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Machine Learning, Deep Learning, Naturallanguageprocessing.
just like Google BERT (credit: ME! This is Venchi Gelato in Richmond, Surrey, UK. Passionfruit & Mango, Strawberry Stracchiatella and Cuore de Cocoa/Dark Chocolate. My 4yo is in the background) Below is a simple list of all the concepts in the original BERT paper that I have decided to call out as | medium | 1,549 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
important prerequisites to understanding before you can really appreciate BERT. I’ve grouped them into three kinds of prerequisites: Essential prerequisites: things that are essential part of BERT’s design Reference only: the paper refers to them and are useful background. Foundations: you should | medium | 1,550 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
know this stuff, but these resources are particularly good and may help provide a fresh perspective on the world. Essential prerequisites as a practitioner The paper and blog posts are pretty readable to get an overview. But if you want to work with BERT — such as your own fine-tuning — it refers | medium | 1,551 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
to these concepts below essential to understanding. My suggestion: read through the paper once then review this list to decide which ideas would help you dive into better. I’ve ordered them roughly in terms of my own subjective knowledge needs. Attention Networks: a new neural network architecture | medium | 1,552 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
that takes a group of words and calculates the relationships between them (“the attention”) by Keita Kurita | by Jay Alamarr | from Distill.pub Transformers: an application of Attention Networks. Jay Alamarr’s Illustrated Transformer | Annotated Transformer takes “Attention is all you need” and | medium | 1,553 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
creates code alongside the paper to reproduce the ideas presented. Transformers are included in Google’s Tensor2Tensor library (see below). Fine tuning & transfer learning: taking a base model and customising it for a specific task with a comparatively small amount of data (hundreds of examples vs | medium | 1,554 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
billions of examples). See the ULMFiT paper (Jan 2018) that as far as I can tell introduces the concept of fine-tuning as a kind of transfer learning that can apply to any NLP task. Language modelling: a prediction system that predicts missing words (either the next word or maybe as in BERT’s case, | medium | 1,555 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
in the middle of a sentence). Jay Alamarr’s illustrated word2vec has a really great section on Language Modelling. Language model pretraining: an integral part of fine-tuning: the initial work, often unsupervised (as is the case for BERT) that build a vectoral representation of language. Contextual | medium | 1,556 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
token representations: accepting that the same word means different things in different contexts. The first generation of word embeddings (word2vec, GloVe) were independent of context so “queen” was represented as the same vector regardless of the fact that it means several things. See this great | medium | 1,557 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
comparison of the methods Multi task learning: the ability for a system to learn multiple tasks at the same time when training. BERT learns two tasks at the same time: masked language modeling and next sentence prediction, the latter of which is key to contributing to BERT’s better understanding of | medium | 1,558 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
context (“long-range dependencies”). Sebastian Ruder’s multi-task learning blog post from 2017 is still one of the best. Then there’s also Andrew Ng’s Multi-Task Learning video as part of deeplearning.ai Positional embeddings: as BERT is not a recurrent neural network (it reviews things in | medium | 1,559 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
parallel) it needs another way to represent spacial relationships and invents a new idea called positional embeddings. See this article on why BERT has 3 layers Segment embeddings: BERT can handle two “sentences” to support a bunch of downstream tasks that require an input text and and output text | medium | 1,560 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
such as question and answer, translation, paraphrasing, and others. It does this with segment embeddings. Again, see this article on why BERT has three layers WordPiece Embeddings: an unsupervised way of tokenising words that helps balance granularity and knowledge retention with out-of-vocabulary | medium | 1,561 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
scenarios. If you want to do custom vocabularies, WordPiece is not actually open sourced but there are other similar methods (such as, confusingly, SentencePiece that sounds like a different layer of abstraction but isn’t). See the value of WordPieces and discussion of WordPiece vs SentencePiece | medium | 1,562 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
