In the sixth season of Netflix&#x27;s Formula 1 documentary &quot;Drive to Survive&quot;, Mercedes driver Lewis Hamilton addresses situations that may have driven him into the arms of Ferrari · SPEEDWEEK.COM provides this translated article advertising-free

Hamilton ignored by Mercedes, therefore to Ferrari?

We are Mercanis the start-up on a mission to disrupt a multi trillion professional service industry

Mercanis

So you’ve been offered a good deal on Mercari, but you’re not quite sure if it’s a reputable website. Or, you’ve heard about different Mercari scams online, and you’re not sure if you should avoid the site altogether or not. First, it’s important to establish that the company itself is legitimate and not a scam. Mercari is an online marketplace where users can buy and sell used items. The company was founded in Japan in 2013 and quickly gained popularity internationally. Users can list their items for sale easily through the Mercari app, which is available for free on both the App Store and Google Play. Is Mercari Safe to Use? Mercari is generally considered a safe platform and provides a secure environment for users to trade safely. However, like any other online marketplace, there may be risks involved, such as dealing with unreliable or dishonest sellers. If you’re a Mercari user, it's important to exercise caution, as the more popular the site gets, the more likely fraudsters will gravitate toward it. Overall, yes, Mercari is safe to use. Six Common Mercari Scams to Avoid on the Platform Yes, Mercari is safe, but it’s not fraud-free. Here are five of the most notorious scams to look out for on the platform: 1) The Fake Listing Scam The number one rule when it comes to shopping online is to not trust every deal you see. Some deals are too good to be true. If you find a handbag you would normally pay $1,100 for in the store and on Mercari, it’s only $100 bucks, you should automatically assume it’s a scam. Unfortunately, there are a lot of people looking for a good deal and are willing to cast all doubt aside to find it. It’s that type of mindset that leads people to fall for scams like this: https://youtu.be/X4bMgLaRORk?si=Edt_rF5jrHsPZbUB&embedable=true Thankfully, you can avoid this scam by: Only deal with sellers who have positive reviews and ratings. Sellers who have a reputation to protect are less likely to pull the wool over your eyes. Try to ask the seller as many questions as you can. Questions are like scam repellant for those trying to make a quick “deal.” Ask the seller for extra photos of the item. If it’s a real item, they shouldn’t mind snapping a few extra pics as proof. 2) The Switcheroo Scam This scam is as old as time when it comes to selling online, but people still do it today, thinking they’re going to get away with it. If you’re a seller on Mercari, this is one you’ll want to look out for. The Switcheroo is when someone buys one of your items and then randomly returns it. When you get the item back, you quickly realize that it’s not the item you shipped out. Instead, you’re left with a cheap knockoff of the item or a broken/ruined version of it.  Or even worse, the scammer sends a completely different item that weighs about the same as the original. If you’ve been a seller online for a while, there’s a good chance that you’ve had to deal with this scam already, and Mercari is no different. The bad news is, if you’re a seller on Mercari, there’s not much you can do about it, as Mercari is known for refunding the buyer once the return looks like it was delivered. It’s still worth trying to for help; just be prepared to write off the item later if there’s nothing the company can do for you. contact Mercari’s customer service 3) The Offsite Payment Mercari Scam This scam is one of the easiest ones to avoid on your part, but only if you know about it. This scam looks innocent at first, but its effects can be devastating. It works like this: You’re interested in an item on Mercari. You contact the seller to make sure they’re the real deal. The seller messages back telling you there’s a problem with their Mercari account, and they need you to pay with Zelle or another outside payment system. You send them money through the third-party payment service. You never get your product, you never hear from the seller again, and you don’t get a refund. The best way to avoid this scam is to make all your transactions on the platform. Don’t believe any of the seller’s lies. You should always be able to send payment through Mercari itself. 4) The Misrepresentation Scam Here’s another classic scam you may run into on Mercari: selling fake or damaged items that appear to be perfectly fine. There are multiple ways that scammers try to pull this off: Altering images through Photoshop. Listing a used item as “new.” Lying about major damage to the item or claiming it works when it clearly doesn’t. There are a few ways to avoid this one. First, check for the “verified seller” icon to make sure it’s not a fake profile. If the profile has a few followers and some good reviews under their belt, that’s even better. If you’re still skeptical, you can also check to see if they have any : Seller Badges Basically, look for anything about the seller that can prove that they have a reputation. If the seller has a reputation, they’re going to want to keep it, and selling fake or damaged items is just not worth it for them. 5) The Changing Address Scam Yes, some sellers do try to pull this off, and many of them succeed. It works like this: You order an item. The “seller” fills a box full of ripped-up paper or garbage. The “seller” uses a third-party shipping company to ship the box full of garbage to your home. The garbage box gets accepted by the carrier. The “seller” calls the shipping company and changes the address to a random house somewhere in your city. The package gets delivered to some random person’s home. From here, it’s a waiting game. The “seller” knows that you only have three days before the return window passes. Three days have passed, and you realize you don’t have your item yet. You contact Mercari, but at this point, it’s too late. To avoid this tricky scam, just remember to keep an eye on your tracking. Your app may say that it was delivered, but it may have been delivered to some random house. If this happens, make sure to inform Mercari right away, and they’ll take care of everything. 6) The Fake Customer Service Representative Scam Here’s an insidious Mercari scam that could do a lot more damage than cheat you out of a few bucks. The fake customer service representative scheme works just like any other type of phishing scam: You get a random message on Mercari or an email informing you about an “issue with your account.” You’re told that you need to resolve the issue immediately, or you risk losing your account altogether. You click on the provided link and fill out the form, including all of the personal information that’s requested. Once you send out the form, the fraudster has everything they need to ruin your life. To avoid this scam, all you need to do is remember that a real Mercari representative would never send you an unsolicited message outside of the platform itself. And if you’re worried that your account may be in danger, you can also contact the company yourself to confirm. How to Report a Mercari Scammer If you or someone you know becomes a victim of a scam on Mercari, don’t just brush it off or pretend like it didn’t happen. It’s better to make the platform a safer place by reporting the scammer to Mercari. You can report any type of fraud you find on the platform by calling 1-888-578-5870. You can also visit the at any time to report your concerns. Mercari Help Center Mercari takes fraud very seriously, and most of the scammers they catch are suspended on the platform for life. Final Thoughts It’s important to reiterate that Mercari is a legitimate website where you can buy and sell goods, just like eBay or Etsy. But just like with any website online, you can’t trust that you’re dealing with a reputable seller or buyer just because they present themselves as one. Feel free to use the platform as much as you’d like; just keep an eye out for the Mercari scams on this list as you do. For more online scams to watch out for, check out these articles: The 8 Most Dangerous Cash App Scams (with Screenshots) Payment Fraud Stats From the World's Most Popular Apps: Zelle, Venmo, Cash App, Paypal, and More 5 Notorious Paypal Invoice Scams & How to Avoid Them

