What is Causal AI and Why You Should Care? Causal AI means both improving machine learning with causal reasoning, and automating causal reasoning with machine learning. Today's learning machines have superhuman prediction ability but aren't particularly good at causal reasoning, even when we train them on obscenely large amounts of data. But now, a convergence of statistical and computational advances has shifted the focus from discourse to algorithms that we can train on data and deploy to software. It is a real path to true, real AI, causal intelligence, or ontological machines, to be run by its machine ontology, the world model engine for general intelligence, learning and knowing, inference and interactions with the world. Its ontological/causal reasoning mechanism is a crucial element to how humans or machines understand, explain, and make decisions and interact with the world.

Today's AI is largely machine learning techniques, deep learning algorithms and deep neural networks can't identify causality, its elements and structures, processes and mechanisms, rules and relationships, data and models, all what makes our world. This leads to all sorts of decision and prediction errors, data and algorithmic biases, the lack of quality data, and implementation failings, or the absence of real machine intelligence and learning. Correlation-based ML; Predictions only; Limited explainability; Spirals out of control in novel situations; Minimal human-machine interaction; Constrained by historical data; No guarantees on fairness; Needs a lot of data; True AI will emerge as Causal AI, State-of-the-Art AI Causal AI True AI: Real AI Platform. Decision-making AI: Causal AI doesn't just predict the future, it shapes it. Explainable AI: Put the "cause" in "because" with next-generation explainable AI.

We build Causal AI-powered products that are trusted by leading organisations across a wide range of industries. Our Causal AI Platform empowers all types of users to make superior decisions through intuitive user interfaces and APIs that adapt to their level of technical expertise. We are creating a world in which humans can trust machines with the greatest challenges in the economy, society, and healthcare. We are looking for motivated and high-achieving Senior Fullstack Software Engineers focusing on bringing causality, explainability and accountability to MLOps as a first on-the-ground engineering member of our CausalOps team, joining product and data scientists. We are a mission-driven, interdisciplinary team with an inclusive culture building technology that improves our world.

Modern AI systems have made it easy to tackle many problems previously thought out of reach of computers. These systems are so good that they have convinced even those working on developing them that they are sentient. However, despite the successes, many of these systems can be thought of as technological parrots. Parrots can mimic their owners, but do not have a true awareness of what they are saying, nor why they are saying it. Similarly, modern AI systems can mimic the patterns they have learnt from previous data, without having the true context of the problem which is being solved, nor understanding why a given prediction is returned.

Understand how Causal AI frameworks and algorithms support decision making tasks like estimating the impact of interventions, counterfactual reasoning and repurposing previously gained knowledge on other domains. AI and Machine Learning solutions have made rapid strides in the last decade and they are being increasingly relied upon to generate predictions based on historical data. However they fall short of expectations when it comes to augmenting human decisions on tasks where there is a need to understand the actual causes behind an outcome, quantifying the impact of different interventions on final outcomes and making policy decisions, perform what if analysis and reasoning for scenarios which have not occurred etc. Let's consider a practical scenario to understand the decision making challenges faced by business and how current AI solutions help address those: While generation of model predictions and explaining key features influencing the outcomes is helpful, it does not allow taking decisions. What will also be of immense help in this situation is to understand the consequences of different actions in hindsight. The above is an example of a counterfactual problems and is more difficult than estimating interventions as the data to answer is not observed and recorded.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner It is a common knowledge that current ML technology fails when applied to dynamic, complex systems. It produces static models that overfit to yesterday's world. The models are data-hungry and unintelligible to humans, as listed below, Static models Historic correlations Black box Observational data only Predictions only And Causal AI comes with the competitive features, Dynamic models Causal drivers Explainable Understands business context Predictions, interventions & counterfactuals Causal AI is a new category of intelligent machines that understand cause and effect ― a major step towards true AI. It is widely recognized that Understanding Causality Is the Next Challenge for Machine Learning. Deep neural nets do not interpret cause-and effect, or why the associations and correlations exist.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How much might cost Real AI Model? Encyclopedic Intelligent Systems has developed the first real model of the Real/Causal AI, including the following elements of its Universal Intelligent Platform, I-World: Machine World Model; Master Algorithm, Causal.World; World Data Framework, World.Data; Global Knowledge Base, World.Net; Domain Knowledge Base, Domain.Net; The Development has reached a stage of a proof of principle, concept and mechanism in which the best AI technology stack of hardware, software and dataware is constructed and tested to explore and demonstrate the feasibility of the Real/Causal AI Model. It creates a world-data mapping of all possible entities, their relationships and behaviors, binding causes and effects. To build truly AI machines of infinitely powerful digital intelligence, we need to encode, program or teach them what the world is with all its complex cause-effect relationships. What makes machine intelligence and learning a true and real AI is the powerful underlying causal master algorithms used to reveal the causal patterns in the world's data universe.

Bernd Greifeneder is the CTO and Founder of Dynatrace, a software intelligence company that helps to simplify enterprise cloud complexity. IT, development and business departments are under more pressure than ever to innovate. However, this has led to applications becoming increasingly complex as organizations move to more dynamic, multicloud environments for greater agility. DevOps and SRE teams need to make sense of this complexity and optimize their services, but this drains the time you can devote to innovation. The move to cloud-native architectures is also making it harder for these teams to quickly identify vulnerabilities.

"What else can I do with AI" is what I have been hearing in professional insights groups recently. The number of solutions is exponentially growing, but AI has not yet affected the insights process as this might indicate. We all have heard of text analytics or facial recognition with AI. More and more applications pop up, and it can feel crowded …. AI is like a magician buster.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How do I know I'm not a robot? Humans are also #robots, social bio-robots, governed by instincts and behavioral algorithms, and trained by all sorts of cultural programs. Humans are advanced autonomous robots that interact and communicate with other humans or other autonomous physical agents by following social behaviors and rules attached to its role. A human robot is a programmable machine capable of carrying out a complex series of actions automatically, guided by the control embedded within. Now robotics develops machines that can substitute for human robots and replicate human actions.