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  • Writer's pictureGeopolitics.Λsia

Neural Network Architectures: The Blueprint

Updated: Jan 18

In an era where large language models and generative AI technologies are increasingly permeating every facet of society, it becomes imperative to take a step back and delve into the scaffolding that supports these intelligent systems. Far from mere computational wizardry, neural networks, the lynchpin of modern AI, have a storied history—a journey from biological inspiration to engineering marvel. Understanding this evolution is not merely an academic exercise; it has implications for investors, developers, and policy makers grappling with a rapidly evolving tech landscape. This article, therefore, serves as a blueprint—a meticulous examination of neural network architectures, from their humble beginnings to their current, sophisticated iterations. While we often discuss industry applications and cutting-edge research, today's focus is even more special. One noteworthy paper explores how AI is aiding the peer review process on platforms like "OpenReview." In essence, AI is becoming both the subject and contributor to scientific discourse.




The simplified Deep Convolutional Inverse Graphics Networks (DC-IGN), a type of neural network architecture aimed at learning a set of disentangled representations from visual data. The model is typically used in unsupervised learning scenarios which it can learn useful features from data without using any labels. The "inverse graphics" part suggests that it tries to understand the underlying 3D scene or features that give rise to a 2D image, effectively attempting to reverse-engineer the graphics rendering process. See paper, Deep Convolutional Inverse Graphics Network, Kulkarni et al. (2015).




The Birth of an Idea: From Theory to Practice


The origins of the perceptron can be traced back to a quest to understand the human brain and its complex network of neurons. Researchers like Warren McCulloch and Walter Pitts were enchanted by the idea of creating a machine that could replicate biological neurons, the fundamental units of the brain. Frank Rosenblatt, who formalized the perceptron, drew significant inspiration from neurobiology and the potential of constructing intelligent machinery. His focus was not only on building an artificial neuron capable of recognizing patterns but also on implementing this theory in the real world, particularly for the classification of visual patterns.


Rosenblatt's 1958 landmark paper introduced both the theoretical framework for the perceptron and empirical results from experiments conducted with the Mark 1 Perceptron, an early machine designed to test the perceptron algorithm. This machine, unlike the software constructs we are familiar with today, was a hardware device specifically engineered to classify visual inputs. Rosenblatt used graphs and results from his experiments to substantiate his theoretical constructs, showcasing the perceptron's capabilities to classify and generalize from its training data. In a time where the modern computer had yet to be fully realized and terms like "machine learning" had not been coined, these empirical tests were groundbreaking.



Mark 1: Perceptron and the empirical performance among α, β and γ system, it appears γ-system performance constancy across varying conditions and its equal constants to the α-system signify its superior robustness and versatility.



In essence, while Rosenblatt's pioneering paper was heavily rooted in theoretical constructs, it didn't stop at mere speculation. Through the Mark 1 Perceptron, he furnished empirical evidence that supported the theoretical properties and capabilities he described. His work became the empirical bedrock upon which the grander theories of machine learning and neural networks would later be built. At the heart of his work was the blend of biological metaphor and empirical validation: a machine equipped with adjustable weights that mimicked the synaptic strengths in biological neurons, and that could be "trained" to recognize simple visual patterns.


Despite the hardware limitations of the time and the nascent state of computational theory, the perceptron laid the foundational stone for what would become the fields of neural networks and machine learning. It was a visionary project that married insights from biology, psychology, and engineering, creating the stepping stones toward artificial intelligence. While the tools and techniques were in their infancy compared to today's standards, the ideas were revolutionary and set the stage for decades of research and innovation to come.







Thus, the perceptron stood at the intersection of theory and practice, marrying the biological inspiration with empirical verification. It generated significant excitement in the academic world because it presented not just a theoretical concept, but a working model that validated the potential of learning algorithms. The perceptron was a significant milestone that opened the door to the possibility that machines could, in fact, learn from data, setting the groundwork for all subsequent advances in artificial intelligence and machine learning.