The Most Typical Mercari Scams You Should Look Out For

সবচেয়ে সাধারণ মার্কারি স্ক্যামগুলির জন্য আপনার সন্ধান করা উচিত

Die typischsten Mercari-Betrügereien, auf die Sie achten sollten

Las estafas de Mercari más típicas a las que debe prestar atención

Les escroqueries Mercari les plus typiques à surveiller

सबसे विशिष्ट मर्करी घोटाले जिन पर आपको नज़र रखनी चाहिए

気を付けるべき典型的なメルカリ詐欺

주의해야 할 가장 일반적인 Mercari 사기

Os golpes Mercari mais típicos que você deve observar

Наиболее типичные случаи мошенничества с Mercari, на которые следует обратить внимание

Dikkat Etmeniz Gereken En Tipik Mercari Dolandırıcılıkları

Những trò lừa đảo Mercari điển hình nhất mà bạn nên đề phòng

您应该警惕的最典型的 Mercari 诈骗

Finding the best cryptocurrencies with massive growth potential to invest in 2024 has become increasingly challenging. More platforms are launching new cryptocurrencies almost daily. Experienced crypto traders and crypto investors are looking for emerging cryptocurrencies, still available on presale, with the knowledge that tokens often record the highest gains after listing. That said, not all projects have the potential to realize such growth in the crypto environment. A cryptocurrency that has the potential to grow exponentially offers significant token utility, functionality, and the designed ability for sustainable demand. Among the best of such coins is Pandoshi (PAMBO). Ongoing Presale; Over 60% of Presale Supply Has Been Sold The ongoing presale makes Pandoshi (PAMBO) the best crypto to invest in. Pandoshi (PAMBO) presale raised about $2 million in the first weeks of launch. Coupled with the fast sales and over 60% presale supply sold in the first three phases, this cryptocurrency may be set to realize 100x growth upon listing. Unique Token-Burning Mechanism Pandooshi is designed according to the best crypto industry practices. It features a unique burn-and-buy mechanism that ensures the supply of the cryptocurrency diminishes raising demand. It is, therefore, a deflationary asset. The burning will stop once 80% of the PAMBO supply has been removed from circulation. As a deflationary token, PAMBO can only be accumulated through purchase and participation in Pandoshi official events. Pandoshi's approach to token economics is highlighted by its intent to substantially decrease the total amount of PAMBO tokens, a move that underscores the token's deflationary aspect while boosting its long-term market value and scarcity. A key milestone for Pandoshi was the recent launch of the beta version of the on the Google Play Store, with an iOS version set to follow shortly. Achieving this during the presale phase has significantly bolstered investor confidence, evidencing the project's capability to fulfill its commitments ahead of schedule. Pandoshi Wallet Launching the wallet at this pivotal juncture underlines the team's dedication and capacity to realize important goals, thereby reinforcing trust in the trajectory of Pandoshi. The wallet's compatibility with both EVM and non-EVM chains is a testament to Pandoshi's innovative spirit and its readiness to be a frontrunner in the ever-evolving world of cryptocurrency. Overall, Pandoshi's strategy of implementing a buy-and-burn policy and reducing the overall PAMBO token supply is a deliberate tactic to enhance the token's rarity and value. This initiative positions the token for potential substantial returns as the ecosystem continues to develop and mature. PAMBO is a potential gem that could yield significant returns in the crypto market. Buy now and enjoy the highest returns once it explodes by 100x in 2024. Click Here To Buy PAMBO Tokens Visit the links below for more information about Pandoshi (PAMBO): Website: Whitepaper: https://pandoshi.com/ https://docs.pandoshi.com/ This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Pandoshi Announces A Unique Token Burn Mechanism

পান্ডোশি একটি অনন্য টোকেন বার্ন মেকানিজম ঘোষণা করেছে

Pandoshi kündigt einen einzigartigen Token-Burn-Mechanismus an

Pandoshi anuncia un mecanismo único de quema de tokens

Pandoshi annonce un mécanisme unique de gravure de jetons

पंडोशी ने एक अद्वितीय टोकन बर्न तंत्र की घोषणा की

Pandoshi が独自のトークンバーンメカニズムを発表

Pandoshi, 독특한 토큰 소각 메커니즘 발표

Pandoshi anuncia um mecanismo exclusivo de queima de token

Пандоши анонсирует уникальный механизм сжигания токенов

Pandoshi Benzersiz Token Yakma Mekanizmasını Duyurdu

Pandoshi công bố cơ chế đốt token độc đáo

Pandoshi 宣布独特的代币销毁机制

MinIO makes a  powerful primary TileDB backend because both are built for performance and scale. MinIO is a single Go binary that can be launched in many different types of cloud and on-prem environments. It's very lightweight, but also feature-packed with things like and , and it provides with various applications. MinIO is the perfect companion for TileDB because of its industry-leading performance and scalability. MinIO is capable of tremendous performance – we’ve benchmarked it at 325 GiB/s (349 GB/s) on GETs and 165 GiB/s (177 GB/s) on PUTs with just 32 nodes of – and is used to build data lakes/lake houses with analytics and AI/ML workloads. replication encryption integrations off-the-shelf NVMe SSDs TileDB is used to store data in a variety of applications, such as Genomics, Geospatial, Biomedical Imaging, Finance, Machine Learning, and more. The power of TileDB stems from the fact that any data can be modeled efficiently as either a dense or a sparse multi-dimensional array, which is the format used internally by most data science tooling. By storing your data and metadata in TileDB arrays, you abstract all the data storage and management pains, while efficiently accessing the data with your favorite programming language or data science tool via our numerous APIs and integrations. Set Up TileDB Let’s dive in and create some test data using TileDB. Install the TileDB module, which should also install the dependency. pip numpy % pip3 install tiledb


Collecting tiledb

  Downloading tiledb-0.25.0-cp311-cp311-macosx_11_0_arm64.whl (10.4 MB)

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Collecting packaging

  Downloading packaging-23.2-py3-none-any.whl (53 kB)

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Collecting numpy>=1.23.2

  Downloading numpy-1.26.3-cp311-cp311-macosx_11_0_arm64.whl (14.0 MB)

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Installing collected packages: packaging, numpy, tiledb

Successfully installed numpy-1.26.3 packaging-23.2 tiledb-0.25.0 Create a test array by running the below Python script, name it . tiledb-demo.py import tiledb

import numpy as np

import os, shutil


# Local path

array_local = os.path.expanduser("./tiledb_demo")


# Create a simple 1D array

tiledb.from_numpy(array_local, np.array([1.0, 2.0, 3.0]))


