#Data ingestion in real time

[jcmarchi]

📽 [Webinar] Cut storage and processing costs for vector embeddings

New Post has been published on https://thedigitalinsider.com/webinar-cut-storage-and-processing-costs-for-vector-embeddings/

📽 [Webinar] Cut storage and processing costs for vector embeddings

Innovative leaders such as NielsenIQ are increasingly turning to a data lakehouse approach to power their Generative AI initiatives amidst rising vector database costs. Join us for a technical deep dive into the pivotal role of vector embeddings in AI and a demo of how you can generate and manage vector embeddings with the cost and scale efficiencies of your lakehouse.

What You Will Learn:

Real-World Applications: In this talk, we’ll cover the challenges of generating, storing, and retrieving high-dimensional embeddings, including high computational costs and scalability issues for production workloads. Kaushik Muniandi, engineering manager at NielsenIQ, will explain how he leveraged a data lakehouse to overcome these challenges for a text-based search application, and the performance improvements he measured.

Introduction to AI Vector Embedding Generation Transformer: Discover how Onehouse solves the above challenges by enabling users to automatically create and manage vector embeddings from near real-time data ingestion streams to lakehouse tables without adding complex setups and extra tools. 

Technical Deep Dive: Get into the nitty-gritty of Onehouse stream captures and how they integrate with leading vector databases, enabling a single source of truth for AI model training, inference, and serving.

Can’t make it? Register anyway to receive the recording! 

*This post is created by Onehouse. We thank the Onehouse team for their insights and ongoing support of TheSequence.

[juliebowie]

Real-Time Data Ingestion: Strategies, Benefits, and Use Cases

Summary: Master real-time data! This guide explores key concepts & strategies for ingesting & processing data streams. Uncover the benefits like improved decision-making & fraud detection. Learn best practices & discover use cases across industries.

Introduction

In today's data-driven world, the ability to analyse information as it's generated is becoming increasingly crucial. Traditional batch processing, where data is collected and analysed periodically, can leave businesses lagging behind. This is where real-time data ingestion comes into play.

Overview Real-Time Data Ingestion

Real-time data ingestion refers to the continuous process of capturing, processing, and storing data streams as they are generated. This data can come from various sources, including sensor networks, social media feeds, financial transactions, website traffic logs, and more.

By ingesting and analysing data in real-time, businesses can gain valuable insights and make informed decisions with minimal latency.

Key Concepts in Real-Time Data Ingestion

Data Streams: Continuous flows of data generated by various sources, requiring constant ingestion and processing.

Event Stream Processing (ESP): Real-time processing engines that analyse data streams as they arrive, identifying patterns and extracting insights.

Microservices Architecture: Breaking down data processing tasks into smaller, independent services for increased scalability and agility in real-time environments.

Data Pipelines: Defined pathways for data to flow from source to destination, ensuring seamless data ingestion and transformation.

Latency: The time it takes for data to travel from its source to the point of analysis. Minimising latency is crucial for real-time applications.

Strategies for Implementing Real-Time Data Ingestion

Ready to harness the power of real-time data? Dive into this section to explore key strategies for implementing real-time data ingestion. Discover how to choose the right tools, ensure data quality, and design a scalable architecture for seamless data capture and processing.

Choosing the Right Tools: Select data ingestion tools that can handle high-volume data streams and offer low latency processing, such as Apache Kafka, Apache Flink, or Amazon Kinesis.

Data Stream Preprocessing: Clean, filter, and transform data streams as they are ingested to ensure data quality and efficient processing.

Scalability and Performance: Design your real-time data ingestion architecture to handle fluctuating data volumes and maintain acceptable processing speed.

Monitoring and Alerting: Continuously monitor your data pipelines for errors or performance issues. Implement automated alerts to ensure timely intervention if problems arise.

Benefits of Real-Time Data Ingestion

Explore the transformative benefits of real-time data ingestion. Discover how it empowers businesses to make faster decisions, enhance customer experiences, and optimise operations for a competitive edge.

Enhanced Decision-Making: Real-time insights allow businesses to react quickly to market changes, customer behaviour, or operational issues.

Improved Customer Experience: By analysing customer interactions in real-time, businesses can personalise recommendations, address concerns promptly, and optimise customer journeys.

Fraud Detection and Prevention: Real-time analytics can identify suspicious activity and prevent fraudulent transactions as they occur.

Operational Efficiency: Monitor machine performance, resource utilisation, and potential equipment failures in real-time to optimise operations and minimise downtime.

Risk Management: Real-time data analysis can help predict and mitigate potential risks based on real-time market fluctuations or social media sentiment.

Challenges in Real-Time Data Ingestion

Real-time data streams are powerful, but not without hurdles. Dive into this section to explore the challenges of high data volume, ensuring data quality, managing complexity, and keeping your data secure.

Data Volume and Velocity: Managing high-volume data streams and processing them with minimal latency can be a challenge.

Data Quality: Maintaining data quality during real-time ingestion is crucial, as errors can lead to inaccurate insights and poor decision-making.

Complexity: Real-time data pipelines involve various technologies and require careful design and orchestration to ensure smooth operation.

Security Concerns: Protecting sensitive data while ingesting and processing data streams in real-time requires robust security measures.

Use Cases of Real-Time Data Ingestion

Learn how real-time data ingestion fuels innovation across industries, from fraud detection in finance to personalised marketing in e-commerce. Discover the exciting possibilities that real-time insights unlock.

Fraud Detection: Financial institutions use real-time analytics to identify and prevent fraudulent transactions as they occur.

Personalized Marketing: E-commerce platforms leverage real-time customer behaviour data to personalise product recommendations and promotions.

IoT and Sensor Data Analysis: Real-time data from sensors in connected devices allows for monitoring equipment health, optimising energy consumption, and predicting potential failures.

Stock Market Analysis: Financial analysts use real-time data feeds to analyse market trends and make informed investment decisions.

Social Media Monitoring: Brands can track social media sentiment and brand mentions in real-time to address customer concerns and manage brand reputation.

Best Practices for Real-Time Data Ingestion

Unleashing the full potential of real-time data! Dive into this section for best practices to optimise your data ingestion pipelines, ensuring quality, performance, and continuous improvement.

Plan and Design Thoroughly: Clearly define requirements and design your real-time data ingestion architecture considering scalability, performance, and security.

Choose the Right Technology Stack: Select tools and technologies that can handle the volume, velocity, and variety of data you expect to ingest.

Focus on Data Quality: Implement data cleaning and validation techniques to ensure the accuracy and consistency of your real-time data streams.

Monitor and Maintain: Continuously monitor your data pipelines for errors and performance issues. Implement proactive maintenance procedures to ensure optimal performance.

Embrace Continuous Improvement: The field of real-time data ingestion is constantly evolving. Stay updated on new technologies and best practices to continuously improve your data ingestion pipelines.

Conclusion

Real-time data ingestion empowers businesses to operate in an ever-changing environment. By understanding the key concepts, implementing effective strategies, and overcoming the challenges, businesses can unlock the power of real-time insights to gain a competitive edge.

From enhanced decision-making to improved customer experiences and operational efficiency, real-time data ingestion holds immense potential for organisations across diverse industries. As technology continues to advance, real-time data ingestion will become an even more critical tool for success in the data-driven future.

Frequently Asked Questions

What is the Difference Between Real-Time and Batch Data Processing?

Real-time data ingestion processes data as it's generated, offering near-instant insights. Batch processing collects data periodically and analyses it later, leading to potential delays in decision-making.

What are Some of The Biggest Challenges in Real-Time Data Ingestion?

High data volume and velocity, maintaining data quality during processing, and ensuring the security of sensitive data streams are some of the key challenges to overcome.

