#learn data structure and algorithms

[empowerwithexpertifie]

Expertifie partners with companies and individuals to address their unique needs, providing training and coaching that helps working professionals achieve their career goals. They teach their students all the relevant skills needed in software jobs, mentor them to crack recruitment processes and also provide them referrals to the best opportunities in the software industry across the globe. Their vision is to empower career growth and advancement for every member of the global workforce as their trusted lifelong learning partners.

The main features of Expertifie courses are:

1.            Instructor-led live interactive training.

2.            Advance level of coding and excellent mentoring from our industry experts.

3.            Receive guidance that makes you skill ready for interviews and on the job scenarios.

4.            Career Support through Mock Interviews and Job Referral.

5.            Coding Sessions and Assignments for each topic.

6.            Up to 4 Mock Interviews.

7.            Sessions by Industry Experts from MAANG.

The courses provided are:

Data Structures & Algorithms

System Design (HLD + LLD)

Full Stack Development

[learnandbuild]

Summer Internship Program 2024

For More Details Visit Our Website - internship.learnandbuild.in

[anhedonyan]

First courses on IT automation, cybersecurity and data analytics are over.

I think the one in cybersecurity has Python too, so I hope I don't get a Python crash course again! XD

[limatsoftsolutionsworld]

Placement Preparation Course for CSE

Dive into essential algorithms, data structures, and coding practices while mastering problem-solving techniques. Our expert-led sessions ensure you're well-equipped for technical interviews. Join our Placement Preparation Course for CSE now to secure your dream job in top-tier companies. Get ahead in the competitive tech world with our proven curriculum and guidance. Your future starts here!

[it-related-online-course]

Best R Programming Training Institute in India

TechnoMaster.in is a leading R programming training institute that offers the best R programming training to students looking to get a secured job in an MNC and corporate giant.

For more details on the availability of our Training Program. Click Below:-

Best R Programming Training Course

The online R Programming certification and training course important concepts in R Programming like data visualization, descriptive analytics techniques, data exploration and predictive analytics with R languages. The R Programming training courses is also helps in exploring about the R packages, data structure in R, cluster analysis, graphic representation, reporting, data manipulation, business analytics, flow of control, machine learning algorithms, variables functions, data structures, import and export data in R, forecasting and diverse statistical concepts through practical R industry scenarios and examples. The courses materials of the TechnoMaster.in R Programming training course are based on the latest industry scenarios and hold every essential concepts on R Programming to clear the TechnoMaster.in R Programming certification exam.

We provide short term, crash and long term online IT courses on all IT technologies at convenient schedules and reasonable fees. The delegates who takes online R Programming training course on the TechnoMaster.in will work on real-time assignments and projects that have impact in the real time scenarios, thus enabling you to get an excellent job in your career efficiently. So, what are you waiting for Simply enroll for the best R Programming certification course and get a secured job without investing much.

Key features of R Programming Online Training

40 Hours of Course Duration

Industry Expert Faculties

100% Job Oriented Training

Free Demo Class Available

Certification Guidance

[naarsoftoffi]

Website and Mobile App Development with ReactJS

The React.js framework is an open source JavaScript framework and library developed by Facebook. It is used to quickly and efficiently build interactive user interfaces and web applications with far less code.

React develops applications by creating reusable components that can be thought of as individual building blocks. These components are the individual parts of the final interface, which together form the overall user interface of the application.

The primary role of React in an application is to treat the view layer of that application, providing the best and most efficient rendering execution. Instead of treating the entire UI as a single entity, ReactJS encourages developers to decompose these complex UI into individual reusable components that form the building blocks of the overall UI. increase. The ReactJS framework combines the speed and efficiency of JavaScript with a more efficient way of manipulating the DOM to render web pages faster and create highly dynamic and responsive web applications.

React has simplified the development process by providing a more organized and structured way to create dynamic, interactive user interfaces with reusable components.

