Judd Spitzer

Tag: AI

  • Defining AI

    Defining AI

    I asked ChatGPT to breakdown the differences between all the terminology out there when we are talking AI. Now some people will get bent out of shape when referring to some things that might be a subset of the over grouping. But while it’s important to understand the difference, don’t let it become the conversation.

    So here is the response. Certainly! Let me break down these terms in simple language with some real-world analogies:

    1. Artificial Intelligence (AI)

    Definition: AI refers to machines or computers that can perform tasks that would typically require human intelligence. These tasks include learning from experience, recognizing patterns, understanding language, solving problems, and making decisions.

    Real-World Analogy: Think of AI as a robot chef. If you teach it how to make a cake by showing it recipes and guiding it through the process, it can then make the cake on its own without you. Over time, it can also improve its baking skills by learning from past mistakes.

    2. Machine Learning (ML)

    Definition: ML is a subset of AI. It involves teaching a machine to recognize patterns and make decisions based on data. Instead of programming the machine to do something step-by-step, you “train” it with data, and it learns on its own.

    Real-World Analogy: Imagine you’re teaching a child how to recognize different types of fruit. Instead of telling the child exactly what each fruit looks like, you show them pictures of apples, bananas, and oranges. Over time, the child learns to identify these fruits based on the patterns in the pictures, like shape, color, and size. In ML, the machine is “learning” in a similar way.

    3. Deep Learning (DL)

    Definition: Deep Learning is a type of machine learning that uses large, multi-layered neural networks to process complex data, such as images, speech, or text. It’s like taking machine learning and making it much more advanced and capable of handling really complex problems.

    Real-World Analogy: Think of deep learning like a series of filters in a photography app. The app starts with a raw picture, and each filter refines the image in specific ways. The first filter might identify edges, the next one might detect faces, and the final filter might enhance color. Deep learning works by having many “layers” that progressively refine data, like filters in an app but for much more complicated tasks.

    4. Large Language Model (LLM)

    Definition: LLMs are a type of AI designed specifically to understand and generate human language. They are trained on vast amounts of text data and can perform a variety of language-related tasks, such as answering questions, writing essays, or translating languages.

    Real-World Analogy: Imagine LLMs as a super-smart librarian who has read an enormous number of books, articles, and websites. When you ask a question, the librarian quickly pulls information from all of that knowledge and gives you a well-formed response. The librarian doesn’t “know” things the way a human does, but can generate answers based on the patterns it’s seen in the text.

    5. Natural Language Processing (NLP)

    Definition: NLP is a branch of AI that focuses on making machines understand, interpret, and generate human language. It’s what allows computers to understand sentences, translate languages, summarize text, and even chat with you.

    Real-World Analogy: NLP is like a translator who helps two people from different countries communicate. One person speaks English, the other speaks Spanish. The translator listens to both and makes sure that the message is accurately conveyed between the two, even though they don’t speak the same language.

    6. Neural Networks

    Definition: Neural networks are a type of algorithm inspired by the human brain. They consist of layers of interconnected “neurons” that work together to process information. This structure helps AI systems recognize patterns and make predictions.

    Real-World Analogy: Think of a neural network like a team of workers in a factory. Each worker has a specific job, like checking the quality of parts or assembling items. When one worker finishes their task, they pass the item to the next worker, and so on, until the item is fully completed. In a neural network, data gets passed through layers of “neurons,” with each layer doing a little bit of processing before passing it along.

    7. Supervised Learning

    Definition: In supervised learning, the AI is trained using labeled data—data that comes with the correct answer. The AI learns to map inputs to the correct outputs by looking at the examples and adjusting itself to reduce errors.

    Real-World Analogy: Imagine a teacher grading a set of math homework. The teacher gives the correct answers to the students beforehand (the “labels”) so that they can compare their answers to the correct ones and learn from any mistakes.

    8. Unsupervised Learning

    Definition: Unsupervised learning is when the AI is given data without labels (no answers provided). The machine tries to find patterns, similarities, or groupings within the data by itself.

    Real-World Analogy: It’s like a child being given a pile of mixed-up fruit (apples, bananas, oranges, etc.) and having to sort them into different baskets based on their characteristics without knowing what they are supposed to look like. The child might group them by color or shape, learning patterns along the way.

    9. Generative AI

    Definition: Generative AI is a type of AI that can create new content—like images, text, music, or even videos—based on patterns it has learned from existing content.

    Real-World Analogy: Think of a music composer who listens to hundreds of songs and then creates a completely new song that sounds similar to the ones they’ve heard, but is entirely unique. Generative AI works the same way by generating new things based on patterns it has learned.

    10. Reinforcement Learning

    Definition: In reinforcement learning, an AI learns by trial and error, similar to how a person might learn a new skill. The AI gets “rewards” for correct actions and “penalties” for incorrect actions, helping it improve over time.

