Shows
Data Science at HomeBrains in the Machine: The Rise of Neuromorphic Computing (Ep. 285)
In this episode of Data Science at Home, we explore the fascinating world of neuromorphic computing — a brain-inspired approach to computation that could reshape the future of AI and robotics. The episode breaks down how neuromorphic systems differ from conventional AI architectures like transformers and LLMs, diving into spiking neural networks (SNNs), their benefits in energy efficiency and real-time processing, and their limitations in training and scalability. Real-world applications are highlighted, including low-power drones, hearing aids, and event-based cameras. Francesco closes with a vision of hybrid systems where neuromorphic chips and LLMs coexist, blending biological inspiration with modern AI....2025-06-1824 minData Science at HomeAutonomous Weapons and AI Warfare (Ep. 275)Here’s the updated text with links to the websites included:
AI is revolutionizing the military with autonomous drones, surveillance tech, and decision-making systems. But could these innovations spark the next global conflict? In this episode of Data Science at Home, we expose the cutting-edge tech reshaping defense—and the chilling ethical questions that follow. Don’t miss this deep dive into the AI arms race!
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Chapters
00:00 - Intro
01:54 - Autonomous Vehicles
03:11 - Surveillance And Reconnaissance
04:15 - Predictive Analysis2024-12-1617 minData Science at HomeHumans vs. Bots: Are You Talking to a Machine Right Now? (Ep. 273)In this episode of Data Science at Home, host Francesco Gadaleta dives deep into the evolving world of AI-generated content detection with experts Souradip Chakraborty, Ph.D. grad student at the University of Maryland, and Amrit Singh Bedi, CS faculty at the University of Central Florida.
Together, they explore the growing importance of distinguishing human-written from AI-generated text, discussing real-world examples from social media to news. How reliable are current detection tools like DetectGPT? What are the ethical and technical challenges ahead as AI continues to advance? And is the balance between innovation and regulation tipping in t...2024-11-2549 minData Science at HomeCareers, Skills, and the Evolution of AI (Ep. 248)!!WARNING!!
Due to some technical issues the volume is not always constant during the show. I sincerely apologise for any inconvenience
Francesco
In this episode, I speak with Richie Cotton, Data Evangelist at DataCamp, as he delves into the dynamic intersection of AI and education. Richie, a seasoned expert in data science and the host of the podcast, brings together a wealth of knowledge and experience to explore the evolving landscape of AI careers, the skills essential for generative AI technologies, and the symbiosis of domain expertise and technical skills in...2024-01-0832 minData Science at HomePredicting Out Of Memory Kill events with Machine Learning (Ep. 203)Sometimes applications crash. Some other times applications crash because memory is exhausted. Such issues exist because of bugs in the code, or heavy memory usage for reasons that were not expected during design and implementation.
Can we use machine learning to predict and eventually detect out of memory kills from the operating system?
Apparently, the Netflix app many of us use on a daily basis leverage ML and time series analysis to prevent OOM-kills.
Enjoy the show!
Our Sponsors
Explore the Complex World of Regulations. Compliance can be overwhelming. Multiple frameworks. Overlapping...2022-09-2019 minData Science at HomeSpeaking about data with Mikkel Settnes from Dreamdata.io (Ep. 170)In this episode Mikkel and Francesco have a really interesting conversation about some key differences between large and small organization in approaching machine learning.
Listen to the episode to know more.
References
https://dreamdata.io/b2b-attribution
https://dreamdata.io/services
https://www.nature.com/articles/s43586-020-00001-2
2021-09-2434 minData Science at HomeHow are organisations doing with data and AI? (Ep. 168)A few weeks ago I was the guest of a very interesting show called "AI Today".
In that episode I talked about some of the biggest trends emerging in AI and machine learning today as well as how organizations are dealing with and managing their data.
The original show has been published at https://www.cognilytica.com/2021/08/11/ai-today-podcast-interview-with-francesco-gadaleta-host-of-data-science-at-home-podcast/
Our Sponsors
Quantum Metric
Stay off the naughty list this holiday season by reducing customer friction, increasing conversions, and personalizing the shopping experience. Want a sneak peak? Visit us at qu...2021-09-0735 min
AI Today PodcastAI Today Podcast: Interview with Francesco Gadaleta, host of Data Science at Home podcastOn the AI Today podcast we regularly interview thought leaders who are implementing AI and cognitive technology at various companies and agencies. However in this episode hosts Kathleen Walch and Ron Schmelzer interview Francesco Gadaleta, host of the Data Science at Home podcast. As his podcast talks about about data and data science trends, Francesco shares with us some of the biggest trends emerging in this area today as well as how organizations are dealing with and managing their data.Continue reading AI Today Podcast: Interview with Francesco Gadaleta, host of Data Science at Home podcast at...2021-08-1134 minData Science at HomeBuilding high-growth data businesses with Lillian Pierson (Ep. 149)
In this episode I have an amazing conversation with Lillian Pierson from data-mania.com
This is an action-packed episode on how data professionals can quickly convert their data expertise into high-growth data businesses, all by selecting optimal business models, revenue models, and pricing structures.
If you want to know more or get in touch with Lillian, follow the links below:
Weekly Free Trainings: We currently publish 1 free training per week on YouTube! https://www.youtube.com/channel/UCK4MGP0A6lBjnQWAmcWBcKQ
Becoming World-Class Data Leaders and Data Entrepreneurs Facebook Group: https://www.facebook.com/groups/data.leaders...2021-04-1925 minData Science at HomeLearning and training in AI times (Ep. 148)Is there a gap between life sciences and data science?
What's the situation when it comes to interdisciplinary research?
In this episode I am with Laura Harris, Director of Training for the Institute of Cyber-Enabled Research (ICER) at Michigan State University (MSU), and we try to answer some of those questions.
You can contact Laura at training@msu.edu or on LinkedIn
2021-04-1331 minData Science at HomeYou are the product [RB] (Ep. 147)
In this episode I am with George Hosu from Cerebralab
and we speak about how dangerous it is not to pay for the services you use, and as a consequence how dangerous it is letting an algorithm decide what you like or not.
Our Sponsors
This episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.
To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and D...2021-04-1145 minData Science at HomePolars: the fastest dataframe crate in Rust - with Ritchie Vink (Ep. 146)In this episode I speak with Ritchie Vink, the author of Polars, a crate that is the fastest dataframe library at date of speaking :) If you want to participate to an amazing Rust open source project, this is your change to collaborate to the official repository in the references.
