When the Montour School District launched America's first Artificial Intelligence Middle School program in the fall of 2018, many questions arose. How? (Just to name a few). But, as a student-centered and future-focused district, the thought process was not if we should teach AI, but what if we don't teach AI? Also, why isn't everyone teaching AI? Through a series of courses developed and implemented by Montour team members and partners, the AI program officially launched in October 2018. To date, hundreds of classes have already been taught to students in areas of AI Ethics, AI Autonomous Robotics, AI Computer Science, and AI Music. The goal for the program is to make an all-inclusive AI program for all middle school students that is relevant and meaningful in a world where children live and prepare them for a future where they will thrive.

Artificial intelligence has been promoted to be leveraged in every industry. AI plays a great role that is nearly impossible to be overtaken by another advanced technology. Experts say that it is better to have a small investment while dealing with advanced technologies like Artificial Intelligence. For example, DeepMind, a subsidiary of Alphabet is working with Unity to train AI systems to handle high challenging tasks. According to research from PwC, by 2030, the global GDP will increase by 14 percent with AI-driven technologies.

The problem of high-dimensional and large-scale representation of visual data is addressed from an unsupervised learning perspective. The emphasis is put on discrete representations, where the description length can be measured in bits and hence the model capacity can be controlled. The algorithmic infrastructure is developed based on the synthesis and analysis prior models whose rate-distortion properties, as well as capacity vs. sample complexity trade-offs are carefully optimized. These models are then extended to multi-layers, namely the RRQ and the ML-STC frameworks, where the latter is further evolved as a powerful deep neural network architecture with fast and sample-efficient training and discrete representations. For the developed algorithms, three important applications are developed. First, the problem of large-scale similarity search in retrieval systems is addressed, where a double-stage solution is proposed leading to faster query times and shorter database storage. Second, the problem of learned image compression is targeted, where the proposed models can capture more redundancies from the training images than the conventional compression codecs. Finally, the proposed algorithms are used to solve ill-posed inverse problems. In particular, the problems of image denoising and compressive sensing are addressed with promising results.

We provide excess risk guarantees for statistical learning in the presence of an unknown nuisance component. We analyze a two-stage sample splitting meta-algorithm that takes as input two arbitrary estimation algorithms: one for the target model and one for the nuisance model. We show that if the population risk satisfies a condition called Neyman orthogonality, the impact of the first stage error on the excess risk bound achieved by the meta-algorithm is of second order. Our general theorem is agnostic to the particular algorithms used for the target and nuisance and only makes an assumption on their individual performance. This enables the use of a plethora of existing results from statistical learning and machine learning literature to give new guarantees for learning with a nuisance component. Moreover, by focusing on excess risk rather than parameter estimation, we can give guarantees under weaker assumptions than in previous works and accommodate the case where the target parameter belongs to a complex nonparametric class. When the nuisance and target parameters belong to arbitrary classes, we characterize conditions on the metric entropy such that oracle rates---rates of the same order as if we knew the nuisance model---are achieved. We also analyze the rates achieved by specific estimation algorithms such as variance-penalized empirical risk minimization, neural network estimation and sparse high-dimensional linear model estimation. We highlight the applicability of our results via four applications of primary importance: 1) heterogeneous treatment effect estimation, 2) offline policy optimization, 3) domain adaptation, and 4) learning with missing data.

Transformative AI technologies have the potential to reshape critical aspects of society in the near future. However, in order to properly prepare policy initiatives for the arrival of such technologies accurate forecasts and timelines are necessary. A survey was administered to attendees of three AI conferences during the summer of 2018 (ICML, IJCAI and the HLAI conference). The survey included questions for estimating AI capabilities over the next decade, questions for forecasting five scenarios of transformative AI and questions concerning the impact of computational resources in AI research. Respondents indicated a median of 21.5% of human tasks (i.e., all tasks that humans are currently paid to do) can be feasibly automated now, and that this figure would rise to 40% in 5 years and 60% in 10 years. Median forecasts indicated a 50% probability of AI systems being capable of automating 90% of current human tasks in 25 years and 99% of current human tasks in 50 years. The conference of attendance was found to have a statistically significant impact on all forecasts, with attendees of HLAI providing more optimistic timelines with less uncertainty. These findings suggest that AI experts expect major advances in AI technology to continue over the next decade to a degree that will likely have profound transformative impacts on society.

Artificial intelligence may still be an emerging technology, but chances are you're already using it in your everyday life. AI is what is powers iPhone's Siri and Google Assistant. Gmail's smart replies, online product suggestions, and directions for the fastest route -- with traffic included -- from one place to another are all examples of AI coming into play. AI, which allows computers and other machinery to learn and adapt to its surroundings, is also active in schools and in classrooms. It runs in many educational and tutoring apps, and digital curriculum tools use this technology to assess a student's performance and suggest an individualized learning plan to help them improve their understanding of a subject.

Does your organization invest a huge amount of time, energy and money in training employees? If yes, there is now a better way to manage such training. Smart computer programs can take all the loads off your shoulders. Chatbots are one such artificial intelligence that can minimize your business efforts and help you graduate to better customer engagement, more effective employee training, greater productivity, and increased bottom line. This demonstrates that AI-powered programs such as chatbots are gradually transforming the business landscape everywhere by simulating human beings.

Artificial intelligence (AI) is a topic of everyone's interest in 2019. This is not only because of the innovations it brings to the table but also the immeasurable contributions towards the tech industry. So, if you are planning to come up with your start up, it's prudent to consider AI actively into it. If you start your own business journey making small investment initially, and gradually pour more, then there is a good chance of getting rewarded with huge returns. Some companies like Google, Salesforce, and Microsoft are good examples of that.

In the movie "Terminator 2: Judgment Day," the titular robot says, "My CPU is a neural net processor, a learning computer." The neural network of which that famous robot speaks is actually a real kind of machine learning method. A neural network is a way to depict a complex mathematical formula, organized into layers. This formula can be trained to do things like recognize images for self-driving cars. For example, watch several seconds of a neural network performing object recognition.

The entire website of Elon Musk's private charitable foundation is shorter than many of the Tesla CEO's contentious tweets. Grants are made in support of: Renewable energy research and advocacy; Human space exploration research and advocacy; Pediatric research; Science and engineering education," the site reads. Documents obtained by the Guardian reveal how the foundation has put that vague mission statement into practice. Together, the documents show that many of the organization's donations have gone far beyond its stated scope. Some have benefited the billionaire's own family and initiatives, others have tackled his pet peeves – the foundation has given more money to Musk's own artificial intelligence research than to any of the more traditional charities it says it supports.