    Real-World Analogy: Imagine you’re playing a video game where you earn points for completing levels and lose points for making mistakes. Over time, you get better at the game because you learn which actions give you the most points.

    Comparisons:

    TermDefinitionReal-World Analogy
    AIMachines performing tasks that typically need human intelligence (e.g., learning, problem-solving).A robot chef learning to bake cakes on its own.
    MLA type of AI that learns from data and improves over time.Teaching a child to identify fruits using pictures.
    DLA more advanced form of ML that uses complex networks to process data (e.g., images, speech, text).Layers of filters in a photography app refining a picture.
    LLMA type of AI focused on understanding and generating human language.A super-smart librarian who answers your questions based on massive amounts of reading.
    NLPAI that helps machines understand and process human language.A translator helping two people speak different languages.
    Neural NetworksLayers of interconnected “neurons” working together to recognize patterns.A factory team, where each worker does a part of the processing.
    Supervised LearningAI learns from data with known answers to make predictions.A teacher grading homework where answers are provided.
    Unsupervised LearningAI finds patterns in data without being told the answers.A child sorting fruit by color or shape without knowing the names of the fruits.
    Generative AIAI that creates new content (e.g., text, images, music).A music composer creating new songs after listening to hundreds of others.
    Reinforcement LearningAI learns through rewards and penalties, improving over time.A gamer getting points for completing levels and learning from mistakes.

    Hopefully, these explanations and comparisons help clarify these concepts!

  • ChatGPT is getting better

    ChatGPT is getting better

    So some time ago, I asked it to do a compare and contrast on the movies surrounding Willy Wonka. It was ok, not crazy in depth, but it did a decent job. Now here we are at around a year plus later, and we have come a long way.

    Here is the response:

    In comparing the original 1971 film Willy Wonka & the Chocolate Factory and the 2005 remake Charlie and the Chocolate Factory, directed by Tim Burton, we can explore differences in style, tone, characterization, and fidelity to the source material. Both films are based on Roald Dahl’s 1964 novel Charlie and the Chocolate Factory, but they take divergent paths in bringing the story to life. These contrasts highlight the evolving nature of filmmaking, as well as shifts in audience expectations over time.

    Tone and Atmosphere

    The 1971 film, directed by Mel Stuart and starring Gene Wilder as Willy Wonka, has a whimsical, almost dreamlike quality that pairs well with the magical world of the chocolate factory. The movie blends light-hearted humor with a touch of darkness, though its overall tone remains warm and family-friendly. The iconic scene of the Chocolate Riverboat ride exemplifies this balance: it starts out cheerful but becomes increasingly bizarre and unsettling, reflecting Wonka’s unpredictable nature.

    In contrast, Burton’s 2005 version leans heavily into a darker, more gothic aesthetic, a hallmark of the director’s style. Johnny Depp’s portrayal of Wonka is eccentric, quirky, and somewhat detached from reality, creating a more alienating and mysterious atmosphere. Burton’s version explores the oddity of Wonka himself in more depth, particularly through flashbacks to his childhood, which attempt to explain his eccentric behavior. This approach brings psychological complexity to the character but alters the overall tone to something more haunting and bizarre than the light-hearted charm of the original.

    Characterization of Willy Wonka

    Gene Wilder’s interpretation of Wonka in the 1971 film is a mixture of whimsy and wisdom. Wilder’s Wonka is whimsical and a bit mad, but there’s an underlying sense of kindness and understanding beneath his eccentric exterior. His portrayal manages to strike a perfect balance between unpredictability and warmth, which keeps the audience guessing about his true intentions while never letting the character feel completely threatening. He appears almost as an enigmatic teacher, guiding the children through their moral failings and rewarding the worthy.

    Johnny Depp’s portrayal, on the other hand, is more socially awkward, aloof, and deeply eccentric. Depp’s Wonka seems more disconnected from reality, driven by unresolved trauma from his strained relationship with his father, a subplot that was absent from the original. This added psychological layer gives Depp’s Wonka more depth but at the cost of making him less relatable or endearing. Depp’s Wonka is less a figure of authority or wisdom and more a character struggling with his own inner demons, which distances him from the moral teachings of Dahl’s story.

    Focus on Charlie and Fidelity to the Source Material

    The 2005 version gives Charlie a stronger presence, emphasizing his role as the moral compass of the story. Freddie Highmore’s Charlie is humble, good-natured, and his family’s financial struggles are more deeply explored in this adaptation. Tim Burton stays more faithful to Dahl’s original story in terms of character development and world-building, such as by using the actual Oompa-Loompa songs from the book and delving more into the fantastical nature of the chocolate factory. The factory itself is shown as a dark, mechanical, almost surreal place, echoing Dahl’s descriptions more closely than the 1971 version, which had a more straightforward, whimsical depiction of the factory.

