Global AI and Data Strategy for International Institutions, National Governments and Global Corporations – BBN Times

Global AI and Data Strategy for International Institutions, National Governments and Global Corporations

As a key member of EU AI Alliance, the EIS Encyclopedic Intelligent Systems Ltd pioneered a Global AI and Data Strategy, to be proposed to the EU Commission as well as International Institutions, National Governments and Global Corporations.

EIS is proposing the Global AI and Data Strategies for International Institutions, National Governments and Global Corporations.

TheGlobal Artificial Intelligenceis emerging as the next General Purpose Technology and General Scale Intelligent Platform.

"AI is a general-purpose technology that has the potential to improve the welfare and well-being of people, to contribute to positive sustainable global economic activity, to increase innovation and productivity, and to help respond to key global challenges. It is deployed in many sectors ranging from production, finance and transport to healthcare and security".

Today's AI Technology is faster, stronger, and over-specialized every day, compromising over-specialized human jobs, as well as human values, relationships, deeds and legacy.

Machines as computers and computerized equipment, like data analytics systems, cognitive robots, and ML systems, programmed to learn like humans, come with better, more efficient, cost-effective solutions for a set of specific problems.

Today specialized robots read the entire internet, run a factory, play games, translate languages, compose music, paint pictures, clean houses, drive our vehicles, disable bombs, provide prosthetic limbs, spot cancer, support surgical procedures, manufacture products, entertain, teach and train us.

Today's specialized AI, automation and robotics development tends to fixate on profit- or warfare-accelerating technologies weaponized AI, digital surveillance, facial recognition, automation software, drones, and technologized weapons and their capacity to serve the wealthy and powerful in a time of ecological collapse, the pandemic, health crises, mass unemployment, widening inequality, and tech billionaires rapidly expanding their wealth.

As a Global Scale Platform and General Purpose Technology, AI Technology "has the potential to improve the welfare and well-being of people, to contribute to positive sustainable global economic activity, to increase innovation and productivity, and to help respond to key global challenges".

It is critically important to know what AI is all about, its nature and principles, scale and scope, in order to avoid defective AI Strategiesused in corporations around the world and

National Data and AI Strategies marked with destructive assumptions of narrow, weak and subjective models of Data, AI, ML, Automation and Robotics.

"Governments around the world see artificial intelligence as a nation defining capability. Countries are looking to their education systems to develop world-class generational AI capability while ensuring equity, privacy, transparency, accountability, economic and social impact".

AIis the science and engineering of intelligence or intellect, its nature, models, theories, algorithms, architectures, methods, techniques, technologies, platforms and applications.

Global AI is a Global Scale Platform and General Purpose Technology, being all about three interrelated universes:

1. Reality/world/environment

2. Intelligence/intellect/mind/understanding

3. Data universe, real world data, human generated data, machine-generated data, IoT data

They are all represented, mapped, coded and processed by computing machinery of any complexity, from digital services to smart phones to the internet and beyond.

Real and non-anthropic AI implies a virtually unlimited mentality, sensation and perception, learning and understanding, as well as a digitally unlimited intelligence, knowledge, power and ubiquity.

Most of them are sold as "end-to-endplatformfor data science and machine learning where you can build and deploy models quickly and manage your ML workflows at scale", just being advanced software libraries, like Google AI Cloud.

Real AIis to automate all and every cognitive and intellectual tasks, including data scientists and machine learning jobs.

Today, data scientist jobs are to spend 80% of their time munging, validating and formatting data, cleaning, moving, checking, organising data before even actually using or writing a single algorithm from a set of traditional statistical algorithms or traditional analytic methods or ML .

And different algorithms learn in different ways, which performance improves as new data regarding observed responses or changes to the environment are fed to the machine, thus increasing its intelligence over time.

Again, machine learning jobs are to automatically detect data patterns by applying selected algorithms and known rules to:

Categorize or catalog things (people, objects, events, etc.)

Predict outcomes or actions based on correlations and identified patterns

Identify unknown patterns and relationships

Detect anomalous or unexpected behaviors

So, the output of a machine learning algorithm is entirely dependent on the data it is exposed to. Change the data, change your model, configure or tune the algorithms parameters for optimal performance, over-tune, retrain or discard; all for lacking any prime models and background knowledge, as in AI Model.

It covers all ML applications, types and basic techniques.

As common applications, from self-driving cars to virtual assistants to facial recognition to high-volume trade to resource optimization to disease or fraud detection.

