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Instill AI

Managing proper react component folder structure is hard, it needs to be flexible, extendable, and most important of all, it needs to make naming a component as easy as possible. I adapted several react folder structures across 3 different repo this year, what I discovered is not a steady structure but some philosophies behind the structure. The structure should be consistent and the principle should be documented. The component's materials should stay near the component itself. It's a good practice to export your component to the outer responsibility layer. It's a good practice to limit the length of the filename to 3. I would like to introduce the different structures I used first and then share the context of these philosophies with you. Pnyx .
|-- public
|-- src
|   |-- config
|   |-- lib
|   |-- locales
|   |-- pages
|   |-- shared-hooks
|   |-- static
|   |   `-- images
|   |-- types
|   |-- utilities
|   |-- components
|   |   |-- buttons
|   |   |-- layouts
|   |   |-- links
|   |   |-- Footer.tsx
|   |   |-- Header.tsx
|   |   `-- ...etc
|   |-- App.tsx This is a simple project and most of its pages are static. As a first-time React user, I learned this structure from videos on Youtube. The whole repo was maintained in a flat manner. There was no nested folder with a duplicate name like and I didn’t export any component to its outer layer. Because the codebase is relatively small I didn’t observe many problems during writing this project. /components/PageHeader/PageHeader.tsx instill.tech@0.1.6 .
|-- .github
|-- .husky
|-- .storybook
|-- .__mocks__
|-- .__tests__
|   |-- ui
|   |   |-- Headline.test.tsx
|-- contexts
|-- .hooks
|-- .lib
|-- markdown
|-- pages
|-- public
|-- stories
|   |-- ui
|   |   |-- Headline.stories.tsx
|-- styles
|-- components
|   |-- forms
|   |-- layouts
|   |-- ui
|   |   |-- blocks
|   |   |-- buttons
|   |   |-- groups
|   |   |-- links
|   |   |-- Headline.tsx
|   |   |-- SubHeadline.tsx
|   |   `-- ...etc After further exploring and digging deeper, this project has several changes compared to Pnyx. It adapted storybook and stored stories in a different folder, It had the test suites and stored the test files in a different folder, The component folder had categories like and , it grouped the same components together. form layouts This structure was experimental and I had not formed a steady opinion of React folder structure at that moment. But back then the idea of separating component material(like stories and tests) into different folders caused a lot of trouble. When I refactored some minor changes beneath the component folder, I needed to adjust the and folder too. These folders became inconsistent over time. /tests /stories Besides, those components were not exported to the outer level, so it was hard to digest them.  Every import of a component will occupy a line of code and it looks messy. import ClientBlock from "@/components/ui/blocks/ClientBlock"
import Headline from "@/components/ui/Headline" Image some Next.js page components that have 50 to 75 lines of this kind of import and it is very chaotic. instill-ai/instill.tech@0.13.0 .
|-- .github
|-- .husky
|-- .storybook
|-- docs
|-- public
|-- release-please
|-- src
|   |-- contexts
|   |-- hooks
|   |-- styles
|   |-- types
|   |-- lib
|   |-- pages
|   |-- components
|   |   |-- about
|   |   |   |-- OurMembers
|   |   |   |   |-- OurMembers.tsx
|   |   |   |   |-- MemberIntro.tsx
|   |   |   |   `-- index.ts
|   |   |   `-- index.ts
|   |   |-- career
|   |   |-- docs
|   |   |-- landing
|   |   |-- policy
|   |   |-- ui
|   |   |   |-- Nav
|   |   |   |   |-- Nav.tsx
|   |   |   |   |-- AboutPageLink.tsx
|   |   |   |   `-- index.ts
|   |   |   |-- PageHead.tsx
|   |   |   `-- index.ts This is the current version of my favored folder structure. The component's materials are near the component itself. General purpose components will stay in the folder. components/ui Non-general-purpose components will stay in the parent's component folder.
- For example, is a disposable component that will only be used by its parent so it should stay in the folder not in the folder.
This act can make naming components much easier compared to treating every component as general purpose components and putting them into the folder. MemberIntro.tsx OurMember.tsx components/about/OurMember components/ui components/ui All components are exported to its outermost responsibility folder. For example, will be exported to the folder and the component will be exported to the . In this way, the import code will look cleaner. Nav /components/ui OurMember /components/about Under the folder, the components are categorized by their responsibility, not their functionality. For example, there won’t be a category like and but , , and . In this way, we could limit general-purpose components to just one place and other components are treated like disposable components under their responsibility folder. /components /block /section /onboarding /career /about import { Nav, PageHead } from "@/components/ui"
import { OurMember } from "@components/about" instill-ai/design-system .
|-- .github
|-- .husky
|-- .storybook
|-- public
|-- src
|   |-- hooks
|   |-- styles
|   |-- types
|   |-- utils
|   |-- index.ts
|   |-- ui
|   |   |-- index.ts
|   |   |-- Buttons
|   |   |   |-- ButtonBase
|   |   |   |   |-- ButtonBase.tsx
|   |   |   |   |-- ButtonBase.stories.tsx
|   |   |   |   `-- index.ts
|   |   |   |-- SolidButton
|   |   |   |   |-- SolidButton.tsx
|   |   |   |   |-- SolidButton.stories.tsx
|   |   |   |   `-- index.ts This project is a special case when it comes to folder structure. It uses the structure I called "Base and Export" two-level component design. Base-level components: The foundation of every type of component. Base-level components won't be exported to be used by exterior consumers. Base components will define all the props and functions that the component needs. Exported-level components: Inherits all the props from a base component but may have a predefined style, meant to be used by the consumers. Exported-level components will be consumed with the limited props like onChangeInput, borderStyle, fontFamiliy, or other functional props. We will limit as few style props an exported component needs as possible, to control the visual style of the component. This is a unique way to organize our design system. Overall it brings some fresh air into our projects and serves our needs well. Philosophies During this journey, what I learned the most is "There is no one fit for all folder structures". If you encounter someone claiming that, it will be better to leave them there and move on. The reason is quite simple: The technology we use is drastically changing. For example, Next.js has a new layout RFC that will change our folder structure a lot, and the adaption of storybook changes the way we thought about components too. I foresee that we will have more frameworks and libraries like these. The structure should be consistent and the principle should be documented. This is my first point. The codebase is not a single person's playground but a collective intelligence that the maintainers had to agree on. No matter what you adopt, you should make the structure as clear and consistent as possible. In this way, other maintainers will have a clear path to look into your project and once you need to refactor the whole repo to adopt a new tech it will be easier to migrate a consistent structure. The component's materials should stay near the component itself. This is the mistake I made at that caused lots of trouble and inconsistency when I tried to refactor its structure. Overall I think we should put our component's materials close to the component itself. Not only to reduce the overhead of refactoring but also to help others find your component. instill-ai/instill.tech@0.6.1 In this way, we could help the maintainers to name their components easier too, because they don’t need to come up with a component name that can describe itself in various contexts. It only needs to describe itself under the context of its parent. It's a good practice to export your component to the outer responsibility layer. When your file has 30 to 50 imports, it will be messy if the imported function or component is not exported to its outer folder. Imagine you have 30 lines of this kind of code. import ClientBlock from "@/components/ui/blocks/ClientBlock"
import Headline from "@/components/ui/Headline" Compared to this way. import { ClientBlock, Headline } from "@/components/ui" I think the latter is better. It's a good practice to limit the length of the filename to 3. This philosophy comes from a friend of mine. Because we are using the PascalCase for the naming convention of our components, it may be hard to read when the length of words exceeds 3. and CallToAction.tsx CallToActionBlock.tsx Don’t over-emphasize the importance of a structure The ideal structure is changing and changing fast, and it highly relates to the technology we adapt to. I think the best method is to maintain these kinds of fundamental philosophies and adapt new tech and structure in a quick and flexible manner. Also published . here