How Can My Business Benefit from Real-Time Data Ingestion?

Real-time insights can revolutionise decision-making, personalise customer experiences, detect fraud instantly, optimise operational efficiency, and identify potential risks before they escalate.

[rajaniesh]

Unlock Powerful Data Strategies: Master Managed and External Tables in Fabric Delta Lake

Are you ready to unlock powerful data strategies and take your data management skills to the next level? In our latest blog post, we dive deep into mastering managed and external tables in Delta Lake within Microsoft Fabric.

Welcome to our series on optimizing data ingestion with Spark in Microsoft Fabric. In our first post, we covered the capabilities of Microsoft Fabric and its integration with Delta Lake. In this second installment, we dive into mastering Managed and External tables. Choosing between managed and external tables is a crucial decision when working with Delta Lake in Microsoft Fabric. Each option…

[nitor-infotech]

Snowpipe: What it is and How to Get Started

Snowpipe is an efficient, secure, and reliable data ingestion service that quickly transfers massive amounts of data from sources such as AWS S3 or Google Cloud Storage into Snowflake tables. Set up Snowpipe quickly and easily to take advantage of this powerful tool so you can start getting better insights from your data.

What is Snowpipe? Snowpipe is a cloud-based data ingestion service that enables you to quickly and easily transfer petabytes of data from sources like AWS S3 or Google Cloud Storage into Snowflake tables. Once you set up Snowpipe, it is able to rapidly ingest and store large volumes of static or streaming data so you can access it for analysis quickly.

Setting Up a Snow Pipe Pipeline The first step to getting started with Snowpipe is to set up a pipeline. In order to do this, you will need to create a stage that describes the source and the format of the data you are transferring. Once the stage is created, you can create a pipe to move the data from your source into the stage. Finally, data will be automatically moved from the stage into specified tables in Snowflake.

How To Automate Data Upload Snowpipe is an automated data ingestion service for loading massive amounts of data stored in external sources such as Amazon S3 or Google Cloud Storage into tables and views in your Snowflake account. Snowpipe makes it easy to load large datasets with minimal effort, set up pipelines that run continuously, and configure automatic alerting so you know when something goes wrong along the way.

Benefits of Using Snowpipe  Snowpipe offers some great benefits for those looking to rapidly move large amounts of data from external sources into their Snowflake tables. Snowpipe’s automated data ingestion service lets you quickly and easily move terabytes of data with minimal effort - something that would have been impossible without the help of Snowpipe. Additionally, Snowpipe can be configured with pipelines that run continuously and alert you if there are any issues with your data transfers.

Working with Data Sources Snowpipe allows you to move your data from a variety of different sources. From a setup perspective, the first step is to create an external stage in your Snowflake account that connects your external source (e.g. AWS S3 or Google Cloud Storage). Once this connection is authenticated, you’ll need to enable automated data ingestion with a custom SQL query. This will allow Snowpipe to move the files from your external source into the designated Snowflake table.

[howi99]

Jaune: *waking up on a beach* I'm... Still alive? Then that mean- *looking around to see if he could see team RWBY or Penny* not here of course, would have been too easy right? Hm... Let's go look around first, that place looks... Strange.

___

Penny: *getting gently moved so she can wake up* No, please, give me 5 more minutes.... *More forcefully* hmmm, i said 5 more minutes...

Jaune: *roll his eyes* PENNY!

Penny: *finally opening her eyes* Ok, i'm awake! Gee, can't even sleep of electric sheeps! Wait... Jaune? *Smiling and hugging him quickly* Oh i'm so happy to see you! I thought i was... Wait, are we dead? *Panic in her eyes* ARE YOU DEAD!? Oh no! Oh nononono, NO!

Jaune: *laughing* Relax, we are fine! Does this look like the pearly gates of heaven?

Penny: *taking a look around her, seeing trees and a really colorful vegetation* ... No, but tell me; did we ingest psychedelic mushrooms?

Jaune: Yeah, i know what you mean, but no. Everything is real.

Penny: It looks like a book in my data- i mean memory. Though now that i try to remember it, i can't seem to remember it.

Jaune: Wouldn't be the first time a fairy tail turned out to be true. Anyway we should move out. If we could find each other, we probably will find team RWBY, right?

Penny: *panicking* They all fell!?

Jaune: Ah, yeah, forgot you felt just after yang and Blake. But yeah we need to find them.

[mostlysignssomeportents]

Hypothetical AI election disinformation risks vs real AI harms

I'm on tour with my new novel The Bezzle! Catch me TONIGHT (Feb 27) in Portland at Powell's. Then, onto Phoenix (Changing Hands, Feb 29), Tucson (Mar 9-12), and more!

You can barely turn around these days without encountering a think-piece warning of the impending risk of AI disinformation in the coming elections. But a recent episode of This Machine Kills podcast reminds us that these are hypothetical risks, and there is no shortage of real AI harms:

https://soundcloud.com/thismachinekillspod/311-selling-pickaxes-for-the-ai-gold-rush

The algorithmic decision-making systems that increasingly run the back-ends to our lives are really, truly very bad at doing their jobs, and worse, these systems constitute a form of "empiricism-washing": if the computer says it's true, it must be true. There's no such thing as racist math, you SJW snowflake!

https://slate.com/news-and-politics/2019/02/aoc-algorithms-racist-bias.html

Nearly 1,000 British postmasters were wrongly convicted of fraud by Horizon, the faulty AI fraud-hunting system that Fujitsu provided to the Royal Mail. They had their lives ruined by this faulty AI, many went to prison, and at least four of the AI's victims killed themselves:

https://en.wikipedia.org/wiki/British_Post_Office_scandal

Tenants across America have seen their rents skyrocket thanks to Realpage's landlord price-fixing algorithm, which deployed the time-honored defense: "It's not a crime if we commit it with an app":

https://www.propublica.org/article/doj-backs-tenants-price-fixing-case-big-landlords-real-estate-tech

Housing, you'll recall, is pretty foundational in the human hierarchy of needs. Losing your home – or being forced to choose between paying rent or buying groceries or gas for your car or clothes for your kid – is a non-hypothetical, widespread, urgent problem that can be traced straight to AI.

Then there's predictive policing: cities across America and the world have bought systems that purport to tell the cops where to look for crime. Of course, these systems are trained on policing data from forces that are seeking to correct racial bias in their practices by using an algorithm to create "fairness." You feed this algorithm a data-set of where the police had detected crime in previous years, and it predicts where you'll find crime in the years to come.

But you only find crime where you look for it. If the cops only ever stop-and-frisk Black and brown kids, or pull over Black and brown drivers, then every knife, baggie or gun they find in someone's trunk or pockets will be found in a Black or brown person's trunk or pocket. A predictive policing algorithm will naively ingest this data and confidently assert that future crimes can be foiled by looking for more Black and brown people and searching them and pulling them over.

Obviously, this is bad for Black and brown people in low-income neighborhoods, whose baseline risk of an encounter with a cop turning violent or even lethal. But it's also bad for affluent people in affluent neighborhoods – because they are underpoliced as a result of these algorithmic biases. For example, domestic abuse that occurs in full detached single-family homes is systematically underrepresented in crime data, because the majority of domestic abuse calls originate with neighbors who can hear the abuse take place through a shared wall.

