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Online Pie & AI: Istanbul - Law in the age of AI

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This event is hosted by Bahcesehir University, Istanbul Bar Association and deeplearning.ai's We continue to work as we targeted by trying to minimize the impact of the challenging process since the beginning of 2020 on our motivation. In this regard, we will realize the activity we planned on May 8, 2020, in a virtual environment. About the event: Participants will be discussed on AI and Social Effects, Risks, and Advantages, A Roadmap for Developing Countries. If you are interested in hosting Pie& AI events in other cities as well, send us a note at events@deeplearning.ai!


Bringing AI Education Online Around the World - AI Trends

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Enver Yucel is the founder of BAU Global, a broad education network headquartered in Turkey, consisting of five universities, three language schools, four academic centers and one boarding school spread across North America, Europe, Africa and Asia. Yucel has devoted his life to education, having served an estimated 150,000 students since starting his first institution with three class rooms in Istanbul in 1974. He is also a member of the Advisory Board of the UN Institute for Training and Research. He was invited to speak at the AI World Conference & Expo in Boston in the fall of 2019, the first Turkish speaker in the four years of the conference. He recently took some time to answer questions posed by AI Trends Editor John P Desmond, who was in the audience for his Boston talk.


Artificial Intelligence for Predicting The Safest Path After an Earthquake

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There has been a lot of research to predict earthquakes and how to increase safety during an earthquake. A question that has remained relatively unexplored is what happens after an earthquake? And how can Artificial Intelligence help? The last question is a big issue to tackle, which we focused on in this challenge. Scientists predict that there will be an earthquake in Istanbul in the near future but the exact date is difficult to identify since Istanbul resides on a fault line.


Using AI for Earthquake Response To Unite Families

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As is often the case in real-world situations, the ideal data didn't exist. When we uncover data roadblocks -- nonexistent, incomplete or inaccurate data -- we invent ways to get around them. The domain expertise of our partners and the creativity of our diverse collaborators come into play. Semih Boyaci, Co-Founder of Impact Hub Istanbul, sees the benefit of working with an inclusive team: "As different community members contribute to the solutions, a significant level of diversity is integrated into the solutions. This not only prevents potential errors in a timely manner but also brings a higher level of creativity to the challenge process."


Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

arXiv.org Machine Learning

Management Information Systems Department, T.C. Kadir Has University, Istanbul, T urkey Abstract Behavior of a malware varies with respect to malware types. Therefore, knowing type of a malware affects strategies of system protection softwares. Many malware type classification models empowered by machine and deep learning achieve superior accuracies to predict malware types. Machine learning based models need to do heavy feature engineering and feature engineering is dominantly effecting performance of models. On the other hand, deep learning based models require less feature engineering than machine learning based models. However, traditional deep learning architectures and components cause very complex and data sensitive models. This paper proposes an ensemble capsule network model based on bootstrap aggregating technique. The proposed method are tested on two malware datasets, whose the-state-of-the-art results are well-known.


Uber C.E.O. Backtracks After Comparing Khashoggi's Killing to an Accident

NYT > Middle East

Mr. Khashoggi, a critic of the Saudi government who wrote for The Washington Post and was a resident of Virginia, was brutally murdered in October 2018 after he entered a Saudi consulate in Istanbul. The C.I.A. has concluded that the Saudi crown prince, Mohammed bin Salman, ordered the journalist's killing. As Axios journalists noted in their interview with Mr. Khosrowshahi, Saudi Arabia is Uber's fifth-largest shareholder, and Yasir al-Rumayyan, the governor of Saudi Arabia's Public Investment Fund and the recently named chairman of the state-owned oil giant Saudi Aramco, sits on Uber's board. In the interview, Mr. Khosrowshahi compared the death of Mr. Khashoggi to the death of a woman who was struck by one of Uber's autonomous vehicles last year. Karen Attiah, an opinions editor for The Washington Post who worked with Mr. Khashoggi, said in a series of tweets on Monday that Mr. Khosrowshahi was "running cover for the Saudi government" and comparing the murder to a technology glitch.


Uber chief tries to backpedal after calling Khashoggi murder 'a mistake'

The Guardian

Dara Khosrowshahi, the chief executive of Uber, has attempted to limit the damage after calling the murder of the journalist Jamal Khashoggi "a mistake" similar to a fatal accident that occurred during tests of his company's self-driving car. Khashoggi, a Saudi national resident in the US, and a severe critic of the Saudi regime who wrote for the Washington Post, was murdered in Istanbul last year after visiting the Saudi Arabian consulate there. His body was dismembered and disposed of. His death has been described by Agnès Callamard, the UN special rapporteur on extrajudicial killings, as a "deliberate, premeditated execution" that warrants further investigation into the responsibility of the Saudi crown prince, Mohammed bin Salman. The prince is a key US ally close to Jared Kushner, Donald Trump's son-in-law and chief adviser.


Graph Domain Adaptation with Localized Graph Signal Representations

arXiv.org Machine Learning

Graph Domain Adaptation with Localized Graph Signal Representations Yusuf Yi git Pilavcı, Eylem Tu g ce G uneyi, Cemil Cengiz and Elif Vural Abstract In this paper we propose a domain adaptation algorithm designed for graph domains. Given a source graph with many labeled nodes and a target graph with few or no labeled nodes, we aim to estimate the target labels by making use of the similarity between the characteristics of the variation of the label functions on the two graphs. Our assumption about the source and the target domains is that the local behaviour of the label function, such as its spread and speed of variation on the graph, bears resemblance between the two graphs. We estimate the unknown target labels by solving an optimization problem where the label information is transferred from the source graph to the target graph based on the prior that the projections of the label functions onto localized graph bases be similar between the source and the target graphs. In order to efficiently capture the local variation of the label functions on the graphs, spectral graph wavelets are used as the graph bases. Experimentation on various data sets shows that the proposed method yields quite satisfactory classification accuracy compared to reference domain adaptation methods. Keywords: Domain adaptation, spectral graph theory, graph signal processing, spectral graph wavelets, graph Laplacian 1 Introduction A common assumption in machine learning is that the training and the test data are sampled from the same distribution. Domain adaptation methods aim to provide solutions to machine learning problems by dealing with this distribution discrepancy. In domain adaptation, a source domain and a target domain are considered where the label information is mostly available for the data samples in the source domain, and few or none of the class labels are known in the target domain. The purpose is then to improve the learning performance in the target domain by making use Y. Y. Pilavcı is with the GIPSA Lab at Universit e Grenoble Alpes, Grenoble. C. Cengiz is with the Dept. of Computer Science and Engineering at Ko c University, Istanbul. Most part of this work was performed while the authors were at METU. 1 arXiv:1911.02883v1 A variety of approaches have been proposed so far for the domain adaptation problem. Some methods are based on reweighing the samples for removing the sample selection bias [1, 2]. Another common solution is to align the source and the target domains through feature space mappings.


Human Capital – TAZI

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Tazi is a global supplier and developer of a unique, understandable continuous automated machine learning product. We are a pioneer in the next generation of continuous autonomous machine learning, putting humans at the epicenter of business solutions. Based in Istanbul, Amsterdam and San Francisco, we are seeking for talented and seasoned individuals. Focussing on culture first, we want someone with a soul, who does the right thing, is openminded and without ego. We are seeking for someone we want to hang-out with, laugh with, jam and work hard with.


Top-10 Artificial Intelligence Startups in Turkey

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What's now called Turkey was once the center of the Ottoman Empire, a global hub of culture and science during its heyday, which lasted over 600 years. It was the birthplace of the first surgical atlas and the first watch that measured time in minutes, and it's where astronomers first calculated the eccentricity of the Sun's orbit. Today, Turkey is better known for its rich cultural heritage, with large numbers of Russian and German tourists haggling over evil eyes, sipping Turkish tea in bazaars, and enjoying the hot water baths of Istanbul. With a population nearing 79 million people, Turkey also has high-quality and relatively cheap resources for developed markets to exploit capitalize on, along with a budding startup ecosystem. Deal sizes might be on the low side, but Turkish tech startups have stepped up to participate in the global AI race.