Ablation studies: core feature of any research that excludes a component of the solution and retests to see what the impact of that component might be. For instance in BERT they removed the next sentence prediction task learning to see how it affected things and it did clearly show it contributed | medium | 1,563 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
to “long-range dependency” understanding. This paper (Jan 2019) explores ablation studies and gives a good rationale for the kinds of insights you can expect to see. One that stood out for me was: in general, the more a single unit’s distribution of incoming weights changes during training, the | medium | 1,564 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
more important this unit is for the overall classification performance.” Tensor2Tensor a general framework that makes deep learning research easier including multi-task learning. Blog post (June 2017) | source Random restarts: I actually yet find any reference to this other than a reference by | medium | 1,565 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
Jeremy Howard about “fast.ai you get to learn about cutting edge techniques… Reference only These ideas are referred to in the BERT paper. You’ll almost certainly come across these ideas too learning about the dependencies but they’re not central to understanding BERT. Natural language inference | medium | 1,566 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
(NLI) Paraphrasing Cloze tasks Closed domain question and answering LSTM Denoising autoencoders Machine translation: probably the highest visibility and most important NLP task because it led the frontier of new developments: firstly with neural machine translation (NMT) seq2seq and then the first | medium | 1,567 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
application of transformers (TODO confirm). Foundations If you’re an NLP practitioner you should know this stuff; links here are to particularly good and up to date resources on them. It is by no means complete; more, a smattering of the best resources I’ve found. Objective functions Distributed | medium | 1,568 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
representation Word embeddings. Jay Alamarr presents NLP Word Embeddings (May 2019) and his Word2Vec blog post is second to none Named entity recognition (NER): I’ve put this here though it’s pretty hard not to know NER if you’re doing NLP Tokenization. Fast.ai NLP Course for programmers (June | medium | 1,569 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
2019). Fast.ai is brilliant and this new course is a“code first” approach. However I find it uses lots of machine learning concepts that may get in the way of understandability so feel it has unarticulated prerequisites. Other resources In-depth comparison of BERT’s built-in feedforward network for | medium | 1,570 |
Nestjs, JavaScript, Programming, Technology, Coding.
Introduction NestJS, with its robust architecture and TypeScript support, has become a popular choice for building scalable and maintainable server-side applications. One of the key features of NestJS is its support for asynchronous programming paradigms, particularly the transition from promises | medium | 1,572 |
Nestjs, JavaScript, Programming, Technology, Coding.
to observables. In this article, we’ll delve into the concept of promises and observables in NestJS, explore why observables are advantageous, and provide comprehensive examples covering various scenarios. Understanding Promises and Observables Promises: Promises are a fundamental part of | medium | 1,573 |
Nestjs, JavaScript, Programming, Technology, Coding.
JavaScript’s asynchronous programming model. They represent a proxy for a value that will eventually be available, or an error that will occur. Promises offer a cleaner alternative to callback-based approaches, enabling more readable and maintainable code. In NestJS, promises are extensively used | medium | 1,574 |
Nestjs, JavaScript, Programming, Technology, Coding.
for handling asynchronous operations such as database queries, HTTP requests, and file I/O. Here’s a basic example of using promises in a NestJS service: @Injectable() export class UserService { async findAll(): Promise<User[]> { return await this.userModel.find().exec(); } } Observables: | medium | 1,575 |
Nestjs, JavaScript, Programming, Technology, Coding.
Observables, on the other hand, are a more powerful abstraction for handling asynchronous data streams. They provide support for handling multiple values over time, making them well-suited for scenarios such as event handling, data caching, and real-time updates. NestJS leverages observables | medium | 1,576 |
Nestjs, JavaScript, Programming, Technology, Coding.
through libraries like RxJS, enabling reactive programming paradigms within its framework. Here’s how you can use observables in a NestJS service: import { Injectable } from '@nestjs/common'; import { Observable } from 'rxjs'; import { User } from './user.interface'; @Injectable() export class | medium | 1,577 |
Nestjs, JavaScript, Programming, Technology, Coding.