But the majority of algorithmic harms are inflicted on poor, racialized and/or working class people. Even if you escape a predictive policing algorithm, a facial recognition algorithm may wrongly accuse you of a crime, and even if you were far away from the site of the crime, the cops will still arrest you, because computers don't lie:

https://www.cbsnews.com/sacramento/news/texas-macys-sunglass-hut-facial-recognition-software-wrongful-arrest-sacramento-alibi/

Trying to get a low-waged service job? Be prepared for endless, nonsensical AI "personality tests" that make Scientology look like NASA:

https://futurism.com/mandatory-ai-hiring-tests

Service workers' schedules are at the mercy of shift-allocation algorithms that assign them hours that ensure that they fall just short of qualifying for health and other benefits. These algorithms push workers into "clopening" – where you close the store after midnight and then open it again the next morning before 5AM. And if you try to unionize, another algorithm ��� that spies on you and your fellow workers' social media activity – targets you for reprisals and your store for closure.

If you're driving an Amazon delivery van, algorithm watches your eyeballs and tells your boss that you're a bad driver if it doesn't like what it sees. If you're working in an Amazon warehouse, an algorithm decides if you've taken too many pee-breaks and automatically dings you:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

If this disgusts you and you're hoping to use your ballot to elect lawmakers who will take up your cause, an algorithm stands in your way again. "AI" tools for purging voter rolls are especially harmful to racialized people – for example, they assume that two "Juan Gomez"es with a shared birthday in two different states must be the same person and remove one or both from the voter rolls:

https://www.cbsnews.com/news/eligible-voters-swept-up-conservative-activists-purge-voter-rolls/

Hoping to get a solid education, the sort that will keep you out of AI-supervised, precarious, low-waged work? Sorry, kiddo: the ed-tech system is riddled with algorithms. There's the grifty "remote invigilation" industry that watches you take tests via webcam and accuses you of cheating if your facial expressions fail its high-tech phrenology standards:

https://pluralistic.net/2022/02/16/unauthorized-paper/#cheating-anticheat

All of these are non-hypothetical, real risks from AI. The AI industry has proven itself incredibly adept at deflecting interest from real harms to hypothetical ones, like the "risk" that the spicy autocomplete will become conscious and take over the world in order to convert us all to paperclips:

https://pluralistic.net/2023/11/27/10-types-of-people/#taking-up-a-lot-of-space

Whenever you hear AI bosses talking about how seriously they're taking a hypothetical risk, that's the moment when you should check in on whether they're doing anything about all these longstanding, real risks. And even as AI bosses promise to fight hypothetical election disinformation, they continue to downplay or ignore the non-hypothetical, here-and-now harms of AI.

There's something unseemly – and even perverse – about worrying so much about AI and election disinformation. It plays into the narrative that kicked off in earnest in 2016, that the reason the electorate votes for manifestly unqualified candidates who run on a platform of bald-faced lies is that they are gullible and easily led astray.

But there's another explanation: the reason people accept conspiratorial accounts of how our institutions are run is because the institutions that are supposed to be defending us are corrupt and captured by actual conspiracies:

https://memex.craphound.com/2019/09/21/republic-of-lies-the-rise-of-conspiratorial-thinking-and-the-actual-conspiracies-that-fuel-it/

The party line on conspiratorial accounts is that these institutions are good, actually. Think of the rebuttal offered to anti-vaxxers who claimed that pharma giants were run by murderous sociopath billionaires who were in league with their regulators to kill us for a buck: "no, I think you'll find pharma companies are great and superbly regulated":

https://pluralistic.net/2023/09/05/not-that-naomi/#if-the-naomi-be-klein-youre-doing-just-fine

Institutions are profoundly important to a high-tech society. No one is capable of assessing all the life-or-death choices we make every day, from whether to trust the firmware in your car's anti-lock brakes, the alloys used in the structural members of your home, or the food-safety standards for the meal you're about to eat. We must rely on well-regulated experts to make these calls for us, and when the institutions fail us, we are thrown into a state of epistemological chaos. We must make decisions about whether to trust these technological systems, but we can't make informed choices because the one thing we're sure of is that our institutions aren't trustworthy.

Ironically, the long list of AI harms that we live with every day are the most important contributor to disinformation campaigns. It's these harms that provide the evidence for belief in conspiratorial accounts of the world, because each one is proof that the system can't be trusted. The election disinformation discourse focuses on the lies told – and not why those lies are credible.

That's because the subtext of election disinformation concerns is usually that the electorate is credulous, fools waiting to be suckered in. By refusing to contemplate the institutional failures that sit upstream of conspiracism, we can smugly locate the blame with the peddlers of lies and assume the mantle of paternalistic protectors of the easily gulled electorate.

But the group of people who are demonstrably being tricked by AI is the people who buy the horrifically flawed AI-based algorithmic systems and put them into use despite their manifest failures.

As I've written many times, "we're nowhere near a place where bots can steal your job, but we're certainly at the point where your boss can be suckered into firing you and replacing you with a bot that fails at doing your job"

https://pluralistic.net/2024/01/15/passive-income-brainworms/#four-hour-work-week

The most visible victims of AI disinformation are the people who are putting AI in charge of the life-chances of millions of the rest of us. Tackle that AI disinformation and its harms, and we'll make conspiratorial claims about our institutions being corrupt far less credible.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/02/27/ai-conspiracies/#epistemological-collapse

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

[elsa16744]

Harnessing the Power of Data Engineering for Modern Enterprises

In the contemporary business landscape, data has emerged as the lifeblood of organizations, fueling innovation, strategic decision-making, and operational efficiency. As businesses generate and collect vast amounts of data, the need for robust data engineering services has become more critical than ever. SG Analytics offers comprehensive data engineering solutions designed to transform raw data into actionable insights, driving business growth and success.

The Importance of Data Engineering

Data engineering is the foundational process that involves designing, building, and managing the infrastructure required to collect, store, and analyze data. It is the backbone of any data-driven enterprise, ensuring that data is clean, accurate, and accessible for analysis. In a world where businesses are inundated with data from various sources, data engineering plays a pivotal role in creating a streamlined and efficient data pipeline.

SG Analytics’ data engineering services are tailored to meet the unique needs of businesses across industries. By leveraging advanced technologies and methodologies, SG Analytics helps organizations build scalable data architectures that support real-time analytics and decision-making. Whether it’s cloud-based data warehouses, data lakes, or data integration platforms, SG Analytics provides end-to-end solutions that enable businesses to harness the full potential of their data.

Building a Robust Data Infrastructure

At the core of SG Analytics’ data engineering services is the ability to build robust data infrastructure that can handle the complexities of modern data environments. This includes the design and implementation of data pipelines that facilitate the smooth flow of data from source to destination. By automating data ingestion, transformation, and loading processes, SG Analytics ensures that data is readily available for analysis, reducing the time to insight.

One of the key challenges businesses face is dealing with the diverse formats and structures of data. SG Analytics excels in data integration, bringing together data from various sources such as databases, APIs, and third-party platforms. This unified approach to data management ensures that businesses have a single source of truth, enabling them to make informed decisions based on accurate and consistent data.

Leveraging Cloud Technologies for Scalability

As businesses grow, so does the volume of data they generate. Traditional on-premise data storage solutions often struggle to keep up with this exponential growth, leading to performance bottlenecks and increased costs. SG Analytics addresses this challenge by leveraging cloud technologies to build scalable data architectures.

Cloud-based data engineering solutions offer several advantages, including scalability, flexibility, and cost-efficiency. SG Analytics helps businesses migrate their data to the cloud, enabling them to scale their data infrastructure in line with their needs. Whether it’s setting up cloud data warehouses or implementing data lakes, SG Analytics ensures that businesses can store and process large volumes of data without compromising on performance.

Ensuring Data Quality and Governance

Inaccurate or incomplete data can lead to poor decision-making and costly mistakes. That’s why data quality and governance are critical components of SG Analytics’ data engineering services. By implementing data validation, cleansing, and enrichment processes, SG Analytics ensures that businesses have access to high-quality data that drives reliable insights.