Websites or Apps developed using ReactJS platform

React has gained stability and popularity due to its ability to build fast, efficient, and scalable web applications. It is currently used in thousands of web applications from established companies to start-ups.

Some notable mentions are:

Facebook

Instagram

Netflix

Reddit

Uber

Airbnb

The New York Times

Khan Academy

Codecademy

SoundCloud

Discord

WhatsApp Web

Due to its adaptability, reusability, and simplicity, ReactJS has emerged as one of the most popular options for creating websites and mobile apps. It enables developers to build complex applications quickly and efficiently while providing a smooth user experience. A lively developer community supports ReactJS, and new libraries, frameworks, and tools are regularly added to its environment. As a result, anyone looking to build contemporary, responsive, and scalable web applications should consider learning and using ReactJS.

ReactJS also provides better speed by utilizing a virtual DOM and streamlining rendering updates. As a result, ReactJS has become the go-to choice for many web developers and businesses, from small startups to large-scale enterprises. Developers can produce dynamic, responsive, and engaging web apps that satisfy the needs of modern users by utilizing React.js.

[authsocial]

Learn data structures and algorithms using java | Sunbeam

The data structure, data structure training near me, data structures and algorithms course in Pune, best data structures and algorithms course in java, best DSA course in c++. Learn data structures and algorithms using java online. In this course, you will understand common data structures like an array, linked list, stack & queue, and their applications and how to implement data structures and algorithms using Java. Data Structures and Algorithms are one of the most important skills that every computer science student must have. It is often seen that people with good knowledge of these technologies are better programmers than others and thus, crack the interviews of almost every tech giant. Now, you must be thinking to opt for a quality DSA Course to build & enhance your data structures and algorithms skills, right?? If yes, then you’ve ended up at the right place. This DSA Course will help you to learn and master all the DSA concepts from the basics to the advanced level! Contact Number:8447901102 Address: Hinjawadi Phase 2, Hinjawadi Pune 411053

[shailendra01]

https://www.worldranklist.com/preview/bookmarking/496309/Data-Structures-and-Algorithms-Certification-Training-ScholarHat

This course is meant for people with an interest in software engineering who want to advance their careers by landing an interview with one of the most prestigious organisations in the world, such as Microsoft, Adobe, Uber, Google, or Amazon. You will learn everything needed to succeed in these key interviews, from coding to problem-solving techniques, during this programme.

[learnandbuild]

Learn and Build Summer Internship Program

For more details visit - Internship.learnandbuild.in

[jcmarchi]

Supercharging Graph Neural Networks with Large Language Models: The Ultimate Guide

New Post has been published on https://thedigitalinsider.com/supercharging-graph-neural-networks-with-large-language-models-the-ultimate-guide/

Supercharging Graph Neural Networks with Large Language Models: The Ultimate Guide

Graphs are data structures that represent complex relationships across a wide range of domains, including social networks, knowledge bases, biological systems, and many more. In these graphs, entities are represented as nodes, and their relationships are depicted as edges.

The ability to effectively represent and reason about these intricate relational structures is crucial for enabling advancements in fields like network science, cheminformatics, and recommender systems.

Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks. By incorporating the graph topology into the neural network architecture through neighborhood aggregation or graph convolutions, GNNs can learn low-dimensional vector representations that encode both the node features and their structural roles. This allows GNNs to achieve state-of-the-art performance on tasks such as node classification, link prediction, and graph classification across diverse application areas.

While GNNs have driven substantial progress, some key challenges remain. Obtaining high-quality labeled data for training supervised GNN models can be expensive and time-consuming. Additionally, GNNs can struggle with heterogeneous graph structures and situations where the graph distribution at test time differs significantly from the training data (out-of-distribution generalization).

In parallel, Large Language Models (LLMs) like GPT-4, and LLaMA have taken the world by storm with their incredible natural language understanding and generation capabilities. Trained on massive text corpora with billions of parameters, LLMs exhibit remarkable few-shot learning abilities, generalization across tasks, and commonsense reasoning skills that were once thought to be extremely challenging for AI systems.

