What is artificial intelligence (AI)?
AI is a field combining computer science and vast datasets to enable problem-solving. It encompasses subfields like machine learning, where algorithms learn from data to make predictions or classifications, and deep learning, which utilizes artificial neural networks inspired by the human brain for even more complex tasks.
From Turing’s test to today’s generative power
1950 – Alan Turing’s – Computing Machinery and Intelligence
The “father of computer science” introduced the famous Turing Test, asking if machines can truly think by mimicking human intelligence in conversation. This remains a controversial yet crucial concept in AI and philosophy, exploring the nature of consciousness and language.
2003 – Stuart Russell and Peter Norvig’s – Artificial Intelligence – A Modern Approach
This influential textbook defines AI through four potential goals:
- Human Approach:
- Systems that think like humans.
- Systems that act like humans.
- Ideal Approach:
- Systems that think rationally.
- Systems that act rationally.
Turing’s test arguably falls under the “act like humans” category, highlighting the ability to emulate intelligent behavior.
The Hype is Real
After cycles of excitement and skepticism, AI is experiencing a resurgence with the likes of OpenAI’s ChatGPT. This generative model represents a leap forward in natural language processing, not just mimicking language but understanding its underlying structure. Similar breakthroughs are occurring in code generation, molecular simulations, and image creation, showcasing the growing versatility of AI.
A Universe of Possibilities
From healthcare to finance to art, the applications of AI are boundless. Yet, amidst the excitement, ethical considerations become paramount. IBM actively participates in the conversation around AI ethics, ensuring responsible development and deployment of this powerful technology.
Exploring Artificial Intelligence Types
Weak AI – The Powerhouse of Today’s Tech
Weak AI, alternatively known as Narrow AI or Artificial Narrow Intelligence (ANI), is designed and trained for specific tasks. Contrary to its name, Weak AI powers many robust applications in our daily lives. Examples include Apple’s Siri, Amazon’s Alexa, IBM Watson, and autonomous vehicles, showcasing the versatility and capabilities of this focused AI.
Strong AI – The Dreams of Tomorrow
Strong AI encompasses two categories – Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). AGI, or general AI, represents a theoretical form of AI with intelligence comparable to humans. It implies self-aware consciousness, problem-solving abilities, learning capabilities, and future planning. Artificial Super Intelligence (ASI), also known as superintelligence, surpasses the intellectual capacities of the human brain. While practical applications of strong AI are currently nonexistent, ongoing research explores its potential development.
Although strong AI remains a theoretical concept, the exploration of its capabilities and possibilities is actively pursued by AI researchers. In the interim, popular culture, such as HAL, the superhuman rogue computer assistant in 2001: A Space Odyssey, provides glimpses into the realm of Artificial Super Intelligence (ASI).
Distinguishing Deep Learning from Machine Learning
While deep learning and machine learning are often used interchangeably, it’s essential to understand the subtle differences between the two. Both fall under the umbrella of artificial intelligence, with deep learning being a specific subset of machine learning.
Deep learning – Diving into the Neural Network Ocean
Deep learning involves the use of neural networks, specifically those with more than three layers, including inputs and outputs. The term ‘deep’ refers to the depth of the neural network, as illustrated in the diagram below.
The key distinction lies in how these algorithms learn. Deep learning streamlines the feature extraction process, reducing the need for manual human intervention and facilitating the utilization of larger datasets. Lex Fridman aptly describes deep learning as ‘scalable machine learning,’ as mentioned in the same MIT lecture referenced earlier. In contrast, traditional or ‘non-deep’ machine learning relies more on human involvement for learning. Human experts play a crucial role in determining the hierarchy of features necessary to discern differences between data inputs, often requiring more structured data for effective learning.
Deep machine learning – The Traditional Architect
Deep machine learning can utilize labeled datasets, employing supervised learning methods to inform its algorithm. However, it doesn’t necessarily require labeled data; it can process unstructured raw data, such as text or images, autonomously determining the hierarchy of features that differentiate various data categories. Unlike traditional machine learning, deep learning doesn’t demand constant human intervention in data processing, opening up opportunities for more innovative and scalable machine learning approaches.
Learning Styles – Supervised vs. Unsupervised
Both giants utilize supervised learning, where labeled data with predefined categories guides the algorithm’s understanding. But deep learning’s true power lies in its ability to shine even in the dark. It can autonomously discover patterns and extract features from unlabeled data like text, images, and videos, a feat beyond most traditional algorithms.
The Future of AI – Where are we headed?
Deep learning’s automated learning and data-hungry appetite pave the way for groundbreaking advancements in areas like computer vision, natural language processing, and even autonomous vehicles. Its ability to learn from vast, messy datasets makes it a game-changer, opening doors to even more innovative and scalable AI applications.
The Evolution of Generative Models in AI
Generative AI, a subset of deep learning, has revolutionized the field by enabling models to process raw data and generate statistically probable outputs. This evolution has been fueled by advancements in deep learning, expanding the application of generative models beyond numerical data to complex types such as images and speech.
Variational Autoencoders (VAEs) – Pioneering Deep Generative Modeling
Introduced in 2013, Variational Autoencoders (VAEs) played a pivotal role in the transition of generative models to deep learning. These models were the first to gain widespread use for realistic image and speech generation, opening the floodgates to scalable deep generative modeling.
The Rise of Exemplary Models – GPT-3, BERT, and DALL-E 2
Early models like GPT-3, BERT, and DALL-E 2 have showcased the immense potential of generative AI. These models have demonstrated the ability to generate diverse outputs, marking a significant leap in the capabilities of generative models across various applications.
Future Trends – Foundation Models and Broad AI Adoption
The future of generative AI lies in foundation models trained on extensive, unlabeled datasets. These models can be applied to different tasks with minimal fine-tuning, moving away from specialized task-oriented systems. This shift towards broad AI is expected to accelerate adoption in enterprises.
Generative AI in Enterprises – Reducing Labeling Requirements
Generative AI is predicted to facilitate widespread adoption in enterprises by reducing labeling requirements. This reduction in barriers makes it easier for businesses to integrate AI-driven automation, enabling deployment in a broader range of mission-critical situations.
IBM’s Vision – Foundation Models in a Hybrid-Cloud Environment
For IBM, the vision involves bringing the power of foundation models to every enterprise. The goal is to facilitate AI deployment in a frictionless hybrid-cloud environment, allowing businesses to harness the potential of generative AI seamlessly and efficiently.