Data governance is equally important, as it defines the policies and procedures for managing data throughout its lifecycle. SG Analytics helps businesses establish robust data governance frameworks that ensure compliance with regulatory requirements and industry standards. This includes data lineage tracking, access controls, and audit trails, all of which contribute to the security and integrity of data.

Enhancing Data Analytics with Natural Language Processing Services

In today’s data-driven world, businesses are increasingly turning to advanced analytics techniques to extract deeper insights from their data. One such technique is natural language processing (NLP), a branch of artificial intelligence that enables computers to understand, interpret, and generate human language.

SG Analytics offers cutting-edge natural language processing services as part of its data engineering portfolio. By integrating NLP into data pipelines, SG Analytics helps businesses analyze unstructured data, such as text, social media posts, and customer reviews, to uncover hidden patterns and trends. This capability is particularly valuable in industries like healthcare, finance, and retail, where understanding customer sentiment and behavior is crucial for success.

NLP services can be used to automate various tasks, such as sentiment analysis, topic modeling, and entity recognition. For example, a retail business can use NLP to analyze customer feedback and identify common complaints, allowing them to address issues proactively. Similarly, a financial institution can use NLP to analyze market trends and predict future movements, enabling them to make informed investment decisions.

By incorporating NLP into their data engineering services, SG Analytics empowers businesses to go beyond traditional data analysis and unlock the full potential of their data. Whether it’s extracting insights from vast amounts of text data or automating complex tasks, NLP services provide businesses with a competitive edge in the market.

Driving Business Success with Data Engineering

The ultimate goal of data engineering is to drive business success by enabling organizations to make data-driven decisions. SG Analytics’ data engineering services provide businesses with the tools and capabilities they need to achieve this goal. By building robust data infrastructure, ensuring data quality and governance, and leveraging advanced analytics techniques like NLP, SG Analytics helps businesses stay ahead of the competition.

In a rapidly evolving business landscape, the ability to harness the power of data is a key differentiator. With SG Analytics’ data engineering services, businesses can unlock new opportunities, optimize their operations, and achieve sustainable growth. Whether you’re a small startup or a large enterprise, SG Analytics has the expertise and experience to help you navigate the complexities of data engineering and achieve your business objectives.

[solardrake]

Iv'e been compelled lately to ingest an ungodly amount of Halo wiki articles, and I've like Autism latched on the Prometheans, which are insect-like Forerunner enemies that don't really get much love in lore. SO I WANTED TO WRITE SOME OF MY OWN!

So, is there a scenario in which Promethean Knights could gain Agency over their existence as formerly human ancilla? Let's discuss some possibilities.

The original Promethean Knights were a highly revered sect of Warrior-Servants in the Forerunner ecumene. Their numbers were limited however, and so the Didact supplemented their ranks by "Conscripting" humans on Omega-Halo and transformed their minds into like those of simulacrum via the composer. Appalled by his actions, the Librarian sealed the Didact away on Shield World Requiem, where he and the Prometheans lay dormant for the next hundred-thousand years. While considered Sentient, Knights have no free-will, and are bound in allegiance to either the Ur-Didact himself or the Remnant of the Librarian.

Well, they're both dead! So who currently has control of them? In the Timeline of Halo 5, Cortana gains control of the Promethean ranks after outsmarting the Warden Eternal and convincing him to aid her in her conquests against Humanity. My first thought is that since Cortana has access to the domain, the Librarian Remnant, and has examined a Knight's data core first hand, that she would maybe feel some kind of sympathy for Knights, right? After all, she herself was created from a flash clone of Doctor Halsey's brain, a process that is similar enough in result. She ALSO saw humans get "composed" in REAL time right in front of her!

Well... one would maybe think that she would feel that way, but judging by her actions in Halo 5, that (poorly written) version of her would rather have their sworn allegiance as her warriors, just as the Didact did. No freedom for our Beetle-like friends there :[

Okay, so what if Cortana and Warden eternal are out of the picture? Warden Eternal's whereabouts are unknown to us, though there are traces of evidence online that he "Died while attacking a UNSC ship", basically written out of existence. Along with Cortana dying during the events of Zeta Halo... That leaves no one to look after the Promethean forces on Genesis! If you recall, Genesis is a Forerunner shield world, in which its greatest merit is the Domain. This is significant, but in order to understand why it's significant, we have to explore what the Domain really is. 

The Domain isn't some Library, it's an "esoteric quantum information repository" that's said to have its own feelings and intentions, though mysterious in nature. It wishes to correct you when it sees you repeating a mistake that someone has made before, and its recollection of events and memories changes with every new generation that accesses it.

"There was a mysterious quality to the Domain; despite its ubiquity in Forerunner culture, its exact nature or origin remained largely nebulous even to Forerunners. Due to these abstruse aspects, the Forerunners treated the Domain as something mystical and transcendent; it was regarded with reverence and connected to the Forerunners' religious beliefs." -Halopedia Article on the Domain

Perhaps the most significant detail about the Domain is its ability to bestow immortality onto any ancilla that touches it. It also was able to give Cortana a physical form, which further enhances our estimates of its abilities. It has also been said by the Didact that the Domain wants, needs to spread knowledge to others. While it does operate on a set of self-imposed rules, it may decide to violate them on occasion. 

The Prometheans were abandoned on Genesis, with no authority figure to look up to, no directive or purpose of existing other than to be discovered by the next tyrant to use them to bolster ranks until their numbers completely erode in battle, tossed aside as tools of war. It was the forerunners that altered their destiny, to strip them of all free-thinking and humanity to fight endless wars among the stars for eons to come. The domain knows this, it must know this after communing with warlords that so desperately wanted to win this war against the flood, feeling their desperation and knowing that these great minds would do anything to save the galaxy from what they inadvertently wrought. The domain knows of the innumerable crimes committed by the Forerunners to achieve this goal. It knows that most of it was in vain. 

Would it feel remorse? Would the domain pity these forgotten soldiers, and wish upon them Agency to dictate the path of their destiny from hereon? Or would they face dormancy, nothing more than a collection of forgotten constructs of an ancient civilization, a civilization that cared little of the pursuits and hopes that each soul that inhabits a jagged metal shell may have had before something bigger than themselves transformed them into something less than deserving of love. 

But they are on Genesis. Genesis, a word which means the formation of something new. Genesis, nonetheless named after the Book of Genesis where the Hebrew God created the heavens and the earth...and people, capable of free thought and motivations that were internal, rather than directed. On Genesis, the Domain would give this gift to the Prometheans as reparation after a hundred thousand years of torment. They could never be human again, not quite, but they could have something close to it. 

[datavalleyai]

Azure Data Engineering Tools For Data Engineers

Azure is a cloud computing platform provided by Microsoft, which presents an extensive array of data engineering tools. These tools serve to assist data engineers in constructing and upholding data systems that possess the qualities of scalability, reliability, and security. Moreover, Azure data engineering tools facilitate the creation and management of data systems that cater to the unique requirements of an organization.

In this article, we will explore nine key Azure data engineering tools that should be in every data engineer’s toolkit. Whether you’re a beginner in data engineering or aiming to enhance your skills, these Azure tools are crucial for your career development.

Microsoft Azure Databricks

Azure Databricks is a managed version of Databricks, a popular data analytics and machine learning platform. It offers one-click installation, faster workflows, and collaborative workspaces for data scientists and engineers. Azure Databricks seamlessly integrates with Azure’s computation and storage resources, making it an excellent choice for collaborative data projects.

