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Introducing GPT-4: A New Era in AI, Programming, and API Integration

ChatGPT released GPT-4

A New Chapter in AI

The much-awaited GPT-4 is here, bringing along a wave of excitement for everyone, not just tech enthusiasts. In this overview, we’ll explore the incredible potential of GPT-4 and its real-world applications, as well as how it can work with various APIs available on APILayer. OpenAI’s newest version follows the success of ChatGPT, OpenAI’s revolutionary AI chatbot.

At the end of this round-up we will share with you code to get started integrating GPT and artificial intelligence with APIs available from API Layer.

To understand the buzz around GPT-4, it’s essential to know the differences between the old and new versions and explore their capabilities.

Inside the World of GPT-4?

If you’re lucky enough to have early GPT-4 access through OpenAI Playground, you’ll see a screen similar to the one below.

GPT-4

GPT-4 is available in the Model dropdown when you filter the mode to ‘Chat’.

There are a few known models of GPT-4, including GPT-4 and GPT-4-32k, and something else called GPT-4-0314. GPT-4-0314 won’t be updated after June 2023. Depending on your needs, if you want the most up-to-date information, it’s best to use GPT-4. However, some users may prefer a stable model without changes, making GPT-4-0314 a suitable choice.

As we dig deeper, we find significant differences between GPT-4 and GPT-4-32k, especially regarding context length. GPT-4-32k has an impressive context length of 32,768 tokens, four times greater than the base GPT-4 model.

Some unreleased GPT-4 versions can interpret images, and according to sources, the most advanced version can generate up to 25,000 words.

With 25,000 words, you can create a detailed thesis, an engaging novel, or an extensive document like a policy paper or business proposal. The possibilities for creating in-depth, impactful documents are vast with GPT-4.

GPT4: The Luxury Car of AI

If you haven’t had a chance to test the more advanced GPT-4 versions, you might feel disappointed when you first see the maximum token size in Playground is 2048. However, the real advantage lies in what you don’t see. GPT-4 is trained on 45 terabytes of data, far more than GPT-3’s 17 terabytes. Reports suggest GPT-4 has 1 trillion parameters, significantly more than GPT-3’s 175 billion parameters.

Simply put, it’s like comparing a compact car to a luxury car. Both can get you from point A to point B, but the luxury car has more power and better performance. Similarly, GPT-4’s increased data and parameters make it a more advanced language model. So, if you want the best AI experience, GPT-4 is worth the wait.

Key Differences Between GPT-4 and GPT-3.5

  1. Parameters: GPT-4 has billions more parameters than GPT-3.5, enabling it to learn and retain more information, improving its understanding of complex tasks. This is particularly helpful for programming tasks, where GPT-3.5 often loses context and overall goals.
  2. Training Data: GPT-4 benefits from a much larger and diverse dataset compared to GPT-3.5, allowing it to comprehend a broader range of topics and handle more nuanced language patterns.
  3. Architecture Improvements: GPT-4’s underlying architecture advancements enhance its ability to process and generate text efficiently, leading to faster and more accurate results.

GPT-4’s incredible advancements include a better sense of humor, amazing academic skills, remarkable legal abilities, and outstanding programming capabilities.

Acing Tests

OpenAI has tested GPT-4 against human exams, such as the LSAT, SATs, and AP tests, with impressive results. For instance, GPT-4 scored in the 90th percentile on all three portions of the bar exam, this is a significant improvement compared to its predecessor, GPT-3.5, which only passed two multiple-choice portions and placed in the 10th percentile.

Mastering Humor

GPT-4 can generate jokes and explain why they’re funny. It can even understand visual jokes, showcasing its ability to process text and images.

Astounding Legal Skills

In addition to scoring high on the Bar Exam, GPT-4 can sue scammers and robocallers for $1500 in under 5 seconds! DoNotPay, a startup using GPT-4 in their legal services, generates “one-click lawsuits” to sue robocallers for $1,500.

Programming Like a Pro

Asking GPT-4 to code games like Pong, Flappy Bird, and Snake demonstrates its capabilities. GPT-4 has proven to be better at programming than GPT-3. A YouTube vlogger demonstrated GPT-4’s ability to code an entire game of Pong in under 60 seconds. While not perfect, GPT-4 outperforms GPT-3.5, which often generated broken or confusing code.

