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Artificial intelligence is a branch of computer science concerned with building intelligent machines capable of performing tasks that typically need human intelligence.
The four types of AI are:
Reactive machines:
Reactive machines are virtual AI principles-based machines. They can perceive and react. They cannot store the memory. So, we cannot rely on past experiences. They are designed to perform only a limited number of specialized tasks. This kind of AI is the most trustworthy and reliable because it reacts the same way to the same stimuli every time. One example of the reactive machine is game playing reactive machine Google’s alpha go.
Limited memory:
Limited memory in Artificial Intelligence is capable of storing previous data and predicting by gathering the last data. It works on the previous data and recollects it if we want to extract it. The three machine learning models that use limited memory are:
Reinforcement learning: It makes predictions better through trial and error.
Long short term memory: It uses past time data and helps predict the item in the sequence.
Evolutionary generative adversarial networks(E-GAN): It grows with time and is modified slightly but not based on previous experiences.
Theory of mind:
It is theoretical. We have not achieved the technology and capabilities to reach this level of Artificial Intelligence.
Self-awarness
The final step in Artificial Intelligence will be self-awareness. It relies on both human researchers and learning how to replicate that.
Examples of Artificial Intelligence:
- Alexa or Siri assistance.
- Self-driving cars.
- Robo-advisors.
- Email spam filters.
How does it works?
Authors Stuart Russell and Peter Noring explored four different approaches that have defined the field of Artificial Intelligence:
- Thinking humanly.
- Thinking rationally.
- Acting humanly.
- Acting rationally.
The first two deal with thought processes and reasoning, while the other two deal with behavior. The ford professor of AI and computer science at MIT, Patrick Winston, defines AI as “algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action.”
Importance of AI in real-time:
AI automates repetitive learning: AI is used to automate manual tasks which are computerized and have a high volume with reliability and without fatigue.
AI adds intelligence: The products that we use can be updated using AI. Example Siri assistance that we employ in IOS. Security intelligence systems used in the home, workplaces.
AI is used to analyze deeper data: It is done by using neural networks with many hidden layers. Making a fraud detection system with five hidden layers is impossible. But, that can be done by using computer power, big data, and AI.
Accuracy in AI: AI has highly great accuracy. It is achieved in AI by using a deep neural network system. For example, our interactions with intelligent assistance devices like Alexa, Siri, and Google are based on deep learning methods. These devices get more accurate by using them more.
AI takes more out from the data: The algorithms that we use are self-learning. We need to apply AI to find solutions using data as assets.
Five AI technologies that are boon to us are:
Artificial Intelligence
Machine learning
Deep learning
Natural language processing
Computer vision
Disadvantages:
- Autonomous weapons using AI systems are hazardous when they are held in the wrong hands.
- The AI is designed to do something beneficial, but it may become destructive whenever we fail to align its goals. For example, if a super-intelligent AI is given a task to a geoengineering project, it might damage the havoc system.
Conclusion:
The advantages and disadvantages of technology always depend on how we use them. It becomes dangerous only if we engage in foolish biological engineering experiments; otherwise, anything is safe.