#machine learning models

[public-cloud-computing]

Transform your analytics with this guide to implementing composable solutions in your organization. Step-by-step instructions for success.

[enterprise-cloud-services]

Master composable analytics with our step-by-step guide and drive innovation through modular, data-driven decision-making in your business.

[rubylogan15]

Transform your analytics with this guide to implementing composable solutions in your organization. Step-by-step instructions for success.

[internett1line]

Machine Learning as a Service (MLaaS): Revolutionizing Data-Driven Decision Making

As businesses continue to generate vast amounts of data, the ability to leverage insights from that data has become a critical competitive advantage. Machine Learning as a Service (MLaaS) is an innovative cloud-based solution that allows companies to implement machine learning (ML) without the need for specialized knowledge or infrastructure. By making powerful ML tools and models accessible…

[usaii]

Breathing Life into Machine Learning Models | USAII®

Want to power machine learning models like a pro? Read on to explore how ML models are deployed, maintained, and further. Understand how a certification can help!

Read more: https://shorturl.at/HCviV

machine learning model, machine learning systems architecture, Model deployment, machine learning system, machine learning operations (MLOps). ML algorithms, ML tools, Machine Learning certifications, ML course

[cymetrixsoftware]

Data Science and Analytics Consulting by Cymetrix

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[smbmatters]

The Power of Big Data: How Technology is Driving Decision-Making

In today's fast-paced world, big data has emerged as a transformative force, revolutionizing the way businesses and organizations operate. By harnessing the power of technology, big data is driving decision-making processes across various sectors, leading to more informed and effective outcomes. This article explores the impact of big data on decision-making and highlights the key technologies enabling this revolution.

Understanding Big Data

Big data refers to the vast volumes of structured and unstructured data generated every second by various sources, including social media, sensors, digital transactions, and more. This data is characterized by its volume, variety, velocity, and veracity, making traditional data processing methods inadequate. The advent of advanced technologies has enabled the collection, storage, and analysis of big data, unlocking valuable insights for decision-making.

The Role of Technology in Big Data

Several cutting-edge technologies play a crucial role in managing and analyzing big data. These technologies not only handle the massive data volumes but also extract meaningful patterns and trends that drive decision-making.

1. Cloud Computing

Cloud computing provides scalable and cost-effective solutions for storing and processing big data. With cloud platforms, businesses can access vast computing resources on-demand, enabling them to analyze large datasets without investing in expensive hardware. Cloud computing also facilitates real-time data processing, crucial for timely decision-making.

2. Artificial Intelligence (AI) and Machine Learning (ML)

AI and ML algorithms are pivotal in analyzing big data. These technologies can identify patterns, predict outcomes, and provide actionable insights. Machine learning models improve over time as they are exposed to more data, making them increasingly accurate and reliable for decision-making.

3. Internet of Things (IoT)

The IoT connects physical devices to the internet, generating continuous streams of data. This data, when analyzed, offers real-time insights into operations, customer behavior, and more. IoT devices help businesses make informed decisions by providing up-to-date information.

4. Data Analytics Platforms

Data analytics platforms like Hadoop, Spark, and Tableau enable the processing and visualization of big data. These tools provide businesses with the ability to analyze complex data sets, generate reports, and visualize trends, making it easier to derive insights and inform decisions.

Big Data in Business Decision-Making

The integration of big data in business processes has led to significant improvements in decision-making. Here are a few examples of how big data is transforming various sectors:

5. Healthcare

In healthcare, big data is used to predict disease outbreaks, personalize treatments, and improve patient care. By analyzing patient data, healthcare providers can make more accurate diagnoses and tailor treatments to individual needs, leading to better health outcomes.

6. Finance

In the financial sector, big data helps in fraud detection, risk management, and investment strategies. Financial institutions use data analytics to monitor transactions, detect anomalies, and make informed investment decisions, enhancing overall financial security and profitability.

7. Retail

Retailers leverage big data to understand customer preferences, optimize inventory, and improve the shopping experience. By analyzing sales data and customer feedback, retailers can tailor their offerings, ensure stock availability, and enhance customer satisfaction.

