Artificial Intelligence explained the easy way
- Video thoughts on an easy definition
- Scientific definition: AI in general is a system’s ability:
- to correctly interpret external data (e.g. when a camera provides data - know the difference between a person and a shadow)
- to learn from such data (e.g. the AI has to learn that it is fine to run over shadows but not over people)
- to use those learnings to achieve specific goals and tasks through flexible adaptation. (e.g. steer a vehicle)
- Key takeaway: AI is not about creating artificial humans but comprises certain ways HOW computers handle data. Some AI systems are really powerful some are not at all.
Create Digital Companions
- Digital Companions are computer systems that are designed to fulfill the needs of humans – to help them to do their work efficiently, comfortably and with minimal errors.
- Video thoughts on Digital Companions
- Psychology and scientific results on how people learn and work are an highly important corner stone for the development of digital companions.
Industrial knowledge graphs
- Without being able to interpret, data are just characters with no meaning.
- Industrial knowledge graphs give AI something like background knowledge. (See a video about the importance of background knowledge)
- Thus, they enable AI to figure out the context of data and further relevant information.
- They can be represented as (mind-map-like) graphs that show how things are interconnected.
- A technical term for certain AI-learning algorithms.
- The AI figures out by itself (in many, many iterations) HOW to solve a problem.
- In the beginning it starts with random actions.
- The AI and receives (positive or negative) feedback depending on how successful its actions were.
- It adapts accordingly and gradually gets better and better
- Try yourself: Watch an AI learn how to play snakes. Take your time, play around with the parameters and experience the differences yourself!
- Neural Networks (NN) are a popular approach to implement AI.
- Watch a video how a NN works
- Using appropriate training algorithms NNs can find solutions to specific problems by themselves
- NNs need thousands of training examples before they deliver satisfying results
- For more infomation do the interactive ExpAInable
Sometimes you need to know why
- Normally AI is like a black box – it gives results but does not tell you why.
- For important decisions this is just not acceptable.
- Explainable AI tells you WHY it made a decision.
- Also see European guidelines for trustworthy AI
- Basic concepts were invented in the 1950s
- AI was almost forgotten during the 1970s and 1980s - there were just not the right use cases (computers too slow, not enough data to train AI).
- The present AI hype is caused by digitalization, masses of data and IoT.
- When one needs to evaluate masses of data, AI is a successful technology.
Why we need AI?
- AI is a technology that helps to solve problems that computers can't handle without AI.
- Video on what AI can do
- AI can handle tons of data and needs tons of data to be trained. Because of that it is so useful for IoT
- Key takeaway: AI is not about replacing humans but especially useful to handle masses of data.
Be aware of Biases
- Humans are prejudiced against a lot of things – and very often not even aware of it.
- Watch a video about unconscious bias.
- AI is just as biased and discriminating as humans are if it was trained by data that – implicitly – contained such biases.
- Nobody needs badly educated AI becoming an artificial racist!
- Therefore: AI needs unbiased data for training!
- An AI-learning approach that works like the evolution. i.e. the fittest will survive
- The AI starts learning in a random configuration and generates a couple of mutations of that configuration. All of these mutations have to carry out a certain task.
The most successful ones “survive “ to the next generation when new mutations are generated
- Normally It takes at least hundreds of generations to receive satisfying results
- Try yourself: watch robots learning to walk – it looks very drunken in the beginning, but 1000 generations later …
Aenne Barnard, Rebecca Johnson
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