#Artificial Intelligence & Machine Learning

[cognithtechnology]

The Rise of Artificial Intelligence: A Deep Dive into AI and Machine Learning

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is the advanced simulation of human intelligence in machines, enabling them to perform tasks that typically require human cognition. These systems can handle complex tasks such as problem-solving, reasoning, learning, and even creative thinking. The ultimate aim of AI is to create systems capable of functioning autonomously, intelligently making decisions without human intervention.

In today’s tech-driven world, AI has become foundational to a wide array of technologies, from virtual assistants like Siri and Alexa to sophisticated systems in healthcare, finance, and beyond. AI can be categorized into two main types: Narrow AI, which is designed for specific tasks such as facial recognition, and General AI, a theoretical concept where machines exhibit human-like intelligence. While narrow AI is in widespread use today, general AI is still in the research phase.

Understanding Machine Learning (ML)

Machine Learning (ML) is a crucial subset of AI that focuses on enabling machines to learn from data. Unlike traditional programming, where specific rules are coded, ML employs algorithms to detect patterns in vast datasets, allowing the system to make predictions or decisions without human intervention. Simply put, ML empowers machines to "learn" and improve over time, refining their performance based on experience.

Also Read: Transforming UX with AI and Machine Learning

There are three primary types of machine learning:

Supervised Learning – Algorithms are trained on labeled data (data with known outcomes), helping the model learn patterns.

Unsupervised Learning – The model identifies hidden patterns in data without predefined labels.

Reinforcement Learning – The model learns through trial and error, receiving rewards for correct actions and penalties for incorrect ones.

How AI and Machine Learning Collaborate

AI and Machine Learning often function together, with AI providing the framework for creating intelligent systems, and ML offering the tools for learning and adaptation. Without machine learning, AI systems would depend entirely on pre-programmed rules, severely limiting their ability to manage dynamic tasks.

Take self-driving cars as an example. These autonomous vehicles rely on AI to analyze data from sensors and cameras, but it's machine learning that enables them to adapt in real time. ML models help the car understand its environment, making decisions like when to stop, change lanes, or avoid obstacles. AI provides the overall intelligence, while ML ensures the car can adjust to ever-changing conditions.

Applications of AI and Machine Learning Across Industries

AI and ML are making a significant impact across various sectors, revolutionizing the way businesses operate and solve problems. Let’s explore how they are transforming key industries.

1. Healthcare AI and ML are reshaping the healthcare industry, with applications ranging from diagnostics to treatment recommendations and robotic surgeries. AI-driven tools can process massive amounts of medical data, providing faster, more accurate diagnostic insights than human practitioners. For instance, ML models can sift through thousands of medical records to predict diseases like cancer and cardiovascular conditions, improving early detection.

2. Finance The finance sector heavily utilizes AI and ML for risk management, fraud detection, and automated trading. ML models can analyze vast amounts of financial data to identify suspicious activity, flagging potential fraud before it escalates. In the trading world, AI systems use real-time data to make informed decisions, often outperforming human traders by identifying patterns and trends that are invisible to the human eye.

3. Autonomous Vehicles Autonomous vehicles, such as those developed by Tesla and Waymo, rely extensively on AI and ML to make real-time decisions. These cars are equipped with a range of sensors—radar, cameras, LiDAR—that collect data to help the vehicle navigate. Machine learning plays a key role in interpreting this data, allowing the car to recognize and respond to various road conditions, traffic signals, and obstacles, improving its driving performance over time.

4. Retail and E-commerce In retail, AI and ML power recommendation engines, dynamic pricing, and personalized marketing strategies. Major platforms like Amazon and Netflix use sophisticated machine learning algorithms to suggest products and content based on user preferences and behavior. Retailers are also leveraging AI for inventory management, demand forecasting, and real-time pricing adjustments to optimize sales.

5. Customer Service AI-powered chatbots and virtual assistants are transforming customer service by providing efficient, automated responses to customer queries. Utilizing natural language processing (NLP), a subset of AI, these bots can understand and respond to customer questions in real time. Over time, machine learning models improve their responses, enhancing accuracy and reducing the need for human agents.

The Future of AI and Machine Learning

The future of AI and Machine Learning holds immense promise as advancements in deep learning and neural networks push the boundaries of what machines can achieve. Deep learning models, which mimic the human brain's structure, are enabling machines to process highly complex data such as images, speech, and text with incredible precision.

One of the most exciting developments is Natural Language Processing (NLP), which allows machines to understand, interpret, and generate human language. AI models like GPT-4 are already making waves in language generation, translation, and conversational AI, pointing to a future where machines can interact with humans in increasingly meaningful ways.

Ethical Considerations in AI and Machine Learning

As AI and ML technologies continue to evolve, ethical concerns are becoming more prominent. These challenges include:

Data Privacy – AI systems require vast amounts of data to function, raising concerns about user privacy. Organizations must be transparent about how they collect and use data while ensuring robust data protection measures are in place.