The Limitations and the Winter: A Tale of Resurgence


The perceptron's pioneering impact was followed by a significant hurdle, often referred to as the "AI winter." Despite its promise, the perceptron had one significant limitation: it could only solve linearly separable problems. This shortcoming became glaringly apparent when Marvin Minsky and Seymour Papert published their seminal book "Perceptrons," which critically examined the constraints of this single-layered neural model. Their critique led to a loss of interest and a decrease in funding for neural network research, casting a long shadow over the field throughout the 1970s. The era was marked by skepticism and reduced enthusiasm, leading many to question the utility of neural networks for solving complex real-world problems.


The Neuron Network Zoo by the Asimov Institute.



However, the chill of the AI winter began to thaw in the 1980s with the advent of backpropagation. This optimization algorithm was a game-changer; it enabled neural networks to update their internal parameters based on the errors in their predictions, effectively allowing the network to "learn" from its mistakes. The introduction of backpropagation led to the development of multi-layer perceptrons or feed-forward neural networks with one or more hidden layers. These new architectures could tackle a much wider variety of problems, rejuvenating interest in the field.





The backpropagation algorithm, a foundational element in the revival of neural network research, can trace its roots back to multiple areas of study, including optimization, control theory, and statistical learning. The basic idea is to propagate the error backward through the network to adjust the weights such that the overall error is minimized. This concept is fundamentally linked to optimization techniques, which have been a staple in statistical learning and control theory for years.



Activation function in one neuron and one layer in matrix notation, see Tikz.



The seminal work often attributed to the popularization of backpropagation is the 1986 paper by David Rumelhart, Geoffrey Hinton, and Ronald Williams titled "Learning representations by back-propagating errors." This paper, which was heavily influenced by optimization techniques, mathematical statistics, and matrix calculus, was a turning point. It demonstrated that a multi-layer perceptron could be trained efficiently using backpropagation, making it possible to train deeper and more capable networks. The algorithm leveraged the concept of the chain rule from calculus to distribute error gradients backward through the network layers.


In the 1980s, several programming languages gained prominence, some of which were used in expert systems. These languages included FORTRAN, Modula-2, Ada, Pascal, LISP, and Prolog.





The late 20th and early 21st centuries saw the evolution of specialized neural network architectures, riding on the increasing computational power and the growing availability of data. Convolutional Neural Networks (CNNs) were designed for image recognition tasks, Recurrent Neural Networks (RNNs) catered to sequence data, Long Short-Term Memory (LSTM) networks which are specialized type of Recurrent Neural Network designed to effectively capture long-term dependencies in sequence data, thanks to their unique gating mechanisms, and Transformers redefined the benchmarks in natural language processing. They are particularly good at handling long-range dependencies in the data, thanks to their self-attention mechanisms. Each of these architectures brought its own set of advantages to the table, expanding the range of problems neural networks could solve.


The term "Deep Learning" gained prominence in the early 2010s, characterized by neural networks with many layers capable of learning high-level features from data. The progress in hardware, particularly GPUs, coupled with the availability of large labeled datasets, made it feasible to train these deep networks. It led to the development of remarkable architectures like GPT and BERT in natural language processing, ResNet in computer vision, and AlphaGo in reinforcement learning, often achieving human-level or even superhuman performance in various tasks.



 

AI Industrial Movement and Geopolitical Aspect



AceGPT


The increasing collaboration in artificial intelligence (AI) between Saudi Arabia's King Abdullah University of Science and Technology (Kaust) and Chinese institutions has raised alarms about potential difficulties in obtaining essential U.S.-made microprocessors. Amid escalating geopolitical tensions, the U.S. has tightened export controls on high-tech hardware, including 7nm microprocessor technology produced by Nvidia and AMD. These chips are crucial for developing generative AI models like chatbots. Although the U.S. has not yet halted exports to the Middle East, experts within Kaust fear that closer ties with China could compromise their access to American-made technology. This comes as the U.S. aims to contain the spread of advanced technology to China, especially against the backdrop of strained U.S.-China relations.



AceGPT stands for Arabic-Chinese-English GPT


Kaust, along with the United Arab Emirates (UAE), is striving to become a regional leader in AI. They are investing in building powerful supercomputers and launching large language models tailored for Arabic speakers. Kaust's most recent venture, AceGPT, is an Arabic-focused language model developed in partnership with the Chinese University of Hong Kong, Shenzhen. However, there is growing concern within Kaust and among Western officials about the implications of technology transfers to China. The prevailing sentiment suggests that maintaining a delicate balance in international partnerships is essential to avoid jeopardizing relations with the U.S., a key security ally for Gulf nations.