Microsoft Azure Data Factory

Microsoft Azure Data Factory (ADF) is a fully-managed, serverless data integration tool designed to handle data at scale. It enables data engineers to acquire, analyze, and process large volumes of data efficiently. ADF supports various use cases, including data engineering, operational data integration, analytics, and data warehousing.

Microsoft Azure Stream Analytics

Azure Stream Analytics is a real-time, complex event-processing engine designed to analyze and process large volumes of fast-streaming data from various sources. It is a critical tool for data engineers dealing with real-time data analysis and processing.

Microsoft Azure Data Lake Storage

Azure Data Lake Storage provides a scalable and secure data lake solution for data scientists, developers, and analysts. It allows organizations to store data of any type and size while supporting low-latency workloads. Data engineers can take advantage of this infrastructure to build and maintain data pipelines. Azure Data Lake Storage also offers enterprise-grade security features for data collaboration.

Microsoft Azure Synapse Analytics

Azure Synapse Analytics is an integrated platform solution that combines data warehousing, data connectors, ETL pipelines, analytics tools, big data scalability, and visualization capabilities. Data engineers can efficiently process data for warehousing and analytics using Synapse Pipelines’ ETL and data integration capabilities.

Microsoft Azure Cosmos DB

Azure Cosmos DB is a fully managed and server-less distributed database service that supports multiple data models, including PostgreSQL, MongoDB, and Apache Cassandra. It offers automatic and immediate scalability, single-digit millisecond reads and writes, and high availability for NoSQL data. Azure Cosmos DB is a versatile tool for data engineers looking to develop high-performance applications.

Microsoft Azure SQL Database

Azure SQL Database is a fully managed and continually updated relational database service in the cloud. It offers native support for services like Azure Functions and Azure App Service, simplifying application development. Data engineers can use Azure SQL Database to handle real-time data ingestion tasks efficiently.

Microsoft Azure MariaDB

Azure Database for MariaDB provides seamless integration with Azure Web Apps and supports popular open-source frameworks and languages like WordPress and Drupal. It offers built-in monitoring, security, automatic backups, and patching at no additional cost.

Microsoft Azure PostgreSQL Database

Azure PostgreSQL Database is a fully managed open-source database service designed to emphasize application innovation rather than database management. It supports various open-source frameworks and languages and offers superior security, performance optimization through AI, and high uptime guarantees.

Whether you’re a novice data engineer or an experienced professional, mastering these Azure data engineering tools is essential for advancing your career in the data-driven world. As technology evolves and data continues to grow, data engineers with expertise in Azure tools are in high demand. Start your journey to becoming a proficient data engineer with these powerful Azure tools and resources.

Unlock the full potential of your data engineering career with Datavalley. As you start your journey to becoming a skilled data engineer, it’s essential to equip yourself with the right tools and knowledge. The Azure data engineering tools we’ve explored in this article are your gateway to effectively managing and using data for impactful insights and decision-making.

To take your data engineering skills to the next level and gain practical, hands-on experience with these tools, we invite you to join the courses at Datavalley. Our comprehensive data engineering courses are designed to provide you with the expertise you need to excel in the dynamic field of data engineering. Whether you’re just starting or looking to advance your career, Datavalley’s courses offer a structured learning path and real-world projects that will set you on the path to success.

Course format:

Subject: Data Engineering Classes: 200 hours of live classes Lectures: 199 lectures Projects: Collaborative projects and mini projects for each module Level: All levels Scholarship: Up to 70% scholarship on this course Interactive activities: labs, quizzes, scenario walk-throughs Placement Assistance: Resume preparation, soft skills training, interview preparation

Subject: DevOps Classes: 180+ hours of live classes Lectures: 300 lectures Projects: Collaborative projects and mini projects for each module Level: All levels Scholarship: Up to 67% scholarship on this course Interactive activities: labs, quizzes, scenario walk-throughs Placement Assistance: Resume preparation, soft skills training, interview preparation

For more details on the Data Engineering courses, visit Datavalley’s official website.

[haripriya2002]

My Journey with Azure IoT Hub: Connecting and Managing IoT Devices at Scale

The Internet of Things (IoT), which enables seamless connectivity and automation across numerous industries, has completely changed the way we engage with technology. I was curious to learn more about the Internet of Things and its possible uses as an aspiring IoT enthusiast. My experience using Azure IoT Hub, Microsoft’s cloud-based IoT platform, and how it assisted me in connecting and managing IoT devices at scale are both discussed in this blog.

Getting Started with Azure IoT Hub

To embark on my IoT journey, I began by understanding the fundamentals of Azure IoT Hub. Azure IoT Hub is a fully managed service that acts as a central hub for bi-directional communication between IoT devices and the cloud. It provides secure, reliable, and scalable connectivity for IoT solutions. Setting up an Azure IoT Hub was my first step. While the process was relatively straightforward, I encountered a few challenges along the way.

Connecting IoT Devices

Once Azure IoT Hub was set up, I delved into the world of IoT devices. I worked with various types of IoT devices, ranging from simple sensors to complex industrial machines. Connecting these devices to Azure IoT Hub required the implementation of device-specific protocols such as MQTT or HTTP. Additionally, I focused on securing device connections and data transmission by utilizing security features provided by Azure IoT Hub.

Real-world examples of IoT devices connected to Azure IoT Hub are aplenty. For instance, in the healthcare industry, wearable devices can transmit patient vitals to Azure IoT Hub, allowing healthcare providers to monitor and respond to critical situations promptly. In smart homes, IoT devices such as thermostats and security cameras can be connected to Azure IoT Hub, enabling remote control and monitoring capabilities.

Managing IoT Devices at Scale

As my IoT project grew, I encountered the need to scale up the number of connected devices. Azure IoT Hub offered robust device management features that simplified the process of managing a large fleet of devices. I could remotely monitor the health, status, and firmware version of each device, enabling efficient troubleshooting and maintenance. Implementing best practices for device management, such as grouping devices based on location or functionality, enhanced the overall operational efficiency of my IoT solution.

Data Ingestion and Processing

Data collected from IoT devices is a valuable asset that can drive actionable insights and informed decision-making. Azure IoT Hub facilitated the ingestion and routing of data to Azure services for further processing and analysis. I had the opportunity to work with Azure Stream Analytics and Azure Functions, which enabled real-time data processing, transformation, and visualization. Leveraging these services allowed me to unlock the true potential of IoT data and derive meaningful insights.

Security and Compliance

Any IoT solution must prioritize security. Azure IoT Hub provided robust security features that ensured end-to-end protection of IoT deployments. These features included device authentication, message encryption, and integration with Azure Active Directory for access control. Additionally, Azure IoT Hub helped me meet compliance and regulatory requirements by providing built-in support for industry standards such as ISO 27001, HIPAA, and GDPR. Throughout my journey, I learned valuable lessons and implemented best practices for securing IoT solutions.

Scalability and Performance

Scaling an IoT solution to handle thousands or millions of devices is a complex task. Azure IoT Hub offered scalability features that allowed me to effortlessly handle large-scale IoT deployments. With Azure IoT Hub’s device-to-cloud messaging capabilities, I could reliably transmit messages to and from a massive number of devices. Moreover, I gained insights into optimizing IoT solutions for performance by considering factors such as message size, frequency, and device capabilities.

Real-World Use Cases

To understand the versatility of Azure IoT Hub, it is crucial to explore real-world use cases. In the manufacturing industry, Azure IoT Hub can be leveraged to connect and monitor machines on the factory floor, ensuring optimal performance and predictive maintenance. In the agriculture sector, IoT devices connected to Azure IoT Hub can collect data on soil moisture levels, temperature, and humidity, enabling farmers to make data-driven decisions for irrigation and crop management. These use cases highlight the valuable role that Azure IoT Hub plays in various domains and industries.

