Note: This a small set of list we have prepared from various sources. If we know any other set of data then please feel free to email us at asif@marktechpost.com.

Scientists are using artificial intelligence to build systems that can analyze the quality of classroom instruction and student engagement. School leaders and education researchers often rely on test scores to judge how well students are learning. But that ignores many important aspects of learning, such as the liveliness of classroom discussion or how engaged and motivated the students are. Expert observers in a classroom can immediately pick up on these unquantifiable moments of great teaching. But human observations are time-consuming and expensive.

While manufacturers have traditionally been hesitant to invest in their operations due to cost, a recent National Association of Manufacturers (NAM) survey of more than 500 manufacturers reveals that 65 percent plan to increase capital spending during the coming years. Where is the money going to go? Experts predict most manufacturers will look toward revamping their facilities to adapt to the demands of today's digital world. This adjustment, which many call the "fourth industrial revolution" or smart manufacturing, will move manufacturers from mass production to customized production via a digital supply network. The digital transformation, a melding of new technologies such as the internet of things (IoT), advanced robotics, artificial intelligence (AI), and 3D printing, is expected to generate more than $370 billion in net global value during the next four years. While digitization is helping to get more out of manufacturers' materials and machines, what are the driving forces behind transformation?

Machine learning has had the entire data science community brimming with questions regarding the framework and the hidden insights that are being used to build the intelligent applications of the future. There are immense possibilities when it comes to grasping the inner workings of algorithms related to machine learning. As mathematics is a big portion of the programming process, there are many important questions to ask and many reasons to include math in the machine learning development process. The knowledge of mathematics is very important to understand and apply machine learning algorithms in different applications. From understanding uncertainty and intervals of prediction to choose a parameter settings with a strategy for validation, mathematics concepts help in implementing machine learning.

Generative Adversarial Networks (GANs) can successfully learn a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on existing work in the area by proposing a novel framework for training the generator against an ensemble of discriminator networks, which can be seen as a one-student/multiple-teachers setting. We formalize this problem within the non-stationary Multi-Armed Bandit (MAB) framework, where we evaluate the capability of a bandit algorithm to select discriminators for providing the generator with feedback during learning. To this end, we propose a reward function which reflects the amount of knowledge learned by the generator and dynamically selects the optimal discriminator network. Finally, we connect our algorithm to stochastic optimization methods and show that existing methods using multiple discriminators in literature can be recovered from our parametric model. Experimental results based on the Fr\'echet Inception Distance (FID) demonstrates faster convergence than existing baselines and show that our method learns a curriculum.

Universally valid ground truth is almost impossible to obtain or would come at a very high cost. For supervised learning without universally valid ground truth, a recommended approach is applying crowdsourcing: Gathering a large data set annotated by multiple individuals of varying possibly expertise levels and inferring the ground truth data to be used as labels to train the classifier. Nevertheless, due to the sensitivity of the problem at hand (e.g. mitosis detection in breast cancer histology images), the obtained data needs verification and proper assessment before being used for classifier training. Even in the context of organic computing systems, an indisputable ground truth might not always exist. Therefore, it should be inferred through the aggregation and verification of the local knowledge of each autonomous agent.

Coreference Resolution is the task of identifying groups of phrases in a text that refer to the same discourse entity. Such referring phrases are called mentions, a set of mentions that all refer to the same 1 discourse entity is called a coreference chain. Annotated corpora are important resources for developing and evaluating automatic coreference resolution methods. Turkish is an agglutinative language and Turkish coreference resolution poses several challenges different from many other languages, in particular the absence of grammatical gender, the possibility of null pronouns in subject and object position, possessive pronouns that can be expressed as suffixes, and ambiguities among possessive and number morphemes, e.g., 'çocukları' can be analysed as'their children' or as'his/her children', depending on context Oflazer and Bozşahin (1994). No coreference resolution corpus exists for Turkish so far. We here describe the result of an effort to create such a corpus based on the METU-Sabanci Turkish Treebank (Say, Zeyrek, Oflazer, and Özge, 2004; Atalay, Oflazer, and Say, 2003; Oflazer, Say, Hakkani-Tür, and Tür, 2003) which is, to the best of our knowledge, the only publicly available Turkish Treebank. Our contributions are as follows.

Recent neural networks such as WaveNet and sampleRNN that learn directly from speech waveform samples have achieved very high-quality synthetic speech in terms of both naturalness and speaker similarity even in multi-speaker text-to-speech synthesis systems. Such neural networks are being used as an alternative to vocoders and hence they are often called neural vocoders. The neural vocoder uses acoustic features as local condition parameters, and these parameters need to be accurately predicted by another acoustic model. However, it is not yet clear how to train this acoustic model, which is problematic because the final quality of synthetic speech is significantly affected by the performance of the acoustic model. Significant degradation happens, especially when predicted acoustic features have mismatched characteristics compared to natural ones. In order to reduce the mismatched characteristics between natural and generated acoustic features, we propose frameworks that incorporate either a conditional generative adversarial network (GAN) or its variant, Wasserstein GAN with gradient penalty (WGAN-GP), into multi-speaker speech synthesis that uses the WaveNet vocoder. We also extend the GAN frameworks and use the discretized mixture logistic loss of a well-trained WaveNet in addition to mean squared error and adversarial losses as parts of objective functions. Experimental results show that acoustic models trained using the WGAN-GP framework using back-propagated discretized-mixture-of-logistics (DML) loss achieves the highest subjective evaluation scores in terms of both quality and speaker similarity.

From the world's largest tech companies to start-ups, everyone is looking for people well-versed with Artificial Intelligence (AI). But a career in this business is no cakewalk: A lot of mathematics, constant leaning and understanding human behaviour are just some of the ways to get a foothold in this fast-growing industry. We spoke to five AI professionals, who tell us that a career in this field is about many different things, from data analysis, text and image recognition to linguistics--and no, evil robots do not figure in the list. AI researcher and founding member, Qure.ai Ghosh, 26, spends his days looking at X-rays. "I am almost a semi-radiologist.

By reading and paying attention to present-day signals from our future, we can best make sense of the higher education IT world in 2038. In 1992, I was in a meeting at Apple Computer and was asked if I wanted to see the next "killer technology" the company would soon release. My Apple colleague left the conference room and came back to unveil the Apple Newton, a handheld device (sort of) that Apple was calling a "Personal Digital Assistant" (PDA) and that featured handwriting recognition. I flipped back the gray metal lid and tried the stylus, writing to my wife, "Dear Pat." My writing, converted to text on the fly, came back as "Deal Pot."