8. Manufacturing

Manufacturers use big data to streamline production processes, predict equipment failures, and improve supply chain efficiency. By analyzing data from sensors and machines, manufacturers can reduce downtime, increase productivity, and lower operational costs.

The Challenges of Big Data

While the benefits of big data are immense, there are also challenges that businesses must address to maximize its potential:

9. Data Privacy and Security

With the increasing volume of data, ensuring data privacy and security is paramount. Businesses must implement robust security measures to protect sensitive information and comply with regulations.

10. Data Quality

The accuracy and reliability of decisions depend on the quality of the data. Ensuring data accuracy, consistency, and completeness is crucial for making sound decisions based on big data.

11. Skilled Workforce

The effective use of big data requires a skilled workforce proficient in data analysis, AI, and machine learning. Investing in training and development is essential for businesses to leverage big data effectively.

Conclusion

Big data is undeniably a game-changer in the realm of decision-making. By leveraging advanced technologies such as cloud computing, AI, and IoT, businesses can unlock valuable insights, drive innovation, and make informed decisions that lead to success. However, addressing challenges related to data privacy, quality, and workforce skills is crucial to fully harness the power of big data. As technology continues to evolve, the impact of big data on decision-making will only grow, shaping the future of various industries and improving our lives in countless ways.

[metaficco]

Machine Learning Demystified: Types & Techniques | Metafic

Have a look at this infographic to know the basics of Machine Learning - from algorithms to datasets. Unravel the mysteries behind training, testing, and model refinement. Source: https://bit.ly/3ICw6hf

[river-taxbird]

AI hasn't improved in 18 months. It's likely that this is it. There is currently no evidence the capabilities of ChatGPT will ever improve. It's time for AI companies to put up or shut up.

I'm just re-iterating this excellent post from Ed Zitron, but it's not left my head since I read it and I want to share it. I'm also taking some talking points from Ed's other posts. So basically:

We keep hearing AI is going to get better and better, but these promises seem to be coming from a mix of companies engaging in wild speculation and lying.

Chatgpt, the industry leading large language model, has not materially improved in 18 months. For something that claims to be getting exponentially better, it sure is the same shit.

Hallucinations appear to be an inherent aspect of the technology. Since it's based on statistics and ai doesn't know anything, it can never know what is true. How could I possibly trust it to get any real work done if I can't rely on it's output? If I have to fact check everything it says I might as well do the work myself.

For "real" ai that does know what is true to exist, it would require us to discover new concepts in psychology, math, and computing, which open ai is not working on, and seemingly no other ai companies are either.

Open ai has already seemingly slurped up all the data from the open web already. Chatgpt 5 would take 5x more training data than chatgpt 4 to train. Where is this data coming from, exactly?

Since improvement appears to have ground to a halt, what if this is it? What if Chatgpt 4 is as good as LLMs can ever be? What use is it?

As Jim Covello, a leading semiconductor analyst at Goldman Sachs said (on page 10, and that's big finance so you know they only care about money): if tech companies are spending a trillion dollars to build up the infrastructure to support ai, what trillion dollar problem is it meant to solve? AI companies have a unique talent for burning venture capital and it's unclear if Open AI will be able to survive more than a few years unless everyone suddenly adopts it all at once. (Hey, didn't crypto and the metaverse also require spontaneous mass adoption to make sense?)

There is no problem that current ai is a solution to. Consumer tech is basically solved, normal people don't need more tech than a laptop and a smartphone. Big tech have run out of innovations, and they are desperately looking for the next thing to sell. It happened with the metaverse and it's happening again.

In summary:

Ai hasn't materially improved since the launch of Chatgpt4, which wasn't that big of an upgrade to 3.

There is currently no technological roadmap for ai to become better than it is. (As Jim Covello said on the Goldman Sachs report, the evolution of smartphones was openly planned years ahead of time.) The current problems are inherent to the current technology and nobody has indicated there is any way to solve them in the pipeline. We have likely reached the limits of what LLMs can do, and they still can't do much.

Don't believe AI companies when they say things are going to improve from where they are now before they provide evidence. It's time for the AI shills to put up, or shut up.

[webmethodology]

Understand the significance of libraries like scikit-learn, Fairlearn, and TensorFlow, and learn how these packages help to interpret machine learning algorithms. Also, Find out the advantages of Python libraries in ML.

[codersdev]

Artificial Intelligence Engineering: Building Intelligent Systems | Coders

Looking to build intelligent systems using Artificial Intelligence? Our AI engineering course provides you with the essential concepts and techniques to build intelligent systems that solve complex problems. From understanding the fundamentals of machine learning and neural networks to natural language processing and computer vision, our course covers everything you need to know. Join Coders today and learn how to build intelligent systems that can revolutionize your industry. Read More-: artificial intelligence engineering

[public-cloud-computing]

Master composable analytics with our step-by-step guide and drive innovation through modular, data-driven decision-making in your business.

[enterprise-cloud-services]

Empower your organization with flexible, composable analytics. Learn how to implement it using our comprehensive step-by-step guide.

[rubylogan15]

Master composable analytics with our step-by-step guide and drive innovation through modular, data-driven decision-making in your business.

[mostlysignssomeportents]

How plausible sentence generators are changing the bullshit wars

This Friday (September 8) at 10hPT/17hUK, I'm livestreaming "How To Dismantle the Internet" with Intelligence Squared.

On September 12 at 7pm, I'll be at Toronto's Another Story Bookshop with my new book The Internet Con: How to Seize the Means of Computation.

In my latest Locus Magazine column, "Plausible Sentence Generators," I describe how I unwittingly came to use – and even be impressed by – an AI chatbot – and what this means for a specialized, highly salient form of writing, namely, "bullshit":

https://locusmag.com/2023/09/commentary-by-cory-doctorow-plausible-sentence-generators/

Here's what happened: I got stranded at JFK due to heavy weather and an air-traffic control tower fire that locked down every westbound flight on the east coast. The American Airlines agent told me to try going standby the next morning, and advised that if I booked a hotel and saved my taxi receipts, I would get reimbursed when I got home to LA.

But when I got home, the airline's reps told me they would absolutely not reimburse me, that this was their policy, and they didn't care that their representative had promised they'd make me whole. This was so frustrating that I decided to take the airline to small claims court: I'm no lawyer, but I know that a contract takes place when an offer is made and accepted, and so I had a contract, and AA was violating it, and stiffing me for over $400.

The problem was that I didn't know anything about filing a small claim. I've been ripped off by lots of large American businesses, but none had pissed me off enough to sue – until American broke its contract with me.

So I googled it. I found a website that gave step-by-step instructions, starting with sending a "final demand" letter to the airline's business office. They offered to help me write the letter, and so I clicked and I typed and I wrote a pretty stern legal letter.

Now, I'm not a lawyer, but I have worked for a campaigning law-firm for over 20 years, and I've spent the same amount of time writing about the sins of the rich and powerful. I've seen a lot of threats, both those received by our clients and sent to me.

I've been threatened by everyone from Gwyneth Paltrow to Ralph Lauren to the Sacklers. I've been threatened by lawyers representing the billionaire who owned NSOG roup, the notoroious cyber arms-dealer. I even got a series of vicious, baseless threats from lawyers representing LAX's private terminal.

So I know a thing or two about writing a legal threat! I gave it a good effort and then submitted the form, and got a message asking me to wait for a minute or two. A couple minutes later, the form returned a new version of my letter, expanded and augmented. Now, my letter was a little scary – but this version was bowel-looseningly terrifying.

I had unwittingly used a chatbot. The website had fed my letter to a Large Language Model, likely ChatGPT, with a prompt like, "Make this into an aggressive, bullying legal threat." The chatbot obliged.

I don't think much of LLMs. After you get past the initial party trick of getting something like, "instructions for removing a grilled-cheese sandwich from a VCR in the style of the King James Bible," the novelty wears thin:

https://www.emergentmind.com/posts/write-a-biblical-verse-in-the-style-of-the-king-james

Yes, science fiction magazines are inundated with LLM-written short stories, but the problem there isn't merely the overwhelming quantity of machine-generated stories – it's also that they suck. They're bad stories:

https://www.npr.org/2023/02/24/1159286436/ai-chatbot-chatgpt-magazine-clarkesworld-artificial-intelligence

LLMs generate naturalistic prose. This is an impressive technical feat, and the details are genuinely fascinating. This series by Ben Levinstein is a must-read peek under the hood:

https://benlevinstein.substack.com/p/how-to-think-about-large-language

But "naturalistic prose" isn't necessarily good prose. A lot of naturalistic language is awful. In particular, legal documents are fucking terrible. Lawyers affect a stilted, stylized language that is both officious and obfuscated.