References
https://github.com/ritchie46/polars
2021-04-0832 minData Science at HomeApache Arrow, Ballista and Big Data in Rust with Andy Grove (Ep. 145)Do you want to know the latest in big data analytics frameworks? Have you ever heard of Apache Arrow? Rust? Ballista? In this episode I speak with Andy Grove one of the main authors of Apache Arrow and Ballista compute engine.
Andy explains some challenges while he was designing the Arrow and Ballista memory models and he describes some amazing solutions.
Our Sponsors
This episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their...2021-03-2630 minData Science at HomePandas vs Rust (Ep. 144)Pandas is the de-facto standard for data loading and manipulation. Python is the de-facto programming language for such operations. Rust is the underdog. Or is it?
In this episode I am showing you why that is no longer the case.
Our Sponsors
This episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.
To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Co...2021-03-1931 min
Data Science at Home[RB] It’s cold outside. Let’s speak about AI winter (Ep. 111)In this episode I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future.
Join us to our Discord channel to discuss your favorite episode and propose new ones.
Thi...2020-07-0336 min
Data Science at HomeRust and machine learning #4: practical tools (Ep. 110)In this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust. Not all of them are mature enough for production environments. I believe that community effort can change this very quickly.
To make a comparison with the Python ecosystem I will cover frameworks for linear algebra (numpy), dataframes (pandas), off-the-shelf machine learning (scikit-learn), deep learning (tensorflow) and reinforcement learning (openAI).
Rust is the language of the future.Happy coding!
Reference
BLAS linear algebra https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
Rust dataframe https://github.com/nevi-me/rust-dataframe
Rustlearn https://github.com/ma...2020-06-2924 min
Data Science at HomeRust and machine learning #3 with Alec Mocatta (Ep. 109)In the 3rd episode of Rust and machine learning I speak with Alec Mocatta. Alec is a +20 year experience professional programmer who has been spending time at the interception of distributed systems and data analytics. He's the founder of two startups in the distributed system space and author of Amadeus, an open-source framework that encourages you to write clean and reusable code that works, regardless of data scale, locally or distributed across a cluster.
Only for June 24th, LDN *Virtual* Talks June 2020 with Bippit (Alec speaking about Amadeus)2020-06-2223 min
Data Science at HomeRust and machine learning #2 with Luca Palmieri (Ep. 108)In the second episode of Rust and Machine learning I am speaking with Luca Palmieri, who has been spending a large part of his career at the interception of machine learning and data engineering. In addition, Luca contributed to several projects closer to the machine learning community using the Rust programming language. Linfa is an ambitious project that definitely deserves the attention of the data science community (and it's written in Rust, with Python bindings! How cool??!).
References
Series Announcement - Zero to Production in Rust https://www.lpalmieri.com/posts/2020-05-10-announcement-zero-to-production-in-rust/
Zero To Production #0: Foreword https://www.l...2020-06-1927 min
Data Science at HomeRust and machine learning #1 (Ep. 107)This is the first episode of a series about the Rust programming language and the role it can play in the machine learning field.
Rust is one of the most beautiful languages I have ever studied so far. I personally come from the C programming language, though for professional activities in machine learning I had to switch to the loved and hated Python language.
This episode is clearly not providing you with an exhaustive list of the benefits of Rust, nor its capabilities. For this you can check the references and start getting familiar with what I think it's going...2020-06-1722 min
Data Science at HomeProtecting workers with artificial intelligence (with Sandeep Pandya CEO Everguard.ai)(Ep. 106)In this episode I have a chat with Sandeep Pandya, CEO at Everguard.ai a company that uses sensor fusion, computer vision and more to provide safer working environments to workers in heavy industry.Sandeep is a senior executive who can hide the complexity of the topic with great talent.
This episode is supported by Pryml.io Pryml is an enterprise-scale platform to synthesise data and deploy applications built on that data back to a production environment.Test ideas. Launch new products. Fast. Secure.2020-06-1516 min
Data Science at HomeCompressing deep learning models: rewinding (Ep.105)As a continuation of the previous episode in this one I cover the topic about compressing deep learning models and explain another simple yet fantastic approach that can lead to much smaller models that still perform as good as the original one.
Don't forget to join our Slack channel and discuss previous episodes or propose new ones.
This episode is supported by Pryml.io Pryml is an enterprise-scale platform to synthesise data and deploy applications built on that data back to a production environment.
References
Comparing Rewinding and Fine-tuning in Neural Network Pruninghttps://arxiv.org/abs/2003.023892020-06-0115 min
Data Science at HomeCompressing deep learning models: distillation (Ep.104)Using large deep learning models on limited hardware or edge devices is definitely prohibitive. There are methods to compress large models by orders of magnitude and maintain similar accuracy during inference.
In this episode I explain one of the first methods: knowledge distillation
Come join us on Slack
Reference
Distilling the Knowledge in a Neural Network https://arxiv.org/abs/1503.02531
Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks https://arxiv.org/abs/2004.059372020-05-2022 min
Data Science at HomePandemics and the risks of collecting data (Ep. 103)Codiv-19 is an emergency. True. Let's just not prepare for another emergency about privacy violation when this one is over.
Join our new Slack channel
This episode is supported by Proton. You can check them out at protonmail.com or protonvpn.com2020-05-0820 min
Data Science at HomeWhy average can get your predictions very wrong (ep. 102)Whenever people reason about probability of events, they have the tendency to consider average values between two extremes. In this episode I explain why such a way of approximating is wrong and dangerous, with a numerical example.
We are moving our community to Slack. See you there!2020-04-1914 min
Data Science at HomeActivate deep learning neurons faster with Dynamic RELU (ep. 101)In this episode I briefly explain the concept behind activation functions in deep learning. One of the most widely used activation function is the rectified linear unit (ReLU). While there are several flavors of ReLU in the literature, in this episode I speak about a very interesting approach that keeps computational complexity low while improving performance quite consistently.
This episode is supported by pryml.io. At pryml we let companies share confidential data. Visit our website.
Don't forget to join us on discord channel to propose new episode or discuss the previous ones.
