(https://axnegar.fahares.com/axnegar/BuDYfSJbReVMyt/4951821.jpg)
#آموزش
#یادگیری_عمیق
🌎Deep Learning CNN’s in Tensorflow with GPUs
In this tutorial, you’ll learn the architecture of a convolutional neural network (CNN), how to create a CNN in Tensorflow, and provide predictions on labels of images. Finally, you’ll learn how to run the model on a GPU so you can spend your time creating better models, not waiting for them to converge.
https://hackernoon.com/deep-learning-cnns-in-tensorflow-with-gpus-cba6efe0acc2
#آموزش
#یادگیری_عمیق
🌎Deep Learning CNN’s in Tensorflow with GPUs
In this tutorial, you’ll learn the architecture of a convolutional neural network (CNN), how to create a CNN in Tensorflow, and provide predictions on labels of images. Finally, you’ll learn how to run the model on a GPU so you can spend your time creating better models, not waiting for them to converge.
https://hackernoon.com/deep-learning-cnns-in-tensorflow-with-gpus-cba6efe0acc2
(https://axnegar.fahares.com/axnegar/ay1PVFtx7OswKm/4954035.jpg)
#هوش_مصنوعی
#چالش
🔵Challenges of #ArtificialIntelligence
Until few years ago, #ArtificialIntelligence (#AI) was similar to nuclear fusion in unfulfilled promise. It had been around a long time but had not reached the spectacular heights foreseen in its initial stages. However now, Artificial intelligence (AI) is no longer the future. It is here and now. It’s realizing its potential in achieving man-like capabilities, so it’s the right time to ask: How can business leaders adapt AI to take advantage of the specific strengths of man and machine?
AI is swiftly becoming the foundational technology in areas as diverse as self-driving cars, financial trading, connected houses etc. Self-learning algorithms are now routinely embedded in mobile and online services. Researchers have leveraged massive gains in processing power and the data streaming from digital devices and connected sensors to improve AI performance. Therefore, the progress in robotics, self driving cars, speech processing, natural language understanding is quite impressive.
But with all the advantages AI can offer, there are still some challenges for the companies who wants to adapt #AI. As AI is a vast domain, lisitng all challenges is quite impossible, yet we’ve listed few generic challenges of Artificial Intelligence here below, such as: AI situated approach in the real-world; Learning process with human intervention; Access to other disciplines; Multitasking; Validation and certification of AI systems.
https://www.xorlogics.com/2017/06/26/challenges-of-artificialintelligence/?utm_content=buffereb35e&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
#هوش_مصنوعی
#چالش
🔵Challenges of #ArtificialIntelligence
Until few years ago, #ArtificialIntelligence (#AI) was similar to nuclear fusion in unfulfilled promise. It had been around a long time but had not reached the spectacular heights foreseen in its initial stages. However now, Artificial intelligence (AI) is no longer the future. It is here and now. It’s realizing its potential in achieving man-like capabilities, so it’s the right time to ask: How can business leaders adapt AI to take advantage of the specific strengths of man and machine?
AI is swiftly becoming the foundational technology in areas as diverse as self-driving cars, financial trading, connected houses etc. Self-learning algorithms are now routinely embedded in mobile and online services. Researchers have leveraged massive gains in processing power and the data streaming from digital devices and connected sensors to improve AI performance. Therefore, the progress in robotics, self driving cars, speech processing, natural language understanding is quite impressive.
But with all the advantages AI can offer, there are still some challenges for the companies who wants to adapt #AI. As AI is a vast domain, lisitng all challenges is quite impossible, yet we’ve listed few generic challenges of Artificial Intelligence here below, such as: AI situated approach in the real-world; Learning process with human intervention; Access to other disciplines; Multitasking; Validation and certification of AI systems.
https://www.xorlogics.com/2017/06/26/challenges-of-artificialintelligence/?utm_content=buffereb35e&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
(https://axnegar.fahares.com/axnegar/t2s8ljz4pzloBl/4954190.jpg)
#یادگیری_عمیق
#هوش_مصنوعی
#شبکه_عصبی
#کورس
🔵Deep Learning: Artificial Neural Networks with Python
This online course is designed to teach you how to create deep learning Algorithms in Python by two expert Machine Learning & Data Science experts. Templates included. This course is split into 32 sections which cover over 179 Artificial Neural Network topics using a video format – receive a certificate of completion at the end of the course. Online learning is very flexible (expiry dates may vary from course to course depending on the course provider).
https://how-to-learn-online.com/artificial-neural-network-with-python
کورس :
https://www.udemy.com/deeplearning/?siteID=9PxUyjpjRL8-WXOxTjtgjAAjSexNfWoxZA&LSNPUBID=9PxUyjpjRL8
#یادگیری_عمیق
#هوش_مصنوعی
#شبکه_عصبی
#کورس
🔵Deep Learning: Artificial Neural Networks with Python
This online course is designed to teach you how to create deep learning Algorithms in Python by two expert Machine Learning & Data Science experts. Templates included. This course is split into 32 sections which cover over 179 Artificial Neural Network topics using a video format – receive a certificate of completion at the end of the course. Online learning is very flexible (expiry dates may vary from course to course depending on the course provider).
https://how-to-learn-online.com/artificial-neural-network-with-python
کورس :
https://www.udemy.com/deeplearning/?siteID=9PxUyjpjRL8-WXOxTjtgjAAjSexNfWoxZA&LSNPUBID=9PxUyjpjRL8
(https://axnegar.fahares.com/axnegar/CbOOpo1ePkVIbw/4954463.jpg)
🔵Detecting Small Signs from Large Images
In the past decade, Convolutional Neural Networks (CNNs) have been demonstrated successful for object detections. However, the size of network input is limited by the amount of memory available on GPUs. Moreover, performance degrades when detecting small objects. To alleviate the memory usage and improve the performance of detecting small traffic signs, we proposed an approach for detecting small traffic signs from large images under real world conditions. In particular, large images are broken into small patches as input to a SmallObject-Sensitive-CNN (SOS-CNN) modified from a Single Shot Multibox Detector (SSD) framework with a VGG-16 network as the base network to produce patch-level object detection results. Scale invariance is achieved by applying the SOS-CNN on an image pyramid. Then, image-level object detection is obtained by projecting all the patch-level detection results to the image at the original scale. Experimental results on a realworld conditioned traffic sign dataset have demonstrated the effectiveness of the proposed method in terms of detection accuracy and recall, especially for those with small sizes.
https://arxiv.org/pdf/1706.08574.pdf
🔵Detecting Small Signs from Large Images
In the past decade, Convolutional Neural Networks (CNNs) have been demonstrated successful for object detections. However, the size of network input is limited by the amount of memory available on GPUs. Moreover, performance degrades when detecting small objects. To alleviate the memory usage and improve the performance of detecting small traffic signs, we proposed an approach for detecting small traffic signs from large images under real world conditions. In particular, large images are broken into small patches as input to a SmallObject-Sensitive-CNN (SOS-CNN) modified from a Single Shot Multibox Detector (SSD) framework with a VGG-16 network as the base network to produce patch-level object detection results. Scale invariance is achieved by applying the SOS-CNN on an image pyramid. Then, image-level object detection is obtained by projecting all the patch-level detection results to the image at the original scale. Experimental results on a realworld conditioned traffic sign dataset have demonstrated the effectiveness of the proposed method in terms of detection accuracy and recall, especially for those with small sizes.