As common learning techniques:

Supervised learning

Bayesian Statistics Decision Trees Forecasting Neural Networks Random Forests Regression Analysis Support Vector

Semi-supervised learning, the inputs and outputs provide the general pattern the machine can extrapolate and apply to the remaining data.

Unsupervised Learning Affinity Analysis Clustering Clustering: K-Means Nearest-Neighbor Mapping

In unsupervised learning, the machine studies data to identify patterns; determines correlations and relationships by parsing the available data, while AI identifies causal patterns, determining real relationships by analyzing the datastreams.

Reinforcement Learning Common Techniques Artificial Neural Networks (ANN) Learning Automata Markov Decision Process (MDP) Q-Learning

Reinforcement learning is like teaching someone to play a game, where the rules and objectives are clearly defined, and exploring different actions and observing resulting reactions the machine learns to exploit the rules to create a proper outcome.

Add here as integral parts of [global] AI such capacities as:

Deep learning, the basis of advanced machine learning systems,

Natural language processing (NLP) and Natural language generation (NLG) to understand and communicate speech, written language, and voice commands.

NLP tools simply perform translation, mapping the words in a command to a dictionary, while AI understands: inferring meaning or intent or context to inform sensible actions or rational or emotional responses.

Cognitive computing as a platform and human-machine interface emulates human behavior using natural language processing, advanced machine learning algorithms (as deep learning) and NLP, NLU, and NLG.

AI is to provide an unlimited scope and scale and generality, all with logic and accuracy, understanding and learning, interpretation and transparency, causation and optimal performance.

AI algorithms perceive the world, its denizens, observing structures, behaviors or the environment, to detect causal patterns, understanding causal relationships, to make generalizations and infer explanations or theories or decisions or causation.

Data science and Machine learning requires the data scientist programming the algorithm, human application of the scientific method and human communication skills. There required humans to answer questions such as: What are we trying to predict? Are resulting correlations predictive? Causal? Are there inherent biases? Can the model and results be applied in real life? What is the proper response?

For instance, when a pattern emerges with global climate or politics or health what is the proper next step or steps?

If Data science and Machine learning make a synergistic exercise between man and machine, Real, True or Global AI Renders Specialists Obsolete, be it data scientists and subject matter experts, analytics, programming engineers or business subject matter experts.

This is the new truth of our new future, like it or not.

What today's AI platforms and applications are badly missing, besides of being overspecialized, from video games to object, speech, face, or sentiment "recognition"?

It is one thing making your really intelligent:

UNDERSTANDING OF THE WORLD AND ITSELF, what makes real mind, intellect, intelligence, and self-knowledge.

The Global AI, as an understanding, truthful and trustworthy AI Platform, will consist of the following parts:

Machine World Model (The WORLD.Schema, World Entities Global REFERENCE; Universal Human-Machine Ontology; USECS, Universal Standard Entity Classification SYSTEM);

Master Algorithm (for symbolic and sub-symbolic machine learning algorithms, as deductive and inductive reasoning, connectionism, evolutionary computation, Bayes' theorem or analogical modelling; Global Causal Network);

World Data Framework (WorldDataArchitecture);

Global Knowledge Base (WorldKnowledgeNet, or WorldNet, embracing WordNet, ConceptNet, MindNet, TextNet, ImageNet, SpeechNet, ActionNet, PeopleNet, SocialNet, InterNet, CityNet, IndustryNet, NationNet,PlanetNet, SpaceNet);

Domain Knowledge Bases (DomainKnowledgeNet);

Narrow Superintelligence Models (ML & DL & ANN Systems, Google AI, Facebook AI, Microsoft AI, Weak Artificial Intelligence Platforms promoted as Machine learning as a service (MLaaS).

MLaaS is a set of cloud services that ML providers offer as a part of cloud computing services. MLaaS providers offer tools including face recognition, data visualization, application programming interface (APIs), predictive analytics, natural language processing, and deep learning. The main attraction of these services is that, like any other cloud service, users can get started with a machine learning system without the need to install software or provision of the servers. Infrastructural concerns like model training, data pre-processing, model evaluation, and ultimately, predictions, can be alleviated with the help of MLaaS.

Also, it is essential to realise that building narrow quasi-AI systems is only a small part of the global AI system.

Amazon Machine Learninghelps automatically classify products in your catalogue using product description data as a training set.

The real AI automatically classifies any things in the world using entity description data as training sets.

One of the greatest breakthroughs of Global AI is to allow computers to analyze all types of data, quantitative or qualitative, structured or unstructured, with access to the whole data universe, an infinite universe of information, an open world of datastreams.

