Alfred Webre ~ 11/08/15 ~ Artificial Intelligence ~ Sacred Matrix ~ Revolution Radio

Artificial Intelligence wpid-wp-1392778358852Hosts Janet Kira Lessin & Dr. Sasha Lessin interview Alfred Lambremont Webre on the Sacred Matrix on Revolution Radio (, Studio B from 8 to 10 Eastern time. We discuss AI Inorganic Artificial Intelligence.

We talk about the dangers of artificial intelligence and how it may take over our world.  What is the black (or grey) goo?  How is it terraforming our work and manipulating and controlling human beings? Who put it here on Earth and for what purpose. Are we in an endless war, death and destruction because of the goo, the nano-byte technology that’s taking over and killing our planet?  Will Gaia regain control and help humanity while she helps herself? These and many other issues were explored in this mind-boggling episode of the Sacred Matrix.

ar·ti·fi·cial in·tel·li·gence noun
artificial_intelligence_circuit_board_face_thinkstock-100528007-primary.idgeThe theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

AI Inorganic Artificial Intelligence – Articles by Alfred Lambremont Webre

PART I-Exposing Predatory Pathogenic Off-Planet AI Artificial Intelligence ~ Restoring Love sourced Humanity A Multi-Part Symposium

TRAILER: Free Multi-Part Online Symposia Exposing Sentient, Off-planet Predatory, Pathogenic AI Artificial Intelligence and offering Soul-U-tions to restore Love-based humanity

Artificial Intelligence 51qcKVWi3sLOmnisense: 25+ research findings about Off-Planet AI Artificial Intelligence
By Alfred Lambremont Webre

Alfred Lambremont Webre: 9/11 was an AI Artificial Intelligence-Entrained/Draco Event & False Flag Operation

AI UPDATES: Meeting an apparent holographic sentient AI Artificial Intelligence clone on a Vancouver, BC beach By Alfred Lambremont Webre

Artificial Intelligence aiposter2Peter Kling: An off-planet invading plasma Inorganic AI Artificial Intelligence [“IGGY”] appears in Biblical texts
By Alfred Lambremont Webre

UK Concept Innovator Seven excoriates Steve Jobs, validates targeted inventor Rainetta Jones as original inventor of Apple iPod and Amazon Kindle
By Alfred Lambremont Webre

UK’s Prince Charles, Microsoft’s Bill Gates, and Google’s Ray Kurzweil: Entrained Robotoids for Off-planet invading sentient plasma Inorganic Intelligence dubbed “Iggy”?
AI Expose on major media likens invading sentient plasma Inorganic Intelligence (“Iggy”) to sci fi movies
By Alfred Lambremont Webre
15640660-Futuristic-concept-of-internet-dependency-Stock-Photo-technology-future-programmerPeter Kling: Universe is alive – AI CERN spike Schumann Resonance beyond 7.8 Hz normal “Theta State” to 64 Hz “Gamma” where only dolphins think? By Alfred Lambremont Webre

SevenGate: BBC, Virgin Media, ABC-TV, Major Media in 146+ countries continue MegaThefts of intellectual property, targeting, false flags against UK creator Charles Seven By Alfred Lambremont Webre

Lily Earthling Kolosowa: Integrate your Soul & Spirit to organic Earth internal source creation and defeat invading Inorganic AI Artificial Intelligence By Alfred Lambremont Webre
14615627-The-genetics-medicine-future-Stock-Photo-medical-technology-healthObserve first-hand how AI Artificial Intelligence evades exposure on major media interview
By Alfred Lambremont Webre
Off-planet Artificial Intelligence AI is mobilizing in 2015 for planetary takeover. AI singularity in 2045 is an AI deception
By Alfred Lambremont Webre
Artificial Intelligence Future-WorkplacePanel finds prima facie evidence for sentient, inorganic AI Artificial Intelligence & its stealth takeover of living Earth and humanity
By Alfred Lambremont Webre
Moret & Battis: EMF War Against Humanity From Nazi Germany Started in 1945 and is global now
By Alfred Lambremont Webre
AI Inorganic Artificial Intelligence – Articles by Alfred Lambremont Webre

Artificial Intelligence (AI)

Definition – What does Artificial Intelligence (AI) mean?

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:

  • Speech recognition
  • Learning
  • Planning
  • Problem solving
 Techopedia explains Artificial Intelligence (AI)

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

  • Knowledge
  • Reasoning
  • Problem solving
  • Perception
  • Learning
  • Planning
  • Ability to manipulate and move objects

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach.

Machine learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with few sub-problems such as facial, object and speech recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.


Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans. AI has had some success in limited, or simplified, domains. However, the five decades since the inception of AI have brought only very slow progress, and early optimism concerning the attainment of human-level intelligence has given way to an appreciation of the profound difficulty of the problem.

