On the heels of Bloomberg's announcement last week on developing their own #chatgpt based on their wealth of financial data, it shouldn't be a surprise that many organizations are investing in building their own proprietary AI generative language models. While I have been talking about AI Governance for the past few years, the need for such a framework is more important than ever. Past conversations focused on model management and governance but the focus should also be the data for training and learning sets. Data especially in unstructured form serves as the largest source for AI models but considering the maturity state in many organizations, how well are organizations labeling and tagging the data for business and operational context? My guess is that its fairly poorly for unstructured data and hopefully improved in terms of structured data.

What is the smartest artificial intelligence ever created? All today's AI is not True AI, be it virtual assistants or autonomous vehicles or predictive applications or large language models or search engines or recommendation systems or language translators or facial recognition systems or q/a systems or gamers. AI has not reached even a proof of concept demonstration phase to verify that its models, concepts or theories have the potential for real-world applications, as the evidence demonstrating that AI projects/products are feasible. Real AI is not some infrastructure (ML platform, algorithms, data, compute) and development stack (from libraries to languages, IDE, workflow and visualisation): Some applied maths, probability theory and statistics; Some statistical learning algorithms, logic regression, linear regression, decision trees and random forests; Machine learning algorithms, supervised, unsupervised and reinforced; ANNs, DL algorithms and models, filtering the input data through many layers to predict and classify information; Optimizing (compressing and quantizing) trained neural network models; Some statistical patterns and inferences; Some programming languages, as Python and R., with their libraries and packages; ML platforms, frameworks and runtimes such as PyTorch, ONNX, Apache MXNet, TensorFlow, Caffe2, CNTK, SciKit-Learn, and Keras; Inferencing SDKs like the Qualcomm Neural Processing SDK, integrated development environments (IDE), such as PyCharm, Microsoft VS Code, Jupyter, MATLAB, etc.; Physical servers, virtual machines, containers, specialized hardware such as GPUs, cloud-based computational resources including VMs, containers, and Serverless computing. Today's AI is so-called "Narrow AI" which is designed to perform a single task, and any knowledge gained from performing that task will not automatically be applied to other tasks.

We don't know what is reality, being, the universe and how the world works. We don't know what relationships are and how correlations and associations, causation and interaction are interrelated. It's the ultimate human quest – to understand everything that there is and how it is reflected by intelligence, human or machine, as the sensemaking and determination of reality. We humans have an existential problem with reality. We experience it all the time, but struggle to define it, let alone understand it.