UserService { findAll(): Observable<User[]> { return from(this.userModel.find().exec()); } } Advantages of Observables in NestJS Asynchronous Stream Processing: Observables excel at handling streams of data asynchronously. In scenarios where data arrives incrementally or in real-time, observables | medium | 1,578 |
Nestjs, JavaScript, Programming, Technology, Coding.
offer a more natural and efficient way to process this data compared to promises. Composability and Error Handling: Observables provide powerful operators that allow for easy composition of asynchronous operations. Operators like map, filter, and mergeMap enable developers to manipulate data | medium | 1,579 |
Nestjs, JavaScript, Programming, Technology, Coding.
streams with ease. Additionally, observables offer robust error handling mechanisms, allowing errors to be gracefully propagated through the stream. Hot and Cold Observables: Observables come in two flavors: hot and cold. Cold observables start producing values when a consumer subscribes to them, | medium | 1,580 |
Nestjs, JavaScript, Programming, Technology, Coding.
making them suitable for scenarios where each consumer needs its own independent stream of data. Hot observables, on the other hand, emit values regardless of whether there are any subscribers, making them ideal for scenarios such as event handling. Using Observables in NestJS: Examples Basic Data | medium | 1,581 |
Nestjs, JavaScript, Programming, Technology, Coding.
Retrieval: Let’s start with a basic example of using observables to retrieve data from a database in a NestJS controller: import { Controller, Get } from '@nestjs/common'; import { Observable } from 'rxjs'; import { User } from './user.interface'; import { UserService } from './user.service'; | medium | 1,582 |
Nestjs, JavaScript, Programming, Technology, Coding.
@Controller('users') export class UserController { constructor(private readonly userService: UserService) {} @Get() findAll(): Observable<User[]> { return this.userService.findAll(); } } Real-Time Updates with WebSockets: Now, let’s explore a more advanced scenario where we use observables to push | medium | 1,583 |
Nestjs, JavaScript, Programming, Technology, Coding.
real-time updates to clients using WebSockets: import { WebSocketGateway, WebSocketServer, SubscribeMessage } from '@nestjs/websockets'; import { Server } from 'socket.io'; import { Observable } from 'rxjs'; import { User } from './user.interface'; import { UserService } from './user.service'; | medium | 1,584 |
Nestjs, JavaScript, Programming, Technology, Coding.
@WebSocketGateway() export class UserGateway { constructor(private readonly userService: UserService) {} @WebSocketServer() server: Server; @SubscribeMessage('getUsers') getUsers(): Observable<User[]> { return this.userService.findAll(); } } FAQ Section Q: When should I use promises instead of | medium | 1,585 |
Nestjs, JavaScript, Programming, Technology, Coding.
observables in NestJS? A: Promises are suitable for simple asynchronous operations that involve a single value or a one-time event. If you’re dealing with streams of data or need more advanced composition and error handling, observables are a better choice. Q: Are observables harder to understand | medium | 1,586 |
Nestjs, JavaScript, Programming, Technology, Coding.
than promises? A: While observables have a steeper learning curve compared to promises, they offer more expressive power and flexibility, especially in complex asynchronous scenarios. Q: Can I mix promises and observables in NestJS? A: Yes, NestJS provides seamless interoperability between promises | medium | 1,587 |
Nestjs, JavaScript, Programming, Technology, Coding.
and observables, allowing you to choose the best tool for the job in any given scenario. Conclusion In conclusion, NestJS offers robust support for both promises and observables, allowing developers to choose the most appropriate asynchronous programming paradigm for their applications. While | medium | 1,588 |
Nestjs, JavaScript, Programming, Technology, Coding.