https://arxiv.org/pdf/1706.08574.pdf
(https://axnegar.fahares.com/axnegar/OLp413Uquydc7T/4960789.jpg)
#هوش_مصنوعی
#مقاله
🔵Perceptual Adversarial Networks for Image-to-Image Transformation
Chaoyue Wang, Chang Xu, Chaohui Wang, Dacheng Tao
(Submitted on 28 Jun 2017)
In this paper, we propose a principled Perceptual Adversarial Networks (PAN) for image-to-image transformation tasks. Unlike existing application-specific algorithms, PAN provides a generic framework of learning mapping relationship between paired images (Fig. 1), such as mapping a rainy image to its de-rained counterpart, object edges to its photo, semantic labels to a scenes image, etc. The proposed PAN consists of two feed-forward convolutional neural networks (CNNs), the image transformation network T and the discriminative network D. Through combining the generative adversarial loss and the proposed perceptual adversarial loss, these two networks can be trained alternately to solve image-to-image transformation tasks. Among them, the hidden layers and output of the discriminative network D are upgraded to continually and automatically discover the discrepancy between the transformed image and the corresponding ground-truth. Simultaneously, the image transformation network T is trained to minimize the discrepancy explored by the discriminative network D. Through the adversarial training process, the image transformation network T will continually narrow the gap between transformed images and ground-truth images. Experiments evaluated on several image-to-image transformation tasks (e.g., image de-raining, image inpainting, etc.) show that the proposed PAN outperforms many related state-of-the-art methods.
https://arxiv.org/abs/1706.09138
#هوش_مصنوعی
#مقاله
🔵Perceptual Adversarial Networks for Image-to-Image Transformation
Chaoyue Wang, Chang Xu, Chaohui Wang, Dacheng Tao
(Submitted on 28 Jun 2017)
In this paper, we propose a principled Perceptual Adversarial Networks (PAN) for image-to-image transformation tasks. Unlike existing application-specific algorithms, PAN provides a generic framework of learning mapping relationship between paired images (Fig. 1), such as mapping a rainy image to its de-rained counterpart, object edges to its photo, semantic labels to a scenes image, etc. The proposed PAN consists of two feed-forward convolutional neural networks (CNNs), the image transformation network T and the discriminative network D. Through combining the generative adversarial loss and the proposed perceptual adversarial loss, these two networks can be trained alternately to solve image-to-image transformation tasks. Among them, the hidden layers and output of the discriminative network D are upgraded to continually and automatically discover the discrepancy between the transformed image and the corresponding ground-truth. Simultaneously, the image transformation network T is trained to minimize the discrepancy explored by the discriminative network D. Through the adversarial training process, the image transformation network T will continually narrow the gap between transformed images and ground-truth images. Experiments evaluated on several image-to-image transformation tasks (e.g., image de-raining, image inpainting, etc.) show that the proposed PAN outperforms many related state-of-the-art methods.
https://arxiv.org/abs/1706.09138
(https://axnegar.fahares.com/axnegar/HlEhn8VsrH81nx/4960815.jpg)
🔵Tools for Making Machine Learning Easier and Smoother
Learn new methods for using deep learning to gain actionable insights from rich, complex data.
During the past decade, enterprises have begun using machine learning (ML) to collect and analyze large amounts of data to obtain a competitive advantage. Now some are looking to go even deeper – using a subset of machine learning techniques called deep learning (DL), they are seeking to delve into the more esoteric properties hidden in the data. The goal is to create predictive applications for such areas as fraud detection, demand forecasting, click prediction, and other data-intensive analyses.
https://data-informed.com/tools-for-making-machine-learning-easier-and-smoother/?utm_content=55415932&utm_medium=social&utm_source=twitter
🔵Tools for Making Machine Learning Easier and Smoother
Learn new methods for using deep learning to gain actionable insights from rich, complex data.
During the past decade, enterprises have begun using machine learning (ML) to collect and analyze large amounts of data to obtain a competitive advantage. Now some are looking to go even deeper – using a subset of machine learning techniques called deep learning (DL), they are seeking to delve into the more esoteric properties hidden in the data. The goal is to create predictive applications for such areas as fraud detection, demand forecasting, click prediction, and other data-intensive analyses.
https://data-informed.com/tools-for-making-machine-learning-easier-and-smoother/?utm_content=55415932&utm_medium=social&utm_source=twitter
Top #robotics and #ai opportunities?
1-#roboadvisors
2-#selfdrivingcars
3-#dataanalytics
(#ArtificialIntelligence and #Robots)
1-#roboadvisors
2-#selfdrivingcars
3-#dataanalytics
(#ArtificialIntelligence and #Robots)
Many papers for the YouTube-8M challenge. You can see what methods are commonly-used for video understanding
https://arxiv.org/find/all/1/OR+au:YouTube_8M+all:+EXACT+YouTube_8M/0/1/0/all/0/1 …
https://arxiv.org/find/all/1/OR+au:YouTube_8M+all:+EXACT+YouTube_8M/0/1/0/all/0/1 …
(https://axnegar.fahares.com/axnegar/OK1VSo5Hxn3OzU/4960973.jpg)
#مقاله
🔵A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches
Mohak Sukhwani, Ravi Kothari
(Submitted on 28 Jun 2017)
We present a parameterized approach to produce personalized variable length summaries of soccer matches. Our approach is based on temporally segmenting the soccer video into 'plays', associating a user-specifiable 'utility' for each type of play and using 'bin-packing' to select a subset of the plays that add up to the desired length while maximizing the overall utility (volume in bin-packing terms). Our approach systematically allows a user to override the default weights assigned to each type of play with individual preferences and thus see a highly personalized variable length summarization of soccer matches. We demonstrate our approach based on the output of an end-to-end pipeline that we are building to produce such summaries.
https://arxiv.org/abs/1706.09193
#مقاله
🔵A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches
Mohak Sukhwani, Ravi Kothari
(Submitted on 28 Jun 2017)
We present a parameterized approach to produce personalized variable length summaries of soccer matches. Our approach is based on temporally segmenting the soccer video into 'plays', associating a user-specifiable 'utility' for each type of play and using 'bin-packing' to select a subset of the plays that add up to the desired length while maximizing the overall utility (volume in bin-packing terms). Our approach systematically allows a user to override the default weights assigned to each type of play with individual preferences and thus see a highly personalized variable length summarization of soccer matches. We demonstrate our approach based on the output of an end-to-end pipeline that we are building to produce such summaries.
https://arxiv.org/abs/1706.09193
(https://axnegar.fahares.com/axnegar/wvM2MjxlPzaIf2/4961077.jpg)
#خبر
🔵Consumer-goods giant Unilever has been hiring employees using brain games and artificial intelligence — and it's a huge success
• Unilever has used artificial intelligence to screen all entry-level employees for the past year.
• Candidates play neuroscience-based games to measure inherent traits, then have recorded interviews analyzed by AI.
• The company considers the experiment a big success and will continue it indefinitely.
For the past year, the Dutch-British consumer-goods giant Unilever has been using artificial intelligence to hire entry-level employees, and the company says it has dramatically increased diversity and cost-efficiency.
"We were going to campus the same way I was recruited over 20 years ago," Mike Clementi, VP of human resources for North America, told Business Insider. "Inherently, something didn't feel right."
https://uk.businessinsider.com/unilever-artificial-intelligence-hiring-process-2017-6?r=US&IR=T
#خبر
🔵Consumer-goods giant Unilever has been hiring employees using brain games and artificial intelligence — and it's a huge success
• Unilever has used artificial intelligence to screen all entry-level employees for the past year.
• Candidates play neuroscience-based games to measure inherent traits, then have recorded interviews analyzed by AI.
• The company considers the experiment a big success and will continue it indefinitely.
For the past year, the Dutch-British consumer-goods giant Unilever has been using artificial intelligence to hire entry-level employees, and the company says it has dramatically increased diversity and cost-efficiency.
"We were going to campus the same way I was recruited over 20 years ago," Mike Clementi, VP of human resources for North America, told Business Insider. "Inherently, something didn't feel right."
https://uk.businessinsider.com/unilever-artificial-intelligence-hiring-process-2017-6?r=US&IR=T
(https://axnegar.fahares.com/axnegar/aCXiJywwrTQ7Zu/4962180.jpg)
#خبر
🔵Scientists made an AI that can read minds
This new deep learning algorithm can analyze brain scans to predict thoughts.
Whether it's using AI to help organize a Lego collection or relying on an algorithm to protect our cities, deep learning neural networks seemingly become more impressive and complex each day. Now, however, some scientists are pushing the capabilities of these algorithms to a whole new level - they're trying to use them to read minds.
https://www.engadget.com/2017/06/29/scientists-made-an-ai-that-can-read-minds/
#خبر
🔵Scientists made an AI that can read minds
This new deep learning algorithm can analyze brain scans to predict thoughts.
Whether it's using AI to help organize a Lego collection or relying on an algorithm to protect our cities, deep learning neural networks seemingly become more impressive and complex each day. Now, however, some scientists are pushing the capabilities of these algorithms to a whole new level - they're trying to use them to read minds.
https://www.engadget.com/2017/06/29/scientists-made-an-ai-that-can-read-minds/
(https://axnegar.fahares.com/axnegar/bSUKcRqot6xEkR/4963861.jpg)
#خبر
#هوش_مصنوعی
#مقاله
🔵Artificially intelligent painters invent new styles of art
Now and then, a painter like Claude Monet or Pablo Picasso comes along and turns the art world on its head. They invent new aesthetic styles, forging movements such as impressionism or abstract expressionism. But could the next big shake-up be the work of a machine?
An artificial intelligence has been developed that produces images in unconventional styles – and much of its output has already been given the thumbs up by members of the public.
The idea is to make art that is “novel, but not too novel”, says Marian Mazzone, an art historian at the College of Charleston in South Carolina who worked on the system.
https://www.newscientist.com/article/2139184-artificially-intelligent-painters-invent-new-styles-of-art/?utm_campaign=RSS%7CNSNS&utm_source=NSNS&utm_medium=RSS&utm_content=news&campaign_id=RSS%7CNSNS-news
مقاله
https://arxiv.org/abs/1706.07068
#خبر
#هوش_مصنوعی
#مقاله
🔵Artificially intelligent painters invent new styles of art
Now and then, a painter like Claude Monet or Pablo Picasso comes along and turns the art world on its head. They invent new aesthetic styles, forging movements such as impressionism or abstract expressionism. But could the next big shake-up be the work of a machine?
An artificial intelligence has been developed that produces images in unconventional styles – and much of its output has already been given the thumbs up by members of the public.
The idea is to make art that is “novel, but not too novel”, says Marian Mazzone, an art historian at the College of Charleston in South Carolina who worked on the system.
https://www.newscientist.com/article/2139184-artificially-intelligent-painters-invent-new-styles-of-art/?utm_campaign=RSS%7CNSNS&utm_source=NSNS&utm_medium=RSS&utm_content=news&campaign_id=RSS%7CNSNS-news
مقاله
https://arxiv.org/abs/1706.07068
🔵Proceedings (9 papers) from First International Workshop on Deep Learning and Music 🎶
https://arxiv.org/html/1706.08675 Great stuff there! 😍 #ML #AI
https://arxiv.org/html/1706.08675 Great stuff there! 😍 #ML #AI
"Bayesian Semisupervised Learning with Deep Generative Models": Toward semi-supervised Bayesian active learning https://arxiv.org/abs/1706.09751
(https://axnegar.fahares.com/axnegar/Z2oBgZuEVZtFWt/4978601.jpg)
#هوش_مصنوعی
#یادگیری_عمیق
#یادگیری_ماشین
#مقاله
🔵راهنمایی برای تشخیص احساس
🔵Recognizing Emotions using Artificial Intelligence
Machine Learning and Deep learning is now being used to detect emotions and facial expressions by analyzing images and videos. Here’s what you need to know.
Machine Learning and Deep Learning are a growing and diverse fields of Artificial Intelligence (AI) which studies algorithms that are capable of automatically learning from data and making predictions based on data. Machine Learning and Deep Learning are two of the most exciting technological areas of AI today. Each week there are new advancements, new technologies, new applications, and new opportunities. It’s inspiring, but also overwhelming. That’s why I created this guide to help you keep pace with all of these exciting developments.
https://blog.produvia.com/recognizing-emotions-using-artificial-intelligence-62b2ea7928a7
#هوش_مصنوعی
#یادگیری_عمیق
#یادگیری_ماشین
#مقاله
🔵راهنمایی برای تشخیص احساس
🔵Recognizing Emotions using Artificial Intelligence
Machine Learning and Deep learning is now being used to detect emotions and facial expressions by analyzing images and videos. Here’s what you need to know.
Machine Learning and Deep Learning are a growing and diverse fields of Artificial Intelligence (AI) which studies algorithms that are capable of automatically learning from data and making predictions based on data. Machine Learning and Deep Learning are two of the most exciting technological areas of AI today. Each week there are new advancements, new technologies, new applications, and new opportunities. It’s inspiring, but also overwhelming. That’s why I created this guide to help you keep pace with all of these exciting developments.
https://blog.produvia.com/recognizing-emotions-using-artificial-intelligence-62b2ea7928a7
(https://axnegar.fahares.com/axnegar/8Af9XZLeu9nGMY/4985969.jpg)
#معرفی_کتاب
🔵FREE R MACHINE LEARNING BOOK
Discover nearly 400 pages of in-depth of tutorials, best practices, and more to discover how to use R to its fullest potential in the world of machine learning
Packed with everything you need to understand the world of machine learning and how to break into it with the power of R this FREE 396 page eBook is the perfect guide to transforming how to turn your data into actionable insight that benefits your business today.
Understand the basic terminology of machine learning and how to differentiate among various machine learning approaches
Classify data using nearest neighbor methods
Learn about Bayesian methods for classifying data
Predict values using decision trees, rules, and support vector machines
Model data using neural networks
https://www.packtpub.com/packt/free-ebook/r-machine-learning
#معرفی_کتاب
🔵FREE R MACHINE LEARNING BOOK
Discover nearly 400 pages of in-depth of tutorials, best practices, and more to discover how to use R to its fullest potential in the world of machine learning
Packed with everything you need to understand the world of machine learning and how to break into it with the power of R this FREE 396 page eBook is the perfect guide to transforming how to turn your data into actionable insight that benefits your business today.
Understand the basic terminology of machine learning and how to differentiate among various machine learning approaches
Classify data using nearest neighbor methods
Learn about Bayesian methods for classifying data
Predict values using decision trees, rules, and support vector machines
Model data using neural networks
https://www.packtpub.com/packt/free-ebook/r-machine-learning
(https://axnegar.fahares.com/axnegar/0AXdTuld1FhRRw/4986478.jpg)
#خبر
#یادگیری_ماشین
🔵Top Machine Learning Interview Questions and Answers for 2017
According to a list released by the popular job portal Indeed.com on 30 fastest growing jobs in technology-
Data science and machine learning jobs dominated the list of top tech jobs.
Data scientist job postings saw an increase of 135% while machine learning engineer job postings saw an increase of 191% in 2017.
3 out of the top 10 tech job positions went to AI and data related positions, with machine learning jobs scoring a strong second place in the list.
More than 10% of jobs in UK this year have been tech jobs demanding data science, machine learning and AI skills.
https://www.dezyre.com/article/top-machine-learning-interview-questions-and-answers-for-2017/357
#خبر
#یادگیری_ماشین
🔵Top Machine Learning Interview Questions and Answers for 2017
According to a list released by the popular job portal Indeed.com on 30 fastest growing jobs in technology-
Data science and machine learning jobs dominated the list of top tech jobs.
Data scientist job postings saw an increase of 135% while machine learning engineer job postings saw an increase of 191% in 2017.
3 out of the top 10 tech job positions went to AI and data related positions, with machine learning jobs scoring a strong second place in the list.
More than 10% of jobs in UK this year have been tech jobs demanding data science, machine learning and AI skills.
https://www.dezyre.com/article/top-machine-learning-interview-questions-and-answers-for-2017/357
#DeepLearning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners https://www.kdnuggets.com/2017/06/deep-learning-demos-code-beginners.html?utm_content=buffer817f1&utm_medium=social&utm_source=t
🔵Lecture Collection - Natural Language Processing with #DeepLearning (Winter 2017) [Stanford]
Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation.
https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6&utm_content=buffer26aab&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation.
https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6&utm_content=buffer26aab&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
🔵Lost in translation
This week Google South Africa announced major upgrades to its Google Translate function – used in 103 languages by more than 500 million users worldwide. For the first time since Google began in 1998, the Google Translate app will now include full isiZulu, isiXhosa and Kiswahili translation functionality. "Machine learning", Google's artificial intelligence team explained, is also being used to drastically improve existing Google search services. The major announcement, slipped in quite casually at a press conference held at Google's local headquarters in Johannesburg, was included in an explanation of machine learning by Blaise Aguera y Arcas, principal scientist for machine learning and artificial intelligence at Google.
https://m.news24.com/news24/SouthAfrica/News/lost-in-translation-20170701-3
This week Google South Africa announced major upgrades to its Google Translate function – used in 103 languages by more than 500 million users worldwide. For the first time since Google began in 1998, the Google Translate app will now include full isiZulu, isiXhosa and Kiswahili translation functionality. "Machine learning", Google's artificial intelligence team explained, is also being used to drastically improve existing Google search services. The major announcement, slipped in quite casually at a press conference held at Google's local headquarters in Johannesburg, was included in an explanation of machine learning by Blaise Aguera y Arcas, principal scientist for machine learning and artificial intelligence at Google.
https://m.news24.com/news24/SouthAfrica/News/lost-in-translation-20170701-3
News24
Lost in translation
As Google incorporates African languages, the conversions ring true to varying degrees as neural techniques improve.
🔵Ray Kurzweil: Our Brain Is a Blueprint for the Master Algorithm
Ray Kurzweil is an inventor, thinker, and futurist famous for forecasting the pace of technology and predicting the world of tomorrow. In this video, Kurzweil suggests the blueprint for the master algorithm--or a single, general purpose learning algorithm--is hidden in the brain. The brain, according to Kurzweil, consists of repeating modules that self-organize into hierarchies that build simple patterns into complex concepts. We don't have a complete understanding of how this process works yet, but Kurzweil believes that as we study the brain more and reverse engineer what we find, we'll learn to write the master algorithm.
https://singularityhub.com/2017/06/30/ray-kurzweil-our-brain-is-a-blueprint-for-the-master-algorithm/
Ray Kurzweil is an inventor, thinker, and futurist famous for forecasting the pace of technology and predicting the world of tomorrow. In this video, Kurzweil suggests the blueprint for the master algorithm--or a single, general purpose learning algorithm--is hidden in the brain. The brain, according to Kurzweil, consists of repeating modules that self-organize into hierarchies that build simple patterns into complex concepts. We don't have a complete understanding of how this process works yet, but Kurzweil believes that as we study the brain more and reverse engineer what we find, we'll learn to write the master algorithm.
https://singularityhub.com/2017/06/30/ray-kurzweil-our-brain-is-a-blueprint-for-the-master-algorithm/
Singularity Hub
Ray Kurzweil: Our Brain Is a Blueprint for the Master Algorithm
Ray Kurzweil is an inventor, thinker, and futurist famous for forecasting the pace of technology and predicting the world of tomorrow. In this video, Kurzweil suggests the blueprint for the master algorithm—or a single, general purpose learning algorithm—is…