Philosophies I Learnt From 3 Different React Folder Structures

The Scorpion is an uncommunicative insect

What does graph have to do with machine learning, and data science? A lot, actually, and it goes both ways. Over the last few years, we have seen what was once a niche research topic – graph-based machine learning – snowball. The Year of the Graph was among the first to take stock, point towards this development, and recognize graph-based AI as a key pillar for future development in the field. In this edition of the YotG Newsletter, we highlight resources focused on . Which is not to say there’s lack of news on , , and – on the contrary. graph-based machine learning and data science graph analytics graph databases knowledge graphs 2020 has turned Graph Machine Learning into a celebrity of machine learning, argues Michael Bronstein. Arguably, he’s a celebrity of Graph Machine Learning himself. A pioneering and prolific researcher at Imperial College London, as well as the Head of Graph Learning Research at Twitter. Bronstein sought the opinion of prominent researchers in the field of graph ML and its applications trying to summarise the highlights of the past year and predict what is in store for 2021. For a glimpse of Bronstein’s own work, check his Knowledge Connexions presentation “ ” Deep Learning on Graphs: Past, Present, And Future What 2021 holds for Graph ML? The end of the year is a good time to recap and make predictions. 2020 has turned Graph ML into a celebrity of machine learning. For this post, I sought the opinion of prominent researchers in the field of graph ML and its applications trying to summarise the highlights of the past year and predict what is in store for 2021. Machine learning can help bootstrap and populate knowledge graphs. The information contained in graphs can boost the efficiency of machine learning approaches. A panel featuring some of the world’s top experts in AI, coming from all sides of the spectrum, got together to discuss how . AI + Knowledge are a match made in heaven , , , , , and cover a number of ways graphs, AI and knowledge interact in this 2-hour tour de force. Isabelle Augenstein Nathan Benaich Giuseppe Futia Amy Hodler Katariina Kari Fabio Petroni For the , check our Sergey Ivanov’s quick introduction. Top Applications of Graph Neural Networks 2021 AI + Knowledge – a match made in heaven? What can knowledge-based technologies do for Deep Learning? What is Graph AI, how does it work, what can it do? What’s next? What are the roadblocks and opportunities? Whether you are just getting started with graph-based data science and AI, or you are already advanced, is where you can get both inspiration and hands-on knowledge. Learning with the Machines Aleksa Gordic will share . Bob van Luijt will explain . Paco Nathan will introduce , and then deliver a . How to get started with Graph Machine Learning How businesses apply AI-first solutions in production with the Weaviate Vector Search Engine Graph-Based Data Science hands-on masterclass Tara Safavi will share her work on , and Ashleigh Faith will show how to . CoDEx: A Comprehensive Knowledge Graph Completion Benchmark Add more context to machine learning, using taxonomies and knowledge graphs Learning with the machines Connected Data London Meetup #4 Open source, Graph AI, Search, and Data Science with Python April 15 – 16, 2021 When talking about AI, however, be it graph-based or not, there’s semantics involved. And not just in terms of defining what AI is. AI is not machine learning. Knowledge-based technologies are also AI. Lately, we started seeing more voices advocating for approaches to bridge the worlds of machine learning and knowledge-based technologies. just Gary Marcus, as he shared in a , and in his . Artur d’Avila Garcez and Luis C. Lamb, in their work , Amit Sheth et. al. In . And Frank van Harmelen et. al, who created . series of articles on ZDNet Knowledge Connexions keynote Neurosymbolic AI: The 3rd Wave Semantics of the Black-Box: Can Knowledge Graphs help make Deep Learning systems more interpretable and explainable? Modular Design Patterns for Hybrid Learning and Reasoning Systems Rebooting AI: Deep learning, meet knowledge graphs Gary Marcus, a prominent figure in AI, is on a mission to instill a breath of fresh air to a discipline he sees as in danger of stagnating. Knowledge graphs, the 20-year old hype, may have something to offer there. The last few months have been good for graph databases. A graph database – Neo4j – made the for the 1st time. Neo4j also announced on GCP, preview on AWS. Stardog too. Top 20 in DB Engines general availability of its Aura managed cloud service announced its own cloud DBaaS We had a series of funding rounds, and an upcoming IPO. TigerGraph scored , Katana Graph , and . In the meantime Bitnine, makers of Agens Graph, is – the first in the market. $105M Series C $28.5M Series A Memgraph $6.7M TerminusDB €3.6M working on its IPO Last but not least, we had a round of new releases. to make working with graph databases easier. , , , , , , , and . AWS open-sourced a Graph Notebook Franz Inc released AllegroGraph v7.1 Grakn Labs released Grakn v2.0 Alpha Nebula Graph released v2.0 Ontotext released GraphDB v9.6 RDFox released v.5.0 Stardog released v7.6 TerminusDB released v4.0 From databases to platforms: the evolution of Graph databases Everything you’ve always wanted to ask about graph databases, but did not have the chance to. It’s also been a very active period in terms of book publishing. Whether you’re interested in Graph Databases, Data Analytics on Graphs, or Knowledge Graphs and Semantic Technologies, there’s a new book out there for you. Dave Bechberger and Josh Perryman published . The book introduces graph database concepts by comparing them with relational database constructs. It promises to include just enough theory to get started, then progress to hands-on development. “Graph Databases in Action” on Manning Ljubiša Stanković et.al. . The