Types of Artificial Intelligence (AI)
The emergence of artificial super-intelligence will change humanity, but it’s not happening soon. Here are the types of AI leading up to that new reality.
Reactive AI
Such algorithms lack memory and are reactive. These offer the same output even after being given a specific input. Machine learning models that use this type of AI – are effective for pattern recognition tasks. But they are incapable of analyzing scenarios that include imperfect information or historical understanding.
- It is applicable for simple classification and pattern recognition tasks.
- Great for scenarios where all parameters are known; beat humans because it can make calculations much faster.
- Incapable of dealing with scenarios including imperfect information or requiring historical understanding.
Limited Memory
Algorithms in limited memory machines work in proportion to the human brain. These are capable of handling complex classification tasks and use historical data to make predictions. Example: Autonomous driving. Limited machines come with narrow intelligence as they lag behind human intelligence in many respects. They require training data to learn and behave.
- Can handle complex classification tasks.
- Able to use historical data to make predictions.
- Capable of complex tasks such as self-driving cars, but still vulnerable to outliers or adversarial examples.
Theory of Mind
This type of AI is capable of understanding human motives and delivers personalized results based on individual needs. Theory of mind is also known as artificial general intelligence (AGI). It can contextualize information and generalize knowledge to resolve varied problems. It also incubates the ability to detect human emotions and empathize with people (a part of sentiment analysis)
- Able to understand human motives and reasoning. Can deliver a personal experience to everyone based on their needs.
- Able to learn with fewer examples because it understands motive and intent.
- Considered the next milestone for AI’s evolution.
Self-aware
This type of artificial intelligence considers the mental state of other entities, as well itself. It is capable of surpassing human cognition by creating more intelligent versions of itself.
Components of AI
| Applications | Types of Models | Software/Hardware for Training and Running Models | Programming Languages for Building Models |
| Image Recognition | Deep Learning | GPU’s | Python |
| Speech Recognition | Machine Learning | Parallel Processing Tools (Like Spark) | TensorFlow |
| Chatbots | Neural Networks | Cloud Data Storage and Computing Platforms | Java |
| Natural Language Generation | C | ||
| Sentiment Analysis |
Artificial Intelligence Solutions
- AI makes production flexible and reliable.
- It is applicable for – (1) structure planning, (2) design of products and machinery, (3) production operations, and (4) manufacturing high-quality, customized products faster and at an affordable price.
- AI enables machines and processes to gather insights from high volumes of scattered data in their surroundings and optimize their processes during live operation.
- It chips in enormous potential giving Industry 4.0 a jumpstart.
- Plants are constantly adapting to new circumstances and can optimize with no need for operator input.
- Artificial intelligence helps to create complex connections in systems that are not evident to the human eye.
- AI also helps to recognize if the product in the manufacturing unit meets quality requirements. It determines the production parameters to be adapted to ensure that this remains the case during the ongoing production process.
Benefits and Applications of AI in Enterprises
- Artificial intelligence offers tremendous potential for industries. In logistics, transporters find their way through factory halls on their own. Industrial plants optimize their power consumption during live operation. Machines perform quality control checks and make the necessary adjustments.
- Organizations incorporate AI into their business operations to save money, invigorate efficiency, generate insights, and create a new market.
- AI-enabled applications enhance customer service, maximizes sales, sharpens cyber-security, optimizes supply chains, frees up workers from routine tasks, improves existing products, and point the way to new products.
- Enterprise leaders use AI to improve their businesses and ensure a return on their investment, irrespective of the challenges:
- Organizations support research initiatives on AI.
- AI-enabled were used to detect hotspots, improve patient care, identify therapies, and develop vaccines accounting for the recent pandemic.
- AI-enabled hardware and software robotics has the potential to rise as companies strive to build resilience against other catastrophes.
- To reap the benefits of AI in enterprises, business leaders must understand how AI works, although its applications are scalable and are still evolving.
In conclusion: What can you take home?
Data changes its form but remains there. It is omnipresent with a great deal of potential. Every AI-enabled application has a lifecycle: Smart recommendations, generative design, anomaly detection, and preventive maintenance, to optimize the way the products are designed and produced. AI Development Companies use it within industrial-grade AI applications adding value and interacting seamlessly with software and automation. Collaboration and open ecosystems potentially leverage such technologies. Follow us for more insights on Artificial Intelligence, neural language, and machine learning applications!