CIO Outlook: Rethinking #SaaS and #DevOps

CIO Outlook: Rethinking #SaaS and #DevOps | @CloudExpo #AI #Microservices | IBM Cloud

  • Ce dernier devrait progresser annuellement d

    I’m a huge fan of growth hacking.

  • I’ve built several companies on the principles of growth hacking.
  • Heck, I might just build several more and put growth hacking to work again.
  • Processeur : Des chercheurs de l’Université de Princeton annoncent avoir créé une puce composée de 25 coeurs facilement intégrable pour construire un ordinateu

    | DIM (Document Information Manager) – CDO (Chief Digital Officer) – Gouvernance numérique

    Transformation numérique : D’après une étude menée par Vanson Bourne, 89% des responsables IT estiment que le numérique est essentiel pour les services informatiques afin d..

  • L’index ISG sur l’externalisation révèle qu’en zone EMEA le marché accuse une baisse de 18 % pour ce deuxième trimestre 2016 (par rapport à 2015)

    Bureautique et Collaboratif : Selon certaines informations, Microsoft préparerait un nouveau produit dénommé Skype Teams, qui pourrait rivaliser avec des apps de messagerie e

    SYS-CON’s WebSphere Developer’s Journal

    Are you investing heavily in inbound content, but don’t have a system to get that content in front of your target audience to read, enjoy and share?

SYS-CON’s WebSphere Developer’s Journal | Le monde du Saas et des Acteurs
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4 challenges Artificial Intelligence must address

4 challenges Artificial Intelligence must address  #AI

  • Huge leaps in AI have accelerated this process dramatically and propagated it to other domains previously imagined to remain indefinitely in the monopoly of human intelligence

    From driving trucks to writing news and performing accounting tasks, AI algorithms are threatening middle class jobs like never before.

  • It’s also true that the AI revolution will create plenty of new data science, machine learning, engineering and IT job positions to develop and maintain the systems and software that will be running those AI algorithms.
  • Teaching new tech skills to people who are losing or might lose their jobs to AI in the future can complement the efforts.
  • Machine Learning, the popular branch of AI that is behind face recognition algorithms, product suggestions, advertising engines, and much more, depends on data to train and hone its algorithms.
  • Unless companies developing and using AI technology regulate their information collection and sharing practices and take necessary steps to anonymize and protect user data, they’ll end up causing harm than good to users.

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Netflix wants to kill buffering dead

.@Netflix wants to kill buffering dead  #MWC17

  • Netflix has spent the last year expanding to virtually the entire world and has transformed itself from a DVD-by-mail service into a video streaming juggernaut with 94 million subscribers.
  • A lot of those customers watch its library of videos on their phones, which is largely why Hastings was at the conference.
  • On data caps: Hastings complimented some of the new unlimited data plans that offer limits on speed as a way to contain the strain on the network.
  • Netflix has invested in getting quality video delivered to a phone with just 500 kilobits per second of data speed, Hastings said.
  • On the future: Netflix attempts to learn about new trends and adapt to them rather than to commit to one vision of the future, Hastings said.

CEO Reed Hastings thinks it’ll go the way of the old dial tone noise for internet access. He also weighs in on piracy, artificial intelligence and more.
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What Does Artificial Intelligence See In A Quarter Billion Global News Photographs?

What does artificial intelligence see in a quarter billion global news photographs?

  • Google’s Cloud Vision API is a commercial cloud service that accepts as input any arbitrary photograph and uses deep learning algorithms to catalog a wealth of data about each image, including a list of objects and activities it depicts, recognizable logos, OCR text recognition in almost 80 languages, levels of violence, an estimate of visual sentiment and even the precise location on earth the image appears to depict.
  • In total, the Vision API applied 9,853 unique labels to the images, with the most popular being “person” (27% of images), “profession” (14%), “vehicle” (10%), “sports” (7%), “speech” (6%), and “people” (5%).
  • The Vision API appears to apply the “person” label primarily in cases where a single person or a small number of people are the primary object of the photograph, such as a speaker standing at a podium.
  • The map below colors each country by the density of human faces in all imagery monitored by GDELT from news media in that country – ie, the total number of recognized human faces in all images from that country is divided by the total count of all images from that country.
  • It also reinforces why only deep learning systems with large numbers of category labels like Google’s Cloud Vision API are sufficient to work with news imagery – a more simplistic system designed to recognize just a few classes of imagery would struggle to provide much utility when applied to the incredible diversity of the world’s news imagery.

What deep learning algorithms can tell us about the visual narratives of the world’s news imagery, from depictions of violence to the importance of people to visual context – a look inside what we see about the world around us
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Create Realistic Synthetic Faces That Look Older With Deep Learning – News Center

New face aging #AI system can help identify people who have been missing for decades.

  • Developers from Orange Labs in France developed a deep learning system that can quickly make young faces look older, and older faces look younger.
  • Using CUDA, Tesla K40 GPUs and cuDNN for the deep learning work, they trained their neural network on 5,000 faces from each age group (0-18, 19- 29, 30-39, 40-49, 50-59, and 60+ years old) taken from the Internet Movie Database and from Wikipedia and then labeled with the person’s age — this helped the system learn the characteristic signature of faces in each age group.
  • A second neural network, called the face discriminator, looks at the synthetically aged face to see whether the original identity can still be picked out.
  • If it can’t, the image is rejected, which they call the process in their paper, Age Conditional Generative Adversarial Network.
  • Grigory Antipov of Orange Labs mentioned the technique could be used in applications such as helping identify people who have been missing for many years.

Developers from Orange Labs in France developed a deep learning system that can quickly make young faces look older, and older faces look younger. A number of techniques already exist, but they are expensive and time consuming.
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Demystifying machine learning part 2: Supervised, unsupervised, and reinforcement learning

#machinelearning use cases:

1. Supervised
2. Unsupervised
3. Reinforcement

  • It is a type of machine learning, where one guides the system by tagging the output.
  • For example, a supervised machine learning system that can learn which emails are ‘spam’ and which are ‘not spam’ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the  ‘spam’ email and distinguish it from those of ‘not spam’ emails.
  • Just as the three year old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’.
  • Now instead of telling the child which toy to put in which box, you reward the child with a ‘big hug’ when it makes the right choice and make a ‘sad face’ when it makes the wrong action (e.g., block in a soft toy box or soft toy in the block box).
  • Based on your problem domain and the availability of data do you know which type of machine learning system you want to build?

Where business and experience meet emerging technology.
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Using TensorFlow in Windows with a GPU

Using #TensorFlow in @Microsoft #Windows with a #GPU  #DeepLearning

  • Particularly, I was curious about my Windows Surface Book (GPU: GeForce GT 940) performance of using the GPU vs the CPU.
  • When TensorFlow was first released (November 2015) there was no Windows version and I could get decent performance on my Mac Book Pro (GPU: NVidia 650M).
  • Read here to see what is currently supported The first thing that I did was create CPU and GPU environment for TensorFlow.
  • To create my CPU TensorFlow environment, I used:

    To create my CPU TensorFlow environment, I used:

    Your TensorFlow code will not change using a single GPU.

  • You can switch between environments with:

    If you are doing moderate deep learning networks and data sets on your local computer you should probably be using your GPU.

In case you missed it, TensorFlow is now available for Windows, as well as Mac and Linux. This was not always the case. For most of TensorFlow’s first year…
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