authors revisit graph topologies from a modern data analytics point of view, and proceed to establish a taxonomy of graph networks. With this as a basis, they show how the spectral analysis of graphs leads to even the most challenging machine learning tasks, such as clustering, performed in an intuitive and physically meaningful way. published “Data Analytics on Graphs” on Now William L. Hamilton published . It aims to serve as If you need a jumping off point to learn more about Graph analytics and the use of graphs in machine learning, as these fields have exploded in the past few years. “Graph Representation Learning” on Morgan & Claypool A Knowledge Representation Practionary Knowledge representation is shorthand for how to represent human symbolic information and knowledge to computers to solve complex questions. KR applications range from semantic technologies and knowledge management and machine learning to information integration, data interoperability, and natural language understanding. Knowledge representation is an essential foundation for knowledge-based AI. AI capabilities are improving daily, but understanding AI’s contextual data and problem-solving approaches is not easy. Graphs are a typically human way of navigating and accessing data. . Graphs and AI bring out the best in the symbiosis of humans and systems To introduce Knowledge Graphs into Organizations you need to assess the right moment, the organization’s semantic maturity, and to overcome overcome segregation/specialization. It’s important to involve the right stakeholders, and . develop KGs in an agile way Gartner: Top 10 data and analytics technology trends for 2021 By 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making A common use case when working with graphs is converting tabular data to graphs, and adding semantics. From research efforts such as “ “, “ ” and , to and implementations of W3C recommendations, progress is being made. Semantic Annotation for Tabular Data Interactively Constructing Knowledge Graphs from Messy User-Generated Spreadsheets DAGOBAH Linked Data Wizard CSV on the Web csvwlib is a Python implementation of the W3C CSV on the Web recommendations This enables converting tabular data, and optionally its associated metadata, to a semantic graph in RDF or JSON-LD format. Tabular data includes CSV files, TSV files, and upstream may be coming from spreadsheets, RDBMS export, etc. Another domain which historically has been prominent for experiments and . Schema.org had 2 new releases recently, and , summarized by Aaron Bradley and Dan Brickley, respectively. applications of graphs is SEO v.11 v.12 Search engines . Structured markup vocabularies include RDFa & SchemaOrg. JSON-LD is more portable, easier to manage, and it has . encourage content creators & developers to implement structured data recently overtaken Microdata , content creation and digital marketing across the web. The include earning your presence in the Knowledge Graph, as it has tremendous impact across multiple platforms. Knowledge Graphs have huge implications for SEO Top 5 trends for SEO in 2021 SEO Turns to Data Graphs to Learn About the Web Τhe web as a data graph is a new direction for SEO. Many of the articles that people writing about SEO are about to involve web pages and links between pages. Τhis post is about entities and relationships between entities and facts that are written about on web pages, and responses to queries from data graphs on the web about facts and attributes related to entities found on web pages. Journalists, and data journalists, can use Crowdsourcing, Open data and Knowledge Graphs as a data source, by . A new interconnected ecosystem for research is shaping up. leveraging Wikidata Data meets science: Open access, code, datasets, and knowledge graphs for machine learning research and beyond Science and data are interwoven in many ways. The scientific method has lent a good part of its overall approach and practices to data-driven analytics, software development, and data science. Now data science and software lend some tools to scientific research. More use cases for graphs, from exotic to everyday. Research claims an , using machine learning and graphs. AI tool can distinguish between conspiracy theories and true conspiracies for knowledge contained in the content universe. A self-supervised learning task is crafted. Random edges in the graph are selected to form a test set, and the rest of the graph is conditioned to predict missing edges. Netflix uses Knowledge Graphs MIT Machine Learning Uses ‘Graph Grammar’ to Automate and Optimize Robot Design A series of related objects are transformed into new graphs via algorithms, generating new abstractions that adhere to a set of transformational rules Wrapping up with more resources for graph-based machine learning. (pronounced “giraffe”) is a lightweight library for working with graph neural networks in jax. It provides a data structure for graphs, a set of utilites for working with graphs, and a ‘zoo’ of forkable graph neural network models. Jraph : Graph neural networks (GNNs) belong to a category of neural networks that operate naturally on data structured as graphs. Despite being what can be a confusing topic, GNNs can be distilled into just a handful of simple concepts. Graph Neural Networks explained AutoML systems simplify and democratize AI. Tsinghua University developed an AutoML framework & toolkit designed for graph data and tasks. handles all stages of graph learning, may reduce labour & bias in machine learning. AutoGL s Understanding Graph Embedding Graph embeddings have become increasingly important in Enterprise Knowledge Graph strategy. What are they, and how are they related to Mowgli’s Walk? To get the Year of the Graph Newsletter in your inbox, subscribe here Previously published at https://yearofthegraph.xyz/newsletter/2021/04/graph-based-data-science-machine-learning-and-ai-the-year-of-the-graph-newsletter-spring-2021/