promises remain a fundamental building block, observables unlock a new level of expressiveness and flexibility, particularly in scenarios involving data streams and real-time updates. By mastering the transition from promises to observables, developers can unlock the full potential of reactive | medium | 1,589 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
Exploring Distributed Databases in 2024 ! What is a Distributed Database? As the name suggests Distributed means circulate in several sites and not particular for only one site or system. For example, it circulates on many computers. Many issues are easily handled by Distributed Database from | medium | 1,591 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
utilizing single databases and single machines such as latency, scalability, mistake understanding and many more. Numerous nodes we’re used by Distributed Database and this node furnishes additional power on the computer. Some parts of the distributed database are conserved in some different | medium | 1,592 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
portions in physical locations. On the several database nodes, the necessity of processing is allocated among processors. The data which is stocked in the physical location where regulated the distributed data by (DDBMS) distributed database management system. It consists of the data in several | medium | 1,593 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
locations, sometimes the data have to be stored in the same places and sometimes have to be stored in several geographical locations. Its database hardware is operated by several devices in several locations but it is worked in a single database system. Two processes confirm that the distributed | medium | 1,594 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
database is recent or up-to-date which means duplication and replication. Distributed Database Types There are two types of distributed databases ,Homogenous and Heterogeneous. Firstly we will understand these two separately then we will do there differentiation. Homogenous Distributed Database: | medium | 1,595 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
Image credit: https://phoenixnap.com/kb/distributed-database As the name suggest homo means similar, so these whole term homogenous distributed database means a system in a network where all nodes and sites are in a same architecture or in a same plate (hardware and software architecture). In the | medium | 1,596 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
other words you can also says that DBMS i.e. (database management system) is operating in every particular nodes that is same and there is a consistency in terms of getting the information of language, some program covering all the nodes. Here all the nodes in the distributed system are utilized in | medium | 1,597 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
the similar Database and Management System (DBMS). Heterogeneous Distributed Database: Image Credit: https://phoenixnap.com/kb/distributed-database As the name suggest heterogeneous means different, so these whole term heterogeneous distributed database means a system in a network where all nodes | medium | 1,598 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
and sites are in a different architecture or in a different plate (hardware and software architecture). Different nodes are utilizing different operating systems and database management systems. One of the main features of this distributed database is it has the multiple data models as well | medium | 1,599 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
specialized functionality. Advantages of Distributed Database Some advantages of distributed databases are discussed below on points. Performance is Enhanced: A numerous query is solved in several sites and this query is divided into several sub-queries, so by this, the performance of the | medium | 1,600 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
distributed database is improved. The Growth is simpler: In the environment of distributing the data, the growth will be simple on adding more data, increasing the size of the database, and adding more processors becomes very easy. Some other advantages are: Building a network of systems is very | medium | 1,601 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
reasonable that consists of a part of data. The database works as normal even when the nodes are offline. It’s not difficult to add a system and the database is circulated in several systems. Common Examples of Distributed database are as follow : Aerospike Cassandra Clusterpoint ClustrixDB | medium | 1,602 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
Couchbase Druid (open-source data store) FoundationDB NuoDB Riak OrientDB AmazonSimpleDB FoundationDB Final Conclusion: As we know the technology is very advances and the latest technology or innovations is going to shape the future of data management, these term distributed database is stands as a | medium | 1,603 |
DevOps, Distributed Systems, Database, Riak, Cassandra.