GPT-4’s Image Input Feature: The Ultimate ‘Will They, Won’t They’ Drama of AI

You may have heard the hype and seen the press releases demonstrating that GPT-4 can interpret images, but can you actually use it for that? Nope, not unless you got early access to on of OpenAI’s super advanced models! For now the image input feature remains elusive. Maybe OpenAI are waiting to surprise us, or maybe they’re struggling to teach GPT-4 to tell a cat from a loaf of bread. Who knows? In the meantime, let’s see what else companies are doing with GPT-4.

API Integrations Power Processes

Several companies have already started incorporating GPT-4 into their products. Stripe is using it to enhance fraud prevention measures, while Duolingo has integrated GPT-4 into its learning app, which was announced the same day OpenAI unveiled GPT-4. Duolingo Max, powered by GPT-4, provides highly personalized language lessons and affordable, accessible English proficiency testing. With GPT-4’s assistance, the platform can more intelligently output text to elevate language learning for over 50 million monthly users.

Microsoft has also integrated OpenAI’s GPT-4 model into Bing, as reported by TechCrunch, providing a ChatGPT-like experience within the search engine.

Although GPT-4 may not be as powerful as some expected, it’s a significant upgrade from GPT-3.5 and is set to become an essential technology for various real-world applications.

GPT-4: Revolutionizing API Integration Across Industries

The groundbreaking GPT-4, a state-of-the-art AI language model, has been making waves in the tech world with its remarkable capabilities and potential applications. One of the key areas where GPT-4 promises to have a significant impact is API integration, enabling seamless communication and data exchange between various software applications and AI systems. By leveraging GPT-4’s advanced capabilities, developers can create innovative solutions that transform industries and enhance productivity.

API integration is the connection of two or more applications via their APIs (application programming interfaces), which allow systems to exchange data sources. API integrations power processes across many sectors and layers of an organization to keep data in sync and increase productivity. With GPT-4’s advanced features and seamless API integration, developers can efficiently build AI-driven solutions that cater to a wide range of industries and user needs, unlocking new opportunities for innovation and growth.

Harnessing GPT-4 and APILayer APIs for Innovation

Developers can combine GPT-4 with various APILayer APIs to create cutting-edge applications. Note that these examples use the GPT-3 API. When GPT-4 becomes available, you can replace the GPT-3 API calls with GPT-4 API calls.

See the examples below of how you can use GPT and GPT4 with other api integration platforms.

  1. Number Verification API: In this example, we integrate the Number Verification API with GPT-3 to create an AI-driven virtual assistant that validates and retrieves phone number information, generating useful insights or recommendations. The function number_verification() takes an API key and a phone number, and it returns the validation result using the Number Verification API. The function generate_response() takes a prompt as input and generates a response using GPT-3.

The main part of the script fetches the validation result of the given phone number, combines it with a prompt, and then generates a response using GPT-3. The output of the script is a human-readable message about the phone number.

import openai

import requests

 

# Set up OpenAI API key

openai.api_key = “YOUR OPENAI API KEY”

 

# Define the function to verify a phone number using the Number Verification API

def number_verification(api_key, phone_number):

    url = f”https://api.apilayer.com/number_verification/validate?number={phone_number}”

    headers = {“apikey”: api_key}

    response = requests.get(url, headers=headers)

    validation_result = response.json()

    return validation_result

 

# Define the function to generate a response using GPT-3

def generate_response(prompt):

    response = openai.Completion.create(

      engine=”text-davinci-003″,

      prompt=prompt,

      max_tokens=500,

      n=1,

      stop=None,

      temperature=0.5,

    )

    return response.choices[0].text

 

# Main script

api_key = “YOUR NUMVERIFY KEY FROM APILAYER”

phone_number = “+353851641380” # replace with the desired phone number

validation_result = number_verification(api_key, phone_number)

prompt = f”Give a detailed view to the user of what carrier the following number belongs to, its trustpilot reputation and known facts about carrier and the location {phone_number}: {validation_result[‘carrier’]} carrier in {validation_result[‘location’]}.”

response = generate_response(prompt)

print(response)

  1. Exchange Rates Data API with GPT

In this example, we integrate the Exchange Rates Data API with GPT-3 to create a smart financial advisor application that offers real-time currency conversion rates and predictions. The function exchange_rates() takes an API key and a base currency and returns the current exchange rates using the Exchange Rates Data API. The function generate_financial_advice() takes a prompt as input and generates financial advice using GPT-3.