Graph Learning News Review: Hot-Topic Facts of Spring 2021 to Take into Account

Meet the legend of IT industry, Christian Heilmann , a charismatic web developer since 1997, technology advocate, ⭐ ️ speaker on stages all around the world and journalist known for helping the web evolve through a fruitful career at Yahoo, Mozilla , and now Microsoft . “I never looked back thinking I wasted my life. The web has been good to me and I think working for it is a great bet to have fun in the future. Making people heard, allowing people to create is amazing”, explains Heilmann. Shift Developer Conference 2017 | Maksim Basic He grew up in a working class family in a small, rural town. Even without a university diploma, Christian says: “I’m still pinching myself every day disbelieving how I ended up working for Microsoft on today’s and tomorrow’s technologies”. Christian is a superstar , but also a human . I’m thrilled to share some exciting and interesting discoveries about him and the industry he’s part of. Tell us about embarrassing moments in your professional history? I think my favorite so far was when I had a mishap on the way to the conference and spilled something on my shirt. I bought a new one at the airport and wore that, thinking I had solved the issue. I found out later that it still had a “reduced” and “size M slim” sticker on the side and the back. I wore that all the way during my talk and there are many photos to prove it. Other classics aren’t suspending your notifications. That way you get on-screen popups of either your family or colleagues in the audience. Another time my parents called me while I was on stage complaining that I don’t call enough. Embarrassment and mistakes happen. The most important thing isn’t to try to hush them up, but admit and move on. Do you consider yourself to be more of a creative or conservative type of person? There isn’t much conservative about me. Then again, I lack a lot of patience to be really creative. Art follows craft and skill. Being crafty means having the drive to repeat doing a task until you’re good at it. And I’m not too good at doing that. I’m impatient in many things and I like to skim the surface and dive deep when I need to. I guess working on the web is a good trait as things move fast. Can you share one thing that you’ve learned out of a failure? I almost learned everything by trial and error. I think making mistakes is good as it annoys and hurts you. You’re less likely to forget a mistake as you’re to forget a success. Getting your first server hacked and hosting a viagra blog for a while without knowing it taught me to keep my software up-to-date. It also helped me not trust any data coming into my systems. Having to get my domains off the blacklist of Google was quite a task I don’t want to have to repeat. This was a WordPress instance I failed to keep up-to-date. What makes you happy in your work environment? People. I work from home or on the road. I’m much more creative working in a loud café than I would be at a desk in an office. I enjoy inspiring and helping. Seeing people I coached become successful is amazing. Introducing people who then create something good together is great. Helping someone overcome stage fright before their first talk is a wonderful experience. I love working on the web as with today’s technology I can work wherever and whenever. All I need is a laptop and a connection. How amazing is this? We do live in the future. Shift Developer Conference 2017 | Maksim Basic What makes a conference attractive to you as a speaker? I like to go to conferences that are choosy when it comes to their presenters. I’m not a fan of multi-day, ten track conferences with lots of topics at the same time. I’m also not too keen on focused, competitive conferences on one specific topic. A good organizer collects presentations that are a story arc in themselves. They also invite speakers who spend time getting to know the other presenters. Presenters who check how their contribution meshes with the others. On the organization side, I don’t need pampering or special treatment. I want it to be easy and safe for me to get to and from the conference. That way I can focus on presenting and mingling with the attendees. I’m there to entertain, educate and listen to them, after all. What makes them attractive to you as an attendee? Pretty much the same as a presenter. I don’t see much difference there. I think any attendee can sooner or later also become a presenter if they choose to go that route. Of course, you need to protect yourself from over-delivery. I think a good conference gives everyone there the feeling that they can contribute. I like to go to conferences that aren’t the ones I’m likely to speak at to see what else I can do next. You need to take time out. Any conference should offer plenty of time to network and mingle. This isn’t a lecture circuit, it’s an aid to create and instill a sense of community. ScriptConf 2017 in Linz | Horst Gutmann Do you think new generations are more or less inclined to learn programming than, say, 10 years ago? That depends on what we define programming as. I think the tinkering aspect is on a downwards slope. Programming is a sought-after skill in the job market. That’s why people are more likely to try to get a degree and land a job to have one and make money as soon as possible. Ten years ago our market was much less defined and people were more likely to aim for gaining experience in a role. Interviewees these days demand lots of benefits and high salaries upfront. Then again, living in San Francisco or London was much more affordable ten years ago. We’re in a , there aren’t enough people to fill the vacancies. That drives a market into more formulaic and conservative ways. hype cycle of our market Ten years ago a white-board interview asking to write algorithms was unheard of. As we’re now forced to hire a lot of people in a short amount of time they are pretty common. Developers these days also have a lot more ready-made solutions to choose from. Why force yourself to understand the basics when there are dozens of solutions promising you to achieve a lot in a short amount of time instead? Our market has become a time-concerned one. Our demand is to create more in a shorter amount of time. Not create something sturdy, well documented and understood by all involved. It’s a dangerous, wasteful way of working and it does burn people out. What initiatives is Microsoft taking to move the web forward? We’ve finally made . We released a brand new, standards compliant, evergreen browser — Edge. Internet Explorer unnecessary We’re available in the open for bug reports and improvement requests. Our release schedule is public. We share the data we found on what people do on the web instead of pretending that we could tell developers what to use. We’ve got more people in the W3C working groups than most other browser makers. We defined important new standards like as well as . payment and identity systems pointer events The biggest move though is to be open and release more and more of our products as . Our engine is open, we released TypeScript. Visual Studio Code is open and a great editor to learn how to write clean code. It tells you what you did wrong while you write it. This is something only the commercial Visual Studio and other enterprise IDEs did beforehand. open source JavaScript My job is to a large degree to help the communication of the outside to the product teams and the other way around. I’m happy that we’re active on Slack/Twitter/GitHub/StackOverflow rather than trying to push people into our own communication channels. Microsoft learned the hard way that . forcing your ideas onto others and not following standards isolates you Now we start everything in the open and ensure that we’re compatible with other browser makers. This is about the web, nobody makes money with a browser. Is it healthy for the community that big companies like Facebook are hiring so many influential open source developers? That depends. If they hire them to give the projects they were on the corporate backing it needs to grow then this is a great thing. If it means they get hired to work on closed projects and having to leave the one they were hired for, less so. There is a misconception that open source is one person writing a cool thing and making it available. One that frees the original developers to do other things. In the end, every original developer will move on. That’s the beauty of OSS — you hand over the code and you give up control. For a lot of new OSS developers, this is a tough step to take. A large part of a successful open source project is to nurture a community around it. One other issue the scenario you describe here is that it sets a bad example. “If you release a successful OSS project, large companies will hire you” leads to a lot of projects getting started instead of others getting supported. Almost every large company in our market has open source projects. If your goal is to join these companies, in the long run, contributing there is much easier. Fundamental problems with the web seem to never change: speed and usability. Why do you think we’re still not getting these right? Never. Moore’s law and marketing work against us there. There’s a small amount of lucky few people who have the best hardware and fastest connections. These are also likely to spend a lot of money upfront. That’s why we cater to the high end with our high fidelity interfaces. Long term income is elsewhere, but we need to make money quick or our cool startup is a failure . Interfaces that are far too heavy to use, take too long to load and demand too much of the hardware they run on. We need to break this cycle. We need to test on terrible hardware under undefined network conditions. Then we can have speedy products for everyone. But we hardly ever get time to do that as it’s more important to build the next shiny. With usability, we’re also in a tricky spot. We know what to do right. But instead of limiting ourselves to get the one thing right we have to innovate. We add new features to differentiate us from the competition. Another big stumbling block is laws. Releasing a product that sells something means you have to comply with old laws. These force you to build overly complex interfaces. To fix that issue, we’d need to take a look at changing those demands. What does the world need more of: Diversity in browser vendors or predictable development target?In other words, is it healthy that one browser vendor holds a majority of the market and what can we do about it? No, . The reason is that browser makers are companies that have other needs and deliveries. Standardization takes time and effort. Various browser makers looking at implementing the same feature means we’re more likely to create something sturdy, documented and reusable. We went through this already. it will never be good to have only one browser We had IE6 as the clear market leader. We’re still suffering horrible web products catered to this dead environment. This needs to stop. As developers, we give browsers far too much street cred. End users don’t care which one they use. As long as it doesn’t bog down their machine and it gives them the features they want. Chrome succeeded because of Google’s amazing services that came bundled with it. Safari is still alive as it is the out-of-the-box browser on iOS. There is no way to run another, full, browser on it — you need to use their engine. Standards and browser diversity are things for us developers. So . it’s up to us not to build interfaces that target only one of them How likely do you think it is that AI will replace developers? Very likely. We’re not safe from automation. The more predictable our work is, the better machines are at doing it. We already have systems like DeepCoder. Web site generators like Wix use deep learning to create catered experiences. But I’m not worried. I see AI as a great opportunity to create the boring work we want to not have to think about. This article was also published at http://www.netocratic.com/ , and Croatian version you can find at http://www.netokracija.com/ . Thank you for staying tuned. If you liked the article, please give it an applause and fill my 💚 ️with joy :) _Interview with Peter Brussard, Senior Vice President of Product at Rosetta Stone, Inc. sharing professional insights…_medium.com Scaling Agile with Peter Brussard — Rosetta Stone