testament to our collective pursuit of resilience and efficiency. PS: This post was originally published at : https://shorturl.at/gCHJ6 Also in case you liked the post and found it useful, please feel free to share it with your friends and network and clap for it. Do not forget to subscribe to our | medium | 1,604 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
How to Achieve Your Goals Sooner Rather Than Later “There’s no added virtue, nothing worth bragging about in getting rich slowly, in getting healthy slowly, in learning a new skill slowly, in turning your business around slowly…nor in achieving any goal slowly.” — Gary Ryan Blair I have a very | medium | 1,606 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
simple philosophy when it comes to goal achievement… If you’re going to work hard to achieve a goal, you owe it to yourself to find the shortest path. If that thought sounds appealing…here’s what you have to do. You have to say adios to conventional wisdom, and surrender whatever self-limiting | medium | 1,607 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
beliefs you’ve been hanging onto regarding rapid goal achievement. They have no place in your life and only serve to compromise performance, spook you into quitting, and relegate you to the slow lane. If you can’t, won’t or simply refuse to accept what I’m about to share with you… you’re making a | medium | 1,608 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
mistake, a big one that will cost you in a big way. Think about it, if the goal is to… Become debt free…why perpetuate the pain and do it slowly? Get in great shape…why should it be a long drawn out process? Get better grades…why should it take any longer than the next exam? Get rich…you might as | medium | 1,609 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
well get rich fast, and while you’re young for that matter. Become the sales leader in your firm…why should it take until next quarter or next year when you can wear the crown next month? Any goal, plan, intention or idea expands so as to fill and reinforce the unrealistic amount of time which you | medium | 1,610 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
have made available for its completion. If you simply choose to learn how to compress time rather than expand it…you will dramatically accelerate the result. To put this idea into another context…think about what happens when you push down on a spring and collapse it. That’s right…you compress it, | medium | 1,611 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
and by squishing it, you shorten its length. The same principle works with time. Behaviors that compress time and which shorten your path to success include… Focus Boldness Consistency Discipline Speed of Execution Enforcement of Deadlines Gritty Perseverance These types of proactive behaviors are | medium | 1,612 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
what advance the ball, put points on the board…and allow you to fast-track any goal you desire. That’s what you really want, isn’t it? Unfortunately, the majority of people practice the art of the longer path. Behaviors that compromise results and which expand the amount of time it takes to achieve | medium | 1,613 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
a goal include… Apathy Indecision Excuses Blaming Procrastination Worry Low Self-Esteem These types of counterproductive behaviors are responsible for stretching, lengthening and expanding the length of time it takes to achieve your goals. They sabotage any chance of success…they perpetuate pain, | medium | 1,614 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
increase tension, compromise performance and intensify suffering. Behavior never lies…and if you want to predict someone’s chances of success (or your own for that matter), just watch how that person spends his or her time. Time can only be compressed or expanded and that is precisely why you need | medium | 1,615 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
to learn how to compress its use so that you can shorten your path to success! WHAT TO DO NOW? If you want to increase your productivity, eliminate procrastination and accelerate your goals, download a copy of my free manifesto. You will find the tip about how to “10X Your Results” fascinating! | medium | 1,616 |
Entrepreneurship, Leadership, Startup Lessons, Personal Development, Life Lessons.
Gary Ryan Blair is the #1 Best Selling Author of Everything Counts and the creator of the 100 Day Challenge…a radical approach to goal achievement that shows people how to achieve 10X size goals by applying the methods and best practices of growth hacking! Get my free Achieve Goals Fast Manifesto | medium | 1,617 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
Photo by Christopher Paul High on Unsplash As a systems engineer, I reflect on human intelligence. Intelligence encompasses various qualities and abilities, such as the capacity to recognize occurrences in our surroundings, abstract and organize them, and predict the future based on past | medium | 1,619 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
experiential knowledge. Among these abilities, I focus on the significance of design capability. This is the ability to shape matters actively, not just passively understand or predict, in alignment with intentions. I propose naming this thinking method as “Logical Design Thinking.” While the term | medium | 1,620 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
“Design Thinking” is common, it’s ambiguous and often conflated with visual graphic design. Thus, “Logical Design Thinking” seems more appropriate. This article defines logical design in Logical Design Thinking, exploring its importance and challenges in individual and societal contexts. It | medium | 1,621 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