    The 1971 film, while beloved, took several liberties with the original story. The title change from Charlie and the Chocolate Factory to Willy Wonka & the Chocolate Factory shifted the focus onto Wonka rather than Charlie. This shift is evident in the way the story is told; Charlie, played by Peter Ostrum, is more of a passive figure compared to his 2005 counterpart. He is good and pure-hearted, but his character is less developed in the original, allowing Wilder’s Wonka to dominate the film.

    Visual and Musical Differences

    Visually, the 1971 version is colorful and playful, yet it bears the limitations of 1970s special effects, relying on more traditional filmmaking techniques and practical effects. Despite this, the film’s charm lies in its handmade, almost theatrical quality. The sets are imaginative but never over-the-top, allowing the film’s characters to take center stage. The musical numbers in the original film, especially “Pure Imagination,” are iconic, emphasizing the sense of wonder and magic inherent in Wonka’s world.

    Tim Burton’s 2005 film, by contrast, makes extensive use of CGI to create a more elaborate and surreal factory. The visuals are much more elaborate and polished, reflecting the advances in technology. The factory is brought to life with a darker, more fantastical flair. However, some viewers feel that this reliance on CGI diminishes the organic feel of the factory that was so central to the charm of the 1971 version. Musically, Danny Elfman’s score for Burton’s version incorporates the original Oompa-Loompa songs from Dahl’s book, but they are much more eclectic and stylized, blending various musical genres and lacking the immediate accessibility of the songs from the 1971 film.

    Themes and Lessons

    Both films tackle similar themes of morality, greed, and the consequences of bad behavior, but they handle these lessons in different ways. The original Willy Wonka & the Chocolate Factory emphasizes these lessons with a sense of humor and warmth, subtly delivering the message without feeling too heavy-handed. The punishments for the children’s vices—gluttony, greed, vanity, and disobedience—are portrayed in a playful, often humorous light, maintaining the whimsical tone of the film.

    The 2005 version, however, offers a more pronounced commentary on the children’s flaws, with a sharper edge to the consequences they face. Burton’s film is less forgiving, presenting the children’s punishments with a darker and more literal tone. Depp’s Wonka appears less emotionally invested in the children’s fate, further amplifying the detached, cynical tone of the film.

    Conclusion

    In sum, while both films are based on the same source material, they cater to different audiences and sensibilities. The original 1971 Willy Wonka & the Chocolate Factory is a more whimsical, nostalgic take that emphasizes warmth, humor, and childlike wonder. It allows Gene Wilder’s Wonka to be a moral guide while still maintaining an air of unpredictability. Tim Burton’s Charlie and the Chocolate Factory, on the other hand, is a darker, more visually elaborate adaptation that delves into the psychology of Willy Wonka, presenting a more eccentric and alienating character. Burton stays closer to Dahl’s original vision in many respects, but in doing so, the film loses some of the whimsical charm that made the 1971 version so beloved. Both films, however, continue to resonate with audiences for different reasons, offering two distinct cinematic interpretations of Dahl’s classic tale.

    The real conclusion is that ChatGPT is going to change the way people think and write. The more we become familiar with the style and tone of Computer Intelligence, the more that we will mimic it. There is a convergence on the horizon.

  • ChatGPT AI is Eliza in Disguise

    You may have heard about the big revolution called AI or artificial intelligence that is sweeping the news and the tech industry. Let me tell you, there is nothing new under the sun here. We are still baby steps into the idea that computers can think for themselves. They don’t. There was a program called Eliza written back in the 60s I believe, or 70s and you can still run it through your browser. Here is the link, https://web.njit.edu/~ronkowit/eliza.html Computers can only create logic from words that people write. It is just more sophisticated today than it was 30 to 40 years ago, but it’s the same thing, take some input, decide based on the logic that humans have programmed, and give output. I think that computers are doing better at reading and understanding. Computers give positive feedback, and yet it isn’t artificial intelligence. It is just a program. We need to stop repackaging this idea that it is AI because it is bullshit. When computers start to react to other inputs that human beings do not give it, then we will start to see real AI. When computers can design and develop new reactions based on what they’re doing that’s real AI. We hear Apple talk about machine learning, and I feel that learning is much more akin to the idea of AI than being able to respond to a query and give you back a paragraph for a page of information that it’s drawn together and parsed together. Does the world need AI? I do think we need some ability to ask computers, real questions and get solid responses. I mean I look at programs like Grammarly, which are amazing. They give incredible feedback on paragraphs that you write and recommendations on writing style for readability and much more. What’s next? Will a computer wake up in the morning and go I think I’m gonna write a novel today or I think I’m gonna do some research on something today? Nothing happens without that human interaction. Computers are a long way from being self-aware, so even though the Terminator is on the horizon, it still isn’t here yet. So don’t worry my friends, we’re all gonna be OK. AI is just 40-year-old technology repackaged and sent to you to raise the stock prices of a few companies.