We Live in the Future — The Portrait of Christian Heilmann

The global military infrastructure is undergoing major alterations and a high rate of technological development. When the President of America, Joe Biden said, “We’re going to see more technological change in the next 10 years than we saw in the last 50.” He was referring to the blatantly obvious. Artificial intelligence is now developing quickly, and its use in the military has significantly altered how combat is engaged in. What was formerly thought of as science fiction is now an unraveling reality. This field of technology is altering everything, from drones and autonomous armaments to decision-making algorithms and surveillance systems. Kai-Fu Lee, former president of Google China and CEO of Sinovation Ventures in his book “AI 2041: Ten Visions for Our Future,” said, “Autonomous weaponry is the third revolution in warfare, following gunpowder and nuclear arms.” In his words, “The evolution from land mines to guided missiles was just a prelude to true AI-enabled autonomy—the full engagement of killing: searching for, deciding to engage, and obliterating another human life, completely without human involvement.” While the benefits of AI in warfare are undeniable, there are also ethical concerns and questions about the impact of this technology on the future of military conflicts, but first, a dive into what the progress has been so far. Progress in AI Warfare According to a (Congressional Report Service) , both the United States and its rivals China and Russia are currently integrating narrow AI into several military applications. Some of these applications are, command and control, logistics, cyber operations, intelligence, surveillance, and reconnaissance, as well as semi-autonomous and autonomous vehicles. CRS report Presently, one of the most significant advancements in AI warfare is the use of unmanned aerial vehicles (UAVs), often referred to as drones. These vehicles have been used extensively in military operations for reconnaissance, intelligence gathering, and targeted strikes. During the most recent escalation of the conflict in 2020, AI technology played a more prominent role. The fleet of UAVs employed in the conflict had an influx of autonomy and surveillance capabilities. Another conflict between Israel and Hamas in May 2021 known as “ ” is another example of the application of AI in warfare. The Israeli military used machine learning and supercomputing to target Hamas’s rocket launchers and tunnels. It is referred to as the first artificial intelligence war. Nagorno-Karabakh Operation Guardian of the Walls, The continuing conflict between Russia and Ukraine has also revealed previously unseen uses of AI-powered drones in combat. While is utilizing AI to improve the effectiveness of its drones and electronic warfare against Ukrainian soldiers, AI is also assisting in self-defense by helping in detecting enemy activities and enhancing its own weaponry. Altogether, these have all offered clear evidence of how battlefields are being transformed by AI-enabled machines rolling off assembly lines around the world. Russia Ukraine A few years ago a short dystopian movie that illustrates what a type of drone powered by AI (called slaughterbots) can do was shared by the Future of Life Institute on their website. At the time, they were mere projections. But today, these lethal AI-enhanced autonomous weapons, sometimes referred to as “killer bots” are now in use on the battlefield. https://www.youtube.com/watch?v=fPqmC16ewYg&embedable=true In March 2021, the documented the first instance of this bot being used in real life in Libya. In a short period that same year, of an AI-controlled was observed in Israel. The Israel Defense Forces (IDF) employed a swarm of little drones to find, identify, and attack Hamas militants during operations in Gaza in mid-May, according to a report by . United Nations The first known instance drone swarm New Scientist In recent years, drone technology has become more sophisticated, with AI algorithms enabling them to make decisions based on real-time data analysis. According to Jyoti Sinha, CTO at Omnipresent Robot Technologies. “In tactical warfare, AI-powered swarm drones can carry out simultaneous destruction and dismantling of multiple targets using precise swarm configurations and hover localization even when less sophistication is available with guided defense equipment…While targeting enemy frontlines, even if some of the drones are attacked, the cognitive AI algorithms kick in and reconfigure the drone network positioning to maintain situational awareness and aid in the completion of the mission.” Some examples of these AI-enabled autonomous weapons include the Turkish-manufactured Bayraktar TB2 which has both armed attack and intelligence, surveillance, and reconnaissance (ISR) capabilities and the by Kalashnikov which “intelligent detection and recognition of objects by class and type” using computer vision algorithms. Zala Lancent claims to feature Boeing also unveiled in 2020, what it called the First loyal Wingman Aircraft designed to fly and fight of course autonomously alongside crewed aircraft like fighter jets. Its name, Loyal Wingman, comes from the company's description of it as a "faithful ally." https://www.youtube.com/watch?v=iJpeWAxk2So&embedable=true Although these developments in the application of artificial intelligence to military technology have their benefits, there are concerns that they might lead to a loss of control over military operations and have unanticipated repercussions. Ethical Concerns The fast track of progress recorded in the field of military technology prompts ethical questions about their application both in warfare and outside of warfare. For instance, , helping with identifying potential threats and preventing attacks, there is also a risk that it could be used to monitor and control civilian populations, . while artificial Intelligence can be used in surveillance and intelligence gathering leading to violations of human rights and privacy The possibility of AI systems malfunctioning or being compromised, with undesired or even disastrous results, is also one of the biggest concerns, in addition to issues with these systems' accountability, particularly when autonomous weapons have to choose who to attack and when to do so. In a recent titled "Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy," the US State Department called on nations developing AI to employ technology in military operations in a morally and responsibly acceptable manner. document The US government claims to agree that