The AI Rush – Serena Capital

Great report on the state of #AI startups in Europe. #MachineLearning #InsurTech

  • Because we know how to have fun ✌️, we were (litteraly) excited about spending days and nights on crunching data of European startups funded in 2016 to eventually get a real sense of what is AI and Data’s real trend in Europe.
  • 2800+ Startups later, we eventually found out 271 AI Data startups and here is our main conclusion: Yes, AI is a thing and it’s happening now.Not convincing enough?
  • Fundraising went crazy, and we always want more of itNumbers stagger : $774 million — Yes 7–7 and 4, nearly 10% of the total 10+ billion invested in European startups in 2016, has been fully dedicated to AI data.
  • Bringing the total investment in AI Data to 2 billion since 2014.2.
  • This trend has been led by early stage investments which played a signifiant role: $215 million have been invested in 171 early stage startups (0 to $5m in funding).

Because we know how to have fun ✌️, we were (litteraly) excited about spending days and nights on crunching data of European startups funded in 2016 to eventually get a real sense of what is AI and…
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How to Build a Recurrent Neural Network in TensorFlow

How to Build a Recurrent #NeuralNetwork in TensorFlow

  • The input to the RNN at every time-step is the current value as well as a state vector which represent what the network has “seen” at time-steps before.
  • The weights and biases of the network are declared as TensorFlow variables, which makes them persistent across runs and enables them to be updated incrementally for each batch.
  • Now it’s time to build the part of the graph that resembles the actual RNN computation, first we want to split the batch data into adjacent time-steps.
  • This is the final part of the graph, a fully connected softmax layer from the state to the output that will make the classes one-hot encoded, and then calculating the loss of the batch.
  • It will plot the loss over the time, show training input, training output and the current predictions by the network on different sample series in a training batch.


This is a no-nonsense overview of implementing a recurrent neural network (RNN) in TensorFlow. Both theory and practice are covered concisely, and the end result is running TensorFlow RNN code.

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Is Artificial Intelligence the Answer to Finding a Cure for Cancer?

#ArtificialIntelligence the Answer to Finding a Cure for #Cancer?  #DataScience @DeepLearn007

  • But one thing that is needed for searching for a cure for cancer, even when using AI, is data.
  • Although there are various treatments available that are improving all the time, there is still no cure for cancer.
  • To make data more available for cancer research purposes, three things need to happen.
  • More standardization and shared access are required to make the best use of this data.
  • With all of the data to hand, researchers estimate that by 2025, as many as 2 billion human genomes could be sequenced.

Cancer is a devastating disease and statistics now suggest that nearly one in every two people worldwide will develop cancer at some point in their lives.
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Intelligent systems: man or machine?

[#HallOfFame 2016] #AI #ethics: Man or Machine? 
  [via @graphcoreai]

  • We need to think of machines that exhibit intelligence as independent entities, and we should think carefully about what that might actually mean.
  • Because thinking about machine intelligence in this way opens up thoughts about new business models, about liability sharing, about machine intelligence insurance and many other issues.
  • But in a world of machine intelligence, we need to take a different view.
  • It may be appropriate for a person or company that buys an intelligent system to own it and to be fully responsible for it.
  • – A (very) large data set of related information that the machine can learn from;

We need to think of machines that exhibit intelligence as independent beings, and we should think carefully about what that might actually mean.
Continue reading “Intelligent systems: man or machine?”

Intelligent systems: man or machine?

#AI #ethics: Man or Machine? 
 [via @graphcoreai]

  • We need to think of machines that exhibit intelligence as independent entities, and we should think carefully about what that might actually mean.
  • Because thinking about machine intelligence in this way opens up thoughts about new business models, about liability sharing, about machine intelligence insurance and many other issues.
  • But in a world of machine intelligence, we need to take a different view.
  • It may be appropriate for a person or company that buys an intelligent system to own it and to be fully responsible for it.
  • – A (very) large data set of related information that the machine can learn from;

We need to think of machines that exhibit intelligence as independent beings, and we should think carefully about what that might actually mean.
Continue reading “Intelligent systems: man or machine?”

Intelligent systems: man or machine?

#AI #ethics: Man or machine?  via @graphcoreai

  • We need to think of machines that exhibit intelligence as independent entities, and we should think carefully about what that might actually mean.
  • Because thinking about machine intelligence in this way opens up thoughts about new business models, about liability sharing, about machine intelligence insurance and many other issues.
  • But in a world of machine intelligence, we need to take a different view.
  • It may be appropriate for a person or company that buys an intelligent system to own it and to be fully responsible for it.
  • – A (very) large data set of related information that the machine can learn from;

We need to think of machines that exhibit intelligence as independent beings, and we should think carefully about what that might actually mean.
Continue reading “Intelligent systems: man or machine?”

Real-Time Digital Advertising That Works

Say hello to your new #marketing team:  | #MarTech #AI

  • Forrester Report: As Paid Search Evolves, Marketers Must Too
  • We use Cookies to enhance your experience on our website.
  • Your brand’s visual elements are configured in real-time into ads predicted to produce the highest sales.
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  • Our state-of-the-art machine algorithms continuously learn from new data, driving $22B in sales.

Criteo’s state-of-the-art technology transforms digital advertising into a personal experience that drives better results.
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New sequence learning data set

New #sequence learning #data #MNIST #MachineLearning #ImageRecognition #github

  • KDnuggets Home > News > 2016 > Sep > Software > New sequence learning data set ( 16:n34 )
  • The data set consists of pen stroke sequences that represent handwritten digits, and was created based on the MNIST handwritten digit data set.
  • The code project used to create the data set (github)
  • This will be relevant for folks who want to experiment with sequence learning and need a publicly available and comprehensible data set.
  • MNIST stroke sequence data set (github)


A new data set for the study of sequence learning algorithms is available as of today. The data set consists of pen stroke sequences that represent handwritten digits, and was created based on the MNIST handwritten digit data set.
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Making data science accessible – Neural Networks

Making #DataScience accessible – Neural Networks:  #abdsc #BigData #MachineLearning

  • Making data science accessible – Neural Networks
  • As with any technique related to data science Neural Networks are one family of many approaches you could take to solve a business problem using large amounts of data.
  • Neural Networks are a family of Machine Learning techniques modelled on the human brain.
  • In a basic neural network, you train the system by running individual cases through one at a time and updating the weights based on the error.
  • Back-propagation involves taking that error back through the network to adjust the individual weights to better reflect the actual outcome.

By Dan Kellett, Director of Data Science, Capital One UK
 
What are Neural Networks?
 
Neural Networks are a family of Machine Learning techniques modelled on…
Continue reading “Making data science accessible – Neural Networks”

Making data science accessible – Neural Networks

Making #DataScience accessible – Neural Networks:  #abdsc #BigData #MachineLearning

  • Making data science accessible – Neural Networks
  • As with any technique related to data science Neural Networks are one family of many approaches you could take to solve a business problem using large amounts of data.
  • Neural Networks are a family of Machine Learning techniques modelled on the human brain.
  • In a basic neural network, you train the system by running individual cases through one at a time and updating the weights based on the error.
  • Back-propagation involves taking that error back through the network to adjust the individual weights to better reflect the actual outcome.

Read the full article, click here.


@KirkDBorne: “Making #DataScience accessible – Neural Networks: #abdsc #BigData #MachineLearning”


By Dan Kellett, Director of Data Science, Capital One UK
 
What are Neural Networks?
 
Neural Networks are a family of Machine Learning techniques modelled on…


Making data science accessible – Neural Networks