A new artificial intelligence (AI) system called DrugCell, developed by researchers at University of California San Diego School of Medicine and Moores Cancer Center can reportedly match tumors to the best drug combinations, in a way that has not bee possible previously. "That's because right now we can't match the right combination of drugs to the right patients in a smart way," said Trey Ideker, PhD, professor at University of California San Diego School of Medicine and Moores Cancer Center. "And especially for cancer, where we can't always predict which drugs will work best given the unique, complex inner workings of a person's tumor cells." Currently, Only four percent of all cancer therapeutic drugs under development earn final approval by the FDA. In a paper "Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells" published in Cancer Cell, Ideker, Brent Kuenzi, PhD, and Jisoo Park, PhD, postdoctoral researchers in his lab, published a paper on their work.

Advances in AI, deep-learning, and robotics are aiding new military capabilities to the armed forces, with its applications ranging from intelligence, …

The technology is being developed by Swiss startup ClearSpace, a spin-off from the Ecole Polytechnique Fédérale de Lausanne (EPFL). Their removal target is the now-obsolete Vespa Upper Part, a 100 kg payload adaptor orbiting 660 km above the Earth. ClearSpace-1 will use an AI-powered camera to find the debris. Its robotic arms will then grab the object and drag it back to the atmosphere before burning it up. "A central focus is to develop deep learning algorithms to reliably estimate the 6D pose (three rotations and three translations) of the target from video-sequences even though images taken in space are difficult," said Mathieu Salzmann, an EPFL scientist spearheading the project.

Our client is a Fintech who was founded on the principle that there is too much complex data online with an insufficient amount of support to help private and professional investors make sense of it. Their solutions allow sifting and crunching data into the simple, concise, and actionable insights needed to succeed in today's markets. Inside their R&D team, they are looking for an NLP Researcher for adding new features and languages to their products. Their solutions used data from multiples sources (financial websites, news, social networks, etc.). Collaborate with the team to create and develop machine learning, deep learning, and NLP models.

You might have seen a recent article from The Guardian written by "a robot". I know that my brain is not a "feeling brain". But it is capable of making rational, logical decisions. I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas!

It is applicable not only for creatures but also for technologies. Technologies in data science have been filled with hypes and biased success stories. Having said that, there are technologies that have lead to the growth of data science: Convolutional Neural Network (CNN). Since AlexNet in 2012, different architectures of CNNs have brought a tremendous contribution to real business operations and academic researches. Residual Networks (ResNet) by Microsoft Research in 2015 brought a real breakthrough to build "deep" CNNs; however, an honorable retirement of this technology would be approaching.

AI legend Yann LeCun, one of the godfathers of deep learning, sees self-supervised learning as the ... [ ] key to AI's future. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today.

It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected.

Long short-term memory (LSTM) The long short-term memory (LSTM) is a special kind of recurrent neural network (Gers et al., 1999; Hochreiter & Schmidhuber, 1997), and is capable of processing sequential data with correlations between points that are far apart. On the one hand, similar to the standard recurrent neural network, the LSTM has a self-looped structure that allows the result of the previous step to participate in the calculation of the subsequent step. On the other hand, the LSTM possesses four interaction layers in its neurons, which makes it able to forget useless information and learn correlations between data points that are far away from each other in sequence. The LSTM is the state-of-the-art model for well log generation in previous studies (Zhang et al., 2018). This agrees well with the perspective of geoscience, since the well logs reflect a formation condition, which possesses internal continuity (spatial dependency). The sequential information in reservoirs is critical for well logs generation. Therefore, the LSTM constitutes the ideal foundation for building a new model for this type of geoscience problem.

Motivated by the huge success of deep neural networks in applications (see, e.g., Schmidhuber (2015), Rawat and Wang (2017), Hewamalage, Bergmeir and Bandara (2020) and the literature cited therein) there is nowadays a strong interest in showing theoretical properties of such estimates. In the last years many new results concerning deep feedforward neural network estimates have been derived (cf., e.g., Eldan and Shamir (2016), Lu et al. (2020), Yarotsky (2018) and Yarotsky and Zhevnerchuk (2019) concerning approximation properties or Kohler and Krzyżak (2017), Bauer and Kohler (2019) and Schmidt-Hieber (2020) concerning statistical properties of these estimates).