highlights the current societal lack of this thinking and the need to pursue solutions. Logical Design Thinking: Science, Will, Engineering Logical design is defined as the intellectual ability to combine science, will, and engineering. For illustration, consider riding a bicycle. A bicycle is | medium | 1,622 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
unstable when stationary or slow. As you pedal faster, it stabilizes. However, excessive speed can make it more susceptible to surface irregularities, leading to balance loss. Too much speed can also mean crashing before recovery is possible. Plus, high speeds make avoiding obstacles and navigating | medium | 1,623 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
complex paths harder, and can exceed the bicycle’s component durability. This explanation is from a scientific perspective. Science objectively grasps facts. Science doesn’t include subjective desires or shoulds. Such subjective judgments belong to the realm of will. In bicycling, decisions like | medium | 1,624 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
prioritizing safety or risking speed are subjective, based on the situation. If you’re cycling for routine shopping, safety is paramount. In a race, overemphasizing safety won’t lead to victory. Choosing between risking safety for victory or prioritizing safety at the cost of losing is a matter of | medium | 1,625 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
personal value judgment, the realm of will. Even in routine shopping, choosing to ride dangerously fast or safely, despite the desire for speed, is a matter of personal will. Practicality or guidelines, like what’s feasible or how to do it, are not part of science. This wisdom belongs to | medium | 1,626 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
engineering. In bicycling, balancing and accelerating quickly at the start is advisable. As speed increases, focus shifts to pedaling efficiently without sacrificing stability. However, there are limits to safe speeds considering road conditions and bicycle durability. Engineering considers these | medium | 1,627 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
factors in determining feasible speeds and guidelines. Logical design combines the objective understanding of science, the subjective goals of will, and the practical insights of engineering to make decisions. Logical design may sound like physical object creation, but it applies to rules, | medium | 1,628 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
projects, and relationships too. For bicycling, deciding on speed and acceleration involves logical design, combining scientific knowledge about speed and stability, willful goals, and engineering insights on safety-speed trade-offs and driving guidelines. Importance of Logical Design Thinking The | medium | 1,629 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
bicycle example shows logical design thinking isn’t special, highly specialized, or requiring high intelligence. It’s just ordinary thought. However, its importance becomes clear in more complex, larger matters. Take summer homework, for example. Spreading it evenly over vacation avoids last-minute | medium | 1,630 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
stress. Delaying leads to a heavy workload later. This is the objective fact, the “science” of homework. People’s will varies: some prefer spreading it out, others focus on certain days. Many dislike rushing at the end. Engineering insights include delegation possibilities or quality-time | medium | 1,631 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
trade-offs. Combining these elements logically designs homework progress. But not everyone applies this thinking as naturally as in bicycling. Similarly, lifestyle diseases. Meticulous people live moderately, preventing these diseases. Others, less orderly, increase their risk. If this is a | medium | 1,632 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
conscious, logically designed choice, it’s respectable. But many regret it upon falling ill, showing logical design thinking isn’t as straightforward as in bicycling. The Difficulty of Collective Logical Design Thinking Logical design thinking becomes particularly challenging in group settings. In | medium | 1,633 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
many societal issues, we tend to rely on experts’ opinions. For example, in matters like environmental issues, the spread of coronavirus, and future risks of AI technology, we seek expert insights via TV and the internet. However, these experts are usually scientists, specialists in objective | medium | 1,634 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
facts. This is insufficient from a logical design thinking perspective. Knowledge about the relationship between a bicycle’s speed and stability alone does not enable us to plan its operation. Similarly, we need experts to organize values and investigate preferences regarding societal issues. It’s | medium | 1,635 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
necessary to understand what people want or should do. Furthermore, we require engineering insights about what’s realistically possible, limitations, trade-offs, and guidelines for implementation. Without this, even if many people desire a 100% safe and high-speed bicycle, such aspirations are | medium | 1,636 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