Future of IoT and Azure IoT Hub

The future of IoT is promising, with emerging trends shaping the landscape. As IoT continues to evolve, Azure IoT Hub will play a crucial role in enabling seamless connectivity, advanced analytics, and artificial intelligence capabilities. Integration with other Azure services and continuous updates from Microsoft ensure that Azure IoT Hub remains at the forefront of IoT innovation. The possibilities for IoT applications are limitless, and Azure IoT Hub will continue to empower developers and organizations to build robust and scalable IoT solutions.

Throughout my journey with Azure IoT Hub, I gained valuable insights and experiences. Azure IoT Hub simplified the process of connecting and managing IoT devices, providing a reliable and scalable platform. The seamless integration with other Azure services allowed me to unlock the full potential of IoT data. Moreover, the security and compliance features provided peace of mind, ensuring that my IoT solution was protected from threats. Overall, Azure IoT Hub has been instrumental in my IoT journey, contributing to enhanced efficiency and productivity.

Recommendations and Tips

For those interested in starting their own IoT journey with Azure IoT Hub, I offer the following recommendations and tips:

Begin with a clear understanding of your IoT use case and requirements.

Familiarize yourself with the documentation and resources provided by Microsoft to gain a solid foundation.

Start small and gradually scale your IoT solution as needed.

Take advantage of the device management and security features offered by Azure IoT Hub.

Leverage other Azure services such as Azure Stream Analytics and Azure Functions to derive meaningful insights from IoT data.

Stay updated on emerging trends and best practices in the IoT space.

To deepen your knowledge of IoT and Azure IoT Hub, I recommend exploring Microsoft’s official documentation, participating in the ACTE Technologies Microsoft Azure training, and attending IoT-focused conferences and events.

Azure IoT Hub has proven to be a powerful and comprehensive platform for connecting and managing IoT devices at scale. Throughout my journey, I witnessed the transformative potential of IoT solutions and the crucial role played by Azure IoT Hub in enabling seamless connectivity, advanced analytics, and robust security. As IoT continues to evolve, Azure IoT Hub will undoubtedly remain at the forefront of IoT innovation, empowering organizations to build scalable and efficient IoT solutions. I encourage readers to embark on their own IoT journeys, leveraging the capabilities of Azure IoT Hub to unlock the full potential of IoT. Join me in embracing the future of IoT and revolutionizing industries through connected devices. Please leave your comments, stories, and inquiries in the space provided below. Let’s continue the conversation and explore the endless possibilities of IoT together.

[keplercryptids]

Hi I rlly appreciated ur post about migraines. I don't have this condition (to my knowledge) but my younger sister has suffered from chronic migraines for yeaaaars and has found little to nothing that actually helps. I'd love to hear (read) your insight on what treatment options worked for you, as well as how to find a specialist that will take you seriously. Thanks!

if your sister hasn't already done this, they should go to their regular doctor, say "i have chronic migraines and i'd like a referral to a neurologist," and try to get an appointment with one who has a migraine specialty. though it's not a guaranteed better experience, my experience at a neurologist has been night and day when compared with a general or family doctor. they actually know about migraines, for starters, lol.

treatment that works for me is the following!

daily supplements, specifically: 500mg magnesium, 100mg CoQ10, + 100mg B2. if your sister is anything like me, they may be reluctant to try woo-woo stuff that gets recommended all the time lmao, but magnesium actually works. as in, it's been studied and there’s actual real data to support it. also, side effects are minimal and it's relatively cheap, so it has a low buy-in. IMO, for these reasons it's worth trying for anyone who gets regular headaches.

ingesting caffeine daily. caffeine is great. (if your sis doesn't already ingest caffeine you probably don't wanna start, but. in a pinch it can cut down on pain.)

imitrex (sumatriptan), prescribed, 25mg taken as needed. this was actually prescribed by my regular doctor a decade ago and literally saved my life. if your sister hasn't tried any meds of the triptan family yet, they should!

when imitrex fails, sometimes i take excedrin migraine but obviously this (and other NSAIDs) shouldn't be a primary rescue method. (ask me about my stomach ulcer.) it also usually doesn't work for me, so there's that too.

staying hydrated, staying fed. <- my number one migraine triggers are "just not taking care of myself" and it's a pain but. water!!! and getting enough sleep, etc etc etc.

botox. i get botox injections every three months at my neurologist. this has cut my number of migraines in half, and is supposed to get better and better over time. it's great and i'm kissing botox with tongue. there's a barrier to entry for insurance to cover it, though: you need to have at least 15 migraines a month, and they usually require that you've tried and failed three classes of migraine preventative before they'll cover it. but. if there's any way for your sister to get on this track, i recommend trying it! botox is another treatment with very minimal/low-risk side effects.

that's a lot of info and i don't know how much of it will be useful. really, asking for a referral to a neurologist and trying out some preventatives should be step one, if your sister hasn't already done that. i have a lot of thoughts about the migraine preventatives that get pushed on people before others (as in, i've reacted badly to ALL of them, they can be really rough side-effects-wise, and i hate that there are so many hoops we have to jump through) so if you have specific questions about any of that, lmk. i hope your sister finds some relief soon!

ETA: a book i recommend a lot is The Keeler Migraine Method by Robert Cowan. it has a lot of great info!

[rajaniesh]

Unveiling the Power of Delta Lake in Microsoft Fabric

Discover how Microsoft Fabric and Delta Lake can revolutionize your data management and analytics. Learn to optimize data ingestion with Spark and unlock the full potential of your data for smarter decision-making.

In today’s digital era, data is the new gold. Companies are constantly searching for ways to efficiently manage and analyze vast amounts of information to drive decision-making and innovation. However, with the growing volume and variety of data, traditional data processing methods often fall short. This is where Microsoft Fabric, Apache Spark and Delta Lake come into play. These powerful…

[see-fee]

psst ai art is not real art and hurts artists

Real life tends to be far more nuanced than sweeping statements, emotional rhetoric, or conveniently fuzzy definitions. “Artists” are not a monolithic entity and neither are companies. There are different activities with different economics.

I’ll preface the rest of my post with sharing my own background, for personal context:

👩‍🎨 I am an artist. I went to/graduated from an arts college and learned traditional art-making (sculpture to silkscreen printing), and my specialism was in communication design (using the gamut of requisite software like Adobe Illustrator, InDesign, Photoshop, Lightroom, Dreamweaver etc). Many of my oldest friends are career artists—two of whom served as official witnesses to my marriage. Friends of friends have shown at the Venice Biennale, stuff like that. Many are in fields like games, animation, VFX, 3D etc. In the formative years of my life, I’ve worked & collaborated in a wide range of creative endeavours and pursuits. I freelanced under a business which I co-created, ran commercial/for-profit creative events for local musicians & artists, did photography (both digital & analog film, some of which I hand-processed in a darkroom), did some modelling, styling, appeared in student films… the list goes on. I’ve also dabbled with learning 3D using Blender, a free, open source software (note: Blender is an important example I’ll come back to, below). 💸 I am a (budding) patron of the arts. On the other side of the equation, I sometimes buy art: small things like buying friends’ work. I’m also currently holding (very very tiny) stakes in “real” art—as in, actual fine art: a few pieces by Basquiat, Yayoi Kusama, Joan Mitchell. 👩‍💻 I am a software designer & engineer. I spent about an equal number of years in tech: took some time to re-skill in a childhood passion and dive into a new field, then went off to work at small startups (not “big tech”), to design and write software every day.

So I’m quite happy to talk art, tech, and the intersection. I’m keeping tabs on the debate around the legal questions and the lawsuits.