Algorithmic Bias – Machine learning models are only as good as the data they are trained on. If that data contains inherent biases, the AI system may perpetuate those biases, leading to unfair decisions, particularly in areas like hiring or law enforcement. It’s crucial for developers to actively address and mitigate bias in their models.

Job Displacement – As AI and ML automate more tasks, there are fears of job displacement, particularly in sectors such as manufacturing and customer service. While automation creates new opportunities, it’s essential to invest in workforce upskilling to keep pace with evolving technology.

Conclusion

AI and Machine Learning are driving groundbreaking innovations across industries, reshaping the way we live, work, and interact with technology. From healthcare and finance to autonomous vehicles and e-commerce, these technologies are transforming industries at a rapid pace. As AI continues to evolve, its potential applications seem limitless. However, it’s crucial to address ethical considerations to ensure these technologies are harnessed for the greater good.

The future of AI and ML is bright, and their impact on our everyday lives is only just beginning to unfold.

[cromacampusinstitute]

With training in artificial intelligence (AI) and machine learning (ML), you can pursue a variety of exciting and high-demand careers. As a machine learning engineer, you can develop algorithms and models that enable computers to learn from data and make predictions. Data scientists extract valuable insights from large datasets, informing strategic decisions for businesses.

[jacelynsia]

Jobs That AI Can’t Replace: The Impact of AI on Workforce

This article will delve into the connection between automation, IT, and the workforce. Explore the roles where human creativity prevails over automation’s prowess. From cybersecurity to software development, certain domains necessitate a human touch. Learn how blending human expertise and artificial intelligence can shape a promising future.

[f-identity]

[Image description: A series of posts from Jason Lefkowitz @jalefkowit@octodon.social dated Dec 08, 2022, 04:33, reading:

It's good that our finest minds have focused on automating writing and making art, two things human beings do simply because it brings them joy. Meanwhile tens of thousands of people risk their lives every day breaking down ships, a task that nobody is in a particular hurry to automate because those lives are considered cheap https://www.dw.com/en/shipbreaking-recycling-a-ship-is-always-dangerous/a-18155491 (Headline: 'Recycling a ship is always dangerous.' on Deutsche Welle) A world where computers write and make art while human beings break their backs cleaning up toxic messes is the exact opposite of the world I thought I was signing up for when I got into programming

/end image description]

[disease]

Frank Rosenblatt, often cited as the Father of Machine Learning, photographed in 1960 alongside his most-notable invention: the Mark I Perceptron machine — a hardware implementation for the perceptron algorithm, the earliest example of an artificial neural network, est. 1943.

[gynoidgearhead]

we need to come up for a good word for ""AI"" that doesn't imply it's artificial or intelligent and highlights the stolen human labor. like what if we call it "theftgen"

(workshop this with me)

[prokopetz]

AIs being able to convincingly pretend to know things isn't a sign of intelligence. Come back when we have an AI that can convincingly pretend to be unaware of things that are common knowledge for the sake of a bit.

[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

[incognitopolls]

For the purposes of this poll, research is defined as reading multiple non-opinion articles from different credible sources, a class on the matter, etc.– do not include reading social media or pure opinion pieces.

Fun topics to research:

Can AI images be copyrighted in your country? If yes, what criteria does it need to meet?

Which companies are using AI in your country? In what kinds of projects? How big are the companies?

What is considered fair use of copyrighted images in your country? What is considered a transformative work? (Important for fandom blogs!)

What legislation is being proposed to ‘combat AI’ in your country? Who does it benefit? How does it affect non-AI art, if at all?

How much data do generators store? Divide by the number of images in the data set. How much information is each image, proportionally? How many pixels is that?

What ways are there to remove yourself from AI datasets if you want to opt out? Which of these are effective (ie, are there workarounds in AI communities to circumvent dataset poisoning, are the test sample sizes realistic, which generators allow opting out or respect the no-ai tag, etc)

–

We ask your questions so you don’t have to! Submit your questions to have them posted anonymously as polls.

[why-ai]

[cognithtechnology]

The Role of AI and Machine Learning in Everyday Life

Explore how AI and Machine Learning are used in daily life, from smart devices to personalized recommendations. Learn how they make life easier.

[textsearchbradleycarlgeigerbrad]

TEXT SEARCH BRADLEY CARL GEIGER AND BRAD GEIGER AND EVERYTHING ASSOCIATED

BRAD GEIGER AND CENTRAL INTELLIGENCE AGENCY

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BRAD GEIGER AND WIKIPEDIA

BRADLEY CARL GEIGER AND WIKIPEDIA

[tumbler-polls]

[zooplekochi]

They call it "Cost optimization to navigate crises"