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S7E153: AI in Project Controls: Separating Fact from Fiction with Alan Mosca

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Manage episode 354515917 series 2652672
Content provided by Val Matthews & Dale Foong, Val Matthews, and Dale Foong. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Val Matthews & Dale Foong, Val Matthews, and Dale Foong or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this week’s pod, we welcomed back Alan Mosca to discuss AI in Project Controls – Separating fact from fiction.

Alan is the co-founder and CTO of nPlan, where he leads technology, research, and product, whilst developing thought leadership about forecasting and risk. Before nPlan, Alan spent 7 years as a technologist in quantitative finance, on live trading systems, research, and front-office in both high-frequency trading and asset management.

Alan has extensive experience in algorithm design and software engineering and holds a BEng in Computer Engineering, MSc in Computer Science, and doctoral research in machine learning theory.

The main topics we discussed on the podcast were as follows:

  • There needs to be a lot of responsibility with using data from AI toolsets
  • Toolsets are available that can auto-plan a successor activity in a schedule based on previous data
  • Large language models only work based on language. Chat GPT took longer to reach the mainstream because of the data checks to ensure outputs were not inappropriate
  • AI will not replace humans on projects. It will only evolve their current roles
  • In the next 3-5 years we will see models that can internalize the meaning of a project
  • AI could be used to measure schedule compliance with the contract
  • Models can’t self-regulate which can lead to biases in data.
  • We’re past the point of having a common data environment
  • Create better things not faster things!
  • Simulation is harder than AI because it requires a greater level of precision
  • One person’s experience is another person’s bias
  • One of the main fictions of AI is that everything will be possible. It will never predict the future, it will only forecast possible outcomes
  • Be a critic! AI outputs are not infallible

Here are links to some of the topics we discussed:

Join us next time when we’re re-joined by Christine McLean to discuss EQ, IQ, and MQ: Unlocking the Power of Softer Skills

For more information, blogs or to support our charities visit www.projectchatterpodcast.com

If you'd like to sponsor the podcast get in touch via our website.

You can also leave us a voice message via our anchor page and let us know if there's something or someone specific that you would like on the podcast.

Proudly sponsored by:
InEight - https://ineight.com/

Stay safe, be disruptive and have fun doing it!

  continue reading

188 episodes

Artwork
iconShare
 
Manage episode 354515917 series 2652672
Content provided by Val Matthews & Dale Foong, Val Matthews, and Dale Foong. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Val Matthews & Dale Foong, Val Matthews, and Dale Foong or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this week’s pod, we welcomed back Alan Mosca to discuss AI in Project Controls – Separating fact from fiction.

Alan is the co-founder and CTO of nPlan, where he leads technology, research, and product, whilst developing thought leadership about forecasting and risk. Before nPlan, Alan spent 7 years as a technologist in quantitative finance, on live trading systems, research, and front-office in both high-frequency trading and asset management.

Alan has extensive experience in algorithm design and software engineering and holds a BEng in Computer Engineering, MSc in Computer Science, and doctoral research in machine learning theory.

The main topics we discussed on the podcast were as follows:

  • There needs to be a lot of responsibility with using data from AI toolsets
  • Toolsets are available that can auto-plan a successor activity in a schedule based on previous data
  • Large language models only work based on language. Chat GPT took longer to reach the mainstream because of the data checks to ensure outputs were not inappropriate
  • AI will not replace humans on projects. It will only evolve their current roles
  • In the next 3-5 years we will see models that can internalize the meaning of a project
  • AI could be used to measure schedule compliance with the contract
  • Models can’t self-regulate which can lead to biases in data.
  • We’re past the point of having a common data environment
  • Create better things not faster things!
  • Simulation is harder than AI because it requires a greater level of precision
  • One person’s experience is another person’s bias
  • One of the main fictions of AI is that everything will be possible. It will never predict the future, it will only forecast possible outcomes
  • Be a critic! AI outputs are not infallible

Here are links to some of the topics we discussed:

Join us next time when we’re re-joined by Christine McLean to discuss EQ, IQ, and MQ: Unlocking the Power of Softer Skills

For more information, blogs or to support our charities visit www.projectchatterpodcast.com

If you'd like to sponsor the podcast get in touch via our website.

You can also leave us a voice message via our anchor page and let us know if there's something or someone specific that you would like on the podcast.

Proudly sponsored by:
InEight - https://ineight.com/

Stay safe, be disruptive and have fun doing it!

  continue reading

188 episodes

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