Article: Towards a Human Artificial Intelligence for Human Development

This paper discusses the possibility of applying the key principles and tools of current artificial intelligence (AI) to design future human systems in ways that could make them more efficient, fair, responsive, and inclusive.

Article: Childhood’s End

All revolutions come to an end, whether they succeed or fail. The digital revolution began when stored-program computers broke the distinction between numbers that mean things and numbers that do things. Numbers that do things now rule the world. But who rules over the machines? Once it was simple: programmers wrote the instructions that were supplied to the machines. Since the machines were controlled by these instructions, those who wrote the instructions controlled the machines.

Article: Gartners Ethics related Predicts for 2019

• Organizations will require a professional code of conduct incorporating ethical use of data and AI.
• Legislation will require that 100% of conversational assistant applications, which use speech or text, identify themselves as being nonhuman entities.
• Consumers in mature markets will rely on artificial intelligence (AI) to decide what they eat, what they wear or where they live.
• Organizations will use explainable AI models to build trust with business stakeholders, up from almost no usage today.
• Fortune 1000 antitrust case will hinge on whether tacit cooperation among autonomous AI agents in competitive markets constitutes collusion.
• Large organizations will hire AI behavior forensic, privacy and customer trust specialists to reduce brand and reputation risk.

Article: Cognitive Hub: the future of work

As organisations and individuals struggle to manage a tidal wave of information and a growing range of devices, Konica Minolta offers a new approach to effective decision-making based on artificial intelligence and the internet of things

Article: Design Thinking Humanizes Data Science

The article ‘Cognitive Hub: The Future of Work’ and the supporting infographic (see Figure 1) provides an interesting perspective on some ‘technology combinations’ that could transform the workplace of the future, all enabled by Artificial Intelligence (AI):
• AI + Internet of Things (IoT) yields workplace decision support
• AI + Human-machine Interaction (HMI) yields augmented collaboration
• AI + Cyber physical systems yields digitalization

Paper: Ten ways to fool the masses with machine learning

If you want to tell people the truth, make them laugh, otherwise they’ll kill you. (source unclear) Machine learning and deep learning are the technologies of the day for developing intelligent automatic systems. However, a key hurdle for progress in the field is the literature itself: we often encounter papers that report results that are difficult to reconstruct or reproduce, results that mis-represent the performance of the system, or contain other biases that limit their validity. In this semi-humorous article, we discuss issues that arise in running and reporting results of machine learning experiments. The purpose of the article is to provide a list of watch out points for researchers to be aware of when developing machine learning models or writing and reviewing machine learning papers.

Paper: Deriving Cyber-security Requirements for Cyber Physical Systems

Today’s cyber physical systems (CPS) are not well protected against cyber attacks. Protected CPS often have holes in their defense, due to the manual nature of today’s cyber security design process. It is necessary to automate or semi-automate the design and implementation of CPS to meet stringent cyber security requirements (CSR), without sacrificing functional performance, timing and cost constraints. Step one is deriving, for each CPS, the CSR that flow from the particular functional design for that CPS. That is the task assumed by our system, Deriving Cyber-security Requirements Yielding Protected Physical Systems – DCRYPPS. DCRYPPS applies Artificial Intelligence (AI) technologies, including planning and model based diagnosis to an important area of cyber security.

Article: Artificial Intelligence Chatbots Could Make Your Doctors Obsolete

Artificial intelligence chatbots are being developed to help diagnose and treat health issues, and could take the place of healthcare providers.

Article: Here’s Why 2019 Will Bring Debates About Ethics of Artificial Intelligence

Theories about the ethics of artificial intelligence (AI) are complicated and ever-evolving. As AI expands in 2019, questions will be raised.

Article: Data Privacy, Ethics: The Time Has Arrived

Cloudera Chief Architect and Hadoop creator Doug Cutting says that the time has arrived for data privacy and ethics rules.

