#predictive AI

[upcoretechnologies]

Generative AI vs Predictive AI: All You Need to Know

Artificial intelligence (AI) is transforming industries by enabling new ways to solve problems and automate tasks. Among the most discussed types of AI are generative AI and predictive AI, each serving distinct purposes and offering unique capabilities. Understanding the differences, applications, and implications of these AI types is crucial for leveraging their full potential in your business. This blog provides an in-depth comparison of generative AI and predictive AI, helping you navigate the landscape of artificial intelligence.

What is Generative AI?

Generative AI refers to a subset of artificial intelligence that focuses on creating new data. This AI type uses existing data to generate text, images, audio, or other forms of content that appear to be created by humans. Generative AI models are trained on large datasets and learn patterns, styles, and structures to produce realistic outputs.

Key Applications of Generative AI

Content Creation: Tools like OpenAI's GPT-4 can write articles, create marketing copy, generate code, and even compose music.

Image and Video Generation: Models such as DALL-E and DeepArt create artwork, enhance photos, and generate video content.

Design and Creativity: Generative AI assists in fashion design, architecture, and product design by providing innovative concepts and variations.

Gaming and Virtual Worlds: AI generates characters, landscapes, and storylines, enhancing the gaming experience.

Simulation and Training: In fields like healthcare and aviation, generative AI creates realistic simulations for training purposes.

Examples of Generative AI Models

GPT-4 (Generative Pre-trained Transformer 4): A language model that generates coherent and contextually relevant text.

DALL-E: An AI model capable of generating images from textual descriptions.

DeepArt: Uses AI to transform photos into artworks in various artistic styles.

What is Predictive AI?

Predictive AI, on the other hand, focuses on forecasting future events or outcomes based on historical data. By analyzing patterns and trends, predictive AI models make informed predictions, which are invaluable for decision-making and strategic planning.

Key Applications of Predictive AI

Finance: Predictive AI forecasts stock prices, detects fraudulent activities, and optimizes investment strategies.

Healthcare: AI predicts disease outbreaks, patient outcomes, and treatment responses, enabling personalized medicine.

Retail: Predictive models analyze consumer behavior to optimize inventory, forecast demand, and enhance customer experiences.

Manufacturing: AI predicts equipment failures, optimizes maintenance schedules, and improves supply chain management.

Marketing: Predictive analytics identify customer preferences, predict trends, and tailor marketing campaigns for better engagement.

Examples of Predictive AI Models

Time Series Analysis Models: Such as ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory), used for forecasting time-dependent data.

Regression Models: Used for predicting continuous outcomes based on input variables.

Classification Models: Such as logistic regression and random forests, used for predicting categorical outcomes.

Differences Between Generative AI and Predictive AI

While both generative and predictive AI utilize data and machine learning algorithms, their goals, methodologies, and applications differ significantly.

1. Purpose

Generative AI: Aims to create new, original content. It's used in scenarios where innovation and creativity are required.

Predictive AI: Aims to forecast future events or trends. It's used in scenarios where understanding and anticipating future outcomes are crucial.

2. Methodology

Generative AI: Uses models that learn from data to generate new, similar data. Techniques include GANs (Generative Adversarial Networks) and variational autoencoders.

Predictive AI: Uses statistical and machine learning models to analyze past data and make predictions. Techniques include regression analysis, classification algorithms, and neural networks.

3. Applications

Generative AI: Content creation, design, simulations, and entertainment.

Predictive AI: Forecasting, risk management, personalized recommendations, and strategic planning.

4. Data Requirements

Generative AI: Requires large datasets to learn patterns and generate high-quality outputs. The more diverse the data, the better the generation.

Predictive AI: Requires historical data relevant to the specific outcome being predicted. Data quality and relevance are critical for accurate predictions.

Future Trends in Generative AI and Predictive AI

As AI continues to advance, both generative and predictive AI are poised to revolutionize various industries further. Here are some emerging trends to watch:

Generative AI

Improved Creativity: With advancements in deep learning, generative AI will produce even more sophisticated and creative outputs, blurring the lines between human and machine-generated content.

Ethical AI: Ensuring ethical use of generative AI, such as preventing deepfakes and maintaining content authenticity, will become a priority.

Personalization: Generative AI will enable highly personalized content creation, from custom marketing materials to individualized learning experiences.

Predictive AI

Enhanced Accuracy: Continued improvements in algorithms and data availability will lead to more accurate and reliable predictions.

Real-Time Predictions: The integration of AI with IoT and edge computing will enable real-time predictive analytics, crucial for dynamic environments like autonomous driving and smart cities.

Explainable AI: As predictive models become more complex, developing methods to interpret and explain AI decisions will be essential for transparency and trust.

Integrating AI into Your Business

To harness the power of both generative and predictive AI, businesses must adopt a strategic approach. Here are some steps to consider:

Conclusion

Generative AI and predictive AI represent two powerful facets of artificial intelligence, each with its own set of applications, benefits, and challenges. Understanding their differences and potential can help businesses leverage these technologies effectively to drive innovation and strategic growth. As AI continues to evolve, staying informed and adaptable will be key to unlocking its full potential in the ever-changing digital landscape.

