Tag: machine learning

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AI and The Super Future

AI keynote speaker & NY Times Bestselling innovation author Jeremy Gutsche dives into artificial intelligence and the AI mechanized future in an AI talk that explores how artificial intelligence trends will change your future, particularly as you combine innovation in AI with robotics, interface, bio enhancement, 3d printing, mind reading, sustainability and thought control.

This AI speech is different than most of Jeremy’s innovation keynote speaker videos in that he dives into a lot more detail about a few specific AI-related trends, versus his normal style of storytelling. Compared to other AI keynote speakers, Jeremy takes a higher level view about how AI impacts a variety of different industries.

His AI & The Super Future keynote was the final keynote at Future Festival World Summit.

In this AI keynote, Jeremy also shares insight from his company’s artificial intelligence transformation. In short, he talks about some of the lessons learned from launching Trend Hunter AI and learning how to leverage your existing data.

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7 Steps of Machine Learning (AI Adventures)

In machine learning the goal of training is to create an accurate model that answers questions correctly, most of the time.

In order to train a model, we need correct data.

The quality and quantity of the data acquired will determine the quality of results.

We need to prepare the data, which includes randomizing. Also some of the data is earmarked for training and other data is used for evaluation.

Choosing a machine learning model is an important step and is pertinent to what is being evaluated.

The training phase is the bulk of the machine learning workflow.

The model usually does a poor job in the beginning, but we can then help it make better decisions by adjusting it over iterative phases to reach better conclusions.

At some point one needs to determine when the adjustments are adequate for one’s needs, since the adjustments can go on forever.

Machine learning is used for answering questions and making predictions using models and data rather than human judgement.

Tensorflow is an open source machine learning library for research and production and is a great place to experiment and learn.

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Machine Learning and Video Advertising

Google is using machine learning to find enough patterns to unlock the ability to figure out how every ad should work with video advertising.

The challenge is that as soon as you make an ad that follows all the rules, then all the rules change.

Data is a tool and like many tools, there’s a lot it can teach us.

Machine learning can help, but it does not replace creativity – it supports it.

The only way to break through with video messaging and creation is to be experimenting all the time.

Currently, they see four primary areas to optimize creative via their video ad experimentations:

VISUAL LANGUAGE

  • Tighter framing
  • Faster pacing
  • Large type over video
  • Use of brightness and contrast

NARRATIVE STRUCTURE

  • Build for capturing attention
  • Provide dessert first (AKA, provide a punch in the face)
  • Provide a reason for viewers to pay attention

TUNING FOR AUDIENCE

  • Make creative choices that reflect what you are about and who you are interested in

CREATIVE SYSTEMS

  • Ad sequencing
  • Best mixture of formats
  • How to employ creative and media assets for maximum impact

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What is Machine Learning?

Artificial Intelligence (AI) is a catchall term that refers to the science of computers with human-like capabilities

Machine learning is a sub-category of AI, or a way of doing AI.

Machine learning is not the same as programming. it’s a way of teaching computers what to do by way of example.

You give the computer a bunch of examples of what you want it to do and it figures out how to do it by itself.

This video provides a description of how machine learning could be used to individually identify ducks and geese in a barn without teaching them all about the details of ducks and geese.

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