The tremendous success of LLMs has catalyzed explorations into leveraging their power for graph machine learning tasks. On one hand, the knowledge and reasoning capabilities of LLMs present opportunities to enhance traditional GNN models. Conversely, the structured representations and factual knowledge inherent in graphs could be instrumental in addressing some key limitations of LLMs, such as hallucinations and lack of interpretability.

In this article, we will delve into the latest research at the intersection of graph machine learning and large language models. We will explore how LLMs can be used to enhance various aspects of graph ML, review approaches to incorporate graph knowledge into LLMs, and discuss emerging applications and future directions for this exciting field.

Graph Neural Networks and Self-Supervised Learning

To provide the necessary context, we will first briefly review the core concepts and methods in graph neural networks and self-supervised graph representation learning.

Graph Neural Network Architectures

Graph Neural Network Architecture – source

The key distinction between traditional deep neural networks and GNNs lies in their ability to operate directly on graph-structured data. GNNs follow a neighborhood aggregation scheme, where each node aggregates feature vectors from its neighbors to compute its own representation.

Numerous GNN architectures have been proposed with different instantiations of the message and update functions, such as Graph Convolutional Networks (GCNs), GraphSAGE, Graph Attention Networks (GATs), and Graph Isomorphism Networks (GINs) among others.

More recently, graph transformers have gained popularity by adapting the self-attention mechanism from natural language transformers to operate on graph-structured data. Some examples include GraphormerTransformer, and GraphFormers. These models are able to capture long-range dependencies across the graph better than purely neighborhood-based GNNs.

Self-Supervised Learning on Graphs

While GNNs are powerful representational models, their performance is often bottlenecked by the lack of large labeled datasets required for supervised training. Self-supervised learning has emerged as a promising paradigm to pre-train GNNs on unlabeled graph data by leveraging pretext tasks that only require the intrinsic graph structure and node features.

Self-Supervised Graph

Some common pretext tasks used for self-supervised GNN pre-training include:

Node Property Prediction: Randomly masking or corrupting a portion of the node attributes/features and tasking the GNN to reconstruct them.

Edge/Link Prediction: Learning to predict whether an edge exists between a pair of nodes, often based on random edge masking.

Contrastive Learning: Maximizing similarities between graph views of the same graph sample while pushing apart views from different graphs.

Mutual Information Maximization: Maximizing the mutual information between local node representations and a target representation like the global graph embedding.

Pretext tasks like these allow the GNN to extract meaningful structural and semantic patterns from the unlabeled graph data during pre-training. The pre-trained GNN can then be fine-tuned on relatively small labeled subsets to excel at various downstream tasks like node classification, link prediction, and graph classification.

By leveraging self-supervision, GNNs pre-trained on large unlabeled datasets exhibit better generalization, robustness to distribution shifts, and efficiency compared to training from scratch. However, some key limitations of traditional GNN-based self-supervised methods remain, which we will explore leveraging LLMs to address next.

Enhancing Graph ML with Large Language Models

Integration of Graphs and LLM –  source

The remarkable capabilities of LLMs in understanding natural language, reasoning, and few-shot learning present opportunities to enhance multiple aspects of graph machine learning pipelines. We explore some key research directions in this space:

A key challenge in applying GNNs is obtaining high-quality feature representations for nodes and edges, especially when they contain rich textual attributes like descriptions, titles, or abstracts. Traditionally, simple bag-of-words or pre-trained word embedding models have been used, which often fail to capture the nuanced semantics.

Recent works have demonstrated the power of leveraging large language models as text encoders to construct better node/edge feature representations before passing them to the GNN. For example, Chen et al. utilize LLMs like GPT-3 to encode textual node attributes, showing significant performance gains over traditional word embeddings on node classification tasks.

Beyond better text encoders, LLMs can be used to generate augmented information from the original text attributes in a semi-supervised manner. TAPE generates potential labels/explanations for nodes using an LLM and uses these as additional augmented features. KEA extracts terms from text attributes using an LLM and obtains detailed descriptions for these terms to augment features.

