Artificial intelligence has come leaps and bounds from a far-away pipe dream to a staple in millions of small-to-medium businesses across the globe, with large-scale international companies not going a day without it. As small business owners, AI may still seem like that distant possibility - but now, more than ever - it's affordable, accessible, and necessary for small businesses to inject into their operations. In today's market, you don't need a sufficient knowledge on artificial intelligence to realize the savings and growth potential of using AI for your business. Believing that AI is no more than just a few chatbots installed on a company's website, or a few auto-generated images to upload to your Facebook page. AI is skyrocketing business growth, and it's best not to get left behind.

Advances in artificial intelligence (AI) are creating opportunities to improve K-12 education. Personalized learning applications can increase student engagement in the classroom and close learning gaps, while AI tools can help teachers reduce their workloads, design better interventions, and reduce burnout. But there are a number of technical, operational, and social challenges that stand in the way of widespread AI usage in schools. Moreover, policymakers have not yet embraced a strategic vision of AI to ensure effective deployment of the technology in the classroom. Join the Center for Data Innovation for a panel discussion about the ways policymakers can address existing concerns while supporting AI use by students, teachers, and administrators.

TensorFlow is a popular open-source framework for machine learning and probably the best tool you can use to implement machine learning and deep learning algorithms and principles. This TensorFlow course offered on Coursera is a part of TensorFlow in Practice Specialization by deeplearning.ai. This course is suitable for software developers who have some experience in Python coding and some knowledge of machine learning and deep learning and who want to build scalable AI-powered algorithms in TensorFlow. It teaches how to use TensorFlow to implement the principles of machine learning and deep learning so learners can start building and applying scalable models to real-world problems.

Every book in this list (apart from the last one which is theory-based) is written around the Python programming language. Python is a great tool for machine learning and data science as its open-source and comes with a wealth of powerful libraries -- NumPy, Pandas, TensorFlow and Keras just to name a few. This post may contain affiliate links and I may earn a small commission when you click on the links at no additional cost to you.

The corporate world has struggled with custom voices that resonate with their brand voice. While the technology has made waves in various areas, speech technology was still under experimentation. Organisations wanting to create'human-like' voices for their brand. The brand voice forms a major part of an organization's branding strategy, and it is crucial to get it right, especially for the industries like call centers, where human-like voices are important. In industries where voice-over is a crucial part of the businesses, AI-powered speech solutions are appreciated for various reasons.

Learning to perform tasks by leveraging a dataset of expert observations, also known as imitation learning from observations (ILO), is an important paradigm for learning skills without access to the expert reward function or the expert actions. We consider ILO in the setting where the expert and the learner agents operate in different environments, with the source of the discrepancy being the transition dynamics model. Recent methods for scalable ILO utilize adversarial learning to match the state-transition distributions of the expert and the learner, an approach that becomes challenging when the dynamics are dissimilar. In this work, we propose an algorithm that trains an intermediary policy in the learner environment and uses it as a surrogate expert for the learner. The intermediary policy is learned such that the state transitions generated by it are close to the state transitions in the expert dataset. To derive a practical and scalable algorithm, we employ concepts from prior work on estimating the support of a probability distribution. Experiments using MuJoCo locomotion tasks highlight that our method compares favorably to the baselines for ILO with transition dynamics mismatch.

Abstract--Artificial Intelligence (AI) has found its applications in a variety of environments ranging from data science to cybersecurity. AI helps break through the limitations of traditional algorithms and provides more efficient and flexible methods for solving problems. In this paper, we focus on the applications of artificial intelligence in authentication, which is used in a wide range of scenarios including facial recognition to access buildings, keystroke dynamics to unlock smartphones. With the emerging AI-assisted authentication schemes, our comprehensive survey provides an overall understanding on a high level, which paves the way for future research in this area. In contrast to other relevant surveys, our research is the first of its kind to focus on the roles of AI in authentication. Learning and neural networks are The traditional password-based authentication method has two main mechanisms used in AI. Learning is the process of slowly faded out due to its inadequate ...

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent’s model. To do so, the agent provides an explanation that can be used to update the model of human such that the agent’s plan is feasible or optimal to the human user. Existing approaches to solve this problem have been based on automated planning methods and have been limited to classical planning problems only. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. In particular, we propose a logic-based framework for explanation generation, where given a knowledge base KBa (of an agent) and a knowledge base KBh (of a human user), each encoding their knowledge of a planning problem, and that KBa entails a query q (e.g., that a proposed plan of the agent is valid), the goal is to identify an explanation ε ⊆ KBa such that when it is used to update KBh, then the updated KBh also entails q. More specifically, we make the following contributions in this paper: (1) We formally define the notion of logic-based explanations in the context of model reconciliation problems; (2) We introduce a number of cost functions that can be used to reflect preferences between explanations; (3) We present algorithms to compute explanations for both classical planning and hybrid systems planning problems; and (4) We empirically evaluate their performance on such problems. Our empirical results demonstrate that, on classical planning problems, our approach is faster than the state of the art when the explanations are long or when the size of the knowledge base is small (e.g., the plans to be explained are short). They also demonstrate that our approach is efficient for hybrid systems planning problems. Finally, we evaluate the real-world efficacy of explanations generated by our algorithms through a controlled human user study, where we develop a proof-of-concept visualization system and use it as a medium for explanation communication.

State-of-the-art multilingual machine translation relies on a shared encoder-decoder. In this paper, we propose an alternative approach based on language-specific encoder-decoders, which can be easily extended to new languages by learning their corresponding modules. To establish a common interlingua representation, we simultaneously train N initial languages. Our experiments show that the proposed approach improves over the shared encoder-decoder for the initial languages and when adding new languages, without the need to retrain the remaining modules. All in all, our work closes the gap between shared and language-specific encoder-decoders, advancing toward modular multilingual machine translation systems that can be flexibly extended in lifelong learning settings.

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