Stephen Miller: How To Leverage Mobile Phones And 3D Data To Build Robust Computer Vision Systems

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Stephen Miller is the Cofounder and SVP Engineering at Fyusion Inc. He has conducted research in 3D Perception and Computer Vision with Profs Sebastian Thrun and Vladlen Koltun while at Stanford University. His area of specialization is AI and Robotics, which included 2 years of undergraduate research with Prof Pieter Abbeel. 

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Episode Links:  

Stephen Miller’s LinkedIn: https://www.linkedin.com/in/sdavidmiller/ 

Stephen Miller’s Twitter: https://twitter.com/sdavidmiller 

Stephen Miller’s Website: http://sdavidmiller.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

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YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

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– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:42) – Started in robotics around 2010, training them to perform human tasks (surgical suturing, laundry folding). Clearest bottleneck was not “How do we get the robot to move properly” but “How do we get the robot to understand the 3D space it operates in?”   

(04:05) – The Deep Learning revolution around that era was very focused on 2D images. But it wasn’t always easy to translate those successes into real world systems: the world is not made up of pixels; it’s made up of physical objects in space.

(06:57) – When the Microsoft Kinect came out; I became excited about the democratization of 3D, and the possibility that better data was available to the masses. Intuitive data can help us more confidently build solutions. Easier to validate when something fails, easier to give more consistent results. 

(09:20) – Academia is a vital engine for moving technology forward. In hindsight, for instance, those early days of Deep Learning -- one or two layers, evaluating on simple datasets -- were crucial to ultimately advancing the state of the art we see today. 

(14:48) – Now that Machine Learning is becoming increasingly commodified, we are starting to see a growing demand for people who can bridge that gap on both sides: conferences requiring code submissions alongside a paper, companies encouraging their engineers to take online ML courses, etc.

(17:41) – As we do finally start to see real-time computer vision productized for mobile phones, it does beg the question: won’t this exacerbate the digital divide? Flagship devices, always-on network connectivity: whether computing on the edge or in the cloud, there is going to be a disparity. 

(20:33) – Because of this, I think the ideal model is to treat AI as one tool among many in a hybrid system. Think smart autocomplete, as opposed to automatic novel writing. AI as an assistant to a human expert: freeing them from the minutia so they can focus on high-level questions; aggregating noise so they can be more consistent and efficient. 

(23:08) – Computer Vision has gone through a number of hype cycles in the last decade –real-time recognition, real-time reconstruction, etc. But the showiest of these ideas seem to rarely leave the realm of gaming, or tech demonstrator. I suspect this is because many of these ideas require a certain level of perfection to be valuable. It’s easy to imagine replacing my eyes with something that works 100% of the time. But what about 90%? At what point is the hassle of figuring out whether I’m in the 10% bucket or the 90% bucket, outweighing the convenience?

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Nell Watson: How To Teach AI Human Values   

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Nell Watson is an interdisciplinary researcher in emerging technologies such as machine vision and A.I. ethics. Her work primarily focuses on protecting human rights and putting ethics, safety, and the values of the human spirit into technologies such as Artificial Intelligence. Nell serves as Chair & Vice-Chair respectively of the IEEE’s ECPAIS Transparency Experts Focus Group, and P7001 Transparency of Autonomous Systems committee on A.I. Ethics & Safety, engineering credit score-like mechanisms into A.I. to help safeguard algorithmic trust.

She serves as an Executive Consultant on philosophical matters for Apple, as well as serving as Senior Scientific Advisor to The Future Society, and Senior Fellow to The Atlantic Council. She also holds Fellowships with the British Computing Society and Royal Statistical Society, among others. Her public speaking has inspired audiences to work towards a brighter future at venues such as The World Bank, The United Nations General Assembly, and The Royal Society.

Episode Links:  

Nell Watson’s LinkedIn: https://www.linkedin.com/in/nellwatson/ 

Nell Watson’s Twitter: https://twitter.com/NellWatson 

Nell Watson’s Website: https://www.nellwatson.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter: https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(2:57)- Even though the science of forensics and police work has changed so much in those last two centuries, principles are great, but it's very important that we create something actionable out of that. We create criteria with defined metrics that we can know whether we are achieving those principles and to what degree.

(3:25)- With that in mind, I’ve been working with teams at the IEEE Standards Association to create standards for transparency, which are a little bit traditional big document upfront very deep working on many different levels for many different use cases and different people for example, investigators or managers of organizations, etcetera.