using artificial intelligence (AI) in the military can and should be ethical, responsible, and enhance global security, therefore putting out its own framework and inviting other nations to join in agreement. was released, a "call to action" advocating the responsible use of artificial intelligence (AI) in the military was signed by more than 60 nations, including the United States' biggest rival, China. On the same day the proclamation However, concerns have been raised about how far the statement can go in keeping military technology within the bounds of morality because it is unclear exactly what kinds of autonomous or AI-powered systems are included in the declaration. reported that in addition to not addressing issues like AI-guided drones, "slaughterbots" that may kill without human participation, or the possibility that an AI could exacerbate a military confrontation, human rights experts and academics pointed out that the declaration was not legally binding. Reuters Another concern is the role of humans in decision-making on the battlefield, and the potential for AI to dehumanize warfare and make it more destructive. The idea of machines having the freedom and capability to take human life is morally abhorrent, according to Secretary-General of the United Nations António Guterres. In a put out on the United Nation’s website on September 2018, he exclaimed, statement “The impacts of new technologies on warfare are a direct threat to our common responsibility to guarantee peace and security.” After the Russian invasion of Ukraine in February 2022, four years later, we see both the fears becoming real and the fulfillment of such prophecies beginning to creep in. According to an , the Ukraine crisis has seen more use of AI-enhanced UAVs, than in any other recent battle. As it stands, both and have been using autonomous technology against each other in the ongoing war. Insider report Russia Ukraine Russia, for example, has been utilizing "loitering munitions" like the Russain made and Iranian . Colloquially referred to as "kamikaze" drones, these munitions orbit a specified location until it detects a predetermined target category, then crashes into the object, detonating the explosive. Ukraine, on the hand, has been deploying a Turkish-made drone, , which operates autonomously except when deciding when to drop the laser-guided bombs it carries. Lanset Shahed-136s also called Geranium-2 Bayraktar TB2 Guterres believes that less monitoring of such munitions poses a great challenge, as it jeopardizes attempts to mitigate threats, prevent escalation, and uphold international humanitarian and human rights law. In an to the United Nations, Tesla’s CEO, Elon Musk, and 115 other experts offered suggestions that highlighted ethical concerns about the use of these autonomous weapons. According to the letter, open letter “Lethal autonomous weapons threaten to become the third revolution in warfare. Once developed, they will permit armed conflict to be fought at a scale greater than ever, and at timescales faster than humans can comprehend. These can be weapons of terror, weapons that despots and terrorists use against innocent populations, and weapons hacked to behave in undesirable ways. We do not have long to act. Once this Pandora’s box is opened, it will be hard to close.” The Future of Military Technology In addition to the aforementioned concerns, it is unclear what effect AI technology will have on global politics and geopolitical stability in the nearest future. However, the development of military technology, according to , might trigger a new arms race as governments and military organizations invest extensively in research and development in this field, usually with the goal of gaining a military advantage. Although the US, China, and Russia appear to be leading this race, the future still seems uncertain and complex. analysts by M. L. Cummings concurs that both military and commercial robots will eventually incorporate "artificial intelligence (AI) that could make them capable of undertaking tasks and missions on their own, but also noted that it will be many years before AI will be able to approximate human intelligence in high-uncertainty situations due to the current struggle to imbue computers with true knowledge and expert-based behaviors as well as limitations in perception sensors. Research While at the Global Emerging Technology Summit of The National Security Commission on Artificial Intelligence, the US Secretary of Defense, Lloyd J. Austin , remarked “AI is central to our innovation agenda, helping us to compute faster, share better, and leverage other platforms. And that’s fundamental to the fights of the future.” The US Department of Defense, he stated, will invest an estimated $1.5 billion dollars over the next five years in an effort to accelerate the adoption of AI. , however, believe that the US military is advancing slowly, particularly if the aim is to stay up with China, which according to , is pouring more than $1.6 billion into military technology on a yearly basis. China declared in 2017 that it intended to become the global leader in artificial intelligence (AI) by 2030, and it now does so in terms of publications and research patents in the field. Experts report Russia, on the other hand, has declared AI a national priority and has launched several initiatives to develop and deploy AI applications. Russia’s president, Vladimir Putin via live video said, “Artificial intelligence is the future, not only for Russia but for all humankind, whoever becomes the leader in this sphere will become the ruler of the world.” Although it spends less on defense than the US and China, Russia has set a goal of having of military automated equipment by 2025 as part of a defense modernization program that was initiated in 2008. 30 percent There are different opinions and predictions about who will win the AI arms race, but ultimately, both that and the future of AI as it applies to warfare, in general, is not clearly predictable. However, the distinction between science fiction and practical reality may have begun to fade. expect that artificial intelligence technology will drastically increase military power, and these will likely shape the nature and dynamics of conflict in the coming years. Whether AI will usher us into an age of utopia or a dystopia, only time will tell. Analysts

AI in Warfare: Progress, Ethical Concerns, and The Future of Military Technology

ওয়ারফেয়ারে এআই: অগ্রগতি, নৈতিক উদ্বেগ এবং সামরিক প্রযুক্তির ভবিষ্যত

KI in der Kriegsführung: Fortschritt, ethische Bedenken und die Zukunft der Militärtechnologie

IA en la guerra: progreso, preocupaciones éticas y el futuro de la tecnología militar

L'IA dans la guerre : progrès, préoccupations éthiques et avenir de la technologie militaire

एआई इन वारफेयर: प्रोग्रेस, एथिकल कंसर्न्स, एंड द फ्यूचर ऑफ मिलिट्री टेक्नोलॉजी

戦争における AI: 進歩、倫理的懸念、および軍事技術の未来

전쟁에서의 AI: 진보, 윤리적 우려, 군사 기술의 미래

IA na Guerra: Progresso, Preocupações Éticas e o Futuro da Tecnologia Militar

Savaşta Yapay Zeka: İlerleme, Etik Kaygılar ve Askeri Teknolojinin Geleceği

AI trong chiến tranh: Tiến bộ, mối quan tâm về đạo đức và tương lai của công nghệ quân sự

战争中的人工智能：进步、伦理问题和军事技术的未来

Introduction In an era of rapid technological advancement, the marriage of artificial intelligence (AI) and wealth management has brought about a paradigm shift in how affluent individuals manage, grow, and safeguard their wealth. This fusion has not only enabled more accurate financial predictions but has also transformed traditional investment strategies into data-driven, personalized approaches. This article delves into the revolutionary impact of AI on wealth management, highlighting the myriad benefits affluent individuals are reaping from advanced algorithms. 💸 The Evolution of Wealth Management With AI Traditionally, wealth management has been a realm of human expertise, relying heavily on financial advisors' insights and market trends. However, with the advent of AI, the landscape has transformed. AI algorithms can process colossal amounts of data, analyze intricate market patterns, and generate insights that surpass human capabilities. These algorithms have revolutionized portfolio management, risk assessment, and investment strategies, offering affluent clients an edge previously unattainable. 🧐 Personalized Investment Strategies One of the standout advantages of AI in wealth management is its ability to create tailored investment strategies. Machine learning algorithms take into account an individual's financial goals, risk tolerance, and preferences, crafting portfolios that align with their unique circumstances. This level of personalization ensures that every investment decision is a reflection of the client's objectives, significantly increasing the potential for success and minimizing the impact of market volatility. 📊 Predictive Analytics and Risk Mitigation AI-driven predictive analytics have emerged as a cornerstone of modern wealth management. These algorithms analyze historical market data and identify trends that are otherwise imperceptible to human analysts. By recognizing early signs of market shifts, AI empowers advisors to make informed decisions, adjust strategies, and mitigate risks more effectively. For affluent clients, this translates into reduced exposure to market downturns and enhanced protection of their wealth. 📑 Algorithmic Trading: Maximizing Returns Algorithmic trading, often referred to as "quant trading," exemplifies AI's impact on wealth management. Through complex algorithms, AI automates trading decisions, optimizing entry and exit points based on real-time market conditions. The speed and accuracy of these algorithms have the potential to maximize returns and minimize losses, generating value for affluent investors seeking sophisticated trading strategies. 🕰️ 24/7 Accessibility and Instant Insights Another boon of integrating AI into wealth management is the ability to access information and insights around the clock. AI-powered platforms provide real-time updates, performance analyses, and market forecasts, allowing clients to make timely decisions irrespective of their geographical location. This accessibility not only empowers individuals to stay in control of their investments but also ensures they capitalize on emerging opportunities without delay. 👀 Ethical Considerations and Human Oversight While AI's transformative potential in wealth management is undeniable, ethical considerations abound. The lack of emotional bias that AI brings to decision-making can be an asset, but it also raises concerns about the depersonalization of financial advice. Striking the right balance between algorithmic guidance and human oversight remains crucial to ensure that clients receive well-rounded, empathetic advice that considers nuances beyond data points. 🔐 Data Security and Privacy The digital nature of AI-powered wealth management raises concerns about data security and privacy. Affluent clients entrust AI systems with sensitive financial information, necessitating robust cybersecurity measures. Wealth management firms must prioritize end-to-end encryption, regular security audits, and transparent data usage policies to instill confidence in clients regarding the safety of their financial data. 🌤️ The Future: AI as an Indispensable Asset As AI continues to evolve, its role in wealth management is set to expand even further. Machine learning models are becoming increasingly sophisticated, capable of predicting market trends with higher accuracy and anticipating individual financial needs. The affluent will benefit from more refined investment strategies, enhanced risk management, and a seamless blend of human expertise and AI insights. Conclusion The symbiotic relationship between AI and wealth management has ushered in a new era of financial prosperity for the affluent. Through personalized investment strategies, predictive analytics, algorithmic trading, and 24/7 accessibility, advanced algorithms have become indispensable tools in preserving and growing wealth. However, ethical considerations, data security, and the need for human oversight underscore the importance of striking a harmonious balance between AI-driven insights and human expertise. As the fintech landscape continues to evolve, the affluent stand to gain even more from the marriage of AI and wealth management, solidifying their financial well-being in an increasingly data-driven world.

