AI is revolutionizing neuromarketing

AI is revolutionizing neuromarketing on @venturebeat  #INBOUND17 #ai #ml

  • In recent years, AI has offered a huge boost to neuromarketing — the science of reading consumers’ minds to gauge their reactions to marketing stimuli.
  • When combined with AI-based facial recognition, biometric insights can offer even more accurate data about consumers’ responses to marketing messaging.
  • IBM’s Watson — the company’s computer system that answers questions delivered in natural language — doesn’t use biometric data to gauge the excitement at an event.
  • What makes these methods more than merely interesting is the fact that they provide the means to obtain data about consumers’ emotional reactions in real time and adjust marketing messages accordingly.
  • Once AI is fully adopted in neuromarketing processes, marketers will have the ability to call on consumers to crowdsource their marketing campaigns without the fear of collecting unreliable data.

An unfortunate fact about humanity is that people lie. While this is a chronic issue for human relations, it’s one that may be less of an issue for marketers of the future, thanks to non-human intervention.
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Google releases new TensorFlow Object Detection API

  • Google is releasing a new TensorFlow object detection API to make it easier for developers and researchers to identify objects within images.
  • Google is trying to offer the best of simplicity and performance — the models being released today have performed well in benchmarking and have become regularly used in research.
  • The handful of models included in the detection API include heavy duty inception-based convolutional neural networks and streamlined models designed to operate on less sophisticated machines — a MobileNets single shot detector comes optimized to run in real-time on a smartphone.
  • Earlier this week Google announced its MobileNets family of lightweight computer vision models.
  • Google, Facebook and Apple have been pouring resources into these mobile models.

Google is releasing a new TensorFlow object detection API to make it easier for developers and researchers to identify objects within images. Google is trying..
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Dubai is introducing robotic policemen, to make up 25% of the force by 2030

Robotic policemen will make up 25% of Dubai's police force by 2030  #AI

  • On Wednesday, May 24, Dubai will launch a new police robot that marks the first phase of the integration of robots into the police force.
  • This modified version of the REEM robot (Designed by PAL robotics and unveiled in 2011) is capable of feeding video to a command center, forwarding reported crimes to police, settling fines, facial recognition, and speaking nine languages.
  • Dubai hopes robots will constitute 25 percent of its police force by 2030, with the next stage being to use them as receptionists in police stations.
  • Brigadier Khalid Nasser Alrazooqi, General Director of Dubai Police’s Smart Services Department, told CNN that they eventually want to release a “fully-functional robot that can work as [a] normal police officer.”
  • In February, China started using the AnBot that uses facial recognition to identify criminals and is capable of following them until the police arrive.

On Wednesday, May 24, Dubai will launch a new police robot that marks the first phase of the integration of robots into the police force. This modified version of the REEM robot (Designed by PAL robotics and unveiled in 2011) is capable of feeding video to a command center, forwarding reported crimes to police, settling fines, facial recognition, and speaking nine languages. It will operate at most malls and tourist attractions.
Continue reading “Dubai is introducing robotic policemen, to make up 25% of the force by 2030”

Dubai is introducing robotic policemen, to make up 25% of the force by 2030

Dubai is introducing robotic policemen, to make up 25% of the force by 2030  #AI

  • On Wednesday, May 24, Dubai will launch a new police robot that marks the first phase of the integration of robots into the police force.
  • This modified version of the REEM robot (Designed by PAL robotics and unveiled in 2011) is capable of feeding video to a command center, forwarding reported crimes to police, settling fines, facial recognition, and speaking nine languages.
  • Dubai hopes robots will constitute 25 percent of its police force by 2030, with the next stage being to use them as receptionists in police stations.
  • Brigadier Khalid Nasser Alrazooqi, General Director of Dubai Police’s Smart Services Department, told CNN that they eventually want to release a “fully-functional robot that can work as [a] normal police officer.”
  • In February, China started using the AnBot that uses facial recognition to identify criminals and is capable of following them until the police arrive.

On Wednesday, May 24, Dubai will launch a new police robot that marks the first phase of the integration of robots into the police force. This modified version of the REEM robot (Designed by PAL robotics and unveiled in 2011) is capable of feeding video to a command center, forwarding reported crimes to police, settling fines, facial recognition, and speaking nine languages. It will operate at most malls and tourist attractions.
Continue reading “Dubai is introducing robotic policemen, to make up 25% of the force by 2030”

Future iPhones and iPads might have a special chip just for processing AI

Future iPhones and iPads might have a special chip just for processing AI

  • Future iPhones and iPads might have a special chip just for processing AI

    Image: lili sams/mashable
    By Raymond Wong2017-05-26 22:58:52 UTC

    Apple’s reportedly working on a new kind of chip — potentially for future iOS devices — that’ll be used just for processing AI, Bloomberg reports.

  • By moving AI processing to a dedicated chip, battery life in devices could also see a boost since the main CPU and GPU wouldn’t be crunching as much data and gobbling as much power.
  • The report says Apple plans to integrate the chip into its devices, but it’s unclear when that’ll happen, and if any iOS devices launching this year will have it.
  • Apple’s AirPods also have a custom W1 chip that helps with pairing them to iOS devices.
  • If the future is AI everywhere (and it definitely looks like that’s where things are headed), it’s in Apple’s best interests to control the stack (like it always does) with its own AI chip.

A new Bloomberg report says Apple’s working on a new chip, called the Apple Neural Engine, that’s devoted to processing AI.
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Understanding the differences between AI, machine learning, and deep learning

Understanding the differences between #AI, #machinelearning, and #deeplearning ▷

  • IBM’s Deep Blue, which beat chess grand master Garry Kasparov at the game in 1996, or Google DeepMind’s AlphaGo, which in 2016 beat Lee Sedol at Go, are examples of narrow AI—AI that is skilled at one specific task.
  • DeepMind, on the other hand, is: It beat the world champion in Go by training itself on a large data set of expert moves.SEE: Machine learning: The smart person’s guide (TechRepublic)Is your business interested in integrating machine learning into its strategy?
  • Amazon, Baidu, Google, IBM, Microsoft and others offer machine learning platforms that businesses can use.Deep Learning Deep learning is a subset of ML.
  • It would take a very massive data set of images for it to understand the very minor details that distinguish a cat from, say, a cheetah or a panther or a fox.As mentioned above, in March 2016, a major AI victory was achieved when DeepMind’s AlphaGo program beat world champion Lee Sedol in 4 out of 5 games of Go using deep learning.
  • Sundown AI, for instance, has mastered automated customer interactions using a combination of ML and policy graph algorithms—not deep learning.Also see… The 6 most exciting AI advances of 2016 (TechRepublic)3 major AI trends to watch in 2017 (TechRepublic)Microsoft’s new breakthrough: AI that’s as good as humans at listening… on the phone (ZDNet)Five ways your company can get started implementing AI and ML (ZDNet)Research: 63% say business will benefit from AI (Tech Pro Research)How Google’s DeepMind beat the game of Go, which is even more complex than chess (TechRepublic)Smart machines are about to run the world: Here’s how to prepare (TechRepublic)Why AI could destroy more jobs than it creates, and how to save them (TechRepublic)7 trends for artificial intelligence in 2016: ‘Like 2015 on steroids’ (TechRepublic)

Artificial intelligence, machine learning, and deep learning have become integral for many businesses. But, the terms are often used interchangeably. Here’s how to tell them apart.
Continue reading “Understanding the differences between AI, machine learning, and deep learning”

Understanding the differences between AI, machine learning, and deep learning

Understanding the differences between AI, machine learning, and deep learning

  • IBM’s Deep Blue, which beat chess grand master Garry Kasparov at the game in 1996, or Google DeepMind’s AlphaGo, which in 2016 beat Lee Sedol at Go, are examples of narrow AI—AI that is skilled at one specific task.
  • DeepMind, on the other hand, is: It beat the world champion in Go by training itself on a large data set of expert moves.SEE: Machine learning: The smart person’s guide (TechRepublic)Is your business interested in integrating machine learning into its strategy?
  • Amazon, Baidu, Google, IBM, Microsoft and others offer machine learning platforms that businesses can use.Deep Learning Deep learning is a subset of ML.
  • It would take a very massive data set of images for it to understand the very minor details that distinguish a cat from, say, a cheetah or a panther or a fox.As mentioned above, in March 2016, a major AI victory was achieved when DeepMind’s AlphaGo program beat world champion Lee Sedol in 4 out of 5 games of Go using deep learning.
  • Sundown AI, for instance, has mastered automated customer interactions using a combination of ML and policy graph algorithms—not deep learning.Also see… The 6 most exciting AI advances of 2016 (TechRepublic)3 major AI trends to watch in 2017 (TechRepublic)Microsoft’s new breakthrough: AI that’s as good as humans at listening… on the phone (ZDNet)Five ways your company can get started implementing AI and ML (ZDNet)Research: 63% say business will benefit from AI (Tech Pro Research)How Google’s DeepMind beat the game of Go, which is even more complex than chess (TechRepublic)Smart machines are about to run the world: Here’s how to prepare (TechRepublic)Why AI could destroy more jobs than it creates, and how to save them (TechRepublic)7 trends for artificial intelligence in 2016: ‘Like 2015 on steroids’ (TechRepublic)

Artificial intelligence, machine learning, and deep learning have become integral for many businesses. But, the terms are often used interchangeably. Here’s how to tell them apart.
Continue reading “Understanding the differences between AI, machine learning, and deep learning”

Why nature is our best guide for understanding artificial intelligence

Why nature is our best guide for understanding artificial intelligence  #ai #machinelearning

  • Google’s AI translation tool seems to have invented its own secret internal language
  • There’s great opportunity in AI, and natural evolution provides a framework for us to study and prepare for the future of machine evolution.
  • For the sake of our comparison (natural evolution to machine evolution), let’s consider data and how it is normalized as “the environment” and the training process as “Natural Selection.
  • Big data company Palantir quietly raised another $20M in November
  • Much like natural evolution, different organisms solve for the same problem differently depending on their environment, but ultimately reach the same outcome.

In living organisms, evolution is a multi-generational process where mutations in genes are dropped and added. Well-adapted organisms survive and those less..
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Artificial Intelligence: Deep learning is not the ultimate fix

#ArtificialIntelligence: #DeepLearning Is Not The Ultimate Fix 
  via @economictimes

  • Pak violates ceasefire 99 times on LoC post-surgical strike
  • Artificial Intelligence: Deep learning is not the ultimate fix
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  • More recently, in a high-profile demonstration, Google’s AlphaGo became the first computer program to beat a human player at Go, a complex board game.

Deep learning is a subset of machine learning, all of whose broad goals are to make computers do things outside of the box of precise programmed instructions.
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TensorFlow in a Nutshell — Part Three: All the Models

TensorFlow in a Nutshell — Part Three

  • X = tf.reshape(X, [-1, 28, 28, 1]) # first conv layer will compute 32 features for each 5×5 patch with tf.variable_scope(‘conv_layer1’): h_conv1 = learn.ops.conv2d(X, n_filters=32, filter_shape=[5, 5], bias=True, activation=tf.nn.relu) h_pool1 = max_pool_2x2(h_conv1) # second conv layer will compute 64 features for each 5×5 patch.
  • Getting the best of both worlds.
  • This type of model can be used for classification and regression problems.
  • The last layer in the network produces the output.
  • Convolution Neural Networks are unique because they’re created in mind that the input will be an image.

The fast and easy guide to the most popular Deep Learning framework in the world.
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