Five Hot AI Startups Step into Spotlight at GTC Europe Inception Awards

Five hot #AI startups step into the spotlight at the #GTC17EU Inception Awards:

  • Then we gave one of them — Gamaya, a 20-person startup harnessing deep learning to help farms improve their productivity and sustainability — a new DGX Station in front of a room packed with more than 160 investors, entrepreneurs and industry observers.
  • The event’s contenders were selected from among the 700 European startups participating in our Inception program, which accelerates the development of startups involved in AI and deep learning.
  • After looking at an initial round of 25 startups, our judges chose companies we believe to be the five hottest in Europe to tell their stories.
  • Besides our winner Gamaya, the startups included presentations from: – – The Inception Awards continue the series of events we’ve held in Silicon Valley and China in conjunction with our GPU Technology Conference world tour.
  • Our Inception virtual accelerator program supports more than 1,900 AI startups with GPUs, deep learning expertise and other resources to help them be successful.

We brought five of the hottest startups in Europe and put them in front of a panel of some of tech’s savviest players at GTC Europe in Munich Tuesday.
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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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Microsoft releases version 2.0 of its deep learning toolkit

Microsoft releases version 2.0 of its deep learning toolkit  #CompBindTech

  • Microsoft today launched version 2.0 of what is now called the Microsoft Cognitive Toolkit.
  • This open-source toolkit, which was previously known as CNTK, is Microsoft’s competitor to similar tools like TensorFlow, Caffe and Torch, and, while the first version was able to challenge many of its competitors in terms of speed, this second version puts an emphasis on usability (by adding support for Python and the popular Keras neural networking library, for example) and future extensibility, while still maintaining — and improving — its speed.
  • Because it was essentially an internal tool, though, it didn’t support Python for example, even though it’s by far the most popular language among machine learning Microsoft originally built this toolkit for speech recognition systems, it was very good at working with time series data for building recurrent neural nets.
  • Huang stressed that the first version of the Cognitive Toolkit outperformed its competitors pretty easily on a number of standard tests.
  • Unsurprisingly, Microsoft is stressing the fact that the Cognitive Toolkit is a battle-tested system that it uses to power most of its internal AI systems, including Cortana, and that it can train models faster than most of its competitors.

Microsoft today launched version 2.0 of what is now called the Microsoft Cognitive Toolkit. This open-source toolkit, which was previously known as CNTK, is..
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AI is learning to speed read

AI is learning to speed read

  • As clever as machine learning is, there’s one common problem: you frequently have to train the AI on thousands or even millions of examples to make it effective.
  • If Gamalon has its way, you could put AI to work almost immediately.
  • The startup has unveiled a new technique, Bayesian Program Synthesis, that promises AI you can train with just a few samples.
  • If you show it very short and tall chairs, for example, it should figure out that there are many chair sizes in between.

As clever as machine learning is, there’s one common problem: you frequently have to train the AI on thousands or even millions of examples to make it effective…
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Fully Convolutional Networks (FCNs) for Image Segmentation

Fully Convolutional Networks (FCNs) for Image Segmentation |

  • Overall, we achieved comparable or better performance with the original paper.
  • The reason why the authors of the paper add skips is because the results produced by the FCN-32s architecture are too coarse and skips are added to lower layers of the VGG-16 network which were affected by smaller number of max-pooling layers of VGG-16 and can give finer predictions while still taking into account more reliable higher level predictions.
  • In the post we want to present Our Image Segmentation library that is based on Tensorflow and TF-Slim library, share some insights and thoughts and demonstrate one application of Image Segmentation .
  • % matplotlib inline from __future__ import division import os import sys import tensorflow as tf import skimage.io as io import numpy as np sys .
  • In order to show some results of Segmentation produced by aforementioned models, let’s apply the trained models to unseen images that contain some objects that represent one of PASCAL VOC classes.

Blog about Machine Learning and Computer Vision. Google Summer of Code blog posts. Scikit-image face detection algorithm implementation.

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Machine Learning 101— Supervised Learning – Shaden Mar’i – Medium

Machine Learning — Supervised Learning.  #BigData #MachineLearning #DataScience #AI

  • Model training using the data set
  • Data sets hold the data which will be used in training your models, whereas, test sets hold the data which you will be using to test and evaluate your models.
  • Data sets join and data wrangling
  • The Restaurant ratings data set along with the customer and Restaurants data set are all used in training the matchbox recommendation model.
  • I have trained my model using the restaurant ratings data sets along with two more data sets; the Restaurant customers and restaurants data sets.

Machine learning is basically teaching computers to solve big problems based on either example data or past experiences. Example data, is purely unlabeled, with unknown and undetected structure. Your…
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Google’s ‘DeepMind’ AI platform can now learn without human input

#Google's #DeepMind #AI platform can now learn without human input ▷

  • Much like the brain, the neural network uses an interconnected series of nodes to stimulate specific centers needed to complete a task.
  • Read next: Facebook makes 360 photos much better with one small update
  • The AI is optimizing the nodes to find the quickest solution to deliver the desired outcome.
  • In a significant step forward for artificial intelligence, Alphabet’s hybrid system – called a Differential Neural Computer (DNC) – uses the existing data storage capacity of conventional computers while pairing it with smart AI and a neural net capable of quickly parsing it.
  • Instead of having to learn every possible outcome to find a solution, DeepMind can derive an answer from prior experience, unearthing the answer from its internal memory rather than from outside conditioning and programming.

Bow to your robot overlords. Google’s parent company, Alphabet, now possesses a smart AI capable of learning without the need for human input.
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