Tenthpin’s 12 Categories of AI

Artificial Intelligence (AI) is transforming the Life Sciences industries, but not all AI is created equal. From task-specific Narrow AI, which powers voice assistants and image recognition, to the conceptual General AI, with human-like intelligence, the spectrum of AI capabilities is vast. Emerging generative AI models, leveraging deep learning and machine learning, are pushing boundaries across domains, from drug discovery to creative content generation. This article explores these distinct AI categories and their transformative potential for Life Sciences.

1. Narrow or Weak AI: AI systems that are designed and trained for a specific task or a narrow set of tasks.
    These systems are not capable of generalizing their knowledge to perform tasks beyond their original scope.
   Examples include voice assistants, image recognition systems, and recommendation algorithms.
2. General or Strong AI: a hypothetical level of AI that exhibits intelligence comparable to that of a human across a wide range of tasks.
   This type of AI would possess the ability to understand, learn, and apply knowledge across various domains, similar to human intelligence.
   General AI is currently more of a concept and does not exist in practical terms as of now.
3. Generative AI: models that can create new, original artifacts like images, video, text, code, molecular structures, and more.
   An emerging cross-cutting advance that builds on ML, DL, NLP and CV while pushing boundaries in multiple domains including drug discovery.
   GI models often require training with large data sets. These can be general/public sets or company own data sets.
4. Machine Learning (ML): a subset of AI that focuses on developing algorithms and models that enable computers to learn from data.
   ML systems can improve their performance on a specific task over time as they are exposed to more data.
   Types of machine learning include supervised learning, unsupervised learning, and reinforcement learning.
5. Deep Learning: a subfield of machine learning that involves neural networks with multiple layers (deep neural networks).
   These deep neural networks can automatically learn hierarchical representations of data, allowing them to capture intricate patterns and features.
   Deep learning has been particularly successful in tasks such as image andspeech recognition.
6. Reinforcement Learning: training AI systems to make decisions by interacting with an environment.
   The system receives feedback in the form of rewards or penalties, allowing it to learn optimal behaviors through trial and error.
   Reinforcement learning has been used in applications like game playing and robotic control.
7. Evolutionary Algorithms: including genetic algorithms, are optimization algorithms inspired by the process of natural selection and evolution.
   These algorithms work by evolving a population of candidate solutions over successive generations. The fittest solutions are selected, undergo crossover (exchange of genetic information), and undergo mutation to create a new generation of solutions.
   Evolutionary algorithms are used for optimization problems, search problems, and tasks where finding the best solution in a vast solution space is challenging.
8. Natural Language Processing (NLP): enabling machines to understand, interpret, and generate human language.
   It includes tasks such as language translation, sentiment analysis, and chatbot interactions.
   NLP plays a crucial role in bridging the communication gap between humans and machines.
9. Computer Vision: equipping machines with the ability to interpret and understand visual information from the world, such as images and videos.
   Applications include image recognition, object detection, and facial recognition.
10. Expert Systems: designed to mimic the decision-making abilities of a human expert in a specific domain.
    They use rules and knowledge bases to provide solutions or recommendations.
11. AI for Robotics: enhance the capabilities of robots, enabling them to perform complex tasks in various environments.
12. AI Ethics and Bias Mitigation: a growing focus on addressing ethical considerations and mitigating biases in AI algorithms to ensure fair and responsible use.