Can an image be stolen if only used in training input, and is never reproduced as output? Can a company be vicariously liable for user-generated content? Legally, style isn’t copyrightable, and for good reason. Copyright law is not one-size-fits-all. Claims vary widely per case.

Flaws in the Anderson vs Stability AI case, aka “stolen images” argument

Read this great simple breakdown by a copyright lawyer that covers reproduction vs. derivative rights, model inputs and outputs, derivative works, style, and vicarious liability https://copyrightlately.com/artists-copyright-infringement-lawsuit-ai-art-tools/

“Getty’s new complaint is much better than the overreaching class action lawsuit I wrote about last month. The focus is where it should be: the input stage ingestion of copyrighted images to train the data. This will be a fascinating fair use battle.”

“Surprisingly, plaintiffs’ complaint doesn’t focus much on whether making intermediate stage copies during the training process violates their exclusive reproduction rights under the Copyright Act. Given that the training images aren’t stored in the software itself, the initial scraping is really the only reproduction that’s taken place.”

“Nor does the complaint allege that any output images are infringing reproductions of any of the plaintiffs’ works. Indeed, plaintiffs concede that none of the images provided in response to a particular text prompt “is likely to be a close match for any specific image in the training data.””

“Instead, the lawsuit is premised upon a much more sweeping and bold assertion—namely that every image that’s output by these AI tools is necessarily an unlawful and infringing “derivative work” based on the billions of copyrighted images used to train the models.”

“There’s another, more fundamental problem with plaintiffs’ argument. If every output image generated by AI tools is necessarily an infringing derivative work merely because it reflects what the tool has learned from examining existing artworks, what might that say about works generated by the plaintiffs themselves? Works of innumerable potential class members could reflect, in the same attenuated manner, preexisting artworks that the artists studied as they learned their skill.”

My thoughts on generative AI: how anti-AI rhetoric helps Big Tech (and harms open-source/independents), how there’s no such thing as “real art”

The AI landscape is still evolving and being negotiated, but fear-mongering and tighter regulations seldom serve anyone’s favour besides big companies. It’s the oldest trick in the book to preserve monopoly and all big corps in major industries have done this. Get a sense of the issue in this article: https://www.forbes.com/sites/hessiejones/2023/04/19/amid-growing-call-to-pause-ai-research-laion-petitions-governments-to-keep-agi-research-open-active-and-responsible/?sh=34b78bae62e3

“AI field is progressing at unprecedented speed; however, training state-of-art AI models such as GPT-4 requires large compute resources, not currently available to researchers in academia and open-source communities; the ‘compute gap’ keeps widening, causing the concentration of AI power at a few large companies.”

“Governments and businesses will become completely dependent on the technologies coming from the largest companies who have invested millions, and by definition have the highest objective to profit from it.”

“The “AGI Doomer” fear-mongering narrative distracts from actual dangers, implicitly advocating for centralized control and power consolidation.”

Regulation & lawsuits benefit massive monopolies: Adobe (which owns Adobe Stock), Microsoft, Google, Facebook et al. Fighting lawsuits, licensing with stock image companies for good PR—like OpenAI (which Microsoft invested $10billion in) and Shutterstock—is a cost which they have ample resources to pay, to protect their monopoly after all that massive investment in ML/AI R&D. The rewards outweigh the risks. They don't really care about ethics, only when it annihilates competition. Regulatory capture means these mega-corporations will continue to dominate tech, and nobody else can compete. Do you know what happens if only Big Tech controls AI? It ain’t gonna be pretty.

Open-source is the best alternative to Big Tech. Pro-corporation regulation hurts open-source. Which hurts indie creators/studios, who will find themselves increasingly shackled to Big Tech’s expensive software. Do you know who develops & releases the LAION dataset? An open-source research org. https://laion.ai/about/ Independent non-profit research orgs & developers cannot afford harsh anti-competition regulatory rigmarole, or multi-million dollar lawsuits, or being deprived of training data, which is exactly what Big Tech wants. Free professional industry-standard software like Blender is open-source, copyleft GNU General Public License. Do you know how many professional 3D artists and businesses rely on it? (Now it’s development fund is backed by industry behemoths.) The consequences of this kind of specious “protest” masquerading as social justice will ultimately screw over these “hurt artists” even harder. It’s shooting the foot. Monkey’s paw. Be very careful what you wish for.

TANSTAAFL: Visual tradespeople have no qualms using tons of imagery/content floating freely around the web to develop their own for-profit output—nobody’s sweating over source provenance or licensing whenever they whip out Google Images or Pinterest. Nobody decries how everything is reposted/reblogged to death when it benefits them. Do you know how Google, a for-profit company, and its massively profitable search product works? “Engines like the ones built by OpenAI ingest giant data sets, which they use to train software that can make recommendations or even generate code, art, or text. In many cases, the engines are scouring the web for these data sets, the same way Google’s search crawlers do, so they can learn what’s on a webpage and catalog it for search queries.”[1] The Authors Guild v. Google case found that Google’s wholesale scanning of millions of books to create its Google Book Search tool served a transformative purpose that qualified as fair use. Do you still use Google products? No man is an island. Free online access at your fingertips to a vast trove of humanity’s information cuts both ways. I’d like to see anyone completely forgo these technologies & services in the name of “ethics”. (Also. Remember that other hyped new tech that’s all about provenance, where some foot-shooting “artists” rejected it and self-excluded/self-harmed, while savvy others like Burnt Toast seized the opportunity and cashed in.)

There is no such thing as “real art.” The definition of “art” is far from a universal, permanent concept; it has always been challenged (Duchamp, Warhol, Kruger, Banksy, et al) and will continue to be. It is not defined by the degree of manual labour involved. A literal banana duct-taped to a wall can be art. (The guy who ate it claimed “performance art”). Nobody in Van Gogh’s lifetime considered his work to be “real art” (whatever that means). He died penniless, destitute, believing himself to be an artistic failure. He wasn’t the first nor last. If a soi-disant “artist” makes “art” and nobody values it enough to buy/commission it, is it even art? If Martin Shkreli buys Wu Tang Clan’s “Once Upon a Time in Shaolin” for USD$2 million, is it more art than their other albums? Value can be ascribed or lost at a moment’s notice, by pretty arbitrary vicissitudes. Today’s trash is tomorrow’s treasure—and vice versa. Whose opinion matters, and when? The artist’s? The patron’s? The public’s? In the present? Or in hindsight?

As for “artists” in the sense of salaried/freelance gig economy trade workers (illustrators, animators, concept artists, game devs, et al), they’ll have to adapt to the new tech and tools like everyone else, to remain competitive. Some are happy that AI tools have improved their workflow. Some were struggling to get paid for heavily commoditised, internationally arbitraged-to-pennies work long before AI, in dehumanising digital sweatshop conditions (dime-a-dozen hands-for-hire who struggled at marketing & distributing their own brand & content). AI is merely a tool. Methods and tools come and go, inefficient ones die off, niches get eroded. Over-specialisation is an evolutionary risk. The existence of AI tooling does not preclude anyone from succeeding as visual creators or Christie’s-league art-world artists, either. Beeple uses AI. The market is information about what other humans want and need, how much it’s worth, and who else is supplying the demand. AI will get “priced in.” To adapt and evolve is to live. There are much greater crises we're facing as a species.

I label my image-making posts as #my art, relative to #my fic, mainly for navigation purposes within my blog. Denoting a subset of my pieces with #ai is already generous on this hellsite entropy cesspool. Anti-AI rhetoric will probably drive some people to conceal the fact that they use AI. I like to be transparent, but not everyone does. Also, if you can’t tell, does it matter? https://youtu.be/1mR9hdy6Qgw

I can illustrate, up to a point, but honing the skill of hand-crafted image-making isn’t worth my remaining time alive. The effort-to-output ratio is too high. Ain’t nobody got time fo dat. I want to tell stories and bring my visions to life, and so do many others. It’s a creative enabler. The democratisation of image-making means that many more people, like the disabled, or those who didn’t have the means or opportunity to invest heavily in traditional skills, can now manifest their visions and unleash their imaginations. Visual media becomes a language more people can wield, and that is a good thing.

Where I’m personally concerned, AI tools don’t replace anything except some of my own manual labour. I am incredibly unlikely to commission a visual piece from another creator—most fanart styles or representations of the pair just don’t resonate with me that much. (I did once try to buy C/Fe merch from an artist, but it was no longer available.) I don’t currently hawk my own visual wares for monetary profit (tips are nice though). No scenario exists which involves me + AI tools somehow stealing some poor artist’s lunch by creating my tchotchkes. No overlap regarding commercial interests. No zero-sum situation. Even if there was, and I was competing in the same market, my work would first need to qualify as a copy. My blog and content is for personal purposes and doesn’t financially deprive anyone. I’ll keep creating with any tool I find useful.

AI art allegedly not being “real art” (which means nothing) because it's perceived as zero-effort? Not always the case. It may not be a deterministic process but some creators like myself still add a ton of human guidance and input—my own personal taste, judgement, labour. Most of my generation pieces require many steps of in-painting, manual hand tweaking, feeding it back as img2img, in a back and forth duet. If you've actually used any of these tools yourself with a specific vision in mind, you’ll know that it never gives you exactly what you want—not on the first try, nor even the hundredth… unless you're happy with something random. (Which some people are. To each their own.) That element of chance, of not having full control, just makes it a different beast. To achieve desired results with AI, you need to learn, research, experiment, iterate, combine, refine—like any other creative process.

If you upload content to the web (aka “release out in the wild”), then you must, by practical necessity, assume it’s already “stolen” in the sense that whatever happens to it afterwards is no longer under your control. Again, do you know how Google, a for-profit company, and its massively profitable search product works? Plagiarism has always been possible. Mass data scraping or AI hardly changed this fact. Counterfeits or bootlegs didn’t arise with the web.

As per blog title and Asimov's last major interview about AI, I’m optimistic about AI overall. The ride may be bumpy for some now, but general progress often comes with short-term fallout. This FUD about R’s feels like The Caves of Steel, like Lije at the beginning [insert his closing rant about humans not having to fear robots]. Computers are good at some things, we’re good at others. They free us up from incidental tedium, so we can do the things we actually want to do. Like shipping these characters and telling stories and making pretty pictures for personal consumption and pleasure, in my case. Most individuals aren’t that unique/important until combined into a statistical aggregate of humanity, and the tools trained on all of humanity’s data will empower us to go even further as a species.

You know what really hurts people? The pandemic which nobody cares about; which has a significant, harmful impact on my body/life and millions of others’. That cost me a permanent expensive lifestyle shift and innumerable sacrifices, that led me to walk away from my source of income and pack up my existence to move halfway across the planet. If you are not zero-coviding—the probability of which is practically nil—I’m gonna have to discount your views on “hurt”, ethics, or what we owe to each other.

We are a non-profit organization with members from all over the world, aiming to make large-scale machine learning models, datasets and related code available to the general public. OUR BELIEFS: We believe that machine learning research and its applications have the potential to have huge positive impacts on our world and therefore should be democratized. PRINCIPLE GOALS: Releasing open datasets, code and machine learning models. We want to teach the basics of large-scale ML research and data management. By making models, datasets and code reusable without the need to train from scratch all the time, we want to promote an efficient use of energy and computing ressources to face the challenges of climate change. FUNDING: Funded by donations and public research grants, our aim is to open all cornerstone results from such an important field as large-scale machine learning to all interested communities.

The “AGI Doomer” fear-mongering narrative distracts from actual dangers, implicitly advocating for centralized control and power consolidation.”

[skilliq]

Artificial Intelligence & Machine Learning’s Future Contribution

It is sometimes included together with Deep Learning, a recent branch of machine learning research. However, given the cutting-edge research taken on in the field of Deep Learning in particular, it is crucial for all AI enthusiasts to comprehend and stay up to date with the goal of bringing Machine Learning closer to one of its original goals Artificial Intelligence.

The main applications of data in the world we live in today are artificial intelligence and machine learning. Due to this, machine learning is one of the most in-demand fields today, and there is a significant demand for people in the field with the necessary knowledge, training, and practical experience. Great Lakes Post Graduate Program in Machine Learning was created with the express purpose of educating professionals in technologies and techniques used in the real world of business.

What is Artificial Intelligence & Machine Learning?

Artificial Intelligence, which includes replicating cognitive processes like perception, learning, and trouble, is a broad term for systems and algorithms that can emulate human intelligence. Deep learning (DL) and machine learning are branches of AI.

Advanced web search engines, voice-activated personal assistants, self-driving cars, and recommendation systems like those used by Spotify and Netflix are some examples of practical uses of AI.

Artificial Intelligence:

The study of intelligent machines that behave like people is the focus of the computer science field known as artificial intelligence or AI. The process of building intelligent machines, often referred to as smart machines, is intended to help in decision-making, which is carefully examined using data that is readily available within an enterprise. It functions in a similar way to how people do when combining information and coming to logical conclusions. However, in this case, the choice was taken after carefully examining a lot of information.

Machine Learning Work:

A subfield of artificial intelligence known as “machine learning” enables computers to learn and grow without being explicitly programmed. Students who pursue courses in machine learning know how to build automatically adapting computer systems by fusing data mining algorithms models.

Why Study AI & ML?

It will be very helpful to combine artificial intelligence, machine learning, and deep learning since they add a lot of value to the present process and offer intelligent directions for people to follow. The top applications for artificial intelligence & machine learning available that are now in use and have shown to be more effective and accurate for career growth. Choosing AI & ML Training Programs in Gujarat can be more beneficial for anyone’s career development.

Benefits of AI & ML Courses

Along with AI, ML is the gasoline we need to power robots. We can use ML to power applications that are easily updated and changed to adapt to new surroundings and tasks — getting things done quickly and effectively.

Studying AI And Machine Learning Promises A Bright Career

Learning Helps You Make A Good Living

Artificial Intelligence And Machine Learning Are A Versatile Discipline

Artificial Intelligence And Machine Learning Is The Skill Of The Century

Capable Of Ingesting A Huge Amount Of Data

Helps In Times Of Disasters

Big Bright Career

The Skill of the Century

Artificial Intelligence & Machine Learning’s Future Contribution

The application of machine learning extends beyond the world of investments. Instead, it is growing in all industries, including banking and finance, IT, media & entertainment, gaming, and the auto sector. There are several sectors where academics are trying to revolutionize the world for the future because the reach of machine learning is so broad. Let’s go over them in more depth.

Robotics

One of the disciplines that consistently captures the attention of both researchers and the general public is robotics. George Devol created the first programmable robot in 1954, which he called Unimate. After that, Hanson Robotics produced Sophia, the first AI robot, in the twenty-first century. Artificial Intelligence and Machine Learning made it feasible for these inventions.

The Quantum Computer

The field of machine learning is still in its infancy. There are many improvements that may be made in this area. Quantum computing is one of many that will advance machine learning. It is a sort of computing that makes use of the entanglement and superposition mechanical properties of quantum mechanics. We can construct systems (quantum systems) that can exhibit several states simultaneously by leveraging the quantum phenomena of superposition. Entanglement, on the other hand, is the situation in which two dissimilar states can be referred to one another. It aids in expressing the relationship between a quantum system’s attributes.

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