Artificial intelligence (AI) gets its "intelligence" by analyzing a given dataset and detecting patterns. It has no concept of the world beyond this dataset, which creates a variety of dangers. One changed pixel could confuse the AI system to think a horse is a frog or, even scarier, err on a medical diagnosis or a machine operation. Its exclusive reliance on the data sets also introduces a serious security vulnerability: Malicious agents can spoof the AI algorithm by introducing minor, nearly undetectable changes in the data. Finally, the AI system does not know what it does not know, and it can make incorrect predictions with a high degree of confidence.

Today's battlefield environment is complex, dynamic and uncertain, and requires efficient support to ensure mission success. This relies on a proper support strategy to provide supported equipment able to fulfill the mission. In the context of defense where both systems and organization are complex, having a holistic approach is challenging by nature, forces and support agencies need to rely on an efficient decision support system. Logistics, readiness and sustainability are critical factors for asset management, which can benefit from AI to reach "Smart In Service" level relying especially on predictive and prescriptive approaches and on effective management of operational re-sources. Smart Support capacities can be then monitored by appropriate metrics and improved by multi-criteria decision support and knowledge management system. Depending on the operational context in terms of information and the objective, different AI paradigms (data-driven AI, knowledge-based AI) are suitable even a combination through hybrid AI.

There are two different types of AI in wide use today. Recent developments have focused on data-driven machine learning, but in the last decades, most AI applications in education (AIEd) have been based on representational / knowledge-based AI. Data-driven AI uses a programming paradigm that is new to most computing professionals. It requires competences which are different from traditional programming and computational thinking. It opens up new ways to use computing and digital devices. But the development of state-of-the-art AI is now starting to exceed the computational capacity of the largest AI developers. The recent rapid developments in data-driven AI may not be sustainable. The impact of AI in education will depend on how learning and competence needs change, as AI will be widely used in the society and economy.

Columbia University professor and robotics engineer Hod Lipson knows the importance of artificial intelligence (AI) on a global level. "It permeates everything we do, from the stock market, from predicting the weather to what product you're going to buy," he said Wednesday during the second day of the virtual Ai4 2020 conference. AI falls into the category of an exponential technology, meaning it accelerates with time. Both biopharma and med-tech companies are increasingly pulling the technology into their business operations, working on programs that can assist in everything from drug discovery and clinical trial recruitment to precision diagnostics and patient compliance efforts. Computing power has doubled every 20 months or so for the past 120 years, Lipson said, moving from mechanical instruments to graphics processing units (GPUs) today.

Human and AI are increasingly interacting and collaborating to accomplish various complex tasks in the context of diverse application domains (e.g., healthcare, transportation, and creative design). Two dynamic, learning entities (AI and human) have distinct mental model, expertise, and ability; such fundamental difference/mismatch offers opportunities for bringing new perspectives to achieve better results. However, this mismatch can cause unexpected failure and result in serious consequences. While recent research has paid much attention to enhancing interpretability or explainability to allow machine to explain how it makes a decision for supporting humans, this research argues that there is urging the need for both human and AI should develop specific, corresponding ability to interact and collaborate with each other to form a human-AI team to accomplish superior results. This research introduces a conceptual framework called "Co-Learning," in which people can learn with/from and grow with AI partners over time. We characterize three key concepts of co-learning: "mutual understanding," "mutual benefits," and "mutual growth" for facilitating human-AI collaboration on complex problem solving. We will present proof-of-concepts to investigate whether and how our approach can help human-AI team to understand and benefit each other, and ultimately improve productivity and creativity on creative problem domains. The insights will contribute to the design of Human-AI collaboration.

Artificial intelligence (AI) is the tech world's golden goose. Machine learning's incredible potential for task automation and productivity has a home in almost every industry, and companies all over the world are tapping into the technology. The integration of AI-powered chatbots and data-driven analytics can improve user experiences and reduce the workload on human service agents, benefiting customers and companies alike. The future of AI technology is already here; here's how companies are leveraging the power of AI. "Artificial intelligence" is broadly defined by the development of a computer program that can "learn" on its own, without input from a human programmer. Despite decades of research dating back to as early as WWII, no computer program currently exists that can match human intelligence.

There was much ado about artificial intelligence (AI) platforms in 2016. Major developments and offerings came out of Microsoft (Cognitive Services), Google (TensorFlow), Amazon (Rekognition, Polly, Lex), IBM (Watson), Salesforce (Einstein), and many more. AI and machine learning (ML) are the hammers that turn just about every business' data problem into a nail. But if 2016 was the year of the platform, 2017 will be the year of data. Looking at AI through the lens of platforms is a bit like looking through the wrong end of a set of binoculars.