Shifting from a cloud-centric AI to the on-device AI, a global AI model is passing a distributed ML/AI/DL learning over wireless networks, to scale the intelligence for mass adoption of AI.

A central or edge cloud sends a state-of-art global AI model to the devices via low-latency and high-capacity 5 G.

Next, each device collects personal data and performs on-device training.

On-device AI capabilities increase exponentially, along with improvements in algorithms and software.

A unifying connectivity fabric for future innovation A platform for existing, emerging, and unforeseen connected services.Transforming how the world connects, computes and communicates

When you merge AI and IoT, you getthe Artificial Intelligence of Things, or AIoT a revolutionary combination that can transform industries, elevate customer experiences and accelerate business performance exponentially. Without AI capabilities, IoT devices and the data they produce would have limited value.

The study was conducted by IDC and sponsored by SAS, with the support of Intel and Deloitte Consulting. They asked 450 business leaders about the impact of artificial intelligence combined with the Internet of Things and found clear evidence of momentum behind this emerging combination of technologies the Artificial Intelligence of Things (AIoT). According to the study respondents, AIoT capabilities are already generating results.

You can think of internet of things devices as the sensory systems of digital nervous system while AI is its brain/mind/intelligence, integrating, coordinating, controlling such "things" as the edge computing nodes, as smart phones, smart cameras, wearable devices, smart TVs, smart drones, refrigerators, digital assistants, sensors, self-driving cars and other equipment, all connected to the Global AI internet Platform.

Practical Examples of 5GAIoT:

The Recommendation on Artificial Intelligence (AI) the first intergovernmental standard on AI was adopted by the OECD Council at Ministerial level on 22 May 2019 on the proposal of the Committee on Digital Economy Policy (CDEP).

It specifies that "Artificial Intelligence (AI) is a general-purpose technology that has the potential to improve the welfare and well-being of people, to contribute to positive sustainable global economic activity, to increase innovation and productivity, and to help respond to key global challenges. It is deployed in many sectors ranging from production, finance and transport to healthcare and security".

Recommendation of the Council on Artificial Intelligence

Artificial Intelligence (AI) technologies and tools play a key role in every aspect of the COVID-19 crisis response. This Recommendation provides a set of internationally-agreed principles and recommendations that can promote an AI-powered crisis response that is trustworthy and respects human-centred and democratic values.For further information on this Recommendation and its relevance to COVID-19 response and recovery, see the background information below.

On the proposal of the Committee on Digital Economy Policy:

1. Thepurpose of this recommendation should be understood as follows:

AI system:An AI system is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy.

AI system lifecycle:AI system lifecycle phases involve:i)design, data and models; which is a context-dependent sequence encompassing planning and design, data collection and processing, as well as model building;ii)verification and validation;iii)deployment; andiv)operation and monitoring. These phases often take place in an iterative manner and are not necessarily sequential. The decision to retire an AI system from operation may occur at any point during the operation and monitoring phase.

AI knowledge:AI knowledge refers to the skills and resources, such as data, code, algorithms, models, research, know-how, training programmes, governance, processes and best practices, required to understand and participate in the AI system lifecycle.

AI actors: AI actors are those who play an active role in the AI system lifecycle, including organisations and individuals that deploy or operate AI.

Stakeholders:Stakeholders encompass all organisations and individuals involved in, or affected by, AI systems, directly or indirectly. AI actors are a subset of stakeholders.

The Recommendation identifies five complementary values-based principles for the responsible stewardship of trustworthy AI and calls on AI actors to promote and implement them:

In addition to and consistent with these value-based principles, the Recommendation also provides five recommendations to policy-makers pertaining to national policies and international co-operation for trustworthy AI, namely:

The Recommendation also includes a provision for the development of metrics to measure AI research, development and deployment, and for building an evidence base to assess progress in its implementation.

Global AI is a Global Scale Platform and General Purpose Technology and a unifying framework for Weak AI, ML and DL.

AI is the science and engineering of intelligence or intellect, its nature, models, theories, algorithms, architectures, methods, techniques, technologies, platforms and applications.

Global AI is a Global Scale Platform and General Purpose Technology, being all about three interrelated universes: reality/world/environment; intelligence/intellect/mind/understanding; its data universe, all represented, mapped, coded and processed by computing machinery of any complexity, from digital services to smart phones to the internet and beyond.

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Global AI and Data Strategy for International Institutions, National Governments and Global Corporations - BBN Times