References
Dynamic ReLU https://arxiv.org/abs/2003.100272020-04-0122 min
Data Science at HomeWARNING!! Neural networks can memorize secrets (ep. 100)One of the best features of neural networks and machine learning models is to memorize patterns from training data and apply those to unseen observations. That's where the magic is. However, there are scenarios in which the same machine learning models learn patterns so well such that they can disclose some of the data they have been trained on. This phenomenon goes under the name of unintended memorization and it is extremely dangerous.
Think about a language generator that discloses the passwords, the credit card numbers and the social security numbers of the records it has been trained on. Or m...2020-03-2324 min
Data Science at HomeAttacks to machine learning model: inferring ownership of training data (Ep. 99)In this episode I explain a very effective technique that allows one to infer the membership of any record at hand to the (private) training dataset used to train the target model. The effectiveness of such technique is due to the fact that it works on black-box models of which there is no access to the data used for training, nor model parameters and hyperparameters. Such a scenario is very realistic and typical of machine learning as a service APIs.
This episode is supported by pryml.io, a platform I am personally working on that enables data sharing without giving u...2020-03-1419 min
Data Science at HomeDon't be naive with data anonymization (Ep. 98)Masking, obfuscating, stripping, shuffling. All the above techniques try to do one simple thing: keeping the data private while sharing it with third parties. Unfortunately, they are not the silver bullet to confidentiality. All the players in the synthetic data space rely on simplistic techniques that are not secure, might not be compliant and risky for production. At pryml we do things differently.2020-03-0813 min
Data Science at HomeWhy sharing real data is dangerous (Ep. 97)There are very good reasons why a financial institution should never share their data. Actually, they should never even move their data. Ever.In this episode I explain you why.2020-03-0110 min
Data Science at HomeBuilding reproducible machine learning in production (Ep. 96)Building reproducible models is essential for all those scenarios in which the lead developer is collaborating with other team members. Reproducibility in machine learning shall not be an art, rather it should be achieved via a methodical approach. In this episode I give a few suggestions about how to make your ML models reproducible and keep your workflow as smooth.
Enjoy the show!Come visit us on our discord channel and have a chat2020-02-2214 min
Data Science at HomeBridging the gap between data science and data engineering: metrics (Ep. 95)Data science and data engineering are usually two different departments in organisations. Bridging the gap between the two is essential to success. Many times the brilliant applications created by data scientists don't find a match in production, just because they are not production-ready.
In this episode I have a talk with Daan Gerits, co-founder and CTO at Pryml.io2020-02-1413 min
Data Science at HomeA big welcome to Pryml: faster machine learning applications to production (Ep. 94)Why so much silence? Building a company! That's why :) I am building pryml, a platform that allows data scientists build their applications on data they cannot get access to. This is the first of a series of episodes in which I will speak about the technology and the challenges we are facing while we build it.
Happy listening and stay tuned!2020-02-0709 min
Data Science at HomeIt's cold outside. Let's speak about AI winter (Ep. 93)In the last episode of 2019 I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future.
Join us to our Discord channel to discuss your favorite episode and propose ne...2019-12-3136 min
Data Science at HomeIt's cold outside. Let's speak about AI winter (Ep. 93)In the last episode of 2019 I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future.
Join us to our Discord channel to discuss your favorite ep...2019-12-3136 min
Data Science at HomeThe dark side of AI: bias in the machine (Ep. 92)This is the fourth and last episode of mini series "The dark side of AI". I am your host Francesco and I’m with Chiara Tonini from London. The title of today’s episode is Bias in the machine
C: Francesco, today we are starting with an infuriating discussion. Are you ready to be angry?
F: yeah sure is this about brexit? No, I don’t talk about that. In 1986 the New York City’s Rockefeller University conducted a study on breast and uterine cancers and their link to obesity. Like in all clinical trials up to that point, the subjects of th...2019-12-2820 minData Science at HomeThe dark side of AI: bias in the machine (Ep. 92)
This is the fourth and last episode of mini series "The dark side of AI".
I am your host Francesco and I’m with Chiara Tonini from London. The title of today’s episode is Bias in the machine
C: Francesco, today we are starting with an infuriating discussion. Are you ready to be angry?
F: yeah sure is this about brexit?
No, I don’t talk about that. In 1986 the New York City’s Rockefeller University conducted a study on breast and uterine cancers and the...2019-12-2820 min
Data Science at HomeThe dark side of AI: metadata and the death of privacy (Ep. 91)Get in touch with us
Join the discussion about data science, machine learning and artificial intelligence on our Discord server
Episode transcript
We always hear the word “metadata”, usually in a sentence that goes like this
Your Honor, I swear, we were not collecting users data, just metadata.
Usually the guy saying this sentence is Zuckerberg, but could be anybody from Amazon or Google. “Just” metadata, so no problem. This is one of the biggest lies about the reality of data collection.
F: Ok the first question is, what the hell is metadata?
Metadata is data about data.
F: Ok… still not clear.Imag...2019-12-2323 minData Science at HomeThe dark side of AI: metadata and the death of privacy (Ep. 91)
Get in touch with us
Join the discussion about data science, machine learning and artificial intelligence on our Discord server
Episode transcript
We always hear the word “metadata”, usually in a sentence that goes like this
Your Honor, I swear, we were not collecting users data, just metadata.
Usually the guy saying this sentence is Zuckerberg, but could be anybody from Amazon or Google. “Just” metadata, so no problem. This is one of the biggest lies about the reality of data collection.
F: Ok the first question is, what the h...2019-12-2323 min
Data Science at HomeThe dark side of AI: recommend and manipulate (Ep. 90)In 2017 a research group at the University of Washington did a study on the Black Lives Matter movement on Twitter. They constructed what they call a “shared audience graph” to analyse the different groups of audiences participating in the debate, and found an alignment of the groups with the political left and political right, as well as clear alignments with groups participating in other debates, like environmental issues, abortion issues and so on. In simple terms, someone who is pro-environment, pro-abortion, left-leaning, is also supportive of the Black Lives Matter movement, and viceversa.
F: Ok, this seems to make sense, right? B...2019-12-1120 min
Data Science at HomeThe dark side of AI: social media and the optimization of addiction (Ep. 89)Chamath Palihapitiya, former Vice President of User Growth at Facebook, was giving a talk at Stanford University, when he said this: “I feel tremendous guilt. The short-term, dopamine-driven feedback loops that we have created are destroying how society works ”.
