"You seem to forget that “artificial” in AI doesn’t mean man-made, it means *faked*. We don’t understand real intelligence sufficiently to do more than a weak emulation. Whether it’s AI, machine learning,…"
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William Vorhies
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William Vorhies's Discussions
Will all that data in the hands of Google or Facebook lead to a good result or bad?
Started this discussion. Last reply by Mab Alam May 28, 2018. 2 Replies 1 Like
Two years ago Google X, the moonshot lab within Google prepared a speculative 9 minute video for in-house consumption called "The Selfish Ledger". Recently the video was leaked and found its way…Continue
Tags: transperancy, privacy, the selfish ledger
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Latest Activity
Just How Much Does the Future Depend on AI?
Summary: Looking at the 12 hottest world-changing segments in the VC-funded world shows that AI will play a key role. Here’s a little more detail. From the inside of the data science profession looking out it’s easy to imagine that almost everything that is or will be important somehow depends on AI. Maybe that’s true, but how do we tell?First of all we’d have to make…See More
Messaoud Zouikri commented on William Vorhies's blog post Causality – The Next Most Important Thing in AI/ML"Thanks to the author for this very interesting topic. Many data scientists today come from the IT sector, and so, when dealing with some topics such as causality they think things in a more techical way and seek solutions by trying sometimes to…"
Messaoud Zouikri commented on William Vorhies's blog post Causality – The Next Most Important Thing in AI/ML"Thanks to the author for this very interesting topic. Many data scientists today come from the IT sector, and so, when dealing with some topics such as causality they think things in a more technical way and seek solutions by trying sometimes to…"
Six AI Strategies – But Only One Winner
Summary: The results are in. There is only one demonstrably successful strategy for creating big wins for AI-first companies. We’ll briefly summarize the other contenders that have fallen by the wayside and then lift the curtain on the winner. For the last three years we’ve been close observers of exactly what makes a successful AI/ML strategy. In addition to our own…See More
Six AI Strategies – But Only One Winner
Summary: The results are in. There is only one demonstrably successful strategy for creating big wins for AI-first companies. We’ll briefly summarize the other contenders that have fallen by the wayside and then lift the curtain on the winner. For the last three years we’ve been close observers of exactly what makes a successful AI/ML strategy. In addition to our own…See More
Can Reinforcement Learning Break Through in 2020
Summary: Reinforcement Learning (RL) is going to be critical to achieving our AI/ML technology goals but it has several barriers to overcome. While reliability and a reduction in training data may be achievable within a year, the nature of RL as a ‘black box’ solution will bring scrutiny for its lack of transparency. Traditional machine learning and even deep learning…See More
"This is me down to a 'T'. I started as a a data scientist processing SEISMIC data for hydrocarbon exploration. The maths behind it was incredible1 SEISMIC data is the most difficult data to process. Some of the maths include FOURIER XFORM,…"
Reimagining Reinforcement Learning – Upside Down
Summary: For all the hype around winning game play and self-driving cars, traditional Reinforcement Learning (RL) has yet to deliver as a reliable tool for ML applications. Here we explore the main drawbacks as well as an innovative approach to RL that dramatically reduces the training compute requirement and time to train. Ever since Reinforcement Learning (RL) was…See More
Reimagining Reinforcement Learning – Upside Down
Summary: For all the hype around winning game play and self-driving cars, traditional Reinforcement Learning (RL) has yet to deliver as a reliable tool for ML applications. Here we explore the main drawbacks as well as an innovative approach to RL that dramatically reduces the training compute requirement and time to train. Ever since Reinforcement Learning (RL) was…See More
"Hi @Vincent, certainly it's possible to get VC funding outside of one of the concentrated metro areas the author mentioned. It's just less likely I'm guessing, and the bar is higher. So even if the cost of living is so high that…"
Profile Information
- Short Bio
- Editorial Director for DataScienceCentral. President and Chief Data Scientist at Data-Magnum, providing predictive analytics and big data infrastructure projects as a service. Bill has been an active commercial predictive modeler since 2001.
- My Web Site Or LinkedIn Profile
- http://www.data-magnum.com
- Professional Status
- C-Level
- Years of Experience:
- 15
- Your Company:
- Data-Magnum, LLC
- Industry:
- big data and predictive analytics
- Your Job Title:
- President & Chief Data Scientist
- Interests:
- Networking, New venture
William Vorhies's Blog
Just How Much Does the Future Depend on AI?
Posted on January 13, 2020 at 11:20am 1 Comment 1 Like
Summary: Looking at the 12 hottest world-changing segments in the VC-funded world shows that AI will play a key role. Here’s a little more detail.
From the inside of the data science profession looking out it’s easy to imagine that almost everything that is or will be important somehow depends on AI. Maybe…
Six AI Strategies – But Only One Winner
Posted on January 7, 2020 at 8:30am 0 Comments 2 Likes
Summary: The results are in. There is only one demonstrably successful strategy for creating big wins for AI-first companies. We’ll briefly summarize the other contenders that have fallen by the wayside and then lift the curtain on the winner.
For the last three years we’ve been close observers of exactly…
Can Reinforcement Learning Break Through in 2020
Posted on December 30, 2019 at 11:18am 0 Comments 0 Likes
Summary: Reinforcement Learning (RL) is going to be critical to achieving our AI/ML technology goals but it has several barriers to overcome. While reliability and a reduction in training data may be achievable within a year, the nature of RL as a ‘black box’ solution will bring scrutiny for its lack of transparency.
…
ContinueReimagining Reinforcement Learning – Upside Down
Posted on December 23, 2019 at 7:30am 0 Comments 0 Likes
Summary: For all the hype around winning game play and self-driving cars, traditional Reinforcement Learning (RL) has yet to deliver as a reliable tool for ML applications. Here we explore the main drawbacks as well as an innovative approach to RL that dramatically reduces the training compute requirement and time to train.
…
ContinueComment Wall (1 comment)
- At 9:08pm on July 27, 2015,
Ziauddin Syed said…
Dear William,
Congratulation to you on your promotion to Editorial Director for the Data Science Central Network (DSC).
Regards,
Zia
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