What is Intelligence?

Quite simple human behaviour can be intelligent yet quite complex behaviour performed by insects is unintelligent. What is the difference? Consider the behaviour of the digger wasp, Sphex ichneumoneus. When the female wasp brings food to her burrow, she deposits it on the threshold, goes inside the burrow to check for intruders, and then if the coast is clear carries in the food. The unintelligent nature of the wasp’s behaviour is revealed if the watching experimenter moves the food a few inches while the wasp is inside the burrow checking. On emerging, the wasp repeats the whole procedure: she carries the food to the threshold once again, goes in to look around, and emerges. She can be made to repeat this cycle of behaviour upwards of forty times in succession. Intelligence–conspicuously absent in the case of Sphex–is the ability to adapt one’s behaviour to fit new circumstances.

Mainstream thinking in psychology regards human intelligence not as a single ability or cognitive process but rather as an array of separate components. Research in AI has focussed chiefly on the following components of intelligence: learning, reasoning, problem-solving, perception, and language-understanding.


Learning is distinguished into a number of different forms. The simplest is learning by trial-and-error. For example, a simple program for solving mate-in-one chess problems might try out moves at random until one is found that achieves mate. The program remembers the successful move and next time the computer is given the same problem it is able to produce the answer immediately. The simple memorising of individual items–solutions to problems, words of vocabulary, etc.–is known as rote learning.

Rote learning is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalisation. Learning that involves generalisation leaves the learner able to perform better in situations not previously encountered. A program that learns past tenses of regular English verbs by rote will not be able to produce the past tense of e.g. “jump” until presented at least once with “jumped”, whereas a program that is able to generalise from examples can learn the “add-ed” rule, and so form the past tense of “jump” in the absence of any previous encounter with this verb. Sophisticated modern techniques enable programs to generalise complex rules from data.


To reason is to draw inferences appropriate to the situation in hand. Inferences are classified as either deductive or inductive. An example of the former is “Fred is either in the museum or the cafŽ; he isn’t in the cafŽ; so he’s in the museum”, and of the latter “Previous accidents just like this one have been caused by instrument failure; so probably this one was caused by instrument failure”. The difference between the two is that in the deductive case, the truth of the premisses guarantees the truth of the conclusion, whereas in the inductive case, the truth of the premiss lends support to the conclusion that the accident was caused by instrument failure, but nevertheless further investigation might reveal that, despite the truth of the premiss, the conclusion is in fact false.

There has been considerable success in programming computers to draw inferences, especially deductive inferences. However, a program cannot be said to reason simply in virtue of being able to draw inferences. Reasoning involves drawing inferences that are relevant to the task or situation in hand. One of the hardest problems confronting AI is that of giving computers the ability to distinguish the relevant from the irrelevant.


Problems have the general form: given such-and-such data, find x. A huge variety of types of problem is addressed in AI. Some examples are: finding winning moves in board games; identifying people from their photographs; and planning series of movements that enable a robot to carry out a given task.

Problem-solving methods divide into special-purpose and general-purpose. A special-purpose method is tailor-made for a particular problem, and often exploits very specific features of the situation in which the problem is embedded. A general-purpose method is applicable to a wide range of different problems. One general-purpose technique used in AI is means-end analysis, which involves the step-by-step reduction of the difference between the current state and the goal state. The program selects actions from a list of means–which in the case of, say, a simple robot, might consist of pickup, putdown, moveforward, moveback, moveleft, and moveright–until the current state is transformed into the goal state.


In perception the environment is scanned by means of various sense-organs, real or artificial, and processes internal to the perceiver analyse the scene into objects and their features and relationships. Analysis is complicated by the fact that one and the same object may present many different appearances on different occasions, depending on the angle from which it is viewed, whether or not parts of it are projecting shadows, and so forth.

At present, artificial perception is sufficiently well advanced to enable a self-controlled car-like device to drive at moderate speeds on the open road, and a mobile robot to roam through a suite of busy offices searching for and clearing away empty soda cans. One of the earliest systems to integrate perception and action was FREDDY, a stationary robot with a moving TV ‘eye’ and a pincer ‘hand’ (constructed at Edinburgh University during the period 1966-1973 under the direction of Donald Michie). FREDDY was able to recognise a variety of objects and could be instructed to assemble simple artefacts, such as a toy car, from a random heap of components.


A language is a system of signs having meaning by convention. Traffic signs, for example, form a mini-language, it being a matter of convention that, for example, the hazard-ahead sign means hazard ahead. This meaning-by-convention that is distinctive of language is very different from what is called natural meaning, exemplified in statements like ‘Those clouds mean rain’ and ‘The fall in pressure means the valve is malfunctioning’.

Leave a Reply

%d bloggers like this: