Today we’re going to touch on a topic that has been in the news a lot lately. Neural networks. Even if you’re not studying computer science or machine learning, it’s useful to have an idea of what these networks are and how they differ from one another. I’ll highlight three common types you might have heard about: feedforward networks, convolutional networks, and recurrent networks. I’ll also briefly mention a newer idea called spiking networks.
Let’s start with the simplest form, the feedforward neural network. Think of it like an assembly line. Information goes in at one end, passes through a series of processing steps, and then comes out the other end as a result. Each step, or layer, applies a simple transformation. This type of network is used for many classification tasks. For example, if you have a set of measurements about a flower, a feedforward network can learn to classify whether it is a rose, a daisy, or a tulip. The key point is that information moves only forward; there is no loop or memory of previous inputs.
Next, let’s look at convolutional neural networks, often called CNNs. These networks are particularly good at processing images. Imagine how you read a page of text: your eyes move across the page, absorbing patterns of letters and words. Similarly, a convolutional network uses a small window that “slides” over an image to detect patterns, such as edges or shapes, without analyzing every pixel individually. This sliding process allows the network to understand spatial relationships, which helps it recognize faces, animals, or other objects in photographs. That’s why CNNs are widely used in applications such as facial recognition or medical image analysis.
Now, consider tasks where the order of information matters, like language translation or speech recognition. For these problems, we often use recurrent neural networks, or RNNs. These networks are designed to remember what they processed previously, much like how you remember the beginning of a sentence to understand its meaning by the time you reach the end. RNNs achieve this by feeding part of their output back into the network, creating a form of short-term memory. This feedback loop helps the network capture context and dependencies over time. However, RNNs can be difficult to train, especially when the sequence is long, because the network needs to hold on to information for extended periods.
Finally, I want to briefly mention spiking neural networks. Unlike the other networks, spiking networks are inspired more directly by biological brains. In the human brain, neurons communicate by sending brief electrical signals, or “spikes,” rather than a continuous flow of information. Spiking neural networks mimic this behavior. They process information as a series of discrete events over time, which can make them very energy-efficient. This efficiency is one reason why researchers are interested in them for low-power devices. However, spiking networks are still largely experimental, and training them effectively is a challenge.
In summary, neural networks come in many forms. Feedforward networks are straightforward and good for basic classification. Convolutional networks excel at recognizing patterns in images. Recurrent networks handle sequences by remembering what came before. And spiking networks, inspired by biology, offer an energy-efficient approach that may become more common in the future. Understanding these differences helps us appreciate why artificial intelligence is such a diverse and rapidly evolving field. Thank you, and in our next session we’ll explore how these networks are trained.
Step 2. Do the test on the lecture
Questions:
1. What does the speaker mainly aim to achieve in this talk?
2. At one point, the professor likens one kind of network to a factory process. What is significant about that analogy?
3. Why does the lecturer refer to the way people scan written material when describing another type of network?
4. According to the lecture, why are loop‑based networks useful for tasks like translating sentences?
5. What can be concluded about biologically inspired networks from the talk?
6. Why does the instructor briefly mention where each architecture is applied?
7. During the lecture, the professor mentions classifying flowers. Why does he give this example?
8. What issue related to loop‑based networks does the professor mention?
9. What can be inferred about the professor when he says this: “Finally, I want to briefly mention spiking neural networks.”
10. Why does the professor begin with the most straightforward kind of network?
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Incorrect Answers: 0
II. Reading Module 1
One letter per gap!
1. Fill in the missing letters in the paragraph
Some experts arg that the deep-learning paradigm is fundament misaligned democratic values. Because modern neural networks require enormous amounts of data and computing power, only a handful corporations can afford develop them. These companies often refuse to disclose train data or model weights, cit trade secre .
Thu , not only the models themselves black boxes, the context of their creation is opaque. Critics fear that, without strict regulation, the AI revolution will be driv by private interests the expense of fairness and human rights. They urge regulators to limit the use of black-box systems in sensitive domains un they can be render transparent and account .
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2. Read a post on a website
Start reading!
Q11. What type of organization is most likely responsible for this advertisement?
Q12. What aspect of Moflin is emphasized most in the ad?
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Incorrect Answers: 0
Read a corporate website's page
The Benefits of Solar CCTV Towers
Solar CCTV installations have multiple applications. Some of the most compelling benefits include:
Lack of reliance on mains power, with zero shortages in unreliable areas.
Ability to recharge continually without manual intervention.
Cost-effectiveness, with zero ongoing running costs or energy bills.
Weatherproof battery casings designed to withstand all conditions.
Flexible installations, from mast-mounted towers to internal CCTV.
Tamper-proof, with no accessible wiring or mains connections.
No fuel means no emissions and no noise pollution
A range of features can be triggered when a detector is activated including automatic video recording, pan-tilt-zoom to preset locations, siren, email notification and ARC incident management.