He was referring to how social media platforms leverage our neurological build-up in the same way slot machines and cocaine do, to keep us using their products as much as possible. They turn us into addicts.
F: how many times do you check your Facebook in a day?
I am not a fan of Facebook. I do not have it on my ph...2019-12-0322 min
Data Science at HomeMore powerful deep learning with transformers (Ep. 84) (Rebroadcast)Some of the most powerful NLP models like BERT and GPT-2 have one thing in common: they all use the transformer architecture. Such architecture is built on top of another important concept already known to the community: self-attention.In this episode I explain what these mechanisms are, how they work and why they are so powerful.
Don't forget to subscribe to our Newsletter or join the discussion on our Discord server
References
Attention is all you need https://arxiv.org/abs/1706.03762
The illustrated transformer https://jalammar.github.io/illustrated-transformer
Self-attention for generative models http://web.stanford.edu/class/cs224n/slide...2019-11-2737 min
Data Science at HomeHow to improve the stability of training a GAN (Ep. 88)Generative Adversarial Networks or GANs are very powerful tools to generate data. However, training a GAN is not easy. More specifically, GANs suffer of three major issues such as instability of the training procedure, mode collapse and vanishing gradients.
In this episode I not only explain the most challenging issues one would encounter while designing and training Generative Adversarial Networks. But also some methods and architectures to mitigate them. In addition I elucidate the three specific strategies that researchers are considering to improve the accuracy and the reliability of GANs.
The most tedious issues of GANs
Convergence to equilibrium
A typi...2019-11-1828 min
Data Science at HomeWhat if I train a neural network with random data? (with Stanisław Jastrzębski) (Ep. 87)What happens to a neural network trained with random data?
Are massive neural networks just lookup tables or do they truly learn something?
Today’s episode will be about memorisation and generalisation in deep learning, with Stanislaw Jastrzębski from New York University.
Stan spent two summers as a visiting student with Prof. Yoshua Bengio and has been working on
Understanding and improving how deep network generalise
Representation Learning
Natural Language Processing
Computer Aided Drug Design
What makes deep learning unique?
I have asked him a few questions for which I was looking for an answer for a long time. For ins...2019-11-1219 min
Data Science at HomeDeeplearning is easier when it is illustrated (with Jon Krohn) (Ep. 86)In this episode I speak with Jon Krohn, author of Deeplearning Illustrated a book that makes deep learning easier to grasp.
We also talk about some important guidelines to take into account whenever you implement a deep learning model, how to deal with bias in machine learning used to match jobs to candidates and the future of AI.
You can purchase the book from informit.com/dsathome with code DSATHOME and get 40% off books/eBooks and 60% off video training2019-11-0544 min
Data Science at Home[RB] How to generate very large images with GANs (Ep. 85)Join the discussion on our Discord server
In this episode I explain how a research group from the University of Lubeck dominated the curse of dimensionality for the generation of large medical images with GANs. The problem is not as trivial as it seems. Many researchers have failed in generating large images with GANs before. One interesting application of such approach is in medicine for the generation of CT and X-ray images.Enjoy the show!
References
Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images https://arxiv.org/abs/1907.013762019-11-0414 min
Data Science at HomeMore powerful deep learning with transformers (Ep. 84)Some of the most powerful NLP models like BERT and GPT-2 have one thing in common: they all use the transformer architecture. Such architecture is built on top of another important concept already known to the community: self-attention.In this episode I explain what these mechanisms are, how they work and why they are so powerful.
Don't forget to subscribe to our Newsletter or join the discussion on our Discord server
References
Attention is all you need https://arxiv.org/abs/1706.03762
The illustrated transformer https://jalammar.github.io/illustrated-transformer
Self-attention for generative models http://web.stanford.edu/class/cs224n/slide...2019-10-2737 min
Data Science at Home[RB] Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 83)Join the discussion on our Discord server
In this episode, I am with Aaron Gokaslan, computer vision researcher, AI Resident at Facebook AI Research. Aaron is the author of OpenGPT-2, a parallel NLP model to the most discussed version that OpenAI decided not to release because too accurate to be published.
We discuss about image-to-image translation, the dangers of the GPT-2 model and the future of AI. Moreover, Aaron provides some very interesting links and demos that will blow your mind!
Enjoy the show!
References
Multimodal image to image translation (not all mentioned in the podcast but recommended by Aaron)
Pix2...2019-10-1837 min
Data Science at HomeWhat is wrong with reinforcement learning? (Ep. 82)Join the discussion on our Discord server
After reinforcement learning agents doing great at playing Atari video games, Alpha Go, doing financial trading, dealing with language modeling, let me tell you the real story here.In this episode I want to shine some light on reinforcement learning (RL) and the limitations that every practitioner should consider before taking certain directions. RL seems to work so well! What is wrong with it?
Are you a listener of Data Science at Home podcast? A reader of the Amethix Blog? Or did you subscribe to the Artificial Intelligence at your fingertips newsletter? In any case...2019-10-1521 min
Data Science at HomeHave you met Shannon? Conversation with Jimmy Soni and Rob Goodman about one of the greatest minds in history (Ep. 81)Join the discussion on our Discord server
In this episode I have an amazing conversation with Jimmy Soni and Rob Goodman, authors of “A mind at play”, a book entirely dedicated to the life and achievements of Claude Shannon. Claude Shannon does not need any introduction. But for those who need a refresh, Shannon is the inventor of the information age.
Have you heard of binary code, entropy in information theory, data compression theory (the stuff behind mp3, mpg, zip, etc.), error correcting codes (the stuff that makes your RAM work well), n-grams, block ciphers, the beta distribution, the uncertainty coeffic...2019-10-1032 min
Data Science at HomeAttacking machine learning for fun and profit (with the authors of SecML Ep. 80)Join the discussion on our Discord server
As ML plays a more and more relevant role in many domains of everyday life, it’s quite obvious to see more and more attacks to ML systems. In this episode we talk about the most popular attacks against machine learning systems and some mitigations designed by researchers Ambra Demontis and Marco Melis, from the University of Cagliari (Italy). The guests are also the authors of SecML, an open-source Python library for the security evaluation of Machine Learning (ML) algorithms. Both Ambra and Marco are members of research group PRAlab, under the supervision of Pr...2019-10-0134 min
Data Science at Home[RB] How to scale AI in your organisation (Ep. 79)Join the discussion on our Discord server
Scaling technology and business processes are not equal. Since the beginning of the enterprise technology, scaling software has been a difficult task to get right inside large organisations. When it comes to Artificial Intelligence and Machine Learning, it becomes vastly more complicated.
In this episode I propose a framework - in five pillars - for the business side of artificial intelligence.2019-09-2613 min
Data Science at HomeReplicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 78)Join the discussion on our Discord server
In this episode, I am with Aaron Gokaslan, computer vision researcher, AI Resident at Facebook AI Research. Aaron is the author of OpenGPT-2, a parallel NLP model to the most discussed version that OpenAI decided not to release because too accurate to be published.
We discuss about image-to-image translation, the dangers of the GPT-2 model and the future of AI. Moreover, Aaron provides some very interesting links and demos that will blow your mind!
Enjoy the show!
References
Multimodal image to image translation (not all mentioned in the podcast but recommended by Aaron)
Pix...2019-09-2337 min
Data Science at HomeTraining neural networks faster without GPU [RB] (Ep. 77)Join the discussion on our Discord server
Training neural networks faster usually involves the usage of powerful GPUs. In this episode I explain an interesting method from a group of researchers from Google Brain, who can train neural networks faster by squeezing the hardware to their needs and making the training pipeline more dense.
Enjoy the show!
References
Faster Neural Network Training with Data Echoinghttps://arxiv.org/abs/1907.055502019-09-1722 min
Data Science at HomeHow to generate very large images with GANs (Ep. 76)Join the discussion on our Discord server
In this episode I explain how a research group from the University of Lubeck dominated the curse of dimensionality for the generation of large medical images with GANs. The problem is not as trivial as it seems. Many researchers have failed in generating large images with GANs before. One interesting application of such approach is in medicine for the generation of CT and X-ray images.Enjoy the show!
References
Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images https://arxiv.org/abs/1907.013762019-09-0614 min
Data Science at Home[RB] Complex video analysis made easy with Videoflow (Ep. 75)In this episode I am with Jadiel de Armas, senior software engineer at Disney and author of Videflow, a Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment.
I have inspected the videoflow repo on Github and some of the capabilities of this framework and I must say that it’s really interesting. Jadiel is going to tell us a lot more than what you can read from Github
References
Videflow Github official repository https://github.com/videoflow/videoflow2019-08-2930 min
Data Science at Home[RB] Validate neural networks without data with Dr. Charles Martin (Ep. 74)In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep neural networks and some impressive findings about the way they work.
The questions that Charles answers in the show are essentially two:
Why is regularisation in deep learning seemingly quite different than regularisation in other areas on ML?
How can we dominate DNN in a theoretically principled way?
References
The WeightWatcher tool for predicting the accuracy of Deep Neural Networks https://github.com/CalculatedContent/WeightWatcher
Slack channel http...2019-08-2744 min
Data Science at HomeHow to cluster tabular data with Markov Clustering (Ep. 73)In this episode I explain how a community detection algorithm known as Markov clustering can be constructed by combining simple concepts like random walks, graphs, similarity matrix. Moreover, I highlight how one can build a similarity graph and then run a community detection algorithm on such graph to find clusters in tabular data.
You can find a simple hands-on code snippet to play with on the Amethix Blog
Enjoy the show!
References
[1] S. Fortunato, “Community detection in graphs”, Physics Reports, volume 486, issues 3-5, pages 75-174, February 2010.
[2] Z. Yang, et al., “A Comparative Analysis of Community Detection Algorithms on Artificial Networks”, Scientific...2019-08-2120 min
Data Science at HomeWaterfall or Agile? The best methodology for AI and machine learning (Ep. 72)The two most widely considered software development models in modern project management are, without any doubt, the Waterfall Methodology and the Agile Methodology. In this episode I make a comparison between the two and explain what I believe is the best choice for your machine learning project.
An interesting post to read (mentioned in the episode) is How businesses can scale Artificial Intelligence & Machine Learning https://amethix.com/how-businesses-can-scale-artificial-intelligence-machine-learning/2019-08-1414 min
Data Science at HomeTraining neural networks faster without GPU (Ep. 71)Training neural networks faster usually involves the usage of powerful GPUs. In this episode I explain an interesting method from a group of researchers from Google Brain, who can train neural networks faster by squeezing the hardware to their needs and making the training pipeline more dense.
Enjoy the show!
References
Faster Neural Network Training with Data Echoinghttps://arxiv.org/abs/1907.055502019-08-0622 min
Data Science at HomeValidate neural networks without data with Dr. Charles Martin (Ep. 70)In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep neural networks and some impressive findings about the way they work.
The questions that Charles answers in the show are essentially two:
Why is regularisation in deep learning seemingly quite different than regularisation in other areas on ML?
How can we dominate DNN in a theoretically principled way?
References
The WeightWatcher tool for predicting the accuracy of Deep Neural Networks https://github.com/CalculatedContent/WeightWatcher
Slack channel http...2019-07-2344 min
Data Science at HomeComplex video analysis made easy with Videoflow (Ep. 69)In this episode I am with Jadiel de Armas, senior software engineer at Disney and author of Videflow, a Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment.
I have inspected the videoflow repo on Github and some of the capabilities of this framework and I must say that it’s really interesting. Jadiel is going to tell us a lot more than what you can read from Github
References
Videflow Github official repository https://github.com/videoflow/videoflow2019-07-1630 min
Data Science at HomeEpisode 68: AI and the future of banking with Chris Skinner [RB]In this episode I have a wonderful conversation with Chris Skinner.
Chris and I recently got in touch at The banking scene 2019, fintech conference recently held in Brussels. During that conference he talked as a real trouble maker - that’s how he defines himself - saying that “People are not educated with loans, credit, money” and that “Banks are failing at digital”.
After I got my hands on his last book Digital Human, I invited him to the show to ask him a few questions about innovation, regulation and technology in finance.2019-07-0941 min
Data Science at HomeEpisode 67: Classic Computer Science Problems in PythonToday I am with David Kopec, author of Classic Computer Science Problems in Python, published by Manning Publications.
His book deepens your knowledge of problem solving techniques from the realm of computer science by challenging you with interesting and realistic scenarios, exercises, and of course algorithms. There are examples in the major topics any data scientist should be familiar with, for example search, clustering, graphs, and much more.
