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<h1 id="conclusion">2.5 Conclusion</h1>
<p>Understanding the technical underpinnings of AI systems—the
underlying models and algorithms, how they work, how they are used, and
how they are evaluated—is essential to understanding the safety, ethics,
and societal impact of these technologies. This foundation equips us
with the necessary grounding and context to identify and critically
analyze their capabilities and potential, as well as the risks that they
pose. It allows us to discern potential pitfalls in their design,
implementation, and deployment and devise strategies to ensure their
safe, ethical, and beneficial use.</p>
<h2 id="summary">2.5.1 Summary</h2>
<p>In this chapter, we presented the fundamentals of artificial
intelligence (AI) and its subfield, machine learning (ML), which aims to
create systems that can learn without being explicitly instructed. We
examined its foundational principles, methodologies, and evolution,
detailing key techniques, concepts, and practical applications.</p>
<p><strong>Artificial intelligence.</strong> We first discussed AI, the
vast umbrella that encapsulates the idea of machines performing tasks
typically associated with human intelligence. AI and its conceptual
origins date back to the 1940s and 1950s when the project of creating
“intelligent machines” came to the fore. The field experienced periods
of flux over the following decades, waxing and waning until the modern
deep learning era was ushered in by the groundbreaking release of
AlexNet in 2012, driven by increased data availability and advances in
hardware.</p>
<p><strong>Defining AI.</strong> The term “artificial intelligence” has
many meanings, and the capabilities of AI systems exist on a continuum.
Five widely used conceptual categories to distinguish between different
types of AI are narrow AI, artificial general intelligence (AGI),
human-level AI (HLAI), transformative AI (TAI), and artificial
superintelligence (ASI). While these concepts provide a basis for
thinking about the intelligence and generality of AI systems, they are
not well-defined or complete, often overlapping and used in different,
conflicting ways. Therefore, in evaluating risk, it is essential to
consider AI systems based on their specific capabilities instead of
broad categorizations.</p>
<p><strong>The ML process.</strong> We presented a general framework for
understanding ML models by considering five aspects of a model: its
task, input data, output, and what type of machine learning it uses. We
then discussed each of these aspects in turn. We explored common tasks
for ML models, including classification, regression, anomaly detection,
and sequence modeling. We highlighted a few of the many types of data
that these models work with and discussed the model development process.
Creating an ML model is a multi-step process that typically includes
data collection, model selection, training, evaluation, and deployment.
Measuring the performance of a model in evaluation is a critical step in
the development process. We surveyed several metrics used to achieve
this, as well as the broader, often conflicting goals that guide this
process.</p>
<p><strong>Types of ML.</strong> We discussed different approaches to
machine learning and how these categories are neither well-defined nor
complete, even though distinctions are often drawn between different
“types” of machine learning. We surveyed four common approaches to ML:
supervised learning, unsupervised learning, reinforcement learning, and
deep learning. At a high level, supervised learning is learning from
labeled data, unsupervised learning is learning from unlabeled data, and
reinforcement learning is learning from agent-gathered data. Deep
learning techniques are used in all three settings, leveraging deep
neural networks to achieve remarkable results.</p>
<p><strong>Deep learning.</strong> We then examined deep learning in
more depth. We saw how, beyond its use of multi-layer neural networks,
deep learning is characterized by its ability to learn hierarchical
representations that provide deep learning models with great flexibility
and power. Machine learning models are functions that capture
relationships between inputs and outputs with representations that allow
them to capture an especially broad family of relationships.</p>
<p><strong>Components of DL models.</strong> We explored the essential
components of deep learning models and neural networks. Through the
example of multi-layer perceptrons (MLPs), we broke down neural
networks, structures composed of layers of neurons, into an input layer,
an output layer, one or more hidden layers, weights, biases, and
activation functions. We highlighted a few significant activation
functions and examined other fundamental building blocks of deep
learning models, including residual connections, convolution, and
self-attention. We also presented influential architectures, such as
CNNs and Transformers.</p>
<p><strong>Processes in DL models.</strong> We discussed how deep
learning models learn and are used in training and inference. We walked
through the steps to training a deep learning model, beginning with
initialization and then cycling through sending information forward to
make a prediction, measuring its error or quality, sending this error
backward, and adjusting parameters accordingly until a stopping
criterion is reached. We discussed training techniques such as
pre-training, fine-tuning, few-shot learning, and zero-shot learning,
and how training typically involves a combination of many methods and
techniques used in conjunction. We considered the importance of
scalability, computational efficiency, and interpretability in
evaluating deep learning models and their suitability for deployment. We
plotted the course of technical and architectural development in the
field, from LeNet in 1989 to BERT and GPT models in 2018. We considered
real-world applications of deep learning in communication and
entertainment, transportation and logistics, and healthcare.</p>
<p><strong>Scaling laws.</strong> Scaling laws describe mathematical
relationships between model performance and key factors like model size
and dataset size in deep learning. These power law equations show that
as models grow in parameters and are trained on more data, their loss
tends to decrease substantially and predictably. Scaling up
computational resources used to train a model can enable an increase in
both model parameters and the amount of data used in training.
Researchers can leverage scaling laws to determine optimal model and
dataset sizes given computational constraints. Scaling laws hold across
many modalities and orders of magnitude, though they do not necessarily
apply to all deep learning models, such as many classification
models.</p>
<p><strong>Speed of AI development.</strong> Trends in compute and algorithmic efficiency suggest that the capabilities of AI systems may continue to improve rapidly and could surpass human performance across a broad range of tasks in coming decades. There is considerable uncertainty among experts about when human-level AI (HLAI) might be achieved, with recent surveys indicating shorter timelines than previously anticipated. Increasing investment in compute and algorithmic advances have driven significant increases in AI capabilities. However, achieving HLAI may require conceptual breakthroughs, and challenges such as the availability of high-quality training data and the economic viability of further investments could lead to a slowdown. Despite these uncertainties, vigilance is warranted due to the high stakes and potential risks associated with advanced AI, even before reaching HLAI.