# Read the array

with tiledb.open(array_local) as A:

	print(A[:]) Run the script % python3 tiledb-demo.py

[1. 2. 3.] This will create a directory called to store the actual data. tiledb_demo % ls -l tiledb_demo/

total 0

drwxr-xr-x  3 aj  staff   96 Jan 31 05:27 __commits

drwxr-xr-x  2 aj  staff   64 Jan 31 05:27 __fragment_meta

drwxr-xr-x  3 aj  staff   96 Jan 31 05:27 __fragments

drwxr-xr-x  2 aj  staff   64 Jan 31 05:27 __labels

drwxr-xr-x  2 aj  staff   64 Jan 31 05:27 __meta

drwxr-xr-x  4 aj  staff  128 Jan 31 05:27 __schema You can continue using it as is but it's no bueno if everything is local because if the local disk or node fails then you lose your entire data. Let's do something fun, like reading this same data from a MinIO bucket instead. Migrating Data to MinIO Bucket We’ll start by pulling mc in our docker ecosystem and then using play.min.io to create the bucket. Pull mc docker image % docker pull minio/mc Test with MinIO Play by listing all the buckets % docker run minio/mc ls play


[LONG TRUNCATED LIST OF BUCKETS] Create a bucket to move our local TileDB data to, name it . tiledb-demo % docker run minio/mc mb play/tiledb-demo


Bucket created successfully `play/tiledb-demo`. Copy the contents of the data directory to the MinIO bucket tiledb_demo tiledb-demo % docker run -v $(pwd)/tiledb_demo:/tiledb_demo minio/mc cp --recursive /tiledb_demo play/tiledb-demo


`/tiledb_demo/__commits/__1706696859767_1706696859767_777455531063403b811b2a2bf79d40e7_21.wrt` -> `play/tiledb-demo/tiledb_demo/__commits/__1706696859767_1706696859767_777455531063403b811b2a2bf79d40e7_21.wrt`

`/tiledb_demo/__fragments/__1706696859767_1706696859767_777455531063403b811b2a2bf79d40e7_21/a0.tdb` -> `play/tiledb-demo/tiledb_demo/__fragments/__1706696859767_1706696859767_777455531063403b811b2a2bf79d40e7_21/a0.tdb`

`/tiledb_demo/__fragments/__1706696859767_1706696859767_777455531063403b811b2a2bf79d40e7_21/__fragment_metadata.tdb` -> `play/tiledb-demo/tiledb_demo/__fragments/__1706696859767_1706696859767_777455531063403b811b2a2bf79d40e7_21/__fragment_metadata.tdb`

`/tiledb_demo/__schema/__1706696859758_1706696859758_74e7040e138a4cca93e34aca1c587108` -> `play/tiledb-demo/tiledb_demo/__schema/__1706696859758_1706696859758_74e7040e138a4cca93e34aca1c587108`


Total: 3.24 KiB, Transferred: 3.24 KiB, Speed: 1.10 KiB/s List the contents of to make sure the data has been copied. tiledb-demo % docker run minio/mc ls play/tiledb-demo/tiledb_demo

[2024-01-15 14:15:57 UTC] 	0B __commits/

[2024-01-15 14:15:57 UTC] 	0B __fragments/

[2024-01-15 14:15:57 UTC] 	0B __schema/ Note: The MinIO Client ( ), or any S3 compatible client, only copies non-empty folders. The reason for this is that in the object storage world the data is organized based on bucket prefixes, so non-empty folders are not needed. In a future blog we’ll dive deeper into how data is organized with prefixes and folders. Hence, you see only these 3 folders and not the rest that we saw in the local folder. mc Now let’s try to read the same data directly from the MinIO bucket using the Python code below, name the file . tiledb-minio-demo.py import tiledb

import numpy as np


# MinIO keys

minio_key = "minioadmin"

minio_secret = "minioadmin"


# The configuration object with MinIO keys

config = tiledb.Config()

config["vfs.s3.aws_access_key_id"] = minio_key

config["vfs.s3.aws_secret_access_key"] = minio_secret

config["vfs.s3.scheme"] = "https"

config["vfs.s3.region"] = ""

config["vfs.s3.endpoint_override"] = "play.min.io:9000"

config["vfs.s3.use_virtual_addressing"] = "false"


# Create TileDB config context

ctx = tiledb.Ctx(config)


# The MinIO bucket URI path of tiledb demo

array_minio = "s3://tiledb-demo/tiledb_demo/"


with tiledb.open(array_minio, ctx=tiledb.Ctx(config)) as A:

	print(A[:]) The output should look familiar % python3 tiledb-minio-demo.py

[1. 2. 3.] We've read from MinIO, next let's see how we can write the data directly in a MinIO bucket, instead of copying it to MinIO from an existing source. Writing Directly to the MinIO Bucket So far we’ve shown you how to read data that already exists, either in local storage or an existing bucket. But if you wanted to start fresh by writing directly to MinIO from the get-go, how would that work? Let’s take a look. The code to write data directly to the MinIO bucket is the same as above except with two line changes. The path to the MinIO bucket where TileDB data is stored must be updated to (instead of ). tiledb_minio_demo tiledb_demo We’ll use the function, as we did earlier with local storage, to create the array to store in the MinIO bucket. tiledb.from_numpy [TRUNCATED]


# The MinIO bucket URI path of tiledb demo

array_minio = "s3://tiledb-demo/tiledb_minio_demo/"


tiledb.from_numpy(array_minio, np.array([1.0, 2.0, 3.0]), ctx=tiledb.Ctx(config))


[TRUNCATED] After making these 2 changes, run the script and you should see the output below % python3 tiledb-minio-demo.py

[1. 2. 3.] If you run the script again it will fail with the below error because it will try to write again. tiledb.cc.TileDBError: [TileDB::StorageManager] Error: Cannot create array; Array 's3://tiledb-demo/tiledb_minio_demo/' already exists Just comment out the following line and you can re-run it multiple times. # tiledb.from_numpy(array_minio, np.array([1.0, 2.0, 3.0]), ctx=tiledb.Ctx(config)) % python3 tiledb-minio-demo.py

[1. 2. 3.]


% python3 tiledb-minio-demo.py

[1. 2. 3.] Check the MinIO Play bucket to make sure the data is in there as expected % docker run minio/mc ls play/tiledb-demo/tiledb_minio_demo/

