Counting Sand

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

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/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

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/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

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/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

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Is Kafka a one-size-fits-all solution? Or does this event sourcing software have an inherent set of strengths? Join Angelo and Kafka guru Anna McDonald as they share use cases and swap stories about how Kafka has radically changed the field of computer science.

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

[00:54] - How did Kafka change the world?

[04:40] - What is so great about big data technology?

[07:00] - Outbox pattern 101

[10:45] - clinical decision support use case

[13:05] - Should I build it or buy it?

[17:05] - Is Kafka a one-size-fits-all for businesses?

[21:55] - Kafka tuning 101

[25:53] - A.I. for Kafka tuning

Helpful links:

https://www.confluent.io/

https://www.youtube.com/channel/UC37UjjtsxpZWS_0QGPKEHdA

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

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

This episode touches on computer simulations, machine learning, and GPU's. How do these aspects of computer science relate and differ? Andy and John dive deep into how they push the boundaries of what is possible and practical in modern medicine by simulating biological systems.

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

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

The episode starts by asking the question, what if we could use computer science to shorten the amount of time it takes to discover new medications? Angelo then shares, "If we meditate on that for just a second, our minds might wander over into the world of machine learning and artificial intelligence, where we can imagine a world where these complicated neural networks or other types of AI are trying to discover a new kind of chemical compound. We might even think about the far future things like quantum because it has application in chemistry because chemistry can be thought of as an optimization problem. Or we could do something like a simulation. What if we could simulate the chemical structures of the world, or we could even simulate the body. We could conceivably introduce new kinds of compounds to the body and see how it reacts."

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

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

The episode touches on the wondrous journey a galaxy undergoes as it evolves through its life cycle. Angelo starts off the episode by asking the question, what's an early-type galaxy? Paolo Bonfini  explains that although you may think that early-type galaxies would be galaxies early in their evolution, they're not, they're galaxies a little later. They're the ultimate evolution of two galaxies coming together. 

Based on the topics touched on in Paolo's paper he then explains the role that supermassive black holes play in galaxy evolution. Paolo explains, "thanks to the recent development in gravitational-wave astronomy, which opened a completely new window of exploration because it's not based on electromagnetic waves, but on gravitational waves, which are a completely different thing. We are now able to explore black holes in more detail and we're able to study when supermassive black holes merged to create a bigger one."

Relating to the idea of bringing new technology forward, Angelo asks has any computer science techniques assisted you to be able to model this or put it together? Paolo explains, "there are a lot of computations involved in this process. People have in mind the romantic view of the astronomer who just looks through the scope of the telescope and notes things down on a piece of paper, but modern astronomy is completely digitalized. And recently it has been even automated by a lot of procedures that they track and scan the sky to create huge catalogs. Even the images themselves, they are captured on digital devices, like, the same as they appear in the phone, basically, the same technology, but just on a more refined scale. And the first process for which you will need a computer is to combine exposures. So you cannot expose a telescope on a specific direction in the sky for a very long time, for several reasons. The summary is that, in order to take an image of some patch in the sky, you will have to take multiple images and then combine them. Now the modern telescopes, they are extremely accurate. So when you combine them, you need to align the stars to a sub pixel resolution. That means that you have to find the center of the star and itself be positioned within a single individual pixel. And when you combine images, you have to align them by with the precision of, let's say, a third of a pixel, which sounds impossible because you're like, how can you do that? But, there are some techniques that allow you to do that. And of course you need a lot of computational power for that. It can take several minutes to do this even a half an hour, let's say, to combine and produce the image that you see on famous websites, like the Hubble. I mean, this is just the first step. You mentioned a thing you need to actually extract, in my case for the study I was doing, in order to assess the lack of stars at the center of a given galaxy you actually have to measure it. So what you have to do is, you have to trace the light profile, starting from the outskirts of the galaxy going gradually towards the center. In this way, you can draw a light curve if you want. It's not exact, it's more like a light profile. So you have some intensity at the edge of the galaxy, which would be low intensity because the light is very diffused and all the center it grows, grows, grows. And at some point you will see doesn't grow as much. That's where you meet the depleted core, but you also need to quantify this because you want to actually extract the information about the amount of depleted mass, like comments that you would expect it to be versus how many you actually measure. So, you have to fit the light profile. And this is done by, okay. In my case, I've been doing this with some kind of basic statistical technique, which is the chi squared fitting. So you have our model and you just fit the model to the observation and once you have the model, you can project only the other path towards the center and you compare it with the actual model that you fit. And from the difference between the two, you have the amount of stars that are missing. So you need to explore a lot of parameters and therefore you need to have this thing automated via computer technology. There is no chance you can get this information doing it by hand."

