Skip to main content

Artificial Intelligence - Expert Perspectives

Sebastain Thrun:
  • Artificial Intelligence represents an opportunity for lessons learned to be applied on all systems and including systems not made yet.
  • Business leaders often want to go a certain direction but available toolsets including education did not keep up.
  • Anything repetitive would likely be done by AI within 20 years.

 Erik Brynjolfsson. Director, MIT Initative on Digital Economy:

  • Book: The Second Machine Age
  • Three trends:
    • Power
    • Data
    • Algorithms
  • Machines with very narrow specialty.
  • Machine learning - when to use and when not to use. Find the problem then find the solution. Identify the questions.
  • Image recognition - machines are way better.
  • Lots of churn under the surface so you have to be working on opportunities even though it looks like nothing is happening.
  • Examples:
    • A team looked flight patterns and was able to predict merger and acquisitions. 
    • A team used logs from locomotives to identify improvements to be made.
    • Finance showed massive improvements - 360,000 hours into few seconds (JP Morgan).
  • Need to watch ethics and privacy issues.

Gautam Khera. AI Executive Leader in Fortune 500 companies:

  • Every organization within a company should be thrilled about the AI journey.

Popular posts from this blog

Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning

 https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/  Source: Udacity AI for Business Leaders Supervised Learning - A category of machine learning which relies on the proper values, or labels , being present for the output data in the ground truth dataset, which the model can “learn” from during its training process. Labels - Values of the output variable/column/piece of data you are interested in Unsupervised Learning - A category of machine learning which uses the underlying characteristics of data itself, rather than already-supplied labels for outputs, to inform the model’s training process. Often uses segmenting or clustering algorithms to determine the output values. Reinforcement Learning - A category of machine learning which uses a series of simulations/cycles to reward optimal behaviors to learn policies for decision making in complex scenarios such as autonomous driving.
Read more

PowerPC 750 on James Webb Telescope

Another PowerPC usage was spotted, this time it is on the James Webb Telescope. The reliable and radiation chip was known to be used on the Orion spacecraft. Most of you know the variant called G3 on Apple PowerPC products.  https://www.talospace.com/2022/01/another-powerpc-in-space.html?m=1 Here is an interesting link with many more examples of the PowerPC usage: https://forums.macrumors.com/threads/a-fun-thread-on-the-powerpcs-longevity-durability-and-performance-for-ongoing-science-work.2170348/
Read more

Data and Differences Between AI, ML, and DL

 Data: Volume - the amount of data that is being produced over any given unit of time Variety - The level of deviation within your data, which can have both positive and negative effects depending on what it is you’re hoping to achieve Velocity - A term referring to how quickly new data is produced. Velocity can also allude to the concept of drift, or, how quickly data underlying a model can change over time Veracity - The accuracy of data that is being collected, a trait which can be affected by faulty inputs, poor organization, or a variety of other factors Value - A holistic measure based on all other underlying characteristics of data and rooted in how likely the data is to help you reach your desired end state Differences between AI, ML, and DL?  NVIDIA: https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/ Source: Udacity AI for Business Leaders
Read more