By improving the quality and expressiveness of input features, LLMs can impart their superior natural language understanding capabilities to GNNs, boosting performance on downstream tasks.

Alleviating Reliance on Labeled Data

A key advantage of LLMs is their ability to perform reasonably well on new tasks with little to no labeled data, thanks to their pre-training on vast text corpora. This few-shot learning capability can be leveraged to alleviate the reliance of GNNs on large labeled datasets.

One approach is to use LLMs to directly make predictions on graph tasks by describing the graph structure and node information in natural language prompts. Methods like InstructGLM and GPT4Graph fine-tune LLMs like LLaMA and GPT-4 using carefully designed prompts that incorporate graph topology details like node connections, neighborhoods etc. The tuned LLMs can then generate predictions for tasks like node classification and link prediction in a zero-shot manner during inference.

While using LLMs as black-box predictors has shown promise, their performance degrades for more complex graph tasks where explicit modeling of the structure is beneficial. Some approaches thus use LLMs in conjunction with GNNs – the GNN encodes the graph structure while the LLM provides enhanced semantic understanding of nodes from their text descriptions.

Graph Understanding with LLM Framework – Source

GraphLLM explores two strategies: 1) LLMs-as-Enhancers where LLMs encode text node attributes before passing to the GNN, and 2) LLMs-as-Predictors where the LLM takes the GNN’s intermediate representations as input to make final predictions.

GLEM goes further by proposing a variational EM algorithm that alternates between updating the LLM and GNN components for mutual enhancement.

By reducing reliance on labeled data through few-shot capabilities and semi-supervised augmentation, LLM-enhanced graph learning methods can unlock new applications and improve data efficiency.

Enhancing LLMs with Graphs

While LLMs have been tremendously successful, they still suffer from key limitations like hallucinations (generating non-factual statements), lack of interpretability in their reasoning process, and inability to maintain consistent factual knowledge.

Graphs, especially knowledge graphs which represent structured factual information from reliable sources, present promising avenues to address these shortcomings. We explore some emerging approaches in this direction:

Knowledge Graph Enhanced LLM Pre-training

Similar to how LLMs are pre-trained on large text corpora, recent works have explored pre-training them on knowledge graphs to imbue better factual awareness and reasoning capabilities.

Some approaches modify the input data by simply concatenating or aligning factual KG triples with natural language text during pre-training. E-BERT aligns KG entity vectors with BERT’s wordpiece embeddings, while K-BERT constructs trees containing the original sentence and relevant KG triples.

The Role of LLMs in Graph Machine Learning:

Researchers have explored several ways to integrate LLMs into the graph learning pipeline, each with its unique advantages and applications. Here are some of the prominent roles LLMs can play:

LLM as an Enhancer: In this approach, LLMs are used to enrich the textual attributes associated with the nodes in a TAG. The LLM’s ability to generate explanations, knowledge entities, or pseudo-labels can augment the semantic information available to the GNN, leading to improved node representations and downstream task performance.

For example, the TAPE (Text Augmented Pre-trained Encoders) model leverages ChatGPT to generate explanations and pseudo-labels for citation network papers, which are then used to fine-tune a language model. The resulting embeddings are fed into a GNN for node classification and link prediction tasks, achieving state-of-the-art results.

LLM as a Predictor: Rather than enhancing the input features, some approaches directly employ LLMs as the predictor component for graph-related tasks. This involves converting the graph structure into a textual representation that can be processed by the LLM, which then generates the desired output, such as node labels or graph-level predictions.

One notable example is the GPT4Graph model, which represents graphs using the Graph Modelling Language (GML) and leverages the powerful GPT-4 LLM for zero-shot graph reasoning tasks.

GNN-LLM Alignment: Another line of research focuses on aligning the embedding spaces of GNNs and LLMs, allowing for a seamless integration of structural and semantic information. These approaches treat the GNN and LLM as separate modalities and employ techniques like contrastive learning or distillation to align their representations.

