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Friday, April 24, 2020

Intelligence Amplification (IA)

Intelligence Amplification (IA) 


Introduction:


Intelligence amplification (IA) is the idea which is composed of technologies that create independent artificial intelligence, technologies can assist human intelligence. Intelligence amplification systems work to enhance one's own intelligence, to boost the role or skill of a human decision-maker in some way. The idea was first proposed in the 1950/60s by cybernetics and early computer pioneers. Intelligence amplification is also known as assistive intelligence, augmented intelligence, cognitive augmentation or machine-augmented intelligence.

  IA(Intelligence Amplification) is sometimes contrasted with AI (artificial intelligence), that is, the project of building a human-like Intelligence in the form of an autonomous technological system such as a Computer or Robot. AI has encountered many fundamental obstacles, practically theoretical, which for AI seem moot, as it needs technology merely as extra support for an autonomous intelligence that has already proven to function.

 The terms “intelligence augmentation” and “intelligence amplification” portrays images of human beings with a computer chip embedded in their skulls or an outfit/device that is attached to their skull/heads. IA does not need to understand the human brain except for extreme enhancement. It can be a user interface issue, Gradual acclimatisation, and Effective intelligence might be more relevant than “real” intelligence
Fig1: Intelligence amplification (IA)

Technology


 Intelligence amplification (IA) uses various technologies such as

1) Natural language Generation (NLG) - NLG is one of the disciplines of Intelligence Amplification that converts all types of data into human-readable text. This software converts data into the text at a very rapid pace, enabling machines to communicate effectively. NLG software has the ability to convert large quantities of data, identify unique patterns and share the information in such a way that it will be easy for human beings to understand. This software is especially used for producing news and other time-sensitive stories which require a very high speed for publishing on the internet

Fig 2: Natural Language Generation(NLG)



2) Speech recognition - Speech recognition is a technology that can convert spoken language into readable text. Voice recognition is one of the parts of speech recognition, which identifies a person based on their voice. Facebook, Amazon, Microsoft, Google and Apple are already offering this feature on various devices like Google Home, Amazon Echo and Siri.

3) Virtual Agents - A virtual agent is basically a computer agent or program that is capable of interacting with humans.

4) IA-Optimized Hardware - IA technology makes hardware much friendlier. It helps through new graphics and central processing units and processing devices specifically designed and structured to execute IA-oriented tasks.

5) Deep Learning Platforms - Deep learning platforms use a unique form of Machine Learning that involves artificial neural circuits with various level of abstraction layers that can mimic the human brain, processing data and creating patterns for decision making.

6) Emotion Recognition - This technology allows the software to “read” the various emotions on a human face by using advanced image processing or audio data processing. We can capture “micro-expressions,” or subtle body language cues defines a person’s feelings.

 Applications


 IA is playing a very important role in our daily lives and also help solve immensely difficult issues in various industries, such as entertainment, education, health, commerce, transport and utilities. IA applications can be grouped into four categories are reasoning, Knowledge, Communication, and Perception.

 • Reasoning: The ability to solve problems through logical deduction. E.g. financial assets management, legal assessment. 
Knowledge: The ability to present knowledge about the world. E.g. purchase prediction, fraud prevention, drug creation.
 • Communication: The ability to understand spoken and written language. E.g. intelligent assistants, voice control.
 • Perception: The ability to infer things about the world via sounds, image, and other sensory inputs. E.g. medical diagnosis, surveillance.

Algorithms

Figure 3: Search Algorithm in IA

IA has some kind of search algorithm in the background to accomplish their tasks.

The search problem consists of
  1. State Space
  2. Start State
  3. Goal Test
  4. Goal state

  • Uninformed Search Algorithms  

The search algorithms have no such additional information on the goal node other than the one that has been provided in the problem definition. The plans are to reach from start state to the goal state differ only by the order and/or length of actions. Uninformed search is also called Blind search. The following uninformed search algorithms are discussed in this section.

1) Depth First Search - Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node and explores as far as possible along each branch before backtracking.

2) Breadth-First Search - Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root and explores all of the neighbour nodes at the present depth prior to moving on to the nodes at the next depth level.

3) Uniform Cost Search- UCS is different than both DFS and BFS. Uniform Cost Search is traversing via different edges might not have the same cost. The method is usually used when we have to find a path where the cumulative sum of costs is least.

  • Informed Search Algorithms 


 The algorithms have information on the goal state, which helps us in more efficient searching. This information is obtained by a method known as Heuristic.

1) Greedy Search - In a greedy search, we expand the node closest to the goal node. The “closeness” is estimated by a heuristic h(x). Heuristic: A heuristic h is defined as- h(x) = distance between node x from the goal node. Lower the value of h(x), the closer is the node from the goal.

2) A* Tree Search - A* Tree Search, or simply known as A* Search, combines the strengths of uniform-cost search and greedy search.

3) A* Graph Search - A* tree search works well, except that it takes time re-exploring the branches it has already explored.

NOTE: The views expressed here are those of the author's and not necessarily represent or reflect the views of DOT Club as a whole

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