Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these approaches were tightly coupled with the capabilities of the artificial intelligence systems at the time. With the proliferation of AI-enabled systems in sometimes critical settings, there is a need for them to be explainable to end-users and decision-makers. We present a historical overview of explainable artificial intelligence systems, with a focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.

The employment of Data Science and Machine Learning technologies is at a peak. We can see several software and tools with various innovative features in the market that serve us with the efficiency of new-age data technologies that can potentially increase a business's efficiency and value proposition. With continuous evolution at scale such solutions too, get revamped with time. Now is the era for automated data science and machine learning software that not only enhance the operational proficiency of such tools but also assist data scientists with great potential. They help automate the repetitive and mundane tasks within the ML or data science processes without compromising model performance and productivity. Therefore, here is the list of top 10 automated data science and machine learning software presented by some key players of the respective market.

The demand for people with knowledge and skills in artificial intelligence (AI) and machine learning (ML) hugely outstrips the supply. This means that learning and gaining qualifications in these subjects can be a great way to enhance your career prospects. However, not everyone has the spare time and money to spend years studying for a degree or other formal qualifications. Today, with the wealth of freely available educational content online, it may not be necessary. There are so many courses, tutorials, and guides available online that it is perfectly possible to gain a thorough grounding in these subjects without paying a penny.

Mr. Sankalp Sunder, CEO Coding Blocks, has written this special guest post article. Coding Blocks is a premier institution that has till date successfully trained over 10k students in artificial intelligence, machine learning and data science. It is a teaching partner at Shiv Nadar University and Lovely Professional University and has placed students in renowned companies like Google, Citibank and Microsoft. Machine Learning (ML) and Artificial Intelligence (AI) are the hot assets of technology that are going to become the driving force of the digital world in the coming time. We know that humans have the tendency to perceive things & retain them in their memory.

Once AI has transformed into a lifelong-learning system across industries, individual users and big corporations alike will notice its more than dynamic insights. Artificial Intelligence (AI) in 2020 and beyond, with its seemingly limitless potential, is set to transform the world – or is it? To fully embrace and reap the rewards of this futuristic technological trend, industries as far afield as a business forecasting and online gambling security, predictive maintenance and customer service will need to identify a clear strategy of AI's inherent value for each of them. The answer may lie in AI's many nuances. For example, is your software able to learn new stuff on the fly?

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We have a PhD level or Advanced courses thread in the sidebar but it's three year old now. There were two other 7-8 month old threads (1, 2) but they don't have many quality responses either. So, can we have a new one here? To reiterate - CS231n, CS229, ones from Udemy etc are not advanced. Advanced ML/DL/RL, attempts at building theory of DL, optimization theory, advanced applications etc are some examples of what I believe should belong here, much like the original sidebar post.

Sophia, the world's most advanced human-like robot participated in the Timberlane Middle School science fair and Family Fun Night to promote STEM education last Saturday. It was Sophia's first time attending a school science fair as a guest and her creator, Hanson Robotics, expressed gratitude to Hopewell Valley for the kind invitation and for showing innovative thinking by including Sophia in this year's activities. Sophia was created by combining innovations in science, engineering, and artistry. She is a framework for robotics and artificial intelligence ("AI") and research, and an agent for exploring the human-to-robot experience in service and entertainment applications. Sophia has also become a much sought-after media personality, helping to advocate for AI research and the role of robotics and AI in our lives.

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.

Complex assignments typically consist of open-ended questions with large and diverse content in the context of both classroom and online graduate programs. With the sheer scale of these programs comes a variety of problems in peer and expert feedback, including rogue reviews. As such with the hope of identifying important contents needed for the review, in this work we present a very first work on key phrase classification with a detailed empirical study on traditional and most recent language modeling approaches. From this study, we find that the task of classification of key phrases is ambiguous at a human level producing Cohen's kappa of 0.77 on a new data set. Both pretrained language models and simple TFIDF SVM classifiers produce similar results with a former producing average of 0.6 F1 higher than the latter. We finally derive practical advice from our extensive empirical and model interpretability results for those interested in key phrase classification from educational reports in the future.