The MoleculeSTM model, for instance, uses a contrastive objective to align the embeddings of a GNN and an LLM, enabling the LLM to incorporate structural information from the GNN while the GNN benefits from the LLM’s semantic knowledge.

Challenges and Solutions

While the integration of LLMs and graph learning holds immense promise, several challenges need to be addressed:

Efficiency and Scalability: LLMs are notoriously resource-intensive, often requiring billions of parameters and immense computational power for training and inference. This can be a significant bottleneck for deploying LLM-enhanced graph learning models in real-world applications, especially on resource-constrained devices.

One promising solution is knowledge distillation, where the knowledge from a large LLM (teacher model) is transferred to a smaller, more efficient GNN (student model).

Data Leakage and Evaluation: LLMs are pre-trained on vast amounts of publicly available data, which may include test sets from common benchmark datasets, leading to potential data leakage and overestimated performance. Researchers have started collecting new datasets or sampling test data from time periods after the LLM’s training cut-off to mitigate this issue.

Additionally, establishing fair and comprehensive evaluation benchmarks for LLM-enhanced graph learning models is crucial to measure their true capabilities and enable meaningful comparisons.

Transferability and Explainability: While LLMs excel at zero-shot and few-shot learning, their ability to transfer knowledge across diverse graph domains and structures remains an open challenge. Improving the transferability of these models is a critical research direction.

Furthermore, enhancing the explainability of LLM-based graph learning models is essential for building trust and enabling their adoption in high-stakes applications. Leveraging the inherent reasoning capabilities of LLMs through techniques like chain-of-thought prompting can contribute to improved explainability.

Multimodal Integration: Graphs often contain more than just textual information, with nodes and edges potentially associated with various modalities, such as images, audio, or numeric data. Extending the integration of LLMs to these multimodal graph settings presents an exciting opportunity for future research.

Real-world Applications and Case Studies

The integration of LLMs and graph machine learning has already shown promising results in various real-world applications:

Molecular Property Prediction: In the field of computational chemistry and drug discovery, LLMs have been employed to enhance the prediction of molecular properties by incorporating structural information from molecular graphs. The LLM4Mol model, for instance, leverages ChatGPT to generate explanations for SMILES (Simplified Molecular-Input Line-Entry System) representations of molecules, which are then used to improve the accuracy of property prediction tasks.

Knowledge Graph Completion and Reasoning: Knowledge graphs are a special type of graph structure that represents real-world entities and their relationships. LLMs have been explored for tasks like knowledge graph completion and reasoning, where the graph structure and textual information (e.g., entity descriptions) need to be considered jointly.

Recommender Systems: In the domain of recommender systems, graph structures are often used to represent user-item interactions, with nodes representing users and items, and edges denoting interactions or similarities. LLMs can be leveraged to enhance these graphs by generating user/item side information or reinforcing interaction edges.

Conclusion

The synergy between Large Language Models and Graph Machine Learning presents an exciting frontier in artificial intelligence research. By combining the structural inductive bias of GNNs with the powerful semantic understanding capabilities of LLMs, we can unlock new possibilities in graph learning tasks, particularly for text-attributed graphs.

While significant progress has been made, challenges remain in areas such as efficiency, scalability, transferability, and explainability. Techniques like knowledge distillation, fair evaluation benchmarks, and multimodal integration are paving the way for practical deployment of LLM-enhanced graph learning models in real-world applications.

[reasonsforhope]

"Is social media designed to reward people for acting badly?

The answer is clearly yes, given that the reward structure on social media platforms relies on popularity, as indicated by the number of responses – likes and comments – a post receives from other users. Black-box algorithms then further amplify the spread of posts that have attracted attention.

Sharing widely read content, by itself, isn’t a problem. But it becomes a problem when attention-getting, controversial content is prioritized by design. Given the design of social media sites, users form habits to automatically share the most engaging information regardless of its accuracy and potential harm. Offensive statements, attacks on out groups and false news are amplified, and misinformation often spreads further and faster than the truth.

We are two social psychologists and a marketing scholar. Our research, presented at the 2023 Nobel Prize Summit, shows that social media actually has the ability to create user habits to share high-quality content. After a few tweaks to the reward structure of social media platforms, users begin to share information that is accurate and fact-based...

Re-targeting rewards

To investigate the effect of a new reward structure, we gave financial rewards to some users for sharing accurate content and not sharing misinformation. These financial rewards simulated the positive social feedback, such as likes, that users typically receive when they share content on platforms. In essence, we created a new reward structure based on accuracy instead of attention.

As on popular social media platforms, participants in our research learned what got rewarded by sharing information and observing the outcome, without being explicitly informed of the rewards beforehand. This means that the intervention did not change the users’ goals, just their online experiences. After the change in reward structure, participants shared significantly more content that was accurate. More remarkably, users continued to share accurate content even after we removed rewards for accuracy in a subsequent round of testing. These results show that users can be given incentives to share accurate information as a matter of habit.

A different group of users received rewards for sharing misinformation and for not sharing accurate content. Surprisingly, their sharing most resembled that of users who shared news as they normally would, without any financial reward. The striking similarity between these groups reveals that social media platforms encourage users to share attention-getting content that engages others at the expense of accuracy and safety...

Doing right and doing well

Our approach, using the existing rewards on social media to create incentives for accuracy, tackles misinformation spread without significantly disrupting the sites’ business model. This has the additional advantage of altering rewards instead of introducing content restrictions, which are often controversial and costly in financial and human terms.

Implementing our proposed reward system for news sharing carries minimal costs and can be easily integrated into existing platforms. The key idea is to provide users with rewards in the form of social recognition when they share accurate news content. This can be achieved by introducing response buttons to indicate trust and accuracy. By incorporating social recognition for accurate content, algorithms that amplify popular content can leverage crowdsourcing to identify and amplify truthful information.

Both sides of the political aisle now agree that social media has challenges, and our data pinpoints the root of the problem: the design of social media platforms."

And here's the video of one of the scientsts presenting this research at the Nobel Prize Summit!

-Article via The Conversation, August 1, 2023. Video via the Nobel Prize's official Youtube channel, Nobel Prize, posted May 31, 2023.

[max1461]

It's like, "why try to reduce chemistry to physics when you can use some kind of ML algorithm to predict chemical properties better than an actual calculation based on QFT or whatever could?". Well, the answer is obviously that that ML algorithm isn't as insightful to us. It's useful but it doesn't tell us what's going on. Trying to better understand the physics-chemistry boundary, and do reductionism, even if in practice a bunch of shit is infeasible to calculate, well, I gather it tells us structural stuff about chemicals, stuff that "plug and chug with an ML algorithm" can't presently give us.

"What's the point of doing linguistic theory if we already have LLMs". Well, because I don't know what's going on inside an LLM and neither do you. They're really good at doing translation tasks and shit but... do they give us insight into how language works? Do we have good reason to think that "things an LLM can learn" correspond closely to "things a human child can learn" linguistically? Does looking at a bunch of transformer weights tell us, e.g., what sorts of linguistic structures are cognitively + diachronically possible? Well, no. To do that we have to look at the actual linguistic data, come up with theories, test them against new data, repeat. Like scientists or whatever.

[mozarting]

Day 2 : Rust and Data Structure

This is second post and I am realizing that documenting the learning progress with blog is so hard for me, I studied error handling in in Rust, I watched second lecture from Algorithms course, I understood the most of it. but when it comes to writing the blog, My brain don't know what to write and how to write ?

What I learned ? What challenges I faced ? What I found interesting ? Key takeaways ?

YEAH RIGHT!! I KNOW THEM. BUT IT'S JUST NOT COMING TO ME AT THE TIME OF WRITING.