Each of the Clearway solar CCTV installations is designed to provide superior image quality, along with automatic sensors and instant reporting available to our NSI Gold Standard alarm-receiving centre (ARC).
Start reading!
Q13. What is one major advantage of solar CCTV towers?
Q 14. Which of the following statements is the most accurate?
15. What may happen after the system senses activity?
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Incorrect Answers: 0
4. Read the text below
Start reading and taking down detail!
Read the text noting down a) Author's Purpose and how they prove their point(s). The logic and author's points are expressed in link phrases at the beginning, in the middle and at the end of each paragraph. In the exam, the text may be longer, with 12-20 questions on it.
Paragraph 1
Over the past decade, neural networks have revolutionized many areas of computing, from natural language processing to image recognition and beyond. Early successes were driven largely by a simple recipe: build bigger models, feed them more data, and let them learn patterns automatically. Researchers observed “scaling laws,” showing that performance improved predictably as models grew in size and were trained on larger data sets. This approach led to well‑known systems like GPT‑4, LLaMA and other so‑called large language models. However, scaling up is not free. It requires enormous amounts of computational power, electricity and data. As a result, the field has begun to ask whether blindly making networks larger is sustainable or even effective. Understanding the limitations of scaling and exploring new types of neural architectures has become one of the central challenges for researchers and engineers today.
Paragraph 2
One of the reasons scaling can stall is rooted in basic mathematics. Modern language models rely on the transformer architecture, which uses multiple layers of matrix operations to process tokens of text. Theoreticians have shown that as the context size grows, the noise in these hidden representations becomes less predictable. In other words, adding more parameters and data eventually yields diminishing returns because random fluctuations start to overwhelm meaningful patterns. Additionally, there is a trade‑off between the bias of a model and its variance: scaling reduces some types of error but introduces others. Scholars have formulated these trade‑offs in terms of a signal‑to‑noise ratio. When that ratio crosses a critical threshold, new capabilities seem to “emerge,” but beyond that point further growth does not help. In the language of engineers, current models may be nearing the top of the S‑curve for performance. As a result, larger networks do not necessarily produce proportionally better results, despite the massive costs required to train them. Instead, researchers are looking for ways to rethink how networks are built and used.
Paragraph 3
Economic factors also push developers away from simple scaling. Training very large models can cost millions of dollars and takes weeks or months on specialized hardware, but deploying them at scale can be even more expensive. Interestingly, the cost of running a trained model—the inferencing cost—has fallen dramatically in recent years. Thanks to software optimization, cheaper graphics processors and new tuning techniques, the cost per million tokens produced by some models has dropped from tens of dollars to just a few cents. Lower costs open the door to a new paradigm: rather than focusing solely on training bigger networks, researchers can invest computational resources during inference, allowing the model to “think” longer by evaluating multiple possible answers or performing a chain‑of‑thought. At the same time, there is a growing recognition that different tasks require different tools. Specialized models designed for narrow domains, often called small foundational models, and tiny models that can run on smartphones or other edge devices, are becoming more common. These models are easier to train, cheaper to deploy and may avoid some of the pitfalls of giant systems.
Paragraph 4
The realization that bigger isn’t always better has spurred innovation in neural network design. One important line of work focuses on diffusion models. Rather than predicting words one by one, diffusion models start from random noise and gradually refine their output over many steps. Originally popular for image generation, diffusion models have been adapted for text and video. Apple researchers recently introduced Matryoshka Diffusion Models, which nest smaller diffusers inside larger ones. By jointly training multiple resolutions of a scene, these models can generate high‑resolution images and videos efficiently. Other groups are exploring hybrid architectures that combine the strengths of language models and diffusion. For instance, the LanDiff model first compresses a scene into a sequence of symbolic “tokens” using a language model, then uses a diffusion process to add perceptual detail, producing coherent and visually rich videos. These examples illustrate how new architectures attempt to preserve quality while reducing the need for ever‑larger parameter counts.
Paragraph 5
Another promising direction mixes neural learning with explicit reasoning. Traditional “connectionist” AI excels at pattern recognition, but struggles with logical rules and causal relationships. To address this, researchers are developing neuro‑symbolic and causal AI systems that integrate deterministic logic with probabilistic neural components. Such systems can impose constraints on the model’s behavior, explaining its decisions in human‑understandable terms, and reducing undesirable outputs like hallucinations. There is also interest in building agentic networks that can plan actions rather than just generating text. Large Action Models (LAMs), for example, attempt to translate natural language instructions into executable tasks, such as booking a flight or managing a calendar. Similarly, Large Concept Models (LCMs) operate at the level of whole sentences or ideas, rather than individual words, allowing them to summarize or expand text more efficiently across multiple languages. These innovations point to a future where networks are not only larger or more powerful, but also more modular, interpretable and responsive to human intentions.