(9:04)- Transparency is really the foundation of all other aspects of AI and Ethics. We need to understand how an incident occurred, or we need to understand how a system performs a function in order to. I analyze how it might be biased or where there might be some malfunction or what might occur in a certain situation or a certain scenario, or indeed who might be responsible for something having gone through it is really the most basic element of protecting ourselves, protecting our privacy, our autonomy from these kinds of advanced algorithmic systems, there are many different elements that might influence these kinds of systems.

(26:35)- We're really coming to a Sputnik moment and AI. We've gotten used to the idea of talking to our embodied smart speakers and asking them about sports results or what tomorrow's weather is going to be. But they're not truly conversational.

(32:43)- Fundamentally technologies and a humane society is about putting the human first, putting human needs first and adapting systems to serve those needs and to truly and better the human condition to not sacrifice everything for the sake of efficiency to leave a bit of slack and to ensure that the costs to society of a new innovation or the costs to the environment are properly taken into effect.

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Ryan McDonald: How To Position People at the Center of AI Native Solutions 

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Ryan McDonald is the Chief Scientist at ASAPP working on NLP and ML research focusing on CX and enterprise. He is also an Associate researcher in the NLP group at Athens University of Economics and Business. Ryan was a Research Scientist in the Language Team at Google for 15 years where he helped build state-of-the-art NLP and ML technologies and pushed them to production. 

He managed research and production teams in New York and London that were responsible for a number of innovations used in Translate, Assistant, Cloud and Search. He was the first NLP research scientist in both New York and London, and helped grow those groups into world-class research organizations. Prior to that, he did his Ph.D. in NLP at the University of Pennsylvania. 

Episode Links:  

Ryan McDonald’s LinkedIn: https://www.linkedin.com/in/ryanmcd/ 

Ryan McDonald’s Twitter: https://twitter.com/asapp 

Ryan McDonald’s Website: http://www.ryanmcd.com 

CX: The Human Factor Report: https://ai.asapp.com/LP-2021-09-CX-The-Human-Factor_Landing-Page.html

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter: https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(3:00)- The kinds of problems that deploying AI runs into for enterprise is more about scalability. Instead of having a single user of the technology, we have hundreds of users of the technology and how can we deliver a unique experience and an excellent experience for each of those users and this necessitates questions around adopting machine learning and natural language processing models to new domains. 

(10:49)- And this is exactly the technology we're building out. How can we sort of regularize that? How can we look at the conversation and the issue that the customer's happening? That's sort of embodied in the dialogue, up to a point in time and then allow AI to make recommendations to the agent; Here is a workflow that we think you should use and all the steps you need to follow in order to solve this issue

(28:33)- So we design everything and that's why it's critical to design these things from the bottom up with AI in mind. All of our artificial intelligence has been designed to serve those latency needs. So to kind of give you a couple of examples, the first is automatic speech recognition. So a huge number of calls that come into call centers are still voice, they're not digital. It's not people call contacting over chat. It's people calling in on their phone. 

(30:41)- So we've focused on building out something called SRU, which is an architecture where we can take super high, accurate AI models and then distill them into these faster architectures, which allows us to get into these millisecond range. So we can get responses back to agents and milliseconds, and that really is going to affect how much they use those suggestions at the end of the day.

(32:38)- Beyond what's happening in the conversation and see everything, all the information and all the actions that the agent can possibly do on their computer. And so agent journey is a product where we, you know, put a piece of software on the agent's computer and it allows us to access into all the tools they're using, how they're using them, how that interacts with the conversation.

(33:49)- Agent journey is our efforts in that space to understand everything holistically that the agent is doing to really make headway in task-oriented dialogue.

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Humphrey Chen: How AI Can Revolutionize the Way We Consume Video 

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Humphrey Chen is the CEO and Co-Founder of CLIPr. He has a BS in Management Science from MIT. His work in tech specializes in the use of technology to make people and companies more productive.    

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Episode Links:  

Humphrey Chen’s LinkedIn: https://www.linkedin.com/in/humphreychen/ 

Humphrey Chen’s Twitter: https://twitter.com/humphreyc?s=20 

Humphrey Chen’s Website: https://aws.amazon.com/es/rekognition/?blog-cards.sort-by=item.additionalFields.createdDate&blog-cards.sort-order=desc 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:36) – CLIPr operating premise is that not all minutes of video content are equally relevant to everyone. So it uses machine learning to fully index that video and make it fully searchable.

(05:02) – Watching a whole video can be inefficient when a participant only wants to watch specific sections. CLIPr team's speeds up and accelerates more efficient automations to be helpful for both consumers and enterprises. 

