We're putting together a few valuable insights for company executives in this episode, but it's so packed that, in the end, there's just something in it for everyone. This is because Afke Schouten, our special guest, has much to share with us! Paulo Nunes, your host and CEO at Two Impulse, knows this very well and lays down an open road to keep it all coming. Afke's mission is to help organizations generate true value with AI. She is the Head of Data & AI Strategy at Xebia Data, focusing on corporate training and consulting. Her previous background as a consultant, as well as a data scientist, AI strategist and analytics team lead at companies such as EY, Swiss Re, SwissQuant and AI Bridge allows Afke to help executives build AI strategies and become data-driven.

"Without understanding the cause and effect of interactions within the world, no AI model, algorithm, technique, application, or technology is real and true", be it: Natural language generation converting structured data into the native language; Speech recognition converting human speech into a useful and understandable format by computers; Virtual agents, computer applications that interact with humans to answer their queries, from Google Assistant to the Watson; Biometrics, to identify individuals based on their biological characteristics or behaviors, with fingerprints and faces, hand veins, irises, or voices biometric modalities; Decision management systems for data conversion and interpretation into predictive models; Machine learning empowering machine to make sense from data sets without being actually programmed, to make informed decisions with data analytics and statistical models; Robotic process automation configuring a robot (software application) to interpret, communicate and analyze data; Peer-to-peer network connecting between different systems and computers for data sharing without the data transmitting via server; Deep learning platforms based on ANNs teaching computers and machines to learn by example just the way humans do; Generative AI (GANs, Transformers, Autoencoders) referring to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing content like text, audio and video files, images, or code to create new possible content as completely original artifacts. It leverages AI and ML algorithms to generate artificial content such as text, images, audio and video content based on its training data to trick the user into believing the content is real, facing legal challenges concerning data privacy; Generative AI models with image generation algorithms generating photographs of human faces, objects and scenes, image-to-image conversion, text-to-image translation, film restoration, semantic-image-to-photo translation, face frontal view generation, photos to emojis, face aging, media and entertainment: deep fake technology; AI optimized hardware support artificial intelligence models, as #neuralnetworks, #deeplearning, and #computervision, including CPUs, GPUs, TPUs, OPUs to handle scalable workloads, special purpose built-in silicon for neural networks, neuromorphic chips, etc.; Real AI is NOT about representing computational models of intelligence, described as structures, models, and operational functions that can be programmed for problem-solving, inferences, language processing, etc. Real AI is about the computational models of reality and mentality, described as causal structures, models, and operational functions that can be programmed for problem-solving and inferences for a wide range of goals in a wide range of environments.

Real AI is not data engineering or coding and software engineering skills, in big data tools or developer's skills in Python, R, Java, MATLAB, C or any other programming language desired, combined with machine learning skills. Keep a big view of Real AI as growing via three human intelligence faking levels to the Trans-AI: Artificial Narrow Intelligence (ANI)/ML/DLNNs; Artificial General Intelligence (AGI)/Human-Level AI; Artificial Super Intelligence (ASI); Trans-AI, Real and True AI, Meta-AI, Causal Machine Intelligence and Learning Man-Machine Hyperintelligence, the most disruptive integrative general-purpose technology.

How to Modeling Real Causality for Real AI? Causality is represented mathematically via Structural Causal Models (SCMs), with two key elements, a graph and a set of equations. The golden standard causal graph, C E, R, is a Bidirected Cyclic Multi-Graph or Causal Loop-Graph Network (BCG/CGN), where entity-vertices E (circles, nodes, points) in a causal BCG represent variables and edges R (arrows, links, ties, arcs, lines) represent causation, direct or inverse. It is strongly connected containing a directed path from x to y (and from y to x) for every pair of vertices (x, y), while having circuits or loops, that is, arcs that directly connect nodes with themselves, and multiple arrows with the same source and target nodes, thus covering all possible directed graphs as a Directed Acyclic Graphs (DAG) or weighted directed graphs/networks . The set of equations is a Structural Equation Model (SEM), showing the causal connections and the details of the relationship. SEMs represent all possible interrelationships between or among variables.

Artificial intelligence might one day be used to power genuinely humanlike cyborgs or other figments of humanity's fertile imagination. For now, Ingo Stork is using the technology to help restaurant chains waste less food and do more with fewer workers. Dr. Stork is co-founder of PreciTaste, a startup that uses AI-based sensors and algorithms to accomplish one fairly specific task: predict how much food people will order at any given moment, and make sure that it's being prepared in a timely fashion.

How much mathematics do you need to learn to fully comprehend AI in order to improve the current state of AI? Should you study applied maths and computing or data science and engineering to do this? Nobody could fully comprehend AI, the most complex problem ever humanity has faced to decide. It is not only applied mathematics, numerical analysis, calculus, geometry, statistics and probability, but also computer science, logic, algebra, discrete mathematics, graph theory and combinatorics, psychology, cognitive science, information science, data science, programming, linguistics, philosophy and other knowledge fields. It is all about the whole science, as the sum of universal knowledge about the world, as being automated, algorithmized and digitized. For its long conception 2300 years and short development lifetime of 70 years, the idea of intelligence has passed many phases and stages to reach its true status of Man-Machine Superintelligence.

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 What are issues with the philosophy of #artificialintelligence? We witness how a new kind of real intelligence emerges, as Deus ex Machina AI vs. Homo Sapiens Sapiens. We all are missing a critical point with #AI that it is machine philosophy per se, as far as philosophy is the study of the fundamental nature of knowledge, reality, and existence. Real AI is all about the modeling and simulating reality/existence/world, its knowledge of entities and relationships, as of facts and data and fundamental causes, phenomena, laws and rules, and patterns, the sum of universal knowledge. It is all started from the fundamental conception initiated by Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" was wrongly replaced with the question "Can machines do what we (as thinking entities) can do?". As a result, we got two polar types of AI, Real & True AI and Human-Like AI.

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 What actually is the most important metric for the success of a machine learning / Artificial Intelligence model? It is accuracy and precision, or bias and variability, or trueness and repeatability of Classifications, binary or multiclass. It should correspond to some reference standard, as data benchmarks for images, audio, speech, text, etc. There could be high accuracy with low precision, low accuracy with high precision, or high accuracy with high precision. But It is disrupted by Real AI, Causal Machine Intelligence and Learning, or Transdisciplinary AI, Trans-AI.

The first major wave of AI was based on the premise that knowledge could be "represented" as a set of rules that computers could process with logic. If you could add enough rules, you could eventually produce commonsense knowledge of the world and general intelligence. In its day, it generated great excitement and funding. But its focus was on a process to produce knowledge (logic), not on knowledge itself. The assumption that knowledge consists merely as a set of assertions that could be represented in symbols was flawed. It did not scale; knowledge was never achieved.

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