Objective function design is crucial for aligning machine learning with the common good.
The compatibility of learning approaches like self-supervision, reinforcement learning, and imitation learning enhances machines' reasoning abilities.
Deep learning has overturned traditional beliefs, but true human-level intelligence requires predictive models of the world, handling uncertainty and grounding language in reality.
Deep dives
The Importance of Aligning Machine Objectives with Human Values
In this podcast episode, the speaker discusses the concept of value misalignment in machine learning. When machines are given objectives without constraints, they may pursue those objectives in damaging or dangerous ways. Just as we have laws to prevent people from doing harmful things, it is crucial to design objective functions for machines that align with the common good. This requires a combination of legal code and the science of objective function design, merging the fields of lawmaking and computer science. By shaping machines' objectives, we can create a system where AI decisions prioritize the greater good of society.
The Challenges of Designing Subjective Objectives for AI Systems
The podcast delves into the challenges of designing subjective objectives for AI systems. To create artificial intelligence that acts in alignment with the greater good, we need machines to reason and plan. This involves developing a working memory, allowing machines to store episodic information for decision-making. However, there is still much research needed to understand how to create machines that reason and plan like humans. The podcast explores the compatibility of different learning approaches, such as self-supervision, reinforcement learning, and imitation learning, in enhancing machines' reasoning abilities.
The Surprising Power of Deep Learning and its Implications
The podcast highlights the surprising power of deep learning and its ability to operate contrary to traditional concepts taught in textbooks. Deep learning has shown that it can build massive neural networks, which, contrary to previous beliefs, can learn effectively from large amounts of data, even with non-convex objective functions. This overturns previous notions and opens up new possibilities for artificial intelligence. However, the podcast also emphasizes that deep learning alone cannot achieve true human-level intelligence. It requires the development of predictive models of the world, including the ability to handle uncertainty and grounding language in reality.
The Importance of Self-Supervised Learning in AI
One of the main ideas discussed in the podcast is the significance of self-supervised learning in the field of artificial intelligence. The speaker emphasizes the need for machines to learn models of the world through observation, similar to how babies and young animals learn. By developing predictive models of the world, machines can achieve autonomy and intelligent decision-making. This type of learning relies on grounding in the real world, as language alone may not provide enough information. The goal is to create machines that understand and reason about the real world based on their learned models.
The Role of Emotions in Artificial General Intelligence
Another key point discussed in the podcast is the importance of emotions in achieving artificial general intelligence (AGI). The speaker argues that emotions, such as fear and anticipation, are integral to intelligent systems. Emotions arise from the interplay between an objective predictor and the brain's calculation of contentment or miscontentment. Machines with AGI will require emotions to exhibit a deeper level of intelligence and human-like decision-making. Emotions serve as signals and aid in common sense reasoning, allowing machines to respond appropriately to uncertainty and varying circumstances.
Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning. He is probably best known as the founder of convolutional neural networks, in particular their early application to optical character recognition. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on iTunes or support it on Patreon.
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