The development of self-driving vehicles is a major indicator of progress in artificial intelligence. While full autonomy is complex because it requires a system to perceive, reason, and incorporate traffic rules, companies have achieved partial autonomy. Currently, deep learning is primarily utilized for the visual perception aspect of these problems, while the remaining reasoning and rule-incorporation logic is heavily tuned by engineers.

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A deep learning model is a powerful type of machine learning model differentiated by its architecture, which consists of many successive transformations of data chained together from top to bottom. This depth of continuous data transformations allows these models to tackle complex tasks by automatically learning multi-level representations. By doing so, deep learning effectively replaces not only the labor-intensive process of manual feature engineering, but also the shallow models that are typically used at the end of traditional machine learning pipelines.

Deep Learning Model

Dive into Deep Learning

Moore's law refers to the historical trend of consistently cheaper and more powerful computation. In the context of machine learning, this exponential growth in compute budget allowed statistical models to spend more computer cycles optimizing parameters, shifting the ideal paradigm from linear models to computationally intensive deep neural networks.

Moore's Law in Deep Learning

Kryder's law describes the rapid advancement and cost reduction of data storage technologies. This availability of inexpensive data storage was a fundamental prerequisite for the deep learning revolution, as it allowed for the accumulation and retention of the massive datasets required to train deep neural networks.

Kryder's Law in Deep Learning

Graphics Processing Units (GPUs) were originally engineered to render graphics for computer gaming, but their highly parallel architecture proved exceptionally well-suited for the matrix operations pervasive in neural networks. Significant progress in GPU computation has triggered much of the recent advancement in deep learning by making previously infeasible, large-scale algorithms highly accessible and efficient.

Graphics Processing Unit (GPU) in Deep Learning

Multi-stage designs, such as memory networks and the neural programmer-interpreter, enable statistical models to execute iterative reasoning. These architectures allow the internal state of a deep neural network to be modified repeatedly across multiple steps, mirroring how a traditional computer processor modifies memory to carry out a chain of computation.

Multi-stage Reasoning in Deep Learning

A Diffusion Model is a deep generative architecture that gradually constructs data samples from random noise. It works by learning the denoising process to reverse a mathematical forward diffusion process (which gradually adds random noise to data). Diffusion models have become highly effective for tasks like photorealistic text-to-image generation.

Diffusion Model

Autonomous Vehicles in Deep Learning

A major catalyst for the recent rapid progress in deep learning has been the immense abundance of available training data. This widespread proliferation of datasets is primarily driven by the deployment of inexpensive digital sensors and the massive scale of internet applications, which continuously generate the vast amounts of information necessary to effectively train complex, multi-layered deep learning models.

Data Abundance in Deep Learning

Deep learning frameworks provide specialized software tools that streamline the construction, training, and deployment of neural networks. The availability of these efficient frameworks has made the design and implementation of whole system optimization significantly easier, which is a critical component in achieving high performance for complex deep learning pipelines.

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