Get the book from https://www.manning.com/books/classic-computer-science-problems-in-python and use coupon code poddatascienceathome19 to get 40% discount.
References
Twitter https://twitter.com/davekopec
GitHub https://github.com/davecom
classicproblems.com2019-07-0228 min
Data Science at HomeEpisode 66: More intelligent machines with self-supervised learningIn this episode I talk about a new paradigm of learning, which can be found a bit blurry and not really different from the other methods we know of, such as supervised and unsupervised learning. The method I introduce here is called self-supervised learning.
Enjoy the show!
Don't forget to subscribe to our Newsletter at amethix.com and get the latest updates in AI and machine learning. We do not spam. Promise!
References
Deep Clustering for Unsupervised Learning of Visual Features
Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey2019-06-2518 min
Data Science at HomeEpisode 65: AI knows biology. Or does it?The successes of deep learning for text analytics, also introduced in a recent post about sentiment analysis and published here are undeniable. Many other tasks in NLP have also benefitted from the superiority of deep learning methods over more traditional approaches. Such extraordinary results have also been possible due to the neural network approach to learn meaningful character and word embeddings, that is the representation space in which semantically similar objects are mapped to nearby vectors. All this is strictly related to a field one might initially find disconnected or off-topic: biology.
Don't forget to subscribe to our Newsletter at a...2019-06-2312 min
Data Science at HomeEpisode 64: Get the best shot at NLP sentiment analysisThe rapid diffusion of social media like Facebook and Twitter, and the massive use of different types of forums like Reddit, Quora, etc., is producing an impressive amount of text data every day.
There is one specific activity that many business owners have been contemplating over the last five years, that is identifying the social sentiment of their brand, by analysing the conversations of their users.
In this episode I explain how one can get the best shot at classifying sentences with deep learning and word embedding.
Additional material
Schematic representation of how to learn a word embedding matrix E by...2019-06-1412 min
Data Science at HomeEpisode 63: Financial time series and machine learningIn this episode I speak to Alexandr Honchar, data scientist and owner of blog https://medium.com/@alexrachnogAlexandr has written very interesting posts about time series analysis for financial data. His blog is in my personal list of best tutorial blogs. We discuss about financial time series and machine learning, what makes predicting the price of stocks a very challenging task and why machine learning might not be enough.As usual, I ask Alexandr how he sees machine learning in the next 10 years. His answer - in my opinion quite futuristic - makes perfect sense.
You can contact Alexandr on
Twi...2019-06-0421 min
Data Science at HomeEpisode 62: AI and the future of banking with Chris SkinnerIn this episode I have a wonderful conversation with Chris Skinner.
Chris and I recently got in touch at The banking scene 2019, fintech conference recently held in Brussels. During that conference he talked as a real trouble maker - that’s how he defines himself - saying that “People are not educated with loans, credit, money” and that “Banks are failing at digital”.
After I got my hands on his last book Digital Human, I invited him to the show to ask him a few questions about innovation, regulation and technology in finance.2019-05-2842 min
Data Science at HomeEpisode 61: The 4 best use cases of entropy in machine learningIt all starts from physics. The entropy of an isolated system never decreases… Everyone at school, at some point of his life, learned this in his physics class. What does this have to do with machine learning? To find out, listen to the show.
References
Entropy in machine learning https://amethix.com/entropy-in-machine-learning/2019-05-2121 min
Data Science at HomeEpisode 60: Predicting your mouse click (and a crash course in deeplearning)Deep learning is the future. Get a crash course on deep learning. Now! In this episode I speak to Oliver Zeigermann, author of Deep Learning Crash Course published by Manning Publications at https://www.manning.com/livevideo/deep-learning-crash-course
Oliver (Twitter: @DJCordhose) is a veteran of neural networks and machine learning. In addition to the course - that teaches you concepts from prototype to production - he's working on a really cool project that predicts something people do every day... clicking their mouse.
If you use promo code poddatascienceathome19 you get a 40% discount for all products on the Manning platform
Enjoy the s...2019-05-1639 min
Data Science at HomeEpisode 59: How to fool a smart camera with deep learningIn this episode I met three crazy researchers from KULeuven (Belgium) who found a method to fool surveillance cameras and stay hidden just by holding a special t-shirt. We discussed about the technique they used and some consequences of their findings.
They published their paper on Arxiv and made their source code available at https://gitlab.com/EAVISE/adversarial-yolo
Enjoy the show!
References
Fooling automated surveillance cameras: adversarial patches to attack person detection Simen Thys, Wiebe Van Ranst, Toon Goedemé
Eavise Research Group KULeuven (Belgium)https://iiw.kuleuven.be/onderzoek/eavise2019-05-0724 min
Data Science at HomeEpisode 58: There is physics in deep learning!There is a connection between gradient descent based optimizers and the dynamics of damped harmonic oscillators. What does that mean? We now have a better theory for optimization algorithms.In this episode I explain how all this works.
All the formulas I mention in the episode can be found in the post The physics of optimization algorithms
Enjoy the show.2019-04-3019 min
Data Science at HomeEpisode 57: Neural networks with infinite layersHow are differential equations related to neural networks? What are the benefits of re-thinking neural network as a differential equation engine? In this episode we explain all this and we provide some material that is worth learning. Enjoy the show!
Residual Block
References
[1] K. He, et al., “Deep Residual Learning for Image Recognition”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016
[2] S. Hochreiter, et al., “Long short-term memory”, Neural Computation 9(8), pages 1735-1780, 1997.
[3] Q. Liao, et al.,”Bridging the gaps between residual learning, recurrent neural networks and visual cortex”, arXiv preprint, arXiv:1604.03640, 2016.
[4] Y. Lu, et al., “Beyond Finite Layer Neural Netw...2019-04-2316 min
Data Science at HomeEpisode 56: The graph networkSince the beginning of AI in the 1950s and until the 1980s, symbolic AI approaches have dominated the field. These approaches, also known as expert systems, used mathematical symbols to represent objects and the relationship between them, in order to depict the extensive knowledge bases built by humans. The opposite of the symbolic AI paradigm is named connectionism, which is behind the machine learning approaches of today2019-04-1616 min
Data Science at HomeEpisode 55: Beyond deep learningThe successes that deep learning systems have achieved in the last decade in all kinds of domains are unquestionable. Self-driving cars, skin cancer diagnostics, movie and song recommendations, language translation, automatic video surveillance, digital assistants represent just a few examples of the ongoing revolution that affects or is going to disrupt soon our everyday life.But all that glitters is not gold…Read the full post on the Amethix Technologies blog2019-04-0917 min
Data Science at HomeEpisode 54: Reproducible machine learningIn this episode I speak about how important reproducible machine learning pipelines are. When you are collaborating with diverse teams, several tasks will be distributed among different individuals. Everyone will have good reasons to change parts of your pipeline, leading to confusion and definitely a number of options that soon explode. In all those cases, tracking data and code is extremely helpful to build models that are reproducible anytime, anywhere. Listen to the podcast and learn how.2019-03-0911 min
Data Science at HomeEpisode 53: Estimating uncertainty with neural networksHave you ever wanted to get an estimate of the uncertainty of your neural network? Clearly Bayesian modelling provides a solid framework to estimate uncertainty by design. However, there are many realistic cases in which Bayesian sampling is not really an option and ensemble models can play a role.
In this episode I describe a simple yet effective way to estimate uncertainty, without changing your neural network’s architecture nor your machine learning pipeline at all.
The post with mathematical background and sample source code is published here.2019-01-2315 min
Data Science at HomeEpisode 52: why do machine learning models fail? [RB]The success of a machine learning model depends on several factors and events. True generalization to data that the model has never seen before is more a chimera than a reality. But under specific conditions a well trained machine learning model can generalize well and perform with testing accuracy that is similar to the one performed during training.
In this episode I explain when and why machine learning models fail from training to testing datasets.2019-01-1715 min
Data Science at HomeEpisode 51: Decentralized machine learning in the data marketplace (part 2)In this episode I am completing the explanation about the integration fitchain-oceanprotocol that allows secure on-premise compute to operate in the decentralized data marketplace designed by Ocean Protocol.
As mentioned in the show, this is a picture that provides a 10000-feet view of the integration.
I hope you enjoy the show!2019-01-0823 min
Data Science at HomeEpisode 50: Decentralized machine learning in the data marketplaceIn this episode I briefly explain how two massive technologies have been merged in 2018 (work in progress :) - one providing secure machine learning on isolated data, the other implementing a decentralized data marketplace.
In this episode I explain:
How do we make machine learning decentralized and secure?
How can data owners keep their data private?
How can we benefit from blockchain technology for AI and machine learning?
I hope you enjoy the show!
References
fitchain.io decentralized machine learnin
Ocean protocol decentralized data marketplace2018-12-2624 min
Data Science at HomeEpisode 49: The promises of Artificial IntelligenceIt's always good to put in perspective all the findings in AI, in order to clear some of the most common misunderstandings and promises. In this episode I make a list of some of the most misleading statements about what artificial intelligence can achieve in the near future.2018-12-1921 min
Data Science at HomeEpisode 48: Coffee, Machine Learning and BlockchainIn this episode - which I advise to consume at night, in a quite place - I speak about private machine learning and blockchain, while I sip a cup of coffee in my home office.There are several reasons why I believe we should start thinking about private machine learning...It doesn't really matter what approach becomes successful and gets adopted, as long as it makes private machine learning possible. If people own their data, they should also own the by-product of such data.
Decentralized machine learning makes this scenario possible.2018-10-2128 min
Data Science at HomeEpisode 47: Are you ready for AI winter? [Rebroadcast]Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in the 90s and since then a long list of experiences at the intersection of computer science and physics, led him to the conclusion that deep learning might not be sufficient nor appropriate to solve the problem of intelligence, specifically artificial intelligence. I read some of his publications and got familiar with some of his ideas. Honestly, I have been attracted by the fact that Filip does not buy the hype around AI an...2018-09-1156 min
Data Science at HomeEpisode 46: why do machine learning models fail? (Part 2)In this episode I continue the conversation from the previous one, about failing machine learning models.
When data scientists have access to the distributions of training and testing datasets it becomes relatively easy to assess if a model will perform equally on both datasets. What happens with private datasets, where no access to the data can be granted?
At fitchain we might have an answer to this fundamental problem.2018-09-0417 min
Data Science at HomeEpisode 45: why do machine learning models fail?The success of a machine learning model depends on several factors and events. True generalization to data that the model has never seen before is more a chimera than a reality. But under specific conditions a well trained machine learning model can generalize well and perform with testing accuracy that is similar to the one performed during training.
In this episode I explain when and why machine learning models fail from training to testing datasets.2018-08-2816 min
Data Science at HomeEpisode 44: The predictive power of metadataIn this episode I don't talk about data. In fact, I talk about metadata.
While many machine learning models rely on certain amounts of data eg. text, images, audio and video, it has been proved how powerful is the signal carried by metadata, that is all data that is invisible to the end user.Behind a tweet of 140 characters there are more than 140 fields of data that draw a much more detailed profile of the sender and the content she is producing... without ever considering the tweet itself.
References You are your Metadata: Identification and Obfuscation of Social Media Users u...2018-08-2121 min
Data Science at HomeEpisode 43: Applied Text Analysis with Python (interview with Rebecca Bilbro)Today’s episode is about text analysis with python. Python is the de facto standard in machine learning. A large community, a generous choice in the set of libraries, at the price of less performant tasks, sometimes. But overall a decent language for typical data science tasks.
I am with Rebecca Bilbro, co-author of Applied Text Analysis with Python, with Benjamin Bengfort and Tony Ojeda.
We speak about the evolution of applied text analysis, tools and pipelines, chatbots.2018-08-1436 min
Data Science at HomeEpisode 42: Attacking deep learning models (rebroadcast)Attacking deep learning models
Compromising AI for fun and profit
Deep learning models have shown very promising results in computer vision and sound recognition. As more and more deep learning based systems get integrated in disparate domains, they will keep affecting the life of people. Autonomous vehicles, medical imaging and banking applications, surveillance cameras and drones, digital assistants, are only a few real applications where deep learning plays a fundamental role. A malfunction in any of these applications will affect the quality of such integrated systems and compromise the security of the individuals who directly or indirectly use them.
In thi...2018-08-0729 min
Data Science at HomeEpisode 41: How can deep neural networks reasonToday’s episode will be about deep learning and reasoning. There has been a lot of discussion about the effectiveness of deep learning models and their capability to generalize, not only across domains but also on data that such models have never seen.
But there is a research group from the Department of Computer Science, Duke University that seems to be on something with deep learning and interpretability in computer vision.
