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Kategori: Sektörler

SAVTEK 2016 Savunma Sanayiinde Derin Öğrenme Uygulamaları Özel Oturum Çağrısı

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Derin öğrenme, görüntü analizi ve doğal dil işleme başta olmak üzere, bir çok konuda önemli uygulamaları olan bir yapay öğrenme alanıdır. Son yıllarda dünyada, özellikle görüntü işleme ve doğal dil işleme alanlarında, en yüksek performansı sergileyen algoritmaların derin öğrenme tabanlı olduğu gözlenmektedir. Bu gelişmeler ışığında, ülkemizde de, başta savunma sanayi olmak üzere, derin öğrenme yaklaşımının bir çok uygulama alanı bulacağı öngörülmektedir. Bu kapsamda, ODTÜ Görüntü Analizi Uygulama ve Araştırma Merkezi (OGAM) ve Aselsan Araştırma Merkezi Akıllı Veri Analitiği Araştırma Program Müdürlüğü, 12-14 Ekim 2016 tarihlerinde ODTÜ KKM’de düzenlenecek SAVTEK 2016 Kongresi bünyesinde bu önemli konu üzerinde akademik paylaşım yapılması ve bilgi alışverişi sağlamak amacıyla “Savunma Sanayinde Derin Öğrenme Uygulamaları” başlıklı özel oturum düzenlemektedir.

SAVTEK 2016 Kongresi ile ilgili bilgilere http://www.savtek.metu.edu.tr/ adresinde ulaşılabilir. Bildirilerin teslim edilmesi için son tarih 29 Nisan 2016 olarak belirlenmiştir.

Özel oturuma bildiri sunmayı planlayan yazarların özel oturum düzenleyicilerini eposta  (ogam@metu.edu.tr & aykutkoc@aselsan.com.tr) ile bilgilendirmesi önemle rica olunur.

Bu oturuma bildirilerinizle katkı sağlamanız bizleri sevindirecektir.

Saygılarımızla,

Prof. Dr. A. Aydın Alatan (ODTÜ OGAM Başkanı) &

Dr. Aykut Koç (Aselsan Araştırma Merkezi Akıllı Veri Analitiği Araştırma Program Müdürü)

NVIDIA Jetson TX1 Kutu Açımı Videosu

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İlk NVIDIA Jetson TX1 ürünümüzü aldık. Kutu açımı videosunu aşağıdan izleyebilirsiniz.

Jetson TX1 Teknik Özellikleri

GPU 1 TFLOP/s 256-core with NVIDIA Maxwell™ Architecture
CPU 64-bit ARM® A57 CPUs
Memory 4 GB LPDDR4 | 25.6 GB/s
Video decode 4K 60 Hz
Video encode 4K 30 Hz
CSI Up to 6 cameras | 1400 Mpix/s
Display 2x DSI, 1x eDP 1.4, 1x DP 1.2/HDMI
Connectivity Connects to 802.11ac Wi-Fi and Bluetooth-enabled devices
Networking 1 Gigabit Ethernet
PCIE Gen 2 1×1 + 1×4
Storage 16 GB eMMC, SDIO, SATA
Other 3x UART, 3x SPI, 4x I2C, 4x I2S, GPIOs

Jetson TK1 Developer Kit for Embedded Systems Computing

Dünyanın en yüksek perfomanslı, en yeni teknolojisine sahip geliştirme platformu ve en gelişmiş gömülü görsel hesaplama özelliğine sahip sistemi.

Daha fazla bilgi için: http://www.nvidia.com/object/jetson-tx1-module.html

Thanks to Alison Lowndes for supplying Jetson TX1.

Makale: Deep Learning on a Raspberry Pi for Real Time Face Recognition

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In this paper we describe a fast and accurate pipeline for real-time face recognition that is based on a convolutional neural network (CNN) and requires only moderate computational resources. After training the CNN on a desktop PC we employed a Raspberry Pi, model B, for the classi cation procedure. Here, we reached a performance of approximately 2 frames per second and more than 97% recognition accuracy. The proposed approach outperforms all of OpenCV’s algorithms with respect to both accuracy and speed and shows the applicability of recent deep learning techniques to hardware with limited computational performance.

Makale: Autonomous Vehicles Need Experimental Ethics: Are We Ready for Utilitarian Cars?

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The wide adoption of self-driving, Autonomous Vehicles (AVs) promises to dramatically reduce the number of traffic accidents. Some accidents, though, will be inevitable, because some situations will require AVs to choose the lesser of two evils. For example, running over a pedestrian on the road or a passer-by on the side; or choosing whether to run over a group of pedestrians or to sacrifice the passenger by driving into a wall. It is a formidable challenge to define the algorithms that will guide AVs confronted with such moral dilemmas. In particular, these moral algorithms will need to accomplish three potentially incompatible objectives: being consistent, not causing public outrage, and not discouraging buyers. We argue to achieve these objectives, manufacturers and regulators will need psychologists to apply the methods of experimental ethics to situations involving AVs and unavoidable harm. To illustrate our claim, we report three surveys showing that laypersons are relatively comfortable with utilitarian AVs, programmed to minimize the death toll in case of unavoidable harm. We give special attention to whether an AV should save lives by sacrificing its owner, and provide insights into (i) the perceived morality of this self-sacrifice, (ii) the willingness to see this self-sacrifice being legally enforced, (iii) the expectations that AVs will be programmed to self-sacrifice, and (iv) the willingness to buy self-sacrificing AVs.

Makale: Deep Learning as an Opportunity in Virtual Screening

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Deep learning excels in vision and speech applications where it pushed the stateof-the-art to a new level. However its impact on other fields remains to be shown. The Merck Kaggle challenge on chemical compound activity was won by Hinton’s group with deep networks. This indicates the high potential of deep learning in drug design and attracted the attention of big pharma. However, the unrealistically small scale of the Kaggle dataset does not allow to assess the value of deep learning in drug target prediction if applied to in-house data of pharmaceutical companies. Even a publicly available drug activity data base like ChEMBL is magnitudes larger than the Kaggle dataset. ChEMBL has 13 M compound descriptors, 1.3 M compounds, and 5 k drug targets, compared to the Kaggle dataset with 11 k descriptors, 164 k compounds, and 15 drug targets.

On the ChEMBL database, we compared the performance of deep learning to seven target prediction methods, including two commercial predictors, three predictors deployed by pharma, and machine learning methods that we could scale to this dataset. Deep learning outperformed all other methods with respect to the area under ROC curve and was significantly better than all commercial products. Deep learning surpassed the threshold to make virtual compound screening possible and has the potential to become a standard tool in industrial drug design.