(06:42) – The tools that CLIPr provides are a way to guarantee target audience engagement rates to be really informative. CLIPr focuses on this video insight when it comes to engagement and interaction around the video itself in a category called video analysis and management.

(08:04) – CLIPr aims to hand out the tools to efficiently find content that matters, bookmark it, share it, react to it, comment on it. 

(08:27) – The tools and the skills required to edit a video are completely opposite from the skills and tools required for editing inside of a document. CLIPr bridges the two effectively, by building a video-based document type.

(11:57) – There has not been as much disruption around video. Some use cases that have been thought out include recording customer meetings; customers’ feedback, integrations with a CRM record, and also, provide a score over time around the actual probability of closing a sale based on the relative perception for the customer reaction.

(14:20) – AI, additionally with the hospitals and the medical universities and researchers alike are still using antiquated technology and they're not extracting insights from these video moments. CLIPr is also useful in telemedicine. For surgeons, CLIPr means high value, highly visual, high-impact in a short time.

(24:26) – Machine learning, in general, it's all about the data and about engagement and interaction and training new models around the data. So, machine learning allows people to create things and bring solutions. Technology is actually going to find meaningful problems to solve more effectively and more efficiently. 

(28:21) – The purpose of services is to build businesses and to augment either with the stable technology or the experimental technology for what will be the future of AI, of natural language processing of emotion, detection of different technologies. Additional progress still needs to happen beyond the data in telemedicine, EMRs or courtrooms.

(31:49) – As new features get uncovered with specific use cases, anyone can benefit from CLIPr video analytics and management platform. There is continued acceleration for product led growth, closing a 5 million seed round with a strategic partner and keeping focus on machine learning and cloud-based services. Rather than just being an endpoint, it analyzes the data, allows for referential utility, allows for collaboration and allows for monthly recurring revenue.

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Dave Bechberger: How Connected Data Impacts Our Daily Interactions   

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Dave Berchberger is a Senior Graph Architect at Amazon Web Services (AWS). He is known for his expertise in distributed data architecture being a thought leader in graph databases, and the co-author of Graph Databases in Action by Manning Publications. Dave uses his 20+ yrs experience working on and managing teams delivering full-stack software solutions to take a holistic approach to solve complex data problems.    

Episode Links:  

Dave Bechberger’s LinkedIn: https://www.linkedin.com/in/davebechberger/ 

Dave Bechberger’s Twitter: https://twitter.com/bechbd?s=20 

Dave Bechberger’s Website: https://www.manning.com/books/graph-databases-in-action?a_aid=bechberger 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:29) – Corporate environments need to be able to help solve certain types of problems that traditional relational databases or other data technologies are not very good at solving. The new approach is to build out high-performance data platforms on top of a mix of technologies, focused around solving them with graphic, graph database technologies. 

(02:53) – Graphs are the mathematical construct of a graph. It's really about networks, connected data of different people connected to other people or things of that nature. It's about building out networks and using those connections to be able to answer specific types of questions and draw insight and information out of that data that isn't necessarily available from other technologies. 

(06:49) – Fraud is another canonical use case, because it is all about figuring out connections and patterns within data, to be able to discern whether this activity is fraudulent or not. 

(08:32) – Other technologies don't do a great job linking together entities in such a way that those links and those connections are also treated as first-class citizens inside that data. Graphs bring those connections in your data up to being “first-class citizens”. 

(09:29) – With a graph, those connections are brought up and given first class status in the languages and queries that you run. It's called traversing them, to be able to move across them, to be able to drive insight from how those connections are made and how those connections basically connect this network of data together.

(12:38) – Using Graphs makes developers able to not only process data in a real-time transactional mode, but being able to use those along with something like graph type analytics, and then use that in conjunction with AI and ML technologies to augment data back into your graph in order to provide a better real-time user experience.

(14:32) – Any enterprise build or any consumer service build is really about creating a better, faster and easier to use experience for your customers. Those are really the driving forces behind any kind of business initiative. Graphs is one of those technologies.

(16:38) – There's certain types of analytics that can be run on top of graphs that are very helpful to be used as inputs into machine learning algorithms of different types. Some examples show working in a fraud area.

(18:20) – Machine learning in general and graphs-based machine learning specifically, is this concept of a graph neural network, which is basically a neural network that instead of taking only vector features as input, it actually takes in a graph itself. So, graphs as an input to be able to create predictive models on the output. It's building a graph of different connected objects inside the algorithm itself as it's training and learning.

(20:33) – To really be able to build graph-based stack applications or applications on top of graph databases, you don't necessarily need to have all of that very academic understanding. And being able to condense that down into a system that helps people start to think about problems that way was really the purpose with Graph Databases in Action by Manning Publications.

(25:54) – The biggest ways graphs are being adopted today is used in conjunction with other technologies, be those relational databases or document databases or key value stores or whatever other technologies that are out there. 

(28:18) – Graphs is one of those technologies that is definitely a double-edged sword because you're able to drive insights and you'll be able to see connections between things. People could use those connections in nefarious type ways.

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Alex Beard: How to Solve for the Global Education Crisis caused by The Pandemic 

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Alex Beard is the Senior Director at Teach For All , and author of the book Natural Born Learners. After starting out as an English teacher in a London comprehensive, He completed an MA at the Institute of Education before joining Teach For All. His book, “Natural Born Learners”, is a user's guide to transforming learning in the twenty-first century, taking readers on a global tour into the future of education, from Silicon Valley to Seoul, Helsinki to Hounslow.   

Episode Links:  

Alex Beard’s LinkedIn: https://www.linkedin.com/in/alex-beard-08901915/ 

Alex Beard’s Twitter: https://twitter.com/alexfbeard?s=20 

Alex Beard’s Website: https://www.alexbeard.org/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:43) –The methods used to teach would probably be familiar to Socrates two and a half thousand years ago in ancient Greece. Few things have been done differently inside the classroom. The gap between what is possible, and what was currently true in the classroom is at the heart of our education crisis.

(03:03) – The pandemic has widened the educational divide. The pandemic has exacerbated the crisis and intensified some of these questions about the future of education.

(06:30) – Education must consider access and quality. But with schools shut down, access becomes an infrastructure through the internet and that's a relatively technical solution.

(07:38) – If you're not going to school, quality of education is knowledge received sitting in your bedroom via your laptop, which has completely disrupted our idea of what a quality education is.

(08:19) – The vast majority of primary and middle school kids are just not equipped with self motivation yet, so quality has to mean something about human to human engagement. Learning, for most people, is better when it's social.

(13:40) – Practitioners have had to develop new pedagogies, new ways of learning, how to engage kids through the medium of technology. You need to know how to engage a student.

(15:16) – We might be strengthening bonds between teachers and parents, as a result of the pandemic to support early learning, virtually, and that involves engaging parents more actively in supporting their kids to learn.

(18:48) – Our intelligence is unlimited, and it's teachers in schools that cultivate that potential. We need to be more explicit about the different roles that teachers play, and set up our system to enable teachers as subject specialists who help kids to do better. 

(21:12) – Teachers need to be experts in tech, at least to understand how they can use the latest tools to outsource bits of their practice to save themselves time. 

(30:22) – AI is sort of an adversary to help us enhance our own creativity. The dangers are more connected to the intentions. It all comes down to human choices if you deploy technology and in certain ways undermine the ability of humans to get better at things. Lots of people are designing to enhance the humans in the loop, which is how we should be thinking about it.

(36:33) – There are great advances to be made in the deployment of technology in education, but the advances will be made not by trying to improve tech, but by trying to improve what the humans who are doing with tech. Investment in people and not an investment in technology. 

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Ivy Lu: How To Organize Data Science Teams and Data Science Projects for Startups 

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Ivy Lu is the head of data science and machine learning at Oxygen. Ivy's onboarding marked the launch of Oxygen’s banking platform. She has bachelor's degree in Geographical Information System from Peking University, a Ph.D in Earth Systems and Geoinformation Science and a Master's degree in Geographic Information Science and Cartography both from George Mason University. 

Episode Links:  

Ivy Lu’s LinkedIn: https://www.linkedin.com/in/ivy9lu/ 

Ivy Lu’s Twitter: https://twitter.com/oxygenbanking 

Ivy Lu’s Website: https://www.blog.oxygen.us/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter: https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:42) – I joined Capital One as a data scientist after my graduation from George Mason University with a PhD in Geographic Information Science. After I moved to the west coast, I joined Apple. So, at Apple, I work on an anti-fraud team where we fight against all kinds of fraud and abuse within the whole Apple ecosystem to bring trust and safety to the Apple customers. Both experiments helped me prepare for my new challenge at Oxygen as a FinTech company. So, that's my career , how I passed from the traditional banking industry to a large technology company. And now I'm at the spin hat company Oxygen. 

(04:05) – A collaboration challenge, since you are the only one and only data scientist on the team, basically, you are collaborating with so many different teams and departments: from operations to marketing customer support or product features. So, you need to collaborate with every single one in the different departments and understand their needs, understand their pain. That also comes related to the first challenge. Collaboration comes with prioritization.

(06:57) – Data science teams should be positioned as the foundation and the cross team within the whole organization. So for each line of the business, data scientists should have domain knowledge about the problem that they are trying to deal with

(09:20) – I collaborate with our fraud team to set up a lot of protections in the core sets. We collaborate with different fraud vendors on how to set up all the parameters, all the controls in place in the fraud vendors for our sign up status. After the sign up flow is pretty under control, I built a preliminary machine learning model for the fraudsters, to detect fraudsters after sign up for the behaviors they show with our card.

(14:48) – I see these days, as data scientists it may require different skills than before. Nowadays, maybe, coding skills are not required anymore with such a good tool for data scientists and for machine learning engineers. But, ultimately, I still think the important thing is the study section background on the machine learning algorithm, the deep understanding of the machine learning algorithms. Also what's important is the deep understanding of the problem they're solving.

(17:41) – There are two types of team structure. One is like the data science team belongs to one centralized team and then people may wear multiple hats. So, one day you may work on project A, then another day and work on project B, versus another one that is more embedded.

(20:33) – We launched a new product called Elements. So we are now offering four tiers of the product, with increasing cashback with different saving APRs, as well as other retail and travel benefits like priority pass, launch access, reimbursements, like digital subscriptions, like Netflix, and the Peloton Digital.  

(23:08) – We are going to raise our series B soon and a series B is all about metrics. Whether your company is going to be sustainable, what's your retention, what's your user growth. So a lot of KPIs and the metrics you send show to not only our internal business, but also to work presents for our VC.

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Asra Nadeem: How the future of media will be enhanced by generative design 

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Asra Nadeem is the Co-Founder of Opus AI, a streaming platform powered by proprietary tech that turns plain text into movies and playable 3D worlds in real-time. She is the first female Pakistani venture capitalist. She has a BA in Economics, and has a Masters in Film/TV/Theater and English Literature from Beaconhouse National University.

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Asra Nadeem’s LinkedIn: https://www.linkedin.com/in/bretgreenstein/

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Asra Nadeem’s Website: https://opus.ai/ 

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Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

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– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:55) –Nadeem’s background and her thesis: There is not any kind of freedom without financial freedom, and technology is a great enabler for that. 

(07:13) – Through a platform that grants access to some of the most brilliant minds in the world for free, anyone can learn and interact now.

(09:02) – ”Naseeb” revolutionized the traditional marriage arrangements in Pakistan, by allowing younger generations to create connections online and get married. 

(11:26) – Formal education has mainly three purposes: learning something, networking and better job opportunities. Those three things are available through technology. 

(13:54) – The Big Names in the tech industry don't request a college degree to work for them, only the skills. It's a different world that is crafting narratives and stories, building stories for the creative industry, and this is a space that's a massive opportunity that has not been tapped into yet.

(14:59) – Opus.ai, an engine that takes any literary text and converts it into a movie. So you have a code without having to know how to code. It can be that tool to enable digital natives who may not have any coding experience in order to democratize content creation.

(23:29) – The technological progress or the leaps and bounds of automation make generative design come of age. Using AI to boost creativity makes anything possible and accessible.

(26:01) – New types of film will be generated and created. And creativity generates, potentially, new jobs. There is no match for human creativity. And this inherent desire to explore new places or explore new worlds, that's something that's very uniquely human and not replicable by a machine. 

(32:14) – Network effects are built into platforms, who want to get users in front of as many people, because that's how they drive ad revenues or eyeballs. Figure out trends that your product market fit, and then that platform creator fit that's working for you. 

(38:14) – The current conditions are opportunities to reinvent, to try new technology and to show that you as a human, can be part of a new wave. We're continuing to move forward into a world that could be without code, could be no code, low code. Build your creative muscle.

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Steven Shillingford is President and CEO of DeepSee.ai, a Knowledge Process Automation (KPA) platform to mine unstructured data, operationalize AI-powered insights, and automate results into real-time action for the enterprise. He is the creator of the Knowledge Process Automation industry category, delivering AI-powered automation and productivity via easy to deploy, cloud-based business flows for critical business operations in the Capital Markets and Insurance verticals. He has led several startup enterprises, building cloud-scale platforms and helped found a successful cybersecurity platform for big data analytics supporting network surveillance systems for a range of verticals, from intelligence agencies to Fortune 500 companies. 

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Podcast Details: 

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Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

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YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

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– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(02:31) – Innovation Cycles used to be about features, but now consumers and enterprises look for innovation around processes

(06:53) – Using AI to surface the information that is most useful through a configurable tool bias towards action.

(13:52) – NLP to support different tools for different types of business problems inside the enterprise

(16:29) – A hybrid approach where people need interaction to lead us to “enhanced accelerated productivity”

(22:26) – Reducing processing time to offload a non-human optimized work to the machine, keeping Computers working on behalf of the humans 

(23:42) – Operationalize data science and the innovation that comes from AI around outcomes to achieve knowledge, reduce cost, mitigate risk and improve customer satisfaction, not only in capital markets or insurance, but across a number of industries

(26:53) – A platform that matches unstructured data in different business models, but same processes. Automation of checkpoints by a machine using the Deepsee platform as in capital markets

(30:27) – Helping research get faster results. Streamlining paper processes to innovate in new therapeutics, new vaccines, medical supplements and medications, as well as the technology used for blockchain

(33:50) – More than document digitization it’s document and data analysis, preserving data provenance across all actions to build trust through transparency and achieve wide-scale adoption.

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Oliver Schabenberger: How Data, Analytics, Decisions and Intelligence Are Connected  

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Oliver Schabenberger is the Chief Innovation Officer at SingleStore. He is a former academician and seasoned technology executive with more than 25 years of global experience in data management, advanced analytics, and AI. Oliver formerly served as COO and CTO of SAS, where he led the design, development, and go-to market effort of massively scalable analytic tools and solutions and helped organizations become more data-driven. 

Previously, Oliver led the Analytic Server R&D Division at SAS, with responsibilities for multi-threaded and distributed analytic server architecture, event stream processing, cognitive analytics, deep learning, and artificial intelligence. He has contributed thousands of lines of code to cutting-edge projects at SAS, including, SAS Cloud Analytic Services, the engine behind SAS Viya, the next-generation SAS architecture for the open, unified, simple, and powerful cloud. He has a PHD from Virginia Polytechnic Institute and State University

Please support this podcast by checking out our sponsors:

Episode Links:  

Oliver Schabenberger’s LinkedIn: https://www.linkedin.com/in/oschabenberger/ 

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Oliver Schabenberger’s Website: https://www.singlestore.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

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YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:38) – From forestry to statistics to Software development to advance analytics

(04:07) – To understand the data is not only to build a mental model, but a probabilistic model of how the data came about, and once that model is accepted, as a good abstraction, then it is used to ask questions about the world. 

(05:39) – Many of the assumptions into our established models and established thinking about industries and supply chains had to be questioned because of unforeseen events like the pandemic. Scenario modeling is not just making a prediction, it must also guide the decisions and the need to provide the right abstractions.

(07:19) – There is an approach steeped in mathematical statistics and probability theory. And a more computationally-driven approach which shows how computer science, as a discipline, changed its focus from focus on compute, to focus on data.

(10:34) – There are transactional systems, analytics systems, machine learning and data science, all somewhat based on existing technology purpose-built for a certain use case, and what we're seeing is the use cases coming together. These worlds need to come together through a data foundation where the workloads can all converge. Silos and empires that need to be connected.

(16:15) – The explosion of neural network technology over the last 15 years due to the availability of big compute and cloud computing has allowed to solve much deeper problems, and we need larger amounts of data to train those models. 

(16:33) – Modern AI, data-driven AI and machine learning applications recognize patterns. Neural networks are trained to detect patterns. The next generation of models might be more contextual or build out from individual component models where humans can interact with the system and understand how it drives its conclusion, and then correct it.

(20:35) – We need to empower all of us to work with data and to contribute to driving the world with data and driving the world with models more. We need to be more data literate. But we also need better tooling that allows low-code and no-code contributions 

(23:28) – The future of data science is decision science. 

(25:38) – We have technology at our disposal, that makes us “prosumers” who consume and produce at the same time. And data should be the same way. We should be able to produce what we need based on data, not just consume. 

(28:28) – Innovation is key to success in technology. Innovation is about turning creativity and curiosity into value, and value has to be tied to the core of what we do, core of the business, core of what our customer needs. 

(30:51) – The elements of building technology: connectivity, automation and culture.

(32:43) – Turn the data into decisions and drive the business, and that is SingleStore’s specialty.

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