Time to Accept Artificial Intelligence as Part of the Family?

Time to Accept #ArtificialIntelligence as Part of the Family? #AI #Fintech #Martech #tech

  • By now, many of us have heard about or might even own one of the popular, sleek multi-functional voice-first devices, such as the Amazon Echo, also known as “Alexa”, the name used when waking the device to give a verbal command.
  • This joke is terrible for many reasons, not the least of which is that I ended up anthropomorphized a digital device, which may be one of the biggest issues with this devices.
  • Related: There’s No Doubt That Amazon Alexa Is the Next Big Thing

    First, according to Voice Labs Voice Report for 2017, 6.5 million voice-first devices — defined as an always-on piece of hardware utilizing artificial intelligence (AI) with primarily a voice interface, both for input and output — were shipped in 2016.

  • While Amazon and Google (and Siri on our iPhones) have an early lead in this sector, there are sure to be new entrants.
  • Here are predictions for the strategies of just the big players:

    The crazy thing is that even with the potential for 24 million devices to be in our homes soon, the potential impact still remains remarkably unclear.

Millions of households are welcoming these new voice-first home assistant devices into and as part of their families — even with all the uncertainties and unintended consequences.
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You can use this machine learning demo to roll Keanu Reeves’ (or anyone’s) eyes

You can use this machine learning demo to roll Keanu Reeves’ (or anyone’s) eyes

  • This time it’s DeepWarp, a demo created by Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, and Victor Lempitsky, that uses deep architecture to move human eyeballs in a still image.
  • The authors of the demo acknowledge that similar projects already exist (like the smile-manipulator FaceApp), but without such a singular, detailed focus.
  • I tried this using images of Keanu Reeves and several dogs, but the demo didn’t work with the dogs.
  • “Our system is reasonably robust against different head poses and deals correctly with the situations where a person wears glasses,” the authors wrote in their study.
  • The authors say they plan to work on making the demo work more quickly in the future.

Another day, another fun internet thing that uses neural networks for facial manipulation. This time it’s DeepWarp, a demo created by Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, and…
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A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

Sergey Brin: I didn’t see AI coming

@Google co-founder Sergey Brin: I didn’t see #AI coming  #wef17

  • Brin said that anyone starting out as a young leader or entrepreneur should focus more on having fun than making money.
  • Committed to improving the state of the world
  • The views expressed in this article are those of the author alone and not the World Economic Forum.
  • Politics, the Fourth Industrial Revolution and how business can make the world more humane
  • Now that AI is to stay, its future and potential uses have become even more difficult to predict.

Sergey Brin, the co-founder of Google and one of the most successful Silicon Valley entrepreneurs, says he did not foresee the artificial intelligence revolution that has transformed the tech industry.
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The Power of Artificial Intelligence is to Make Better Decisions

The Power of Artificial Intelligence is to Make Better Decisions —@byronreese @gigaom #AI

  • According to Accenture, AI will define future customer experience .
  • “The power of AI is the power to make better decisions.
  • Reese shared a historical example of AI and whether AI should be used to make decision.
  • If AI has the power to make better decisions, then any business that has to make decisions, will be able to make more informed and fast decisions.
  • Reese notes that AI in business is really heating up.

Artificial intelligence (AI) is the new UI, according to Accenture’s Technology Vision 2017 report, identifying trends that are essential to business suc…
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A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • You need to be a member of Data Science Central to add comments!
  • In machine learning, computers apply statistical learning techniques to automatically identify patterns in data.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.
  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • There are clearly patterns in the data, but the boundaries for delineating them are not obvious.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”