The LLM I accidentally used to rewrite my legal threat transmuted my own prose into something that reads like it was written by a $600/hour paralegal working for a $1500/hour partner at a white-show law-firm. As such, it sends a signal: "The person who commissioned this letter is so angry at you that they are willing to spend $600 to get you to cough up the $400 you owe them. Moreover, they are so well-resourced that they can afford to pursue this claim beyond any rational economic basis."

Let's be clear here: these kinds of lawyer letters aren't good writing; they're a highly specific form of bad writing. The point of this letter isn't to parse the text, it's to send a signal. If the letter was well-written, it wouldn't send the right signal. For the letter to work, it has to read like it was written by someone whose prose-sense was irreparably damaged by a legal education.

Here's the thing: the fact that an LLM can manufacture this once-expensive signal for free means that the signal's meaning will shortly change, forever. Once companies realize that this kind of letter can be generated on demand, it will cease to mean, "You are dealing with a furious, vindictive rich person." It will come to mean, "You are dealing with someone who knows how to type 'generate legal threat' into a search box."

Legal threat letters are in a class of language formally called "bullshit":

https://press.princeton.edu/books/hardcover/9780691122946/on-bullshit

LLMs may not be good at generating science fiction short stories, but they're excellent at generating bullshit. For example, a university prof friend of mine admits that they and all their colleagues are now writing grad student recommendation letters by feeding a few bullet points to an LLM, which inflates them with bullshit, adding puffery to swell those bullet points into lengthy paragraphs.

Naturally, the next stage is that profs on the receiving end of these recommendation letters will ask another LLM to summarize them by reducing them to a few bullet points. This is next-level bullshit: a few easily-grasped points are turned into a florid sheet of nonsense, which is then reconverted into a few bullet-points again, though these may only be tangentially related to the original.

What comes next? The reference letter becomes a useless signal. It goes from being a thing that a prof has to really believe in you to produce, whose mere existence is thus significant, to a thing that can be produced with the click of a button, and then it signifies nothing.

We've been through this before. It used to be that sending a letter to your legislative representative meant a lot. Then, automated internet forms produced by activists like me made it far easier to send those letters and lawmakers stopped taking them so seriously. So we created automatic dialers to let you phone your lawmakers, this being another once-powerful signal. Lowering the cost of making the phone call inevitably made the phone call mean less.

Today, we are in a war over signals. The actors and writers who've trudged through the heat-dome up and down the sidewalks in front of the studios in my neighborhood are sending a very powerful signal. The fact that they're fighting to prevent their industry from being enshittified by plausible sentence generators that can produce bullshit on demand makes their fight especially important.

Chatbots are the nuclear weapons of the bullshit wars. Want to generate 2,000 words of nonsense about "the first time I ate an egg," to run overtop of an omelet recipe you're hoping to make the number one Google result? ChatGPT has you covered. Want to generate fake complaints or fake positive reviews? The Stochastic Parrot will produce 'em all day long.

As I wrote for Locus: "None of this prose is good, none of it is really socially useful, but there’s demand for it. Ironically, the more bullshit there is, the more bullshit filters there are, and this requires still more bullshit to overcome it."

Meanwhile, AA still hasn't answered my letter, and to be honest, I'm so sick of bullshit I can't be bothered to sue them anymore. I suppose that's what they were counting on.

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/2023/09/07/govern-yourself-accordingly/#robolawyers

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

CC BY 3.0

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

[prokopetz]

I suppose the thin silver lining to the discoverability of online resources going to shit because of SEO explotation is that all the folks who responded to reasonable questions with snarky "let me Google that for you" links which now lead to nothing but AI-generated gibberish look like real assholes now.