[2024-01-15 16:45:04 UTC] 	0B __commits/

[2024-01-15 16:45:04 UTC] 	0B __fragments/

[2024-01-15 16:45:04 UTC] 	0B __schema/ There you go, getting data into MinIO is that simple. Did you get the same results as earlier? You should have, but if you didn't there are a few things you can check out. Common Pitfalls We’ll look at some common errors you might encounter while trying to read/write to MinIO. If your access key and secret key are incorrect, you should expect to see an error message like below tiledb.cc.TileDBError: [TileDB::S3] Error: Error while listing with prefix 's3://tiledb-demo/tiledb_minio_demo/__schema/'... The request signature we calculated does not match the signature you provided. Check your key and signing method. Next, you need to ensure the hostname and port are correct, without a proper endpoint these are the errors you would encounter. Incorrect Hostname: tiledb.cc.TileDBError: [TileDB::S3] Error: … Couldn't resolve host name Incorrect Port: tiledb.cc.TileDBError: [TileDB::S3] Error: … Couldn't connect to server Last but not least, one of the most cryptic errors I’ve seen is the following tiledb.cc.TileDBError: [TileDB::S3] Error: … [HTTP Response Code: -1] [Remote IP: 98.44.32.5] : curlCode: 56, Failure when receiving data from the peer After a ton of debugging it turns out that if you are connecting using http but the MinIO server has TLS activated then you will see the above error. Just be sure the connection scheme is set to the right configuration, in this case, config["vfs.s3.scheme"] = "https". Racks on Racks on Racks There is a rap song (you can search for it) where they rap about having stacks on stacks on stacks of * cash. But there is another rap song where they claim they have so many stacks of cash that they can’t be called “stacks” anymore, they are now “racks”. Essentially when your stacks get so big and so high you need racks on racks on racks to store your stacks of cash. cough* This is an apt comparison because your stacks of data mean as much (or more) to you as the stacks of cash they're rapping about. If only there was something like MinIO to keep all your objects – physical or virtual – safe and readily accessible. With MinIO in the mix, you can easily scale TileDB to with relative ease. You also get all the features that make MinIO great like , , , , among others right out of the box. By having all your data in MinIO, you decrease required storage complexity and therefore realize considerable savings on data storage costs, while at the same time running MinIO on commodity hardware provides the best possible performance-to-cost ratio. MinIO supercharges your TileDB engine with industry-leading performance that makes querying a joy. multiple racks across multiple datacenters Security and Access Control Tiering Object Locking and Retention Key Encryption Service (KES) We’ve added the code snippets used in this blog to a . If you have any questions on how to connect MinIO to TileDB or migrate data into MinIO be sure to reach out to us on ! git repository Slack Also appears . here