Referencing the famous space observatory, Hubble, Pablo explains what it was like to work with such a brilliant piece of machinery. He shares, "it's really amazing because the Hubble telescope was launched in the nineties and just to give you an idea is roughly the size of a bus. There is a replica of it you can visit at, I think it's the Aerospace Museum in Washington, so if you're curious. The main mirror is 2.3 meters in diameter, just to give you an idea, the larger the diameter, the higher the resolution you can achieve. On Earth, there are bigger telescopes. The biggest telescope we have on Earth is currently 10 meters. It's on the Canary Islands. On Earth you have the atmosphere on top of you and this makes everything flicker a little bit because you know, there is air moving, and these big masses of atmosphere move and this shifts the path of the light and this causes the images to be more confused. If you are instead outside the atmosphere, you don't have that problem and you really achieve the limiting resolution of your instrument. So the Hubble Space Telescope is particularly famous because of its resolution. It doesn't have a large collective area, it’s only two meters, let's say, so it doesn't collect a lot of light per second. So it doesn't have, let's say, the same contrast as ground-based telescopes, but it has extremely high resolution. So when you open an image and you're saying, okay, I want to look at this galaxy and I will work on this, which is at the center of the field of view because you pointed there. But, at the edges of it, you see a lot of tiny objects and if you zoom in you can see the structure. Maybe you see a lot of spiral galaxies around the merging objects in the background. And it's not at the center of your research. You're looking at the big galaxy at the center that you're studying. But, you know, it's like a small pleasure, small candy that you have for the eye. You're looking at these things around and you are like, well, man, this is incredible. There are so many things in the universe and I'm here focusing on these big galaxies at the center, but whatever else is happening in the background, and this is really the, I think it's the most impressive thing."

Angelo concludes the episode by discussing the ups and downs of crafting a research paper. Paolo touches on the rollercoaster of emotions one undergoes due to the sheer volume of work that needs to be done. to the most rewarding aspect of writing such a paper. He explains, "you know that you are at the forefront of this research, and I think this is when the reward comes when you're actually presenting and you see the people being curious and asking you directly at the conference, “What is this?” “How did you get there?” “It's very interesting. Let's work together.” “This is an idea to make it even better” and so on."

Our Guest - Thank you!:

Paolo Bonfini - https://www.linkedin.com/in/paolo-bonfini-phd-085a6a179/

Paolo's  Paper:

Connecting traces of galaxy evolution: the missing core mass-morphological fine structure relation

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

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Angelo begins this episode with the predictions of Moore’s Law.  In the early years, systems were restricted based on the CPUs ability to keep up.  As the CPUs continued to advance, the bottlenecks ended up around data movement.  Data movement of information from disc to memory and memory to cache become the big bottlenecks. Then of course, disks got faster and eventually you'd have so much RAM on a machine that it was just memory movement inside of RAM.  Eventually, we believe the bottleneck will return to the CPU.

Quantum computing is on the rise—that, we believe, is a game changer for Moore’s Law.  Because we're no longer talking about conventional computer chips and transistors, instead we're talking about something completely different.

Additionally, as machine learning and artificial intelligence systems make advancements, or like Angelo’s thesis using AI to tune data systems, the advancement of speed and acceleration will be impactful in order of magnitude from traditional systems.

Talent and staffing will also change as we adapt to the future. Angelo admires Google’s practices of hiring ability over experience because the problems we face tomorrow are different than today.  The key thing is to be able to independently make progress because there isn't much room for babysitting. It's too hard to predict where the next fire will be. Angelo explains further why he hires ability over experience every single time, because it is true, someone who has ability, someone who's brilliant and has the hunger to learn new things can be programmed like a stem cell. They can just inject themselves into whatever problem they might have.

Angelo transitions into his own personal story and his quest for fulfillment and happiness. He introduces a personal story of a boy who was dying during the Nazi occupied island in Chios, Greece.  A doctor took pity on this boy and secretly nursed him to health.  We later learn that this boy is Angelo’s father.  Angelo shares, “My father grew up in a world much different than mine. His siblings related stories of famine and suffering, but he never ever spoke of those things. What he chose to relate were accounts of human triumph, perseverance, hope, aspiration. The sea was his salvation, carrying him from Chios as a sailor, eventually to the United States.”

So, what is our true potential? Intellectual achievements can be ignored or forgotten. But to be a successful family person, a husband, a father, a human, Angelo needed to be something more, something enduring.

Education builds the qualities of perseverance, hard work, and accomplishment. There is no doubt you'll accomplish many things, but think about what it is that you're really trying to do. You see, building technical solutions isn't just about doing interesting stuff.  Ultimately we're building these things for a reason. We're building technology. For example, if you're doing a healthcare application, it's going to touch somebody's life. That's the point of this breakthrough, right? You want to increase throughput, for example, in decision support, something Angelo spends a lot of time on. We want to say, increase throughput, build a system that can compute faster and bigger sets of data. Why are we doing that? Just because of the challenge of the data? No, we want to find out if a clinical intervention is working so that we can feed that information forward to those making the guidelines.

You see, that's the real reason behind doing this. The great resignation has shown us that people care more about what it is they're doing and why they're doing it than just simply being interesting work. We owe it to our family to use our gifts, talents, and opportunities to the best of our ability, but to use them on something that matters.

Angelo is really excited that we're going to have interesting conversations around things like the universe, data centers, energy and how they work. There's a reason the hard problem exists. Don't fixate on the fact that it's a problem. Although there is joy in having a problem and solving it.

We're trying something a little bit new this season and we would love to hear which kinds of episodes you like most. Do you like interviews or do you like some of the educational discussional episodes?

We're going to start a YouTube channel to help deep dive on topics like LSM Trees or RocksDB which are better served with diagrams than with just voice.  Seeing the math for yourself or seeing the way that they operate for yourself on video is much more helpful. We're going to have supplementary content, bonus material that you can find on our YouTube channel, and we'll also have some bonus podcast episodes.  We look forward to your feedback. Tell us what you like about the show, which topics you prefer, and what you wish we would dive a little deeper on.  And we'll really try to do that.

Citations

Gordon Moore, Co-Founder of Intel

Heisenberg, Uncertainty Principle

Powell, James. (2008). The Quantum Limit to Moore's Law. Proceedings of the IEEE. 96. 1247 - 1248. 10.1109/JPROC.2008.925411.

Merritt, Rick. (2013). Moore’s Law Dead by 2022, Expert Says of EETimes

Atomic Hire (2019)

Further Reading

Moore’s Law Ending

Work and Culture at Google

Google Strategy to Hire

About the Host

Angelo Kastroulis is an award-winning technologist, inventor, entrepreneur, speaker, data scientist, and author best known for his high-performance computing and Health IT experience. He is the principal consultant, lead architect, and owner of Carrera Group, a consulting firm specializing in software modernization, event streaming (Kafka), big data, analytics (Spark, elastic Search, and Graph), and high-performance software development on many technical stacks (Java, .net, Scala, C++, and Rust). A Data Scientist at heart, trained at the Harvard Data Systems Lab, Angelo enjoys a research-driven approach to creating powerful, massively scalable applications and innovating new methods for superior performance. He loves to educate, discover, then see the knowledge through to practical implementation.

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

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Angelo begins this episode with reflections on history and what brought us to the AI Winter.  Why do we need a balance between research and practice?  You don’t want to rediscover what has already been discovered or settle for something that could be better if you took the time to research a bit more.  

In episode 4 we meet Angelo’s friend Andy Lee who talks about computer science predicting our biological age.  Andy actually met Greg Fahy who talked about longevity.  The study focused on injecting the thymus gland with a growth hormone that produced regeneration effects.  The effects were measured through the epigenetic clock known as DNA methylation.

In Episode 6, Jim Shalaby talks with Angelo about how COVID-19 changed healthcare forever.  Patients don’t have to wait in waiting rooms, they don’t have to find transportation to get there, and the patient has access to the clinicians.  

The hard problems associated with explainability in artificial neural networks, we talked about in Episode 8.  Angelo’s friend Nikos explained to us about five classic problems, one of which includes data privacy.  Another big issue is developing a machine learning system to create adversarial attacks on the existing system.

In episode 7, Angelo’s friend Manos shared how complicated it is for people to invoke their right to have their data removed from a system.  Typically those systems have to schedule deletions to remove the data through tombstones and a process called compacting.

What is on the horizon and what should we be paying attention to?  We are going to run against barriers of technology. For instance, Moore's law is coming to an end. What do we do about that?  What is happening in the short-term and how do we get past this barrier to the next?  And then how do we blow away all those barriers with moonshots like quantum computing?

Finally, wrapping up our first season, Angelo wants to reflect on gratitude.  Gratitude for you our listeners.  Thank you so much for joining us on this journey. We really want to hear about your thoughts. The show is evolving just as the world is and we want to make sure that we're covering topics that you're interested in.

We would love for you to follow, rate, and review the show on your favorite podcast platform so that others can find us too. Thank you so much for listening.

Our Guests - Thank you!:

Nikos Myrtakis on  LinkedIn

Manos Athanassoulis on LinkedIn and Boston University
Jim Shalaby on Twitter and LinkedIn

Andy Lee on Twitter and LinkedIn

About the Host

Angelo Kastroulis is an award-winning technologist, inventor, entrepreneur, speaker, data scientist, and author best known for his high-performance computing and Health IT experience. He is the principal consultant, lead architect, and owner of Carrera Group, a consulting firm specializing in software modernization, event streaming (Kafka), big data, analytics (Spark, elastic Search, and Graph), and high-performance software development on many technical stacks (Java, .net, Scala, C++, and Rust). A Data Scientist at heart, trained at the Harvard Data Systems Lab, Angelo enjoys a research-driven approach to creating powerful, massively scalable applications and innovating new methods for superior performance. He loves to educate, discover, then see the knowledge through to practical implementation.

Host: Angelo Kastroulis

Executive Producer: Kerri Patterson; 

Producer: Albert Perrotta;

Communications Strategist: Albert Perrotta;

Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth