In this paper, we disseminate a new handwritten digits-dataset, termed Kannada-MNIST, for the Kannada script, that can potentially serve as a direct drop-in replacement for the original MNIST dataset. In addition to this dataset, we disseminate an additional real world handwritten dataset (with $10k$ images), which we term as the Dig-MNIST dataset that can serve as an out-of-domain test dataset. We also duly open source all the code as well as the raw scanned images along with the scanner settings so that researchers who want to try out different signal processing pipelines can perform end-to-end comparisons. We provide high level morphological comparisons with the MNIST dataset and provide baselines accuracies for the dataset disseminated. The initial baselines obtained using an oft-used CNN architecture ($96.8\%$ for the main test-set and $76.1\%$ for the Dig-MNIST test-set) indicate that these datasets do provide a sterner challenge with regards to generalizability than MNIST or the KMNIST datasets. We also hope this dissemination will spur the creation of similar datasets for all the languages that use different symbols for the numeral digits.

Ischaemic stroke is a medical condition caused by occlusion of blood supply to the brain tissue thus forming a lesion. A lesion is zoned into a core associated with irreversible necrosis typically located at the center of the lesion, while reversible hypoxic changes in the outer regions of the lesion are termed as the penumbra. Early estimation of core and penumbra in ischaemic stroke is crucial for timely intervention with thrombolytic therapy to reverse the damage and restore normalcy. Multisequence magnetic resonance imaging (MRI) is commonly employed for clinical diagnosis. However, a sequence singly has not been found to be sufficiently able to differentiate between core and penumbra, while a combination of sequences is required to determine the extent of the damage. The challenge, however, is that with an increase in the number of sequences, it cognitively taxes the clinician to discover symptomatic biomarkers in these images. In this paper, we present a data-driven fully automated method for estimation of core and penumbra in ischaemic lesions using diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) sequence maps of MRI. The method employs recent developments in convolutional neural networks (CNN) for semantic segmentation in medical images. In the absence of availability of a large amount of labeled data, the CNN is trained using an adversarial approach employing cross-entropy as a segmentation loss along with losses aggregated from three discriminators of which two employ relativistic visual Turing test. This method is experimentally validated on the ISLES-2015 dataset through three-fold cross-validation to obtain with an average Dice score of 0.82 and 0.73 for segmentation of penumbra and core respectively.

Abstract--Recently, NLP models have been shown to be susceptible to adversarial attacks. In this paper, we explore adve rsarial attacks on neural machine translation (NMT) systems. Given a sentence in the source language, the goal of the proposed att ack is to change multiple words while ensuring that the predicte d translation remains unchanged. In order to choose the word from the source vocabulary, we propose a soft-attention bas ed technique. The experiments are conducted on two language pa irs: English-German (en-de) and English-French (en-fr) and two state-of-the-art NMT systems: BLSTM-based encoder-decod er with attention and Transformer . The proposed soft-attenti on based technique outperforms existing methods like HotFlip by a significant margin for all the conducted experiments The res ults demonstrate that state-of-the-art NMT systems are unable t o capture the semantics of the source language.

There are many headline studies about artificial intelligence making strides in medicine, but the reality can be somewhat more prosaic.

Artificial Intelligence for Good – Also Makes Business Sense 2. 3 Ways You Can Use Artificial Intelligence to Grow Your Business Right Now 3. Tangible Use Cases Are Key to AI Adoption 4. Why Genuine Human Intelligence is Key for the Development of AI 5. NVIDIA GPUs Powering the Most Energy Efficient Supercomputers in the World 4. 4 ARTIFICIAL INTELLIGENCE FOR GOOD – ALSO MAKES BUSINESS SENSE Artificial intelligence is being used as a potential solution to many of the world's most pressing problems, from poverty to hunger. "Another aimed the opioid epidemic currently plaguing the US, by harnessing machine learning to determine which patients were more likely to become addicts after being prescribed opioid treatments. " "Other initiatives include driving data-driven research into multiple sclerosis, developing AI-driven systems to assist those on low incomes with managing their finances, assisting the UN in driving its sustainable development goals and predict outbreaks of Zika virus." READ ARTICLE 5. 5 3 WAYS YOU CAN USE ARTIFICIAL INTELLIGENCE TO GROW YOUR BUSINESS RIGHT NOW Utilizing AI in your business marketing is more cost- effective and simpler to implement than you think. "Finding a logical starting point for bringing artificial intelligence integration into your marketing strategy is intimidating, no matter what your budget looks like. However, for the SMB and startup entrepreneur without the wiggle room to experiment, getting started often feels incredibly risky or out of reach. But we're reaching a point where it's becoming a necessity for any brand looking to stay competitive."

There are many headline studies about artificial intelligence making strides in medicine, but the reality can be somewhat more prosaic. What gets used in hospitals and clinicians' offices may be much simpler, and a lot less like AI than you would think. In the latest issue of Nature magazine, DeepMind researchers published the results of a deep learning project that can predict kidney failure of patients in the hospital up to 48 hours before the onset of symptoms, with far greater accuracy than existing computer programs for such predictive uses. Also this week, the DeepMind team published the results of a third-party survey of the use of a computer program called "Streams," which uses no artificial intelligence but which can be useful to physicians for things such as being alerted to warning signs about a patient. The first project, the deep learning one, has some ways to go to be put into practice, while the Streams software is already in use by doctors and hospital staff.

In 2017, the cognitive scientist and entrepreneur, Gary Marcus, argued that AGI needs a moonshot. In an interview with Alice Lloyd George, he said, "Let's have an international consortium kind of like we had for CERN, the large hadron collider. What if you had $7 billion dollars that was carefully orchestrated towards a common goal." Marcus felt that the political climate of the time made such a collective effort unlikely. But the moonshot analogy for AGI has taken hold in the private sector and captured the public imagination. In a 2017 talk, the CEO and co-founder of DeepMind, Demis Hassabis, evoked the moonshot analogy to describe his company as "a kind of Apollo program effort for artificial intelligence." Hassabis unpacks his vision with pitch deck efficiency: First they'll understand human intelligence, then they'll recreate it artificially.

You will be a member of the Algorithms Team of our Research and Technology department. The work will be focused on developing and implementing new deep learning-based algorithms in medical and dental 3D X-ray imaging. You will be responsible for staying informed on the latest developments and publications in the field; defining new research problems; and testing new methods using data from our X-ray imaging devices. In cooperation with the team, you will be implementing elegant and efficient algorithms in Python, OpenCL/CUDA, and/or C to solve various research problems and implement the solutions into our systems. In addition, you will be doing your part to ensure that the necessary documentation is produced in line with the applicable medical device regulation.

The massive integration of information technologies, under different aspects of the modern world, has led to the treatment of vehicles as conceptual resources in information systems. Since an autonomous information system has no meaning without any data, there is a need to reform vehicle information between reality and the information system.This can be achieved by human agents or by special intelligent equipment that will allow identification of vehicles by their registration plates in real environments. Among intelligent equipment, mention is made of the system of detection and recognition of the number plates of vehicles.The system of vehicle number plate detection and recognition is used to detect the plates then make the recognition of the plate that is to extract the text from an image and all that thanks to the calculation modules that use location algorithms, segmentation plate and character recognition.The detection and reading of license plates is a kind of intelligent system and it is considerable because of the potential applications in several sectors which are quoted: The detected plates are compared to those of the reported vehicles. In order to detect licence we will use Yolo ( You Only Look One) deep learning object detection architecture based on convolution neural networks. This architecture was introduced by Joseph Redmon, Ali Farhadi, Ross Girshick and Santosh Divvala first version in 2015 and later version 2 and 3. Yolo is a single network trained end to end to perform a regression task predicting both object bounding box and object class.

In the last couple of years, Uber has quietly become one of the most active contributors to open source machine learning technologies. From training frameworks like Horovod, statistical languages like Pyro or conversational stacks like the Plato Research Dialogue System, Uber has been pushing boundaries of innovation in the machine learning space with practical technologies rather than exoteric research. One Uber's most popular contributions to the machine learning ecosystem has been Ludwig, a framework for training and testing machine learning models without the need to write code. Recently, Uber released a second version of Ludwig that includes major enhancements in order to enable mainstream no-code experiences for machine learning developers. The goal of Ludwig is to simplify the processes of training and testing machine learning models using a declarative, no-code experience.