[yuusukewada]

(AIを利用したemailマーケティングから)

[contact-guy]

CHRISTMAS EVE, 1890 - part 3 - part 1 - part 2 - bit of a cliffhanger on this one - Watson's Sketchbook will return with THE BLUE CARBUNCLE!

(this is the Watson's Sketchbook series!)

[mintjeru]

hot girl summer 🔥

open for better quality | no reposts

[kugisakiss]

watched m26 hehe, sorry for the word vomit

if anyone was wondering how i was counting how many movies they appeared in, i made a little timeline when i was trying to figure it out for myself ↓

all dcmk movies are released on golden week which is in april. shout out to the detectiveconanworld wiki i couldn't have done it without you x

the real enemy is conan because he's got a perfect 100% movie spotlight

[westbifire]

Found these gems in my notes app from January

They're predictions i have for different routes

Vere taking Mc to his home (idk he i feel like he doesn't actually bring anyone)

Leander taking Mc to crypt for a date (could be creepy)

Mc helps out at the bar with Leander and becoming close with some hounds (they also are there to keep any eye on Mc)

Mhin gets Mc some leather gloves with small embroidered feather detail

Mc having tea with Kuras at his clinic after a long day of clinic work

Vere catches Mhin and Mc sneaking in the Senobium and desides to tag along to cause chaos

Leander gets drunk and reveales his secrets depending on your relationship its either soft and cuddly or hot and flirty, but I think at the end he reveales something that tips Mc off about his true intentions (what ever they are)

[jjoneechan]

Hunt And Run Snf Comic

"come find me"

"i'll always find you"

[live-emotion]

Winter Blossom's MV has been added to Live Emotion

[iwoulddieforienzo]

Something really great about the persona 2 cast is that they all individually fucking SUCK to talk to casually. Every single one of them. They are all infuriating. We have:

Tatsuya, who will stare at you blankly if you try to initiate conversation (IS) and will dip without saying a word afterward (EP)

Batsuya, who will scoff and brush you off/otherwise act dismissive

Eikichi, who might honestly be the best to talk to in the IS crew and that is not saying much, who WILL talk extremely loudly over you (probably not on purpose?) and will not be paying particularly close attention to the conversation beyond whatever he wants to say (gets points for talking about his gf. gets points taken away for constantly talking about his gf)

Lisa, who will automatically assume bad faith and will be rude to you the entire conversation unless you manage to defuse her temper (good luck)

Jun, who is uncommunicative at BEST and requires an encyclopedic knowledge of flowers, metaphor and body language just to get a HINT on what he’s thinking, and who will be extremely polite but completely unhelpful. If you tried asking him what he wants for dinner I guarantee it will be the longest 30 minutes of your life as he goes “oh I have no opinion :) whatever you want. :))” EXCEPT HE DOES HAVE OPINIONS. He has SO MANY OPINIONS. He is Expecting you to be able to pick up on his “obvious” clues. He will be passive aggressive if you don’t. (Jun babygirl you suck so bad I love u)

Maya, who is a delight but will very quickly become grating if you try to talk to her about anything serious as she hits you with the white suburban mom's "how to live a happy, healthy life" lifecoach slogans. You can’t even mention, like, stepping in a puddle or something without her hitting you with the positivity beam.

Yukino is great actually. 10/10. She’s fabulous we love her. Incredible conversationalist, chill and fun and easy to get along with. But she’s from Persona One, she doesn’t Count.

Ulala, who WILL bring up her relationship problems in every conversation within 10 minutes at least once. Any longer and she will start talking about Maya.

Do I even need to explain Baofu. Have you seen him.

And finally, Katsuya, who is a cop and a kiss ass and Very Obvious about these things. Also he can't talk to women. He can barely talk to men. Help Him.

And yet they all work wonderfully as a group. They are so annoying I love them

[veemimis]

Idk what AINI game I played to get here

[reality-detective]

The new neural link movie.. Atlas with Jennifer Lopez… They always tell you in movies… And they’re telling you the AI is coming for you 🤔

[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

[carlyraejepsans]

HELPP i found out about character AIs and im losing it over calling deltarune sans "a more manipulative personality" 😭😭 like whatt we've only seen this man like 2 times

sorry im being a hater hgfjdks but you're the sans understander

lmaoo character.ai, look where the fall of ai dungeon got us, smh. thank you for the compliment though!

[angomay]

[ID: The “Shout out to Women’s Day” meme, edited to read, “Shout out to ai girls in media fr 🤞🏾 / Gotta be one of my favorite genders.” A collage of several characters has been edited on top. From left to right, top to bottom, they include: The Weapon from Halo, Lyla from Spider-Man: Across the Spider-Verse, Lyla from Spider-Man 2099, GLaDOS from Portal, Aiba from AI: The Somnium Files, Cortana from Halo and Tama from AI: Nirvana Initiative. End ID]