References
Prediction Analysis Lab Duke University https://users.cs.duke.edu/~cynthia/lab.html
This looks like that: deep learning for interpretable image recognition https://arxiv.org/abs/1806.105742018-07-3118 min
Data Science at HomeEpisode 40: Deep learning and image compressionToday’s episode will be about deep learning and compression of data, and in particular compressing images. We all know how important compressing data is, reducing the size of digital objects without affecting the quality. As a very general rule, the more one compresses an image the lower the quality, due to a number of factors like bitrate, quantization error, etcetera. I am glad to be here with Tong Chen, researcher at the School of electronic Science and Engineering of Nanjing University, China.
Tong developed a deep learning based compression algorithm for images, that seems to improve over state of the...2018-07-2417 min
Data Science at HomeEpisode 39: What is L1-norm and L2-norm?In this episode I explain the differences between L1 and L2 regularization that you can find in function minimization in basically any machine learning model.2018-07-1921 min
Data Science at HomeEpisode 38: Collective intelligence (Part 2)In the second part of this episode I am interviewing Johannes Castner from CollectiWise, a platform for collective intelligence. I am moving the conversation towards the more practical aspects of the project, asking about the centralised AGI and blockchain components that are essential part of the platform.
References
Opencog.orgThaler, Richard H., Sunstein, Cass R. and Balz, John P. (April 2, 2010). "Choice Architecture". doi:10.2139/ssrn.1583509. SSRN 1583509
Teschner, F., Rothschild, D. & Gimpel, H. Group Decis Negot (2017) 26: 953. https://doi.org/10.1007/s10726-017-9531-0
Firas Khatib, Frank DiMaio, Foldit Contenders Group, Foldit Void Crushers Group, Seth Cooper, Maciej Kazmierczyk, Miroslaw Gilski, Szymon Krzywda, Hel...2018-07-1746 min
Data Science at HomeEpisode 38: Collective intelligence (Part 1)This is the first part of the amazing episode with Johannes Castner, CEO and founder of CollectiWise. Johannes is finishing his PhD in Sustainable Development from Columbia University in New York City, and he is building a platform for collective intelligence. Today we talk about artificial general intelligence and wisdom.
All references and shownotes will be published after the next episode.Enjoy and stay tuned!2018-07-1230 min
Data Science at HomeEpisode 37: Predicting the weather with deep learningPredicting the weather is one of the most challenging tasks in machine learning due to the fact that physical phenomena are dynamic and riche of events. Moreover, most of traditional approaches to climate forecast are computationally prohibitive. It seems that a joint research between the Earth System Science at the University of California, Irvine and the faculty of Physics at LMU Munich has an interesting improvement on the scalability and accuracy of climate predictive modeling. The solution is... superparameterization and deep learning.
References
Could Machine Learning Break the Convection Parameterization Deadlock?
Gentine, M. Pritchard, S. Rasp, G. Reinaudi, and G. Yacalis Earth and Environmental Engine...2018-07-0926 min
Data Science at HomeEpisode 36: The dangers of machine learning and medicineHumans seem to have reached a cross-point, where they are asked to choose between functionality and privacy. But not both. Not both at all. No data, no service. That’s what companies building personal finance services say. The same applies to marketing companies, social media companies, search engine companies, and healthcare institutions.
In this episode I speak about the reasons to aggregate data for precision medicine, the consequences of such strategies and how can researchers and organizations provide services to individuals while respecting their privacy.2018-07-0322 min
Data Science at HomeEpisode 35: Attacking deep learning modelsAttacking deep learning models
Compromising AI for fun and profit
Deep learning models have shown very promising results in computer vision and sound recognition. As more and more deep learning based systems get integrated in disparate domains, they will keep affecting the life of people. Autonomous vehicles, medical imaging and banking applications, surveillance cameras and drones, digital assistants, are only a few real applications where deep learning plays a fundamental role. A malfunction in any of these applications will affect the quality of such integrated systems and compromise the security of the individuals who directly or indirectly use them.
In thi...2018-06-2929 min
Data Science at HomeFounder Interview – Francesco Gadaleta of FitchainCross-posting from Cryptoradio.io
Overview
Francesco Gadaleta introduces Fitchain, a decentralized machine learning platform that combines blockchain technology and AI to solve the data manipulation problem in restrictive environments such as healthcare or financial institutions.Francesco Gadaleta is the founder of Fitchain.io and senior advisor to Abe AI. Fitchain is a platform that officially started in October 2017, which allows data scientists to write machine learning models on data they cannot see and access due to restrictions imposed in healthcare or financial environments. In the Fitchain platform, there are two actors, the data owner and the data scientist. They b...2018-05-2431 min
Data Science at HomeFounder Interview – Francesco Gadaleta of FitchainCross-posting from Cryptoradio.ioOverviewFrancesco Gadaleta introduces Fitchain, a decentralized machine learning platform that combines blockchain technology and AI to solve the data manipulation problem in restrictive environments such as healthcare or financial institutions.Francesco Gadaleta is the founder of Fitchain.io and senior advisor to Abe AI. Fitchain is a platform that officially started in October 2017, which allows data scientists to write machine learning models on data they cannot see and access due to restrictions imposed in healthcare or financial environments. In the Fitchain platform, there are two actors, the data owner and the data...2018-05-2431 min
Data Science at HomeFounder Interview – Francesco Gadaleta of FitchainCross-posting from Cryptoradio.io
Overview
Francesco Gadaleta introduces Fitchain, a decentralized machine learning platform that combines blockchain technology and AI to solve the data manipulation problem in restrictive environments such as healthcare or financial institutions.Francesco Gadaleta is the founder of Fitchain.io and senior advisor to Abe AI. Fitchain is a platform that officially started in October 2017, which allows data scientists to write machine learning models on data they cannot see and access due to restrictions imposed in healthcare or financial environments. In the Fitchain platform, there are two actors, the data owner and the data scientist. They both r...2018-05-2431 min
Crypto RadioFrancesco Gadaleta of FitchainFrancesco Gadaleta introduces Fitchain, a decentralized machine learning platform that combines blockchain technology and AI to solve the data manipulation problem in restrictive environments such as healthcare or financial institutions. Show notes: http://cryptoradio.io/fitchain This episode is sponsored by: http://cryptoradio.io/play 2018-05-2331 min