Supercharge TileDB Engine with MinIO

MinIO সহ সুপারচার্জ টাইলডিবি ইঞ্জিন

Laden Sie die TileDB-Engine mit MinIO auf

Superalimentar el motor TileDB con MinIO

Boostez le moteur TileDB avec MinIO

मिनिओ के साथ सुपरचार्ज टाइलडीबी इंजन

MinIO で TileDB エンジンをスーパーチャージする

MinIO로 TileDB 엔진 강화

Turbine o mecanismo TileDB com MinIO

Усовершенствуйте движок TileDB с помощью MinIO

MinIO ile TileDB Motorunu Güçlendirin

Công cụ TileDB siêu nạp với MinIO

使用 MinIO 增强 TileDB 引擎

Recommendation systems have become an integral and indispensable part of our lives. These intelligent algorithms are pivotal in shaping our online experiences, influencing the content we consume, the products we buy, and the services we explore. Whether we are streaming content on platforms like , discovering new music on , or shopping online, recommendation systems are quietly working behind the scenes to personalize and enhance our interactions. Netflix Spotify The unique element of these recommendation systems is their ability to understand and predict our preferences based on historical behavior and user patterns. By analyzing our past choices, these systems curate tailored suggestions, saving us time and effort while introducing us to content/products that align with our interests. This enhances user satisfaction and fosters discovery, introducing us to new and relevant offerings that we might not have encountered otherwise. At a high level, developers understand that these algorithms are powered by machine learning and deep learning systems (interchangeably called neural networks), but what if I tell you there is a way to build a recommendation engine without going through the pain of deploying your neural net or machine learning model? This question is specifically relevant in the context of early and mid-stage startups because they don't have tons of structured data to train their models. And as we already know, most machine learning models will not give accurate predictions without proper training data. I recently built and deployed a basic recommendation engine for a , which led to a 40% jump in our key metrics. At the time of writing this blog, the system is generating more than 30 million recommendations per month. Even though this recommendation system was built for a social network, you can apply the basic architecture to any use case, such as product recommendations, music recommendations, content recommendations on text and video platforms, or anything else. Let me start by describing the problem statement. voice-first social network Problem statement from the engineering perspective I had an extensive and because we were building the recommendation system for a product that is already used by thousands of users daily. But to keep this blog short and on point, I will write only the high-level requirements and then discuss the solution of the same. If you are building a recommendation system for your product (simple or neural net-based) and are stuck somewhere, please feel free to contact me on or and I will be more than happy to answer your questions. product requirement document subsequent engineering requirements document Twitter , Linkedin At a high level, we had the following requirements from an engineering perspective - The system should be able to capture a user's interests in the form of keywords. The system should also be able to classify the level of interest a user has with specific keywords. The system should be able to capture a user's interest in other users. It should be able to classify the level of interest a user has in content created by another user. The system should be able to generate high-quality recommendations based on a user's interests. The system should be able to ensure that the recommendations already viewed/rejected by the user shouldn't re-appear again for X number of days. The to ensure that the posts from the same creators aren't grouped on the same page. The system should try its best to ensure that if a user consumes ten posts (our page size), all of those should be from different creators. system should have logic The system should be fast. Less than 150 milliseconds of P99 latency. All the other non-functional requirements, such as high availability, scalability, security, reliability, maintainability, etc, should be fulfilled. Again, this is a highly oversimplified list of problem statements. In reality, the documents were 3000+ words long as they also covered a lot of edge cases and corner cases that can arise while integrating this recommendation engine into our existing systems. Let's move on to the solution. Solution - High-level working of the recommendation engine I will discuss the solutions to the problem one by one and then will describe the overall working of the entire system. Our first problem is capturing the user's interests and defining their interest level with a specific interest For this, we created something called a . To put it simply, a social graph stores the relationships and connections between different entities in a social network. These entities can be different users or a relationship of users with a specific interest. Social graphs are a powerful way to understand and structure the relationships within a particular system. For the sake of brevity, I will not explain the social graph in detail, but I will recommend you google it and learn more about it. Following is a simplified version of the social graph I built for our recommendation engine. social graph As you can see from the above image, we are storing a lot of information, such as the number of interactions (likes, comments, shares) and recency of these interactions (when they happened last) as relationship data between two users as well between a user and an interest. We are even storing the relationship between two different interest keywords. I used , a managed graph database by AWS, to store this social graph. You can use any other graph database, such as , etc. Amazon Neptune Neo4j, JanusGraph, ArrangoDB These interest keywords are predominantly nouns. There is a system in place that breaks down the contents of a post into these keywords(nouns). It's powered by a natural-language processing (NLP) service that uses machine learning to break text into entities, key phrases, etc. Again, you can use any managed NLP services (several available) to accomplish the same. You don't need to learn or deploy your machine-learning models! If you already understand machine learning, then you can go check . AWS comprehend; open-source NLP models as well on Huggingface Our second problem is generating high-quality recommendations based on a user's interest The following diagram is a simplified high-level representation of how the system works. While the above looks easy, there is a lot more going on at each step, and those things have to be carefully thought through and then programmed to ensure that the system is performing optimally. Let me explain step by step: Step 1 - Converting post content into vector embeddings To generate these recommendations, first, we have to convert the contents of a post into something called - . With the recent uptick in the branding of LLMs, OpenAI( the makers of ChatGPT) and , Vector embeddings are becoming an everyday term. I will not go into the details of what they are and how they work, but I highly recommend reading more about them. But generating viable candidates for a feed also has to account for things like content privacy and moderation (removing profane words, abuses, sexual content, harassment, filtering blocked users, etc). Vector embeddings Vector databases For generating the vector embeddings, you can use any prominent embedding model such as the , or any text embedding model, depending on your use case. We went with because of its friendly pricing, performance and operational ease. OpenAI embedding model Amazon titan open-source Amazon Titan Step 2 - Query the user's interest Now, this is where things get interesting. You would want to design the queries based on your specific business needs. For example, we give more weightage to the recency of engagement while querying interests than the number of engagements with a specific keyword or user. We also run multiple parallel queries to find different types of interest of the user - keyword or other user. Since we generate multiple feeds for a single user, we also run some queries promoting a specific topic according to the trend (for example, you will see many Christmas-related posts near Christmas or earthquake-related posts if some earthquake has happened). Needless to say, this topic will only come up in the query results if the user has expressed some interest in them in their journey. So, choose the logic that suits your business use case and the behavior that you want to drive and run multiple queries to get a big enough list of all the user's interests. Step 3 - Do an ANN search based on the interests found Vector databases are predominantly used for performing a particular type of search called (ANN). Again, the way you categorize various interests and whether you are doing one big ANN search or parallel difference searches should entirely be based on your use case and business requirements. I recommend doing more than cohort-based searches and then ordering the results (we will discuss this later in this blog) for the best end-user experience. What ANN search does, in this case, is find other posts on the platform that are similar (closer) to the interests of the user. Approximate nearest neighbor search Step 4 - Store the results in a cache database with ordering. Cache database because one of the problems that we need to solve is speed. We used for storing the unique IDs of the posts for a specific user. We used redis sorted sets because the order of posts in a user's feed is critical. Also, another problem that you have to solve is that the" to ensure that the posts from the same creators aren't grouped on the same page". To avoid repetition of content from the same creator, we have written a simple algorithm which ensures that if a specific creator's post is inserted at any position in a particular user's feed (sorted set), we don't insert another post from the same creator for successive ten positions (we have a page size of 10 while serving the feed to the end user, so we kept it static to avoid complexity). sorted sets redis system should have logic For deciding the order of a specific recommendation of the user, we factored in the following things - : It's determined by an arithmetic formula that takes various data points from the social graph. All of this is engagement data like the timestamp of the last likes created, number of likes created, last comment, etc. User engagement behavior is the indicator of their interest in something. The strength of the relationship with a specific interest (or another user) for this user To determine this, we have created an algorithm that takes various factors such as engagement, engagement-to-impression ratios, number of unique users who engaged, etc., to generate an engagement score of that post at a platform level. The popularity of the post on the platform: In some feeds, we prioritize popularity; in others, we prioritize the social graph. But mostly, all of them are a healthy mix of the two. How the System Works As you can see from the diagram above, the system has been intentionally kept very simple. Following is how the system works - When user A creates a post, the post service, after saving that post, triggers a pub/sub event to a queue, which is received by a background service meant for candidate generation. We use for the pub/sub functionality. Google Pub/Sub This background service receives this asynchronously and performs functionalities discussed earlier - Privacy checks, moderation checks, and keyword generation and then generates the vector embeddings and stores them in the vector database. We are using (discussed later). AstraDB as our vector database Whenever a user engages (like/comment/share, etc.) after updating our main NoSQL database, the post-service triggers a pub/sub event to the recommendation engine service. This recommendation engine service updates the graph database and then updates the recommended feed of the user in near real-time by performing the ANN search and updating the Redis database. So, the more users interact, the better the feed keeps getting. . Those checks are performed while we query the Graph database. This service also updates the engagement score asynchronously. Engagement scores are re-calculated on users viewing the post as well. There are checks to ensure that the recommendations are not biased towards a specific list of keywords Since all of the above steps are performed asynchronously behind the scenes, these computations have no impact on the end-user experience. The feed is finally served to the end user through a feed service. Since this service just performs a lookup on redis and our main NoSQL database ( ), its P99 latency is less than 110 milliseconds. Both these databases return query results in single-digit millisecond latency irrespective of scale. DyanmoDB Tools and technologies used Some services have been written in , while others have been written in (with typescript). Go programming language NodeJS We are using as our vector database. We arrived at this decision after evaluating multiple other databases, such as and . Apart from its excellent query and indexing capabilities on vector and other data types, it offers a pocket-friendly serverless pricing plan. It runs on top of a Cassandra engine, which we use as a database in several other features on our platform, and it gives a CQL query interface, which is very developer-friendly. I highly recommend trying it for your vector use cases. AstraDB by Datastax pinecone, milvus weaviate We use for our asynchronous communication because, at our current scale (few lakh total users, few thousand daily active users), it's highly cost-effective. I have run it at a scale of a few lakh users with thousands of events per second. It works well, and it's effortless to use and extend. Google pub/sub - Speed, simplicity and powerful data structure. I don't think I need to discuss why redis in 2024. Redis - Again, it is highly scalable and easy to use, and we run it in the serverless mode where, despite hundreds of thousands of queries per minute, our total bill is quite low. It also offers very powerful indexing capabilities and single-digit millisecond latency in reads and writes. DynamoDB Problems to be solved in the future As you can imagine, this same setup can be tweaked to build a basic recommendation engine for any use case. But, since ours is a social network, we will require some tweaks down the line to make this system more efficient. Machine learning/ Deep learning algorithms will be needed at the social graph level to predict the keywords and users most relevant for the user. Currently, the data set is too small to predict anything accurately as it is a very new product. However, as the data grows, we will need to replace the current simple queries and formulas with the output of machine learning algorithms. Relationships between various keywords and users must be fine-tuned and made more granular. They are at a very high level right now. But they will need to be deeper. We will need to explore the second and third-degree relationships in our graph to refine the recommendations first. We are not doing any fine-tuning in our embedding models right now. We will need to do that in the near future. End note I hope you found this blog helpful. If you have any questions, doubts or suggestions, please feel free to contact me on , or . Do share this article with your friends and colleagues. Twitter Linkedin Instagram Also published . here

