Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

By Felipe FloresTechnology

Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science episodes

August 06, 2019#63 Set Yourself Multi-Year Professional Challenges with Felipe Flores – Founder & Podcast Host
In this episode, Anthony Ugoni, one of Australia’s more prominent leaders in analytics interviews Felipe. Felipe came to Australia as a backpacker and ended up falling in love with the place. With Spanish as his first language, the only English he could say was the jacket is black. Then, Felipe explains some of his odd jobs and working freelance IT. At university, Felipe wanted to specialize in data, but all of his friends told him it was dead. So, he ended up specializing in hardware, even though all of his work was in data. When Felipe went to do his thesis, he happened to stumble into a project involving brain wave activity. The electrical engineer did all the research and design, the signals would be passed to Felipe’s computer, where he made his first application of machine learning.
Then, Felipe explains how he and a colleague of his made the decision to quit their jobs at a small consulting firm. They decided to start their own firm, despite knowing very little about business. The first year they almost went bankrupt about four times and made lots of mistakes. They wanted to be in analytics but were unsure how to sell their services. The two spent six months creating a piece of software. When they went to show prospects they found out people did not like the entire product. So they decided to focus on their consulting business.

Enjoy the show!

We speak about:
[02:40] Felipe’s background

[06:10] Education and specializations

[14:30] Quick delivery of value

[17:20] A series of odd jobs and IT freelancing

[24:20] Setting up his own consulting company

[33:15] Highs and lows of Clear Blue Water

[37:30] Executive Director & Head of Data Science at ANZ

[47:55] Supportive and open culture at work

[52:40] Understanding the business at a new job

[54:45] Inspiration behind Data Futurology

[62:00] Explainable AI

Resources:
Felipe’s LinkedIn: https://www.linkedin.com/in/felipefloresanalytics/?originalSubdomain=au
Episode #21 Antony Ugoni: https://www.datafuturology.com/podcast/21

Quotes:
“If I’m an engineer, people will think I’m smart.”

“A colleague of mine and I decided to set up our own consulting company. Professionally, it was the best and worst thing I’ve ever done.”

“Sales is built on trust and a human connection.”

“I had not done a good job of being a leader and creating a culture.”

“How can we make data scientists today, the CEOs of tomorrow?”

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology

Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1 hr 5 min
July 30, 2019#62 Full Stack Data Science with Gregory Hill – Global Head of Analytics
Dr. Gregory Hill leads the Analytics function at Brightstar's Global Services division, developing and delivering their data & analytics strategy, innovation programs, and product development initiatives. He works across their lines of business, including supply chain optimization, product portfolio management, financial services, buy-back and trade-in, leasing, and omnichannel solutions. He also manages Brightstar's analytics team in support of their key global accounts with pre-sales, solution design, and service delivery. His expertise is in the application of advanced analytics techniques (including machine learning, predictive modelling, mathematical optimization, econometrics, and operations research) to commercial problems. These applications span forecasting, pricing, fraud, market segmentation, customer satisfaction, and propensity modelling.

In this episode, Gregory explains how he started in the data space. He was aware of all the theoretical work being done around data but did not know how it worked in an industry aspect. The real challenge of putting mathematical models to practice lies in the organizational and people elements of it. Computer science and electrical engineering do not teach you how to overcome organizational challenges and individual motivations and incentives. Going back to get his Ph.D., Greg wanted to do something requiring qualitative research. So he targeted informational systems and economics. His fieldwork leads him to interview executives of larger banks, publicly listed companies, and government agencies. He came up with an economic framework that improved customer data quality.

Enjoy the show!

We speak about:
[02:00] How Greg started in the data space

[11:10] Leaving academics and getting involved in the industry

[13:20] Greg’s work background

[18:25] The four P’s of marketing

[20:40] Transiting from gut instinct to a data-driven approach

[27:55] Thinking through cause and effect

[30:45] What Greg’s team looks like

[39:00] Lessons learned from managing data scientists

[42:25] Active in local data science meetups + guest speaking

[44:25] Working globally + peeling back opportunities to use data science techniques

Resources:
Greg’s LinkedIn: https://www.linkedin.com/in/gregoryhill/?originalSubdomain=au
Brightstar: https://www.brightstar.com

Quotes:
“My thesis was not a project; it was a lifestyle.”

“I didn’t want to be an academic, I wanted to get back into the industry.”

“It was a combination of arrogance and laziness.”

“At the end of the day, it boils down to if I change X, will Y change?”

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology

Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
50 min
July 23, 2019#61 Data Science Strategy in the Military, Startups and Tech/Digital Leaders with Sveta Freidman - Data Analytics & Science Director/Mentor
Sveta Freidman is a data scientist and business intelligence leader with extensive experience in consulting and client-based environments. She has a vast experience working in different industries, including gambling, retail, health, and online businesses (startups). Sveta is a data strategist with a passion for connecting people to the data they need to make decisions, build better products, and execute marketing strategies.
In this episode, Sveta explains why she decided to study statistics, she had a passion for mathematics. During her time in Israel’s military, she collected data from different places and made sense from it. Her commercial experience comes from various startups she joined. When joining a startup, you have to wear many hats. Sometimes you have to be a data engineer, data scientist, or a data analyst. Then, Sveta moved to Australia and found a startup, Envato, where she built all the data from scratch.

Enjoy the show!

We speak about:
[01:40] How Sveta started in the data space

[07:15] Sveta’s professional background

[17:10] Investing in local talent

[20:45] How to hire for a startup

[24:30] Questions for hiring interviews

[29:25] Working for Carsales

[31:55] People not trusting the data

[35:20] Solving the issue of trust

[40:30] Finding bias in the data

[44:50] Make sure you look at the data every day

Resources:
Sveta’s LinkedIn: https://www.linkedin.com/in/sveta-freidman-5981593
Carsales: https://www.carsales.com.au

Quotes:
“Statistics is everywhere.”

“You can be a great data scientist, but you need to understand the culture.”

“I give the candidate a business problem to see how they will react to it.”

“Your algorithms are good as long as your data is good.”

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology

Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
51 min
July 16, 2019#60 Building Self-Driving Cars in Silicon Valley with Vladimir Iglovikov, Ph.D. – Senior Computer Vision Engineer and Kaggle Grandmaster
Vladimir Iglovikov graduated from university with a degree in theoretical physics, he moved to Silicon Valley in search of a data science role in the industry. This led him to his current position in Lyft’s autonomous vehicle division where he works on computer vision related applications. In the past few years, he has invested a lot of time in Machine Learning competitions leading to his title of Kaggle Grandmaster.
In this episode, Vladimir explains how difficult it was to find work in Silicon Valley. He had harsh requirements for a salary, no one looked at his resume. Companies in Silicon Valley are willing to pay big bucks, but at the same time, they require the person to be skilled in software engineering, machine learning, and statistics. His biggest issue when applying for jobs was assuming that all people are similar to the people in academics. At his interviews, he felt no connection with the interviewers. After sending his resume to over 200 different companies, someone finally bit just before his visa expired. Vladimir worked at Bidgely for 8 months then moved to TrueAccord and eventually got his job at Lyft.
Enjoy the show!

We speak about:
[02:00] How Vladimir started in the data space

[12:30] Transferring from academia to industry

[21:40] Benefits of having soft skills

[25:45] How Vladimir manages stress

[31:30] Kaggle is like lifting weights

[35:30] The hiring process for data scientists

[40:45] Excitement for machine learning

[46:00] Autonomous driving

[47:55] Pursuing a startup

[51:40] Aiming to maximize mistakes in a day

[61:00] Social life comes first

Resources:
Vladimir’s LinkedIn: https://www.linkedin.com/in/iglovikov/
Kaggle: https://www.kaggle.com/iglovikov

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1 hr 3 min
July 09, 2019#59 Creating the Link Between Business and Data with Tony Gruebner - GM Analytics, Insights and Modelling
Tony Gruebner is the GM Analytics of Insights and Modelling and the Exec Sponsor of Personalisation at Sportsbet. He established a department of 40+ skilled analysts and data scientists tasked with creating innovative data products focused at improving the experience for their customers and supporting the business by providing relevant and timely information and insights that steer decision making across all levels of the business. He has served on the Executive Leadership Team from 2016.
In this episode, Tony explains how he started in data and what led him to get his job at Sportsbet. Tony got a call from a recruiter asking if he wanted to do work with analytics, in a company that does sports and is heavily digital. All of those factors checked the box for Tony, and he took the entry-level analyst role. Over time, the need for analytics has grown, so he has been able to develop some analytics teams.
Enjoy the show!

We speak about:
[01:20] How Tony got started in data

[08:20] Tony’s skills come from the commercial side

[11:10] Linking data science and the business

[14:30] Communicating how data science works

[17:00] Steps to getting others to understand data science

[20:40] Getting the best talent for your team

[24:00] Structuring teams and the department

[28:10] Transiting from analytical roles to commercial roles

[35:30] Working on global expansion

[38:10] Solving with artificial intelligence

[42:30] Passionate about using numbers to reach an outcome

[44:00] Modelling failures with Sportsbet

[47:50] Imposter syndrome in data science

[50:05] Data science is rapidly changing and exciting

Resources:
Tony’s LinkedIn: https://www.linkedin.com/in/gruebz/
Sportsbet: https://www.sportsbet.com.au
Tony’s Twitter: https://twitter.com/gruebz?lang=en

Quotes:
“There is no one path that always works.”

“There are literally thousands of things data scientists couldn’t potentially tackle in any business.”

“If you’re not making mistakes, then you aren’t pushing the envelope hard enough.”

“Not having imposter syndrome is a sign of lack of knowledge.”

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
54 min
July 02, 2019 #58 Explainable AI Methods for Unstructured Data
Today we have a different type of episode, this is a presentation that Felipe did at the Chief Data and Analytics Officer Conference in Canberra, and it is on explainable AI. First, Felipe explains how Amazon used a secret AI recruiting tool that had a bias against women. Also, the U.S. government used an algorithm predicting how likely people in the criminal justice system would reoffend. What they found is that it targeted specific racial groups. The algorithm isn’t racist or sexist, the data is.
Regarding job applications, as your company scales up, the need to automate the process of looking at the applications becomes necessary. Sometimes, bias will creep into the automated decision-making algorithm. The bias can even be narrowed down to the person’s name. For example, somebody with name Felipe might get scored lower than somebody with the name Tyler. Lean into the inequality and predict the bias. You can plug in the CV information, and ask the algorithm to predict the person’s race and gender. Then, find out what key inputs they are flagging to determine this and remove them from the algorithm.
Then, Felipe explains how algorithms can tackle unstructured data approaches. When discussing images, an algorithm was able to correctly identify a wolf from a husky 5 out of 6 times. However, when uncovering how the algorithm determined which was which, it was merely looking at if the animal was in the snow or not. If the picture had snow in it, then it must be a wolf. To determine how this algorithm was functioning, Felipe used LIME - Local Interpretable Model-Agnostic Explanations. It works for classifications and came out of a study from MIT. Later, Felipe discusses using EL15 and how transparency is essential for the public to understand how the algorithms could affect them.
Enjoy the show!
We speak about:
[03:40] Large companies and their biases
[05:40] Racism and sexism is in our data
[08:45] Uncovering inputs of the bias
[10:45] Unstructured data approaches
[14:30] Using ELI5
[19:20] The right to an explanation
Quotes:
“We teach our algorithms on how to replicate our decisions.”
“The algorithms show the inequality that we have in the world today.”
“Explainable AI is more ethical in the sense that it is more transparent.”
“Explainable AI helps us avoid blunders and informs us how the algorithm perceives the data.”
Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!
And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
23 min
June 25, 2019#57 From Academic to Data Science Leader with Yuval Marom - Analytics and Data Science Professional
Yuval is an Analytics and Data Science professional with extensive commercial and academic experience. His interests and goals are to be working on interesting and practical problems where there is a need to discover and act on meaningful patterns in data, through advanced analytics and data science. I'm the founder and co-organiser of two meetups: Data Science Melbourne and MelbURN, a user group for Melbourne-based users of the R statistical and data mining programming language.
In this episode, Yuval tells us about how both of his parents are statisticians and inspired him to fall in love with data science. Growing up, he used Pascal to build spaceship games, and it motivated his passion for programming. Eventually, Yuval went for his Ph.D. and focused on applying how animals learn and behave to robotics. Simulated and physical experiments were pretty basic because robotics were not as advanced as they are today. Later, Yuval realized academia was not necessarily his calling, he was more interested in applying solutions to interesting problems. However, in recent years, research innovation and solving problems are becoming much more intertwined.
Enjoy the show!

We talk about:
[01:40] How Yuval fell in love with data science

[05:45] Social learning in biology

[08:05] Lessons learned from completing a Ph.D.

[13:10] Research innovation vs. solving problems

[15:40] Embrace simplicity

[18:00] Small business advantages

[21:45] Skills to develop before management

[26:00] Results oriented work

[30:45] Different flavors of management

[32:50] Connection to community

[40:20] Learning to interact with stakeholders + managerial skills

[44:00] Benefits of building connections + education

[48:00] Assume people are at work with good intentions

[52:00] Allocate time for professional development

[59:30] Focus on retention

Resources:
Data Science Melbourne
MelbURN
Yuval’s LinkedIn
University of New South Wales

Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1 hr 4 min
June 19, 2019#56 Every Business is an AI Business with Dr. Eric Daimler – Serial Entrepreneur, Technology Executive, Investor and Policy Advisor
Dr. Eric Daimler is an authority in Artificial Intelligence & Robotics with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage. Daimler is the author of the forthcoming book Every Business is an AI Business, a guidebook for entrepreneurs, engineers, policymakers, and citizens on how to understand—and benefit from—the unfolding revolution in AI & Robotics. A frequent speaker, lecturer, and commentator, he works to empower communities and citizens to leverage AI & Robotics. For a more sustainable, secure, and prosperous future.
In this episode, Eric explains how he has a vivid memory of getting a computer at the age of nine. He loves the machine, and even at such a young age saw the freedom a computer allows. Early in his career, Eric knew he wanted to work with brilliant and motivated people. When he was in New York, he saw the Netscape browser and instantly recognized the world was going to change. This inspired him to get out and find opportunities on the west coast.
Enjoy the show!

We speak about:
[02:10] How Eric started in the technology space

[05:15] Moving from one career path to another

[09:50] Eric’s most significant failure as an investor

[13:30] Picking the timing

[18:15] AI is larger than what currently exists

[21:30] Embracing the technology behind AI

[29:45] Hurdles for companies who are adopting AI

[41:30] Reactions from people learning about AI

[48:40] Shortage of truck drivers + how technology is making driving easier

[54:00] AI in the medical field

[61:30] Using a categorical approach

Resources:
Eric’s LinkedIn: https://www.linkedin.com/in/ericdaimler/
Eric’s Twitter: https://twitter.com/ead
Website: http://conexus.ai/

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology

Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1 hr 5 min
June 13, 2019#55 The Truth about AI from the People Building it with Martin Ford, Futurist and New York Times Bestselling Author Focused on Artificial Intelligence (AI), Robotics and the Future Economy
Martin Ford is a prominent futurist, New York Times bestselling author, and leading expert on artificial intelligence and robotics and their potential impact on the job market, economy and society. His 2015 book, "Rise of the Robots: Technology and the Threat of a Jobless Future" won the Financial Times and McKinsey Business Book of the Year Award and has been translated into more than 20 languages.
In this episode, Martin discusses his best-selling books and describes some of the themes he writes about. For instance, in Rise of the Robots he talks about “The Triple Revolution” which was a report presented to U.S. President Lyndon B. Johnson fifty years ago that argued this would be a dramatic change to the economy; however, it never really panned out. Martin’s argument for artificial intelligence started back in 2009 after writing his first book titled The Lights in the Tunnel. Ultimately, artificial intelligence will become so powerful that it can have a significant impact on employment that will compete with a large fraction of the workforce.
Enjoy the show!

We speak about:
[02:50] Martin’s background

[05:45] The themes behind Martin’s writing

[08:35] Machine learning is when algorithms can make decisions

[12:00] Amazon is susceptible to automation

[16:45] The most common occupation error is driving some kind of vehicle

[18:15] The type of work that will be left for humans

[21:45] Universal basic income

[28:55] Building explicit incentives to earn more income; paying people more to pursue education

[33:25] Artificial intelligence will be the primary force shaping our futures

[38:35] The solution is not to teach everyone how to code

[41:30] Architects of Intelligence: The truth about AI from the people building it

[46:00] Deep learning is the biggest thing to happen to artificial intelligence

[52:20] Controlling data and an entirely new industry called data banks

[53:15] Negative implications of artificial intelligence

[64:40] You do not want to be doing something predictable

Resources:
Martin’s Website: https://mfordfuture.com/about/
Martin’s LinkedIn: https://www.linkedin.com/in/martin-ford-5a70428/
Martin’s Twitter: https://twitter.com/MFordFuture
TED Talk: https://www.ted.com/talks/martin_ford

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology

Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1 hr 9 min
June 06, 2019#54 How to build a Data Department from scratch with Annie South – General Manager of Data at ME Bank
Annie South is the General Manager of Data at ME Bank. She is an Information Management professional with twenty years’ experience of complex information environments spanning the full spectrum of structured data to unstructured information. Annie has in-depth technical knowledge of various specialisms, including metadata, data warehousing, data governance, data quality, enterprise architecture, data lineage, Big Data, data analytics, and regulatory requirements.
In this episode, Annie explains the things she does to ensure her career is future ready because nobody can predict what jobs will look like years from now. Do not specialize in a particular technology but specialize in a capability. The technologies that you are using today will not be the technologies they are using tomorrow. If you specialize in a particular technology set, and it is decreasing in popularity, you will end up with fewer opportunities in the market. Annie tells people wanting career advice that when people look at your resume, they are looking for a consistent arc. That could mean staying consistent in an industry or constant engagement in the workforce. Another thing Annie looks for in applicants is kindness, this quality is something that cannot be taught.
Enjoy the show!

We speak about:
[01:20] How Annie got into the world of data

[10:00] Insight for people starting in the data space

[12:50] Organizations are not predictable

[14:50] Annie’s team at ME Bank

[27:50] Turning recruitment on its head

[33:20] Transitioning from teaching to general manager

[39:05] Sort out your personality and experiment with leadership

[46:30] Imposter syndrome

[49:10] Experimenting with diversity in the workforce

[53:30] Challenges with discrimination in the workplace

[61:10] Define yourself; do not be defined by others

Resources:
Annie’s LinkedIn: https://www.linkedin.com/in/annesouth/
ME Bank: https://www.mebank.com.au
IT Jobs Watch: https://www.itjobswatch.co.uk

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology

Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1 hr 9 min

About Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

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Artificial intelligence is a tremendously beneficial technology that's advancing at an incredibly rapid pace.

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Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science episodes

About Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science