Paragraph 6
Finally, scientists are turning to biology for inspiration in solving the scaling problem. Neuromorphic computing seeks to replicate how human brains work by building hardware that processes information through spikes—brief electrical pulses—rather than continuous numbers. Spiking neural networks communicate sparsely, consuming far less energy than traditional artificial networks. Recent research has shown that these networks can now be trained using gradients, the same basic method used for other deep learning models. Coupled with advances in digital neuromorphic chips and in‑memory computing, spiking networks could power low‑energy devices like smart watches or autonomous sensors. Alongside this, studies of cortical organization suggest that the brain’s sparsely connected, recurrent architecture may be more efficient for certain tasks than the fully connected layers used in conventional AI. Researchers have found that networks mimicking these sparse structures can learn more rapidly and represent information more robustly when data or computation is limited. The exploration of spiking systems, sparse connectivity and brain‑like architectures indicates that solving the scaling problem may require not only algorithmic innovations but also a reimagining of the hardware on which neural networks run. Taken together, these developments suggest a future in which artificial intelligence is not just bigger, but smarter, more efficient and more diverse in its underlying designs.
Questions:
1. Why does the author mention that the cost per million tokens for running a language model has fallen dramatically to a few cents? [Question Type: Author’s Purpose]
The passage uses the decline in cost to show that falling inference expenses are shifting research from building ever‑larger models toward using computation at inference time and exploring new strategies.
2. According to paragraph 2, which of the following is NOT identified as a factor limiting the effectiveness of simply scaling up neural networks? [Question Type: Negative Factual Information]
Paragraph 2 explains that noise, bias‑variance trade‑offs and diminishing returns limit scaling; it does not claim that adding layers always reduces randomness.
3. According to paragraph 3, how does the recent drop in the cost of running trained models influence research priorities? [Question Type: Detail]
Paragraph 3 notes that cheaper inferencing encourages researchers to allocate compute during inference and to explore smaller or specialized models, not to abandon new architectures or hardware.
4. According to paragraph 4, what advantage do nested diffusion models and hybrid architectures offer? [Question Type: Detail]
Nested diffusion and hybrid models train across different resolutions to improve efficiency and quality, rather than discarding high‑resolution data or replacing transformers completely.
5. Which of the sentences below best expresses the essential information in the highlighted sentence in paragraph 6? Incorrect choices change the meaning in important ways or leave out essential information. [Question Type: Paraphrase]
The highlighted sentence argues that both algorithmic innovations and new hardware (for example, neuromorphic chips) will be needed; answer A captures this idea.
6. The word “modular” in paragraph 5 is closest in meaning to: [Question Type: Vocabulary]
“Modular” describes something composed of separate units or modules that can be combined in different ways.
7. According to paragraph 5, which of the following is NOT one of the goals of integrating deterministic logic with neural learning? [Question Type: Negative Factual Information]
Paragraph 5 notes that combining rules with neural nets aims to make models more reliable and action‑oriented; it does not advocate replacing neural components entirely with symbolic reasoning.
8. Which paragraph discusses the trade‑off between bias and variance and introduces the concept of a signal‑to‑noise ratio? [Question Type: Detail]
The bias‑variance trade‑off and signal‑to‑noise ratio are discussed in paragraph 2 when explaining why scaling up has limits.
9. In the paragraph below, there is a missing sentence. Look at the paragraph and indicate (A, B, C, or D) where the following sentence could be added to the passage. This means that simply increasing the number of layers will not always result in better performance. [Question Type: Sentence Insertion]
One of the reasons scaling can stall is rooted in basic mathematics. (A) Modern language models rely on the transformer architecture, which uses multiple layers of matrix operations to process tokens of text. (B) Theoreticians have shown that as the context size grows, the noise in these hidden representations becomes less predictable. (C) In other words, adding more parameters and data eventually yields diminishing returns because random fluctuations start to overwhelm meaningful patterns. (D) Additionally, there is a trade‑off between the bias of a model and its variance, and scholars have formulated these trade‑offs in terms of a signal‑to‑noise ratio.
The inserted sentence clarifies the idea of diminishing returns, so it best fits after the sentence explaining that additional parameters yield diminishing improvements (option C).
10. Directions: An introductory sentence for a brief summary of the passage is provided below. Complete the summary by dragging the letters of the 3 answer choices that express the most important ideas into the box. [Question Type: Summary]
Recent research on neural networks suggests that scaling up models is not always effective.
A. Scaling laws show that noise and diminishing returns limit the benefits of simply adding more parameters; new mathematical frameworks like signal-to-noise ratio illustrate these limitations.
B. Lower inferencing costs and the development of specialized models have shifted focus toward small and domain-specific architectures that may be deployed on edge devices.
C. Diffusion models, hybrid architectures, and concept-level models offer efficient alternatives to giant transformer-based networks, enabling high-resolution generation and better semantic control.
D. All new architectures replace the need for traditional neural networks, making future research on transformers unnecessary.
E. Spiking neural networks, sparse connectivity and neuromorphic hardware suggest that solving scaling challenges involves changes in both algorithms and hardware design.