The U.S. is actively seeking to pull Gulf nations away from Chinese influence, offering alternatives such as infrastructure projects that connect India and Europe through the Middle East. Meanwhile, Kaust is ramping up its own outreach to China, with its president Tony Chan advocating for deeper Sino-Saudi relations in academia and technology. The institution insists that its ties with various nations, including China, are thriving and are in compliance with international controls. As both the U.S. and China vie for technological and geopolitical influence in the Gulf, entities like Kaust find themselves at the crossroads of complex international dynamics.



Other AI Industrial Movements


72% CEOs Back Generative AI: A recent KPMG survey reveals that U.S. CEOs are bullish on AI investment, with 72% listing generative AI as a top priority and expecting a return in 3 to 5 years. This enthusiasm follows the breakthroughs like ChatGPT, signaling AI's disruptive potential across industries. While the technology is seen as transformative for revenue growth and operational efficiency, there's ongoing debate about its impact on employment. At the same time, CEOs are increasingly favoring a return to the office; 62% expect their teams to work in-office permanently within three years, a significant rise from last year. Despite the fluidity in work modes and the tech landscape, CEOs appear committed to melding traditional office culture with cutting-edge AI developments.



Source: KPMG



CEOs Embrace AI: Almost half of CEOs, according to an EdX survey, think AI can and should take over the majority of their responsibilities. These executives believe automating their roles would allow them more time for strategic leadership. But while 79% are anxious about falling behind if they don't adapt to AI, experts argue that machines can't yet emulate human strategic thinking. Essentially, the survey reveals that executives see AI not as an option but as a necessity, although true leadership qualities remain irreplaceable.


LLaVA vs. GPT-4(V)ision: A collaborative effort from Stanford, UW-Madison, and Columbia has yielded LLaVA, an open-source AI that could potentially compete with GPT-4 in visual and language comprehension. Despite having less training data, the system shows promise in its free availability and capability. LLaVA essentially demonstrates that when it comes to advancing vision-language AI, being open-source is not only viable but may also set new benchmarks.


Decoding AI Neurons: Anthropic has developed a way to interpret neurons in large language models, offering insights into their reasoning process. The method decomposes neurons into simpler features, like DNA or legal text, which can be manipulated for expected behaviors. Beyond its significance for AI safety, this breakthrough could lead to higher levels of control and customization, making the AI’s thought process not just a black box but a controllable entity.


AI in Healthcare: Google has beefed up its Vertex AI platform with new healthcare-centric features. The enhanced system allows for cross-referencing various types of medical data, from electronic health records to clinical notes. Coupled with strong security measures, this suggests Google is making a serious play to revolutionize healthcare through AI, offering a data-driven, secure, and intuitive platform that could redefine patient care.


 

AI Frontier Research Monitoring



Today, we turn our attention from our routine surveillance of arXiv to a paper that's certainly going to be the talk of the scientific community. Its focus? Whether Large Language Models (LLMs) like GPT-4 can actually provide valuable feedback on scientific research papers yet to be published on OpenReview. For the uninitiated, OpenReview is a platform that provides public, transparent, and in-depth peer review services in scholarly publishing.






The paper emanates from a cadre of researchers across disciplines, notably Stanford University and Northwestern University among others. The essence of the study is not only timely but hits a nerve in the scientific community grappling with the bottleneck of quality peer reviews. The paper leverages GPT-4 in an automated pipeline to comment on scientific manuscripts and compares these comments to those of human peer reviewers. Astonishingly, the overlap in points raised by the AI model is often comparable to that between two human reviewers, particularly for weaker papers. Furthermore, more than half of the 308 researchers surveyed found the AI-generated feedback helpful, while 82.4% considered it more beneficial than some human feedback. However, it's not all roses; GPT-4 does exhibit limitations in critiquing method design.


What's the takeaway? The evidence strongly suggests that we're merely at the tip of the iceberg. LLMs like GPT-4 are edging into an area where they can become a vital complement to human expertise. While they can't yet replace the invaluable depth of human feedback, their utility, particularly in a resource-tight or time-sensitive scenario, is not to be dismissed lightly. The era of AI-facilitated scientific inquiry isn't coming; it's already here.



 





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