Article: Autonomous Testing Is Like Autonomous Driving: The AI Needs Human Assistance

The US Society of Automotive Engineers (SAE) has defined a scale to describe the autonomous capabilities that self-driving cars have – i.e., their levels of automation.

Article: Algorithms should contribute to the Happiness of Society

The following is the opening speech of Arjan van den Born, Academic Director of the Jheronimus Academy of Data Science (JADS), that we worked on for the Den Bosch Data Week of which I am the cofounder and curator.

Article: Algorithmic ethics: lessons and limitations for leaders

To unleash automation’s decision-making potential we must examine its limitations

Article: AI Policy Making Part 4: A Primer On Fair and Responsible ML and AI

Bias. Discrimination. Inequality. Inequitable. Irresponsible. Unethical. Unfair. How are all these terms connected to Machine Learning (ML) and Artificial Intelligence (AI)? In this post I’ll address three topics that are critical for decision and policy makers- fairness, responsibility and transparency (or explainability) of ML and AI. Our life today is being ruled by algorithms and most of them are a mysterious black box for us. There’s a realization that automated decision making (autonomous systems) will only be acceptable if there is fairness and transparency. A number of media articles, books and research reports by think tanks and books have raised awareness about unfair and unethical AI and algorithmic ethics.

Article: Data Science in International Development. Part I: Working with Text

Today, headlines are filled with claims about the power of Artificial Intelligence (AI) to do things only humans could do before. Recognizing objects in images, responding to voice queries, or interpreting complex text instances, to mention a few. But how do AI applications work? What are the AI solutions being used in International Development and Security? In this post, we summarize some of the basic techniques for computers to process and react to human language using Machine Learning, using real-world scenarios from several of our projects at AKTEK.

Article: Logical Positivism and the Scientific Method in Genetic Algorithmics

The genetic algorithm owes its form to biomimicry, not derivation from first principles. So, unlike the workings of conventional optimization algorithms, which are typically apparent from the underlying mathematical derivations, the workings of the genetic algorithm require elucidation. Attempts to explain how genetic algorithms work can be divided in two: those based to a lesser or greater extent on the scientific method, and those that reject the scientific method in favor of logical positivism.

Article: 5 Ways Artificial Intelligence and Chatbots Are Changing Education

Artificial Intelligence (AI) and Chatbots are changing the world in more ways we can ever imagine. Completing a diversified range of tasks, AI and Chatbots have become a normal element in our everyday life. The technology has played an important role in the development of varied fields including education and online tutoring. Through artificial intelligence, educators and educational institutes have been able to offer a personalized learning environment to students. AI-driven tools not only improve student interaction and collaboration but also act as a game changer in the innovative ed-tech world. Here are 5 ways artificial intelligence and chatbots are influencing and changing education.

Book: Foundations of Trusted Autonomy

This book establishes the foundations needed to realize the ultimate goals for artificial intelligence, such as autonomy and trustworthiness. Aimed at scientists, researchers, technologists, practitioners, and students, it brings together contributions offering the basics, the challenges and the state-of-the-art on trusted autonomous systems in a single volume. The book is structured in three parts, with chapters written by eminent researchers and outstanding practitioners and users in the field. The first part covers foundational artificial intelligence technologies, while the second part covers philosophical, practical and technological perspectives on trust. Lastly, the third part presents advanced topics necessary to create future trusted autonomous systems. The book augments theory with real-world applications including cyber security, defence and space.

Book: Artificial Intelligence Accelerates Human Learning – Discussion Data Analytics

Focusing on students’ presentations and discussions in laboratory seminars, this book presents case studies on evidence-based education using artificial intelligence (AI) technologies. It proposes a system to help users complete research activities, and a machine-learning method that makes the system suitable for long-term operation by performing data mining for discussions and automatically extracting essential tasks. By illustrating the complete process – proposal, implementation, and operation – of applying machine learning techniques to real-world situations, the book will inspire researchers and professionals to develop innovative new applications for education.