Anyways, I still tried!!! and I will do it again tomorrow.

🦀 Rust Book: Error Handling

Today, I studied the 9th chapter of the rust book: Error handling, So, rust groups errors into two categories : recoverable and non-recoverable.

Rust has this Result type which is an enum with two variants Ok and Err. I also got to know the ? operator, which at looked like magic, it’s a shortcut to pass errors up the chain. Pretty cool, How this example

turns into just few lines of code by using ?

This chapter was easy to follow. I typed and ran all the examples, I spent 3 hours with it.

📘MIT 6.006 Introduction to Algorithms : Lecture 2

I watched 2nd lecture from the course. It was about Data Structures.

I got to know difference between interfaces and data structures. Now, I want to implement static and dynamic array and linked list from scratch.

Tomorrow's plan

I'll start the 10th chapter in rust book. Generic types and Traits and continue the 3rd lecture in the Algorithms course. Well, I want to start working on a project with Rust, right now I am thinking about writing interpreter. But, I'll wait until I complete the 12th chapter from the book.

[xiabablog]

Thanks Sivani for the video! Well explained, loved the example array you gave 😎👍🏾💗

Binary Search Algorithm

I made a new video explaining the Binary Search Algorithm. My aim is to explain Binary Search Algorithm more straightforwardly in this video. I have also tried to explain this visually with the help of an example array I took. I hope this video helps you understand about Binary Search Algorithm.

Do check it out!

[probablyasocialecologist]

AI can turn some impressive party tricks, but it's unsuited for solving serious problems in the real world. This is true of predictive AI, whose correlations are data-driven conspiracy theories, and of large language models like ChatGPT, whose plausible waffle is always trying to pull free of the facts. The real issue is not only that AI doesn't work as advertised, but the impact it will have before this becomes painfully obvious to everyone. AI is being used as form of 'shock doctrine', where the sense of urgency generated by an allegedly world-transforming technology is used as an opportunity to transform social systems without democratic debate. Faced with social structures whose foundations have been eaten away by decades of privatisation and austerity, the political response is to pump money into 'frontier AI' while hyping it up as the most awe-inspiring technology since the Manhattan Project. The Prime Minister says he will "harness the incredible potential of AI to transform our hospitals and schools" while ignoring leaking roofs in the NHS and the literally collapsing ceilings in local schools. This focus on the immaterial fantasies of AI is a deliberate diversion. When large language models are touted as passing basic medical exams, it's because they've absorbed answers from across the internet. They are incapable of the embodied understanding and common sense that underpin medicine, education or any other form of care. One thing that these models definitely do, though, is transfer control to large corporations. The amount of computing power and data required is so incomprehensibly vast that very few companies in the world have the wherewithal to train them. To promote large language models anywhere is privatisation by the back door. The evidence so far suggests that this will be accompanied by extensive job losses, as employers take AI's shoddy emulation of real tasks as an excuse to trim their workforce. The goal isn't to "support" teachers and healthcare workers but to plug the gaps with AI instead of with the desperately needed staff and resources. Real AI isn't sci-fi but the precaritisation of jobs, the continued privatisation of everything and the erasure of actual social relations. AI is Thatcherism in computational form. Like Thatcher herself, real world AI boosts bureaucratic cruelty towards the most vulnerable. Case after case, from Australia to the Netherlands, has proven that unleashing machine learning in welfare systems amplifies injustice and the punishment of the poor. AI doesn't provide insights as it's just a giant statistical guessing game. What it does do is amplify thoughtlessness, a lack of care, and a distancing from actual consequences. The logics of ranking and superiority are buried deep in the make up of artificial intelligence; married to populist politics, it becomes another vector for deciding who is disposable.

[shailendra01]

This course is intended for people with an interest in software engineering who want to advance their careers by landing an interview with one of the most prestigious organisations in the world, such as Microsoft, Adobe, Uber, Google, or Amazon. In order to prepare you for these important interviews, this programme will teach you everything from coding to problem-solving techniques.