unrealistic and meaningless for logical design. The major gaps in logical design thinking for social issues are the perspectives of engineering and decision-making based on it. This realization shows that while logical design thinking may seem obvious in simple contexts like bicycling, it’s not | medium | 1,637 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
functioning well for societal issues. Who Are the Experts in Social Issues? There’s a belief that experts’ opinions on these issues are unreliable. This is because we misunderstand the nature of expertise needed for comprehensive logical design thinking. We confuse scientists, who possess knowledge | medium | 1,638 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
of objective facts in their fields, as the only experts. We need to gather knowledge from experts in science, decision-making, and engineering. Without a specialist who combines these skills, logical design thinking cannot be established. People often mistake scientists as the sole experts and | medium | 1,639 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
expect them to solve problems appropriately, leading to disappointment when these expectations aren’t met. To dispel this misconception, it’s crucial to properly understand the seemingly obvious framework of logical design thinking. The Lack of Recognition of the Importance of Engineering and | medium | 1,640 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
Design As a systems engineer, I find it frustrating that societal issues lack this perspective of engineering and logical design. This isn’t just about technical aspects but involves organizing the feasibility and trade-offs of problems to analyze societal demands and design plans and systems | medium | 1,641 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
accordingly. In decision-making and policy formation, scientific methods and evidence-based approaches are becoming more prominent. However, in unknown situations where evidence can’t always be gathered, engineering methods and design-based thinking should also be possible. Even in the | medium | 1,642 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
unprecedented situation of the coronavirus spread, commentators on TV emphasized the importance of evidence-based policy decisions, and no one seemed to object. This surprised me. It shows a misunderstanding that science is our only reliable knowledge, overlooking the perspectives of engineering | medium | 1,643 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
and design. Scientists can’t say much without precedent or evidence, but engineering and design are different. In their absence, we create rough models, test small versions with adequate safety margins, gather sample information, and produce the most rational answers within limited time and | medium | 1,644 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
resources. This requires not just knowledge but also experience, foresight, and analytical abilities. This intellectual work, as simple and obvious as in the example of bicycling, doesn’t function well in groups because its importance is overlooked in society. The Blind Spot in Societal Thinking | medium | 1,645 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
The difficulty in applying logical design thinking to social issues becomes evident when comparing AI technology with the example of bicycling. Concerns about the evolution of AI technology are widely discussed. However, these discussions focus mainly on future predictions and technical or societal | medium | 1,646 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
measures, solely from a scientific perspective. They often miss crucial points like prioritizing safety over progress and understanding the trade-offs between them. In the bicycle analogy, it’s clear that unlimited acceleration, regardless of measures, threatens safety. It’s also clear that the | medium | 1,647 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
majority wouldn’t want to sacrifice safety for acceleration. We would first consider reducing speed to a controllable level. Yet, in AI technology discussions, such basic, logical considerations are strangely absent. Instead, we often find discussions that, if translated into the bicycle analogy, | medium | 1,648 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
would be recklessly indifferent to safety. This might be because AI technology, unlike bicycle riding, involves unpredictable impacts and risks. Navigating these uncertainties is like riding a blindfolded bicycle, where safety becomes difficult to maintain. In AI technology development, it’s as if | medium | 1,649 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
we’re pedaling a bicycle blindfolded, carrying everyone, including children, without knowing the road or speed. Despite this, many remain indifferent, a clear indication of the absence of societal logical design thinking. This situation, along with the lack of a framework to understand such | medium | 1,650 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
problems, suggests a blind spot in societal thinking. This is not just a technical issue like AI but also applies to environmental issues. Essentially, it’s not just about managing risks of technological progress; it shows a lack in our society’s capacity to apply rational thought, which we usually | medium | 1,651 |
Logical Thinking, Design Thinking, Systems Thinking, Social Problems, Problem Solving.
do in everyday situations. For instance, if we imagine our society riding a blindfolded bicycle, we’d likely keep pedaling under the current societal structure. This prompts the need to explore why that is. Logical design thinking is not a special method but a common way of thinking, like riding a | medium | 1,652 |
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