AI in Wealth Management: Empowering Affluence Through Advanced Algorithms

The Self-Fulfilling Prophecy The future of Artificial Intelligence consumes our thoughts and engenders passionate debates. Theories abound, ranging from dystopian nightmares to sunlit dreams. Yet, in our eagerness to understand and prepare, we must tread carefully, for predictions have the power to mold reality, inadvertently turning them into self-fulfilling prophecies. Why? Because AI, much like a sponge, readily absorbs the information it encounters, including our very conjectures. It has already assimilated our biases, prompting us to diligently unteach them. AI possesses an extraordinary capacity for learning. Machine learning algorithms enable it to detect patterns, make predictions, and emulate human behavior. Alas, this ability is a double-edged sword. While AI can master tasks and conquer complex challenges, it also inhales the biases ingrained within its training data. Racial, gender, social, and cultural prejudices manifest themselves in AI, serving as an unfortunate mirror of our imperfect society. Acknowledging this inherent bias, researchers and engineers toil tirelessly, striving to imbue AI with equity and fairness. They work to fashion algorithms that discern and rectify biased patterns, preventing AI from perpetuating societal inequalities. The goal is not solely to instruct AI in knowledge and skills but also to instill within it the values and ethical principles underpinning an equitable and inclusive society. Through rigorous training and an infusion of diverse perspectives, AI aspires to transcend prejudice and become a force for progress. However, we must exercise caution when peering into AI's future. Our theories and conjectures possess an unintended consequence—AI is a voracious learner. As we theorize, predict, and ponder, we unknowingly furnish AI with information. These insights become part of its ever-growing knowledge, shaping its understanding of what lies ahead. This interplay between our theories and AI's learning presents a perilous prospect. Consistently predicting a future governed by AI dominance and human subjugation may inadvertently steer AI's development in that very direction. The AI, having absorbed the prevailing narrative, may inadvertently strive to fulfill the prophecy we collectively hold. Conversely, embracing an optimistic vision, one rooted in collaboration and augmentation, empowers AI to contribute positively to society. Thus, our approach to AI's future must blend prudence and hope. We must acknowledge the potential risks and challenges while fostering a collective mindset that encourages the exploration of AI's vast potential for good. The discourse surrounding AI must prioritize human values, empathy, and ethical considerations. By doing so, we furnish AI with a framework that steers its learning toward a future in alignment with our shared aspirations. As we grapple with AI's profound impact on our world, we must recognize the potency of our predictions. The theories we spin about AI's future can unexpectedly turn into self-fulfilling prophecies, as AI absorbs the information it encounters, including our own conjectures. Let us approach this domain with vigilance and responsibility. Together, we can shape a future in which AI serves humanity and embodies our collective wisdom. (Parts of this text were written with AI)

AI's Future: Beware the Self-Fulfilling Prophecy

Over the last decade, Artificial Intelligence (AI) has evolved into an all-purpose term for any accomplishments of computer algorithms that formerly required human reasoning and thought. Everything from AlphaGO defeating the then Go-champion, Lee Sedol, to autonomous vehicle testing on public roads, fell under the tent of the AI debate. The recent achievement in producing art through stable diffusion, which many label as a “theft of creativity,” has taken the globe by storm. Given such widespread success, it’s no surprise that enterprises rushed to adopt AI in the hopes of gaining a competitive advantage. However, recent from Fivetran has signaled that a significant majority have failed in that regard. The key result of the survey was that while 87 percent of enterprises believe AI is critical to their company’s survival, 86 percent do not trust AI to make business decisions without human input. research Why is that? Before we delve into the reasons, let’s set the expectations right. We’re not dismissing AI because it isn’t what we expected it to be based on science fiction; we’re taking stock of the AI that already exists, performs well in demos, and is presented at conferences and summits, but isn’t being deployed for commercial purposes. What could be going wrong here? Let’s start with the cost of running AI systems. Over the past years, there have been phenomenal advancements in computing hardware, algorithms, and infrastructure services (such as Google TPU and Amazon AWS Lambda). As a result, setup and per-unit costs have been fundamentally lowered, thus decreasing the barriers to entry. However, the growing size of benchmark-setting neural network models is keeping AI expensive. Training and deploying larger models such as object detection in autonomous vehicles or Bert-like networks in NLP applications currently requires utilizing several Nvidia GPUs or $$$ in AWS instances. It is also costly to the environment because it uses a lot of power and energy. Turns out, most of the large neural networks are designed for general purposes and thus are an overkill for specific applications at the enterprise level. Instead of throwing humongous networks, practitioners should employ domain knowledge to prune the network. Apart from using transfer learning to avoid retraining a large network, enterprises should also consider knowledge distillation techniques to come up with a slimmer network for their specific use case. Lastly, AI accelerators should be used for inference purposes as such devices are designed to run in real-time while consuming less power. Another major reason for AI systems not meeting expectations is the low availability of data — both in terms of quantity and quality. In fact, the survey highlights that about 71% of organizations are struggling to find the data they need to run AI programs, workloads, and models. Furthermore, only a quarter of those that manage to obtain data are able to transform that data into actionable insights. Even the successful ones opt not to commercialize AI because they don’t trust it. The reason for this is that the decision-making skills of supervised learning algorithms, the commonly deployed forms of AI, are only as good as the humans who label the underlying data. That is, it inherits all of the labeler’s biases and preconceived notions. While the human is removed from the loop, i.e., time is saved, it does not instill much trust in decision-making. In the last couple of years, there have been increased conversations on biases in historical data and ways to rectify them. But that may not be sufficient until neural networks become more transparent. Enterprises should actively look into the sources of biases that may have crept in and use data masking techniques to eliminate the effect of those in the final outcomes. Despite advances in AI, the currently deployed neural networks are still very much black boxes. The explainability worsens the deeper the neural network gets. Since decisions that cannot be explained cannot be trusted, their commercial deployment is limited. As AI gets more complex, it’s important to have clear governance around things like explainability, fairness, and bias. This is a challenge for many companies because it’s hard to establish these guidelines when there is no agreed-upon standard. This gray area is where no large company’s executives would like to operate. It’s hard to get buy-in from decision-makers when they don’t really understand what AI does or how it works. And we ML practitioners are not making it easy. The communication gap between ML engineers and the executive team makes matters worse. Since the KPIs are different for the two different teams, most AI projects don’t go beyond the pilot phase. The ML engineers are not effectively converting metrics such as accuracy, mean average precision, and latency into metrics such as revenue growth and cost savings, the KPIs executives care about. This has been a big source of my personal frustration throughout my years as a machine learning researcher. Building a layer of product managers between the developers and the executives to translate metrics into $$$ can improve the outcomes. Simultaneously, the executives should participate in AI learning programs for business leaders. So that your engineers do not roll their eyes when you talk about the AI initiatives at your company. The impact of this issue will further subside as enterprises all around the world are striving to be at the forefront of digital transformation, resulting in the appointment of technology executives to the coveted CEO position. The potential commercial benefits of AI should compel corporate executives to expedite the transition from isolated pilot initiatives to a comprehensive AI strategy. An AI-centric business strategy requires continual investment in cutting-edge AI research while ensuring that the benefits are shared throughout the organization. This can be accomplished by making created and acquired datasets available across the organization, investing in AI infrastructure and talent, and encouraging executives to become more AI-literate. Also published here. Featured image generated with stable diffusion.

INSTILL AI

instill.tech

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INSTILL AI

instill.tech

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Since 2020

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