From: lexfridman
The fields of [[ai_and_machine_learning | Artificial Intelligence (AI) and Machine Learning (ML)]] have undergone significant transformations over the years, heavily influenced by advancements in hardware and computing capabilities. A major aspect of this evolution is the role that [[advancements_in_deep_learning | deep learning]] and machine learning play in shaping the future and applications of AI.
## Evolution of AI: From Logic-Based Systems to Learning-Based Models
In the 1990s, the field of AI was primarily focused on Boolean logic and knowledge engineering. Success in AI was largely achieved through manually programmed systems where experts were interviewed to encode their knowledge into machines <a class="yt-timestamp" data-t="10:00">[10:00]</a>. However, as computing power increased, the focus shifted towards probability-based methods and machine learning, which allowed systems to learn from data rather than relying solely on pre-programmed instructions <a class="yt-timestamp" data-t="11:00">[11:00]</a>.
## Current State: The Dominance of Deep Learning
Deep learning, a subset of machine learning, has gained prominence due to its success in handling tasks involving big data. While [[artificial_intelligence_and_deep_learning | deep learning and AI]] may not solve every problem, they have revolutionized areas like perception systems and complex pattern recognition <a class="yt-timestamp" data-t="00:01:01">[00:01:01]</a>.
### Advancements in Hardware
The growth of deep learning has been accompanied by advancements in hardware such as GPUs, TPUs, and ASICs, which have enabled the processing of large-scale models and massive data sets <a class="yt-timestamp" data-t="01:01:01">[01:01:01]</a>.
## Future Directions and Philosophical Considerations
One key philosophical shift in AI development has been the focus on utility functions—deciding the objectives for AI systems now poses a more significant challenge than merely optimizing existing models <a class="yt-timestamp" data-t="03:01:01">[03:01:01]</a>. As AI systems become more sophisticated, issues like fairness, bias, and societal impact come into play, requiring advancements that transcend mere technical optimization.
## Challenges in AI
Deep learning faces challenges, particularly in balancing capabilities across various fields. While it excels in tasks related to perception, questions remain about its effectiveness in areas such as commonsense reasoning or tasks that require a broader AI perspective <a class="yt-timestamp" data-t="14:01:01">[14:01:01]</a>.
Furthermore, the concept of trust and explainability in AI systems continues to be a critical concern. Ensuring that decisions made by machine learning models are interpretable and trustworthy is pivotal for widespread adoption <a class="yt-timestamp" data-t="20:02:01">[20:02:01]</a>.
## Moving Forward
As we progress, integrating machine learning into broader AI systems and exploring areas such as [[future_directions_and_hopes_for_deep_learning_and_ai | future directions in deep learning and AI]] will be crucial. This includes developing AI applications that are not only technically advanced but also align with human values and societal goals <a class="yt-timestamp" data-t="27:01:01">[27:01:01]</a>.
The role of deep learning and machine learning in AI is continuously evolving, influenced by technological progress and societal needs. As these fields advance, they will play an increasingly integral role in shaping the landscape of AI.
> [!info] For Further Exploration
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> Check out resources on [[advancements_in_deep_learning | advancements in deep learning]] and the [[principles_of_deep_learning_and_ai_education | principles of AI education]] to understand how these technologies are integrated into modern AI systems.