[27:50] - The uses of trained AI models

[29:30] - Health care industry data usage

[33:25] - AI to speed up research

[33:50] - What is rural AI?

Guest Links:

https://www.linkedin.com/in/ericdaimler/

https://conexus.com/

Our Team:

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

What if there was a way to reduce the amount of energy consumed and produced from servers around the world. Would these new methods positively or negatively impact the environmental footprint of today’s big data ecosystems?

In a time crunch? Check out the time stamps below:

[02:15] - Research Paper 

[05:55] - Power consumption of data centers and methods to save energy 

[08:50] - Server cooling methods 

[12:00] - Energy production from data transportation 

[13:55] - The impact of location and climate through venting and cooling computers. 

[15:38] - Edge devices and cloud computing 

[20:47] - Cost and energy optimization

[21:45] - Machine Learning + A.I. productive maintenance

[24:45] - Automobile processing unit, big data

Our Team:

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Over the last couple of years, Harvard Data Systems Lab has been focused on cutting-edge research and applications of complex data systems, focusing on such areas as artificial intelligence and machine learning pipelines. In this episode of Counting Sand, Angelo and Stratos dive deep into what they have learned and what’s next in these fields.

In a time crunch? Check out the time stamps below:

[01:00] - What’s new at Harvard Data Systems Lab?

[08:20] - What are examples of general data structure applications?

[14:13] - How do we decrease the time spent from research to application?

[20:23] - What are the benefits of machine learning?

[22:15] - What are some helpful tips when writing a thesis?

[25:00] - How important is the creative process when writing a research paper?

Helpful links:

Harvard Data Systems Lab: http://daslab.seas.harvard.edu/

Harvard Data Systems Lab Twitter: https://twitter.com/HarvardDASlab

Our Team:

Host:Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Our Team:

Host:Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth