Explainable AI by Felipe Flores

Felipe Flores.jpg

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.”

Now you can support Data Futurology on Patreon!  

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Thank you to our sponsors: 

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Felipe Flores is based in Melbourne, Victoria, Australia.

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

Every Business is an AI Business with Dr. Eric Daimler – Serial Entrepreneur, Technology Executive, Investor and Policy Advisor

Eric Daimler.jpg

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.

Eric’s most significant failure as an investor was with a sports company. It was an idea of aggregating the worldwide demand for niche sports into an audience on the web that would allow for more significant marketing dollars. It was a fantastic idea and seemed like the appropriate time to go for it. One of his biggest takeaways from being an investor is that timing matters a lot. The bandwidth wasn’t there, so the experience ended up being quite weak.

There is a great deal of money looking to chase the next big thing. If you are looking for a house in a new city, and someone outbids you, you will lose the house. That doesn’t mean you aren’t going to move; it just means you need to start looking for a new home. If you receive a pitch and then some name brand firm takes it away from you, that doesn’t mean you can’t work in that field anymore. Now you have done your due diligence, you understand the market better and can look for other investment opportunities in the area.

AI is a system. We have to embrace this technology in its totality, the survival of our species depends on it. We have famously been able to survive to 2019.  There was a prediction back in the 1800s that we would have starved by now because the population was growing faster than our food production. Increase in productivity comes from technology and automation. When Eric is speaking, he likes to ask the audience what comes to mind when he mentions the term AI. People just don’t know what the word means. One of the critical issues that need to be addressed when companies employ AI is the recognition that our understanding of technology may change. Even the meaning of data has changed over the last year. Later, Eric explains how people have a long way to go regarding embracing AI, how technology is making driving easier, and AI in the medical field.


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/


Quotes:

  • “Most people get the timing very wrong.”

  • “I am starting to look at investments where others are not looking.”

  • “I wanted to change the Hollywood narrative around AI.”

  • “We were naive about our privacy in 2016.”


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 will@datasourceservices.com.au 

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Dr. Eric Daimler is based in San Francisco, California.


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



Martin Ford – Author and Futurist

Martin Ford.jpg

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.

Then, Martin discusses the impact of jobs, the economy, privacy, and democracy with the influx of automation. There was a recent announcement from OpenAI stating they have created a sophisticated deep-learning system that was able to generate narrative content. In other words, the system can create reviews, articles, and poetry proving AI can be creative. The company withheld the technology because of fear people would use the system to turn the internet into garbage full of fake news and fake reviews.

Later, Felipe and Martin discuss the most common occupational error, driving. Self-driving cars will threaten Uber drivers, taxi drivers, and truck drivers. It may take a bit longer than some people are saying, the technology will be coming within ten to fifteen years. Martin believes the hardest jobs to automate are in three categories: genuine creativity, relationship-building, and skilled trade jobs. We put so much emphasis on going to college; however, the safest jobs are going to be the electrician and plumber. Many people do not thrive at university, half of them are not finding jobs that leverage their education after graduation. Then, Martin explains universal income; giving everyone a minimal level of income to allow them to survive in the absence of a traditional job that would provide money. Stay tuned to hear Martin discuss deep learning, data banks, and the negative implications of artificial intelligence.

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

Books:

Rise of the Robots: Technology and the Threat of a Jobless Future

Architects of Intelligence: The truth about AI from the people building it

The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future

Quotes:

  • “It would be a huge mistake to assume only blue-collar workers will be impacted.”

  • “Education is not going to be enough in the long run.”

  • “The last thing we need is a dumbed down population and less informed voters.”

  • “I believe artificial intelligence will be the best thing to happen to humanity.”

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 will@datasourceservices.com.au 

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Martin Ford is based in Sunnyvale, California.

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


Peter Elger – Founder and CEO

Peter Elger.jpg

Peter Elger is the founder and CEO of four Theorem; his focus is on delivering business value to his clients through the application of cutting edge serverless cloud architectures and machine learning technology. His experience covers everything from architecting large-scale distributed software systems, to leading the internationally-based teams that built them.

In this episode, Peter tells us how his first real passion was in physics. After graduating with a BSc in Physics and a master’s degree in Computer Science, he worked for several years at the Joint European Torus (JET), the world's largest operational magnetically confined plasma physics / nuclear fusion experiment. They were doing big data, but at the time they did not refer to it as such; they dealt with around four to five terabytes of scientific data. Peter then transitioned to Indigo Stone as a Senior Technical Architect. Indigo Stone was a software disaster recovery firm which exited in 2007 to EMC.

Peter explains how it is essential to keep your technical skills up-to-date and why some of his favorite days are when he gets to code despite being the CEO of his company. If you can actually be the bridge between the business and the technology, you are an invaluable asset to any company. The freedom to innovate is what led Peter to his entrepreneurial ventures; previously, he had no real experience being his own boss. Peter says it is dangerous to think you can do everything; you have might a broad skill set, but you need to recognize that you have gaps. This is why Peter has always started businesses with co-founders. Currently, his co-founder is a world-class technologist and someone who understands the human dimension. All of his current co-founders and people Peter has worked with previously.

Later, Peter explains why he wrote about applying AI to existing platforms in his book, AI as a Service. He also recently wrote a chapter on how to apply machine learning to existing systems and the patterns that arise within two systems. Then, Peter describes why you do not need knowledge of AI to use AI as a service. In the past, everyone wanted their own security department; however, people already have the same technology that is vastly better than your own. This applies to how you can utilize AI within your existing business systems. Peter wants people to be interested in machine learning, but only a basic understanding is essential to use it within a business context. Finally, Peter discusses his approach to finding the most capable team members, being open and upfront with all staff members, and removing the ego to allow his team to perform their best work.


Enjoy the show!


We speak about:

  • [01:45] How Peter started in the data space

  • [06:50] Transition to disaster recovery  

  • [08:55] Interactive radio and marketing applications

  • [13:40] Maintaining a grip with technical skills

  • [16:20] The entrepreneurial bug came organically to Peter

  • [18:40] Transition to entrepreneurship  

  • [21:45] What to look for in a co-founder

  • [26:00] Building analytics with machine learning

  • [29:00] A tale of two technologies

  • [33:00] Applying AI to existing platforms

  • [35:10] Knowledge of AI is not necessary to use AI as a service  

  • [37:50] Capable team members are difficult to find

  • [40:10] Sharing management meetings with all staff members  

  • [44:05] Experiences with handling politics in organizations

  • [48:50] Removing ego + allowing the team to do their best work

  • [50:30] Scheduling work to maximize the impact


Resources:

Peter’s LinkedIn: https://www.linkedin.com/in/peterelger/?originalSubdomain=ie

Peter’s Twitter: https://twitter.com/pelger

Peter’s Book: AI as a Service


Quotes:

  • “Computers don’t lie, and they do what you tell them.”

  • “In order to sell to someone, you need to understand what their problems are.”

  • “Business is all based on delivering value and building relationships.”

  • “Good software engineers have been doing things before they had a name.”


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 will@datasourceservices.com.au 

Fyrebox - Make Your Own Quiz!


Peter Elger is based in Ireland.



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