The main part of the script fetches the current exchange rate between USD and EUR, combines it with a prompt, and then generates financial advice using GPT-3. The output of the script is a human-readable message about the current exchange rate and what actions to take.

import openai

import requests

 

def exchange_rates(api_key, base_currency):

    url = f”https://api.apilayer.com/exchangerates_data/latest?base={base_currency}”

    headers = {“apikey”: api_key}

    response = requests.get(url, headers=headers)

    data = response.json()

    rates = data[“rates”]

    return rates

 

def generate_financial_advice(prompt):

    openai.api_key = “YOUR OPENAI API KEY”

    response = openai.Completion.create(

        engine=”text-davinci-003″,

        prompt=prompt,

        max_tokens=1024,

        n=1,

        stop=None,

        temperature=0.5,

    )

    advice = response.choices[0].text

    return advice

 

api_key = “YOUR API LAYER EXCHANGE RATES API KEY”

base_currency = “USD”

rates = exchange_rates(api_key, base_currency)

usd_to_eur = rates[“EUR”]

prompt = f”The current exchange rate between USD and EUR is {usd_to_eur}. What actions should I take?”

advice = generate_financial_advice(prompt)

print(advice)

  1. Weatherstack API with GPT

In this example, we integrate the Weatherstack API with GPT-3 to develop an advanced weather forecasting application that provides highly accurate, localized weather predictions and activity suggestions based on the forecast. The function weather_forecast() takes an API key and a location and returns the current weather data using the Weatherstack API. The function generate_activity_suggestion() takes a prompt as input and generates activity suggestions using GPT-3.

import openai

import requests

 

def weather_forecast(api_key, location):

 

    url = f”http://api.weatherstack.com/current?access_key={api_key}&query={location}”

 

    response = requests.get(url)

 

    data = response.json()

 

    return data[“current”]

 

def generate_activity_suggestion(prompt):

 

    openai.api_key = “YOUR OPENAI API KEY”

 

    response = openai.Completion.create(

 

        engine=”text-davinci-003″,

 

        prompt=prompt,

 

        max_tokens=100,

 

        n=1,

 

        stop=None,

 

        temperature=0.5,

 

    )

 

    message = response.choices[0].text.strip()

 

    return message

 

# Replace with your API key and location

 

weatherstack_api_key = “YOUR WEATHERSTACK API KEY”

 

location = “New York”

 

weather_data = weather_forecast(weatherstack_api_key, location)

 

temperature = weather_data[“temperature”]

 

weather_description = weather_data[“weather_descriptions”][0]

 

prompt = f”The current weather in {location} is {weather_description} with a temperature of {temperature}°C. What activities do you recommend?”

 

suggestion = generate_activity_suggestion(prompt)

 

print(suggestion)

 

  1. YouTube API with GPT

This script uses API Layer’s YouTube API. The script searches for YouTube videos related to “ChatGPT” and then uses GPT-3 to generate a recommendation on which video to watch first and why. Make sure to replace your_youtube_api_key and your_openai_api_key with your actual API keys.

 

import requests

import openai

 

# Set up YouTube API

YOUTUBE_API_KEY = “YOUR YOUTUBE API KEY FROM APILAYER”

 

# Set up OpenAI API

openai.api_key = “YOUR OPENAI API KEY”

 

# Function to search YouTube videos

def search_youtube_videos(query):

    url = “https://api.apilayer.com/youtube/search?q=”+ str(query)

    print(query)

    payload = {}

    headers= {

      “apikey”: YOUTUBE_API_KEY

    }

 

    

    

    response = requests.request(“GET”, url, headers=headers, data = payload)

 

    if response.status_code == 200:

        return response.json()

    else:

        raise Exception(f”Error: {response.status_code}”)

 

# Function to generate recommendation using GPT-3

def generate_recommendation(videos):

    video_titles =

[“title”] for video in videos[“contents”]]

    video_descriptions =

[“descriptionSnippet”] for video in videos[“contents”]]

 

    # Print video titles

    print(“Video titles:”)

    for i, title in enumerate(video_titles):

        print(f”{i+1}. {title}”)

    

    print()

 

    prompt = f”Based on the following YouTube video titles and descriptions,{”.join([f’n- {title}: {description}’ for title, description in zip(video_titles, video_descriptions)])} tell me in English which one should I watch first and why?”

    response = openai.Completion.create(

        engine=”text-davinci-003″,

        prompt=prompt,

        max_tokens=500,

        n=1,