How to Build a Basic Recommendation Engine without Machine Learning 

কিভাবে মেশিন লার্নিং ছাড়া একটি বেসিক রেকমেন্ডেশন ইঞ্জিন তৈরি করবেন

So erstellen Sie eine einfache Empfehlungs-Engine ohne maschinelles Lernen

Cómo construir un motor de recomendación básico sin aprendizaje automático

Comment créer un moteur de recommandation de base sans apprentissage automatique

मशीन लर्निंग के बिना एक बुनियादी अनुशंसा इंजन कैसे बनाएं

機械学習を使用せずに基本的なレコメンデーション エンジンを構築する方法

머신러닝 없이 기본 추천 엔진을 구축하는 방법

Como construir um mecanismo de recomendação básico sem aprendizado de máquina

Как создать базовую систему рекомендаций без машинного обучения

Makine Öğrenimi Olmadan Temel Öneri Motoru Nasıl Oluşturulur

Cách xây dựng công cụ đề xuất cơ bản mà không cần học máy

如何在不使用机器学习的情况下构建基本的推荐引擎

Ready to embrace the new world of search? SGEs (Search Generative Engines) are designed to understand your intent and deliver clear, concise, and close-to-human answers. Still, in its limited access Beta phase, gives a good indication of what the future holds for us. As such, it is important to be prepared and understand the ins and outs of this soon-to-come revolution. Google SGE Let's start with a brief history of the evolution of search engines and move on to the look and feel of Google SGE Beta search results and a basic understanding of the SGE inner workings. A Brief History of Search Engines : The Directory Era (Early 1990s) Think of an enormous internet phone directory, organized down to the last T by a human editor. That's basically what early search engines like Yahoo! and AltaVista were. Though revolutionary for their time, the model of manually organized information was scalable only until the web finally reached a point of explosive growth. From Chaos to Order : The Indexing Age (Late 1990s-2000s) With the arrival of search engines like Google and AltaVista, robots known as "crawlers" took over, automatically scouring the web and indexing websites based on keywords. This marked a significant leap, offering more dynamic and comprehensive search results. Enter the Crawlers : The Rise of Semantics (2000s-2010s) As keyword limitations became apparent, search engines started incorporating natural language processing (NLP) to understand the meaning behind keywords, paving the way for more intuitive and relevant results. Think "best pizza near me" instead of just "pizza." Beyond Keywords (2010s-Present) The introduction of personalized search and voice search assistants like Siri and Alexa marked a shift towards understanding user intent and context. Search engines began delivering results tailored to individual preferences and voice queries, mimicking natural conversation. The Age of Personalization and Voice Search: : Enter SGE (2020s-Present) And finally, we arrive at the dawn of Search Generative Engines. Building upon the advancements of its predecessors, SGE leverages sophisticated AI and machine learning to not just index and understand, but to generate responses tailored to individual needs. As shown in the example below, it goes beyond providing links; it synthesizes information, extracts key insights, and organizes them to best fit what the user wants. At least, according to the data stored by Google to profile the user. The Paradigm Shift What Do Google SGE Search Results Look Like Google SGE is still in Beta mode, so it is tempting to lift the hood and get advanced knowledge of the inner workings of what the future of search engines holds. With SGE, gone is the time when documents were written with keywords and strict algorithms. Where traditional search is, in a nutshell, more keyword-based than conceptual, the SGE integrates NLP, AI, and ML to understand the meaning and context behind the searcher's query. For example, saying, "What is the best laptop for a writer?" returns a synthesized answer with a description of the characteristics to consider. The list of recommended laptops that follows is based on such characteristics and displays each one next to the recommended product. This accelerates the selection time as it saves the time needed to click on each site and enables a quick comparative evaluation at a glance. Next to the list of recommended products are suggested "Best of" websites that, presumably, would provide an alternative perspective to the one offered by Google SGE. The list of recommended products is quite long, which means the user has to scroll for quite a while before getting to the regular Google search results. And even then, before getting to just the listing, you must go through the FAQ section. Between the time I wrote this article and the time I uploaded it to Hackernoon - 36 hours later -, the interface had slightly changed. So, as I wanted to take a better screenshot, it returned the result below that includes sponsored results above the new SGE engine. Fun fact: This highlights the different options Google SGE Beta is experimenting with, making it challenging to anticipate the format it will have when Google SGE is released to all users. For non-commercial searches, the results are quite different. That makes sense, as general knowledge, though valuable, has little market value. So, let's ask a random question unlikely to have commercial implications. For example, "How did troglodytes live?" As you can see, the road to the “People also ask” section is much shorter than for commercial products. What is interesting is that went clicking on the “read more” arrow of the opening section, GSE defaults back to a combination of the  Bard-like interface - with suggested follow-up questions and the invitation to create your follow-up question – and access to additional sources of information next to its snippet definition. So, for both formats like the recent BARD, the ‘People also ask’, and indexed sections are still available, they are now stacked in reverse chronological order. Given the previous surprising difference between the two search result formats at a day’s interval, I tried a new search with the same search query. Though the format of the result is the same as for the previous result, its content has changed. Further thoughts will have to be invested in that specific aspect of SGEs. At this stage, the exact future of search engines we are used to today is anyone's guess. Even is revisiting this version of SGE and using Beta users’ feedback, scrolling, and clicking patterns and comparing them to the previous version to fine-tune their SGE model. Google For now, the best we can do to guess is to attempt to take a peek at the engine behind the process. Peeking at the of Inner Workings Google SGE There are three underlying components to SGE, each building on the previous one capabilities. This equips SGE with the ability to understand your queries' natural language, with word nuances, grammar, and sentiment. In so doing, it "reads" your question just as any human would, by extracting the real meaning and intention behind your search. Natural Language Processing (NLP): SGE continually learns and adapts from the analysis of an enormous amount of text and data to extract patterns and relationships. This enables the model to predict the information you would want to be looking for even when the query is not properly posed. Machine Learning: AI takes the insights from NLP and ML to author not just on-point answers but also informative and tailored to you. SGE is meant to be your personal research assistant with the latest technology and a constantly growing knowledge base. It does far more than just retrieving information. It understands your needs, delivers insights that answer your questions, and helps you further explore, learn, and make informed decisions. Artificial Intelligence: Beyond Text: A Multimodal Experience As we can see in the examples above, SGEs are not limited to text formats. They can generate various response formats, An SGE might present an interactive map highlighting recommended destinations, or curate a personalized video itinerary based on your interests. There are unconfirmed rumors about a soon-to-be-released voice narration that could, for example, guide you through the must-see locations. How Could SGEs Impact SEO in the Future? SGEs promise to herald a user experience revolution. As seen above, this revolution's main characteristics are: Boosting Accuracy and Relevance Personalization on Steroids Conversational capabilities But for all its undeniable advantages, SGEs will need to be scrutinized for: How can we ensure that SGEs are free from bias and deliver fair and inclusive results to all users? Bias and Fairness: : How can users understand how SGEs arrive at their answers and ensure the information they receive is accurate and reliable? Transparency and Explainability : How can we safeguard user privacy and ensure that the vast amount of data collected by SGEs is used responsibly and ethically, and without relying on copyrighted sources? Privacy and Data Protection This being said, SEO is bound to be the first field to have to adapt fast to the new search landscape. Optimizing SEO for SGEs Today, optimizing SEO for discoverability is a fundamental requirement for businesses’ success or for any project that depends on traffic to a website. SEO professionals are already familiar with moving from keywords to longtail keywords, maximizing the attractivity of their titles to incite clicks (all the while avoiding clickbait titles with low engagement rates), structuring content with H titles, etc., obtaining backlinks, and optimizing the website for fast loading, responsiveness and so forth. All of this is to place their content on the coveted search results’ first page. Adapting to the new era of SGEs will require adapting fast. My previous article on SGEs is dedicated exclusively to .  At the time of writing that article, Google SGE Beta was not yet available, but the main principles remain. SEO in the LLM-Powered Search Engine age The article goes into more detail on what to do to adapt fast by shifting from keywords to context. My next article should focus on how to leverage SGEs for strategic business advantage. Make sure to never miss an article by following me on Hackernoon (Subscribe button under my profile above) and on . LinkedIn

Adapting to Google's Search Generative Engines (SGEs) - Part 1

Google-এর সার্চ জেনারেটিভ ইঞ্জিন (SGEs)-এর সাথে মানিয়ে নেওয়া - পার্ট 1

Anpassung an die Search Generative Engines (SGEs) von Google – Teil 1

Adaptación a los motores generativos de búsqueda (SGE) de Google - Parte 1

S'adapter aux moteurs de recherche génératifs (SGE) de Google - Partie 1

Google के सर्च जेनरेटर इंजन (एसजीई) को अपनाना - भाग 1

Google の検索生成エンジン (SGE) への適応 - パート 1

Google의 검색 생성 엔진(SGE)에 적응하기 - 1부

Adaptação aos mecanismos geradores de pesquisa (SGEs) do Google - Parte 1

Адаптация к поисковым генеративным системам Google (SGE). Часть 1.

Google'ın Arama Üretken Motorlarına (SGE'ler) Uyum Sağlama - Bölüm 1

Thích ứng với Công cụ tạo tìm kiếm (SGE) của Google - Phần 1

适应 Google 的搜索生成引擎 (SGE) - 第 1 部分

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com;
(2) Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com;
(3) Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com. Table of Links Abstract and Introduction
Problem Setup and Related Work
HyperTransformer
Experiments
Conclusion and References
A Example of a Self-Attention Mechanism For Supervised Learning
B Model Parameters
C Additional Supervised Experiments
D Dependence On Parameters and Ablation Studies
E Attention Maps of Learned Transformer Models
F Visualization of The Generated CNN Weights
G Additional Tables and Figures A EXAMPLE OF A SELF-ATTENTION MECHANISM FOR SUPERVISED LEARNING Self-attention in its rudimentary form can implement a cosine-similarity-based sample weighting, which can also be viewed as a simple 1-step MAML-like learning algorithm. This can be seen by considering a simple classification model [5] here we use only local features hφl (ei) of the sample embedding vectors ei This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com; (2) Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com; (3) Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com. This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com; (2) Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com; (3) Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com. Table of Links Abstract and Introduction Problem Setup and Related Work HyperTransformer Experiments Conclusion and References A Example of a Self-Attention Mechanism For Supervised Learning B Model Parameters C Additional Supervised Experiments D Dependence On Parameters and Ablation Studies E Attention Maps of Learned Transformer Models F Visualization of The Generated CNN Weights G Additional Tables and Figures Abstract and Introduction Abstract and Introduction Problem Setup and Related Work Problem Setup and Related Work HyperTransformer HyperTransformer Experiments Experiments Conclusion and References Conclusion and References A Example of a Self-Attention Mechanism For Supervised Learning A Example of a Self-Attention Mechanism For Supervised Learning B Model Parameters B Model Parameters C Additional Supervised Experiments C Additional Supervised Experiments D Dependence On Parameters and Ablation Studies D Dependence On Parameters and Ablation Studies E Attention Maps of Learned Transformer Models E Attention Maps of Learned Transformer Models F Visualization of The Generated CNN Weights F Visualization of The Generated CNN Weights G Additional Tables and Figures G Additional Tables and Figures A EXAMPLE OF A SELF-ATTENTION MECHANISM FOR SUPERVISED LEARNING Self-attention in its rudimentary form can implement a cosine-similarity-based sample weighting, which can also be viewed as a simple 1-step MAML-like learning algorithm. This can be seen by considering a simple classification model [5] here we use only local features hφl (ei) of the sample embedding vectors ei

HyperTransformer: A Example of a Self-Attention Mechanism For Supervised Learning

To begin with, let's imagine a businessman who has fallen under US sanctions. A sanctioned businessman is trying to buy a sanctioned bicycle from a sanctioned country and make it non-sanctioned in the place where it is located. How to make it non-sanctioned? We are not interested in this story. What we are interested in is how financial transactions will take place. How will the businessman continue to make international transfers? He or she will probably open shell companies, for example, in Dubai, because the UAE is now very similar to Switzerland and tries to stay out of someone's business unless that someone is showing off. These shell companies should be linked to some not the cleanest bank, preferably not legally located in Dubai (only a branch). Let's call it X-Bank. Then transfer to some Caribbean country, i.e. to an offshore zone, the money for the under-sanctioned bike to "Y-Bank", which is linked to shell companies on the other side. Let's take a closer look at the transaction To be able to make a wire transfer in U.S. dollars for a bicycle, "X-Bank" needs to have an account at a large U.S. correspondent bank, such as Goldman Morgan. Similarly, the bank that accepts "Y-Bank" transactions must have accounts with a US correspondent bank. A Correspondent bank is a bank that acts on behalf of another bank (client bank) to execute financial transactions, including international transfers. In the case of dollar transfers, the correspondent bank is usually located in the U.S., allowing it to conduct dollar transactions directly. Technically, when a sanctioned businessman bought a sanctioned bike for dollars from a sanctioned country through shell companies that work with not the cleanest banks, all transactions took place inside the US. . Everything is very complicated. There are a lot of legal risks. True, sanctions continue to be easily circumvented traditionally. Bottom line How is this done through the USDT? If a sanctioned businessman uses USDT, the transactions would take place on the USDT network, and correspondent bank accounts would not need to be used. The entire transaction with live money would occur inside Tether's balance sheet, which is the issuer of USDT. It turns out that dollars don't move anywhere from a legal standpoint. Even though USDT addresses are blocked as part of sanctions, they are still very shallow and too pinpointed. Transactions can be made to a large number of addresses at once and then transferred through crypto bridges, where they will eventually be lost. You can even pull funds from multiple wallets to centralized sites and take them anywhere. To stop transfers through USDT, US authorities would have to interact directly with Tether, but the company is too big to collapse. You can't walk into an organization with $100 billion in net assets and $100 billion in liabilities and start shaking it down. A lot of money, companies, and lives are tied up in Tether. Could Tether start banning addresses by location? Yes! Tether is a smart contract that allows the developer to freeze any amount of USDT in any wallet. However, Tether (USDT), like other cryptocurrencies and tokens running on blockchain platforms, does not have a built-in mechanism to track the geolocation of wallet addresses in its smart contracts. Smart contracts, including those used by Tether, operate based on the inputs they receive and the rules encoded within them, without access to external data such as the physical location of users, unless users provide such data through an off-chain mechanism and then transmitted to the blockchain via an oracle. If a system in which users voluntarily associate their wallet addresses with geolocation and this data is stored off-chain, then the oracle could provide this data to the smart contract. However, this method relies heavily on user participation and honesty. It turns out that if the businessman is under sanctions, the elementary knowledge of changing internet traces, which every user knows, makes him or her elusive to US sanctions, allowing him or her to make settlements right under the noses of US regulators. Just 2 levels of encryption can permanently hide traces of an account's origin, so it's unclear how to freeze entire locations and countries. Prohibiting Tether from interacting with a sanctioned jurisdiction is possible, but not for USDT users. More importantly, even if user data is collected by oracles and transferred to the blockchain, it would legally destroy Tether. All those companies that collect any user data should, in theory, be responsible for that data. How Tether will be able to protect user data is unknown, which would make it automatically banned in most jurisdictions on planet Earth. Conclusion The upshot is that Tether and other stablecoins are safe in terms of sanctions risks for those who know how to maintain their privacy. This continues to keep the door open for any sanctioned individuals, organizations, and countries to interact with the US financial system. The above reduces the legal risks of using all stablecoins, as if you are not committing criminal activity, no one will block your address point by point, nor will there be a mass blocking. P.S. All characters are fictitious, and the story is provided for informational purposes only.

Stablecoins Vs. US Sanctions: How Do Circumvention Mechanisms Work With Tether (USDT)?

As the buzz around starts to settle, industry leaders are growing aware of the upcoming era of artificial intelligence. With a market value of , the AI industry is on the rise, projected to reach Generative AI $197 billion in 2023 $1,812 billion by 2030. In this article, I will focus on the future of AI and the potential rise of —a technology that will have a bigger global impact than current Generative AI tools. artificial general intelligence What is artificial general intelligence? Artificial General Intelligence is a branch of AI research focused on developing systems that have the capability to closely copy human-level cognitive abilities, including unparalleled adaptability and the potential for self-improvement. However, the closest approximation to AGI is now like GPT-4, which generate human-like responses to prompts and engage in continuous dialogue with users. Nevertheless, these models still lack even basic cognitive abilities. found in large language models There are 4 common approaches in artificial general intelligence research, categorizing them as follows: This AGI approach considers symbolic thought the core of human general intelligence, enabling broad generalization. Abstract thinking, a key aspect of human intelligence, may also play a role in artificial intelligence. Symbolic approach: The emergentist approach to AGI states that the human brain, consisting of neurons, self-organizes into complex patterns based on sensory experiences. Replicating such a structure might lead to a similar form of intelligence. Emergentist approach: The hybrid AGI approach views the brain as a composite system where different components and principles collaborate to achieve a more powerful outcome. Hybrid AGI research covers a lot of diverse approaches. Hybrid approach: The universalist approach to AGI focuses on the mathematical essence of general intelligence. It suggests that once AGI is theoretically solved, the principles used can be scaled down for practical applications. Universalist approach: The purpose of these approaches is to help us understand human intelligence and apply this knowledge to advance AI. How is it different from artificial intelligence? While artificial intelligence covers various technologies related to machine and computer understanding, the idea of artificial general intelligence is Similar to humans, AGI will learn, make conclusions, and generate new insights from acquired information. still a theoretical goal. Currently, most AI systems have a narrow focus. They are designed for specific tasks and applications and are dependent on human programming for training and accuracy. For example, large language models can engage in human-like conversations and handle complex tasks, but they lack the cognitive ability to analyze information and draw insights, a capability expected from artificial general intelligence. Are there any real-life examples of AGI? Even though the AGI concept hasn’t fully left the theoretical territory, there are some examples of narrow AI that can outperform humans in specific areas: Supercomputers like Watson use AI to handle complex scientific tasks, from modeling the Big Bang theory to simulating the human brain. IBM's Watson: AI-powered vehicles recognize and interact with other traffic elements, following road rules to navigate. : Self-driving cars ROSS mines data from different text documents, analyzes information and provides precise responses to complex legal questions in seconds. ROSS Intelligence: Specializing in playing the board game Go, AlphaGo famously defeated world champion Lee Sedol in 2016. AlphaGo: Dadabots is an AI algorithm that can create its own music when given a set of existing compositions. Music AIs: What are AGI benefits for enterprises? AGI is loosely described as AI systems that have a reasonable level of self-awareness, making decisions autonomously, and solving complex problems across different contexts. Enterprises are from current AI capabilities, and these advantages will only grow once AGI becomes a reality. already gaining benefits Improved automation AGI will automate a broad range of tasks, from data entry and analysis to decision-making. This will enhance operational efficiency, reduce manual labor, and decrease operational costs. Organizations already use AI tools for quick document processing, data summarization, content creation, and analytics. Through research on artificial general intelligence, all these capabilities will expand. Upgraded problem-solving and risk management AGI will handle complex business challenges, process extensive datasets, and offer actionable insights for effective risk management. It will improve detection of cybersecurity breaches, fraud attempts, or operational irregularities, providing immediate mitigation. Unlike current models, AGI will also be able to generate new solutions. Accelerated innovation Improved artificial intelligence will speed up innovation by generating creative ideas, optimizing research, and exploring business possibilities. The introduction of AGI will let business leaders experiment with problem-solving, learn from failures, and succeed in timely innovative pursuits. Additionally, AGI will significantly influence industries through its transformative speed and impact. Enterprises adopting AGI will achieve a competitive edge, innovating, optimizing processes, and adapting to changing market conditions. For example, marketing teams will be able to make more informed decisions, while supply chain managers will have more opportunities for analyzing alternative delivery routes. Financial institutions will gain high-quality insights into market trends. Cost and resource optimization AGI will let businesses achieve better cost savings by automating tasks and streamlining processes. For example, the use of AGI systems will allow cutting customer support costs with chatbots, minimizing inventory holding costs through demand forecasting, and reducing energy consumption with smart building management. AGI's real-time data analysis will inform resource allocation decisions, optimizing supply chain logistics, human resource allocation, and inventory management. Continuous learning and adaptation AGI will be able to learn from new data, providing access to expert-level knowledge and insights across diverse domains, particularly valuable in industries requiring rare or costly expertise. In the future, potential AGI applications may include advanced chatbots, digital assistants, and autonomous vehicles, all requiring high reasoning and autonomous decision-making capabilities. The future of AI The future of AI is going to bring a major shift in technology with various possibilities and responsibilities to take care of. Artificial general intelligence will be a key part of this change, as it has the power to change industries, businesses, and our everyday routines. If industry leaders want to make the most of this change, their priority should be to prepare for AGI's impact and open their minds to new ideas and improvements that lie ahead.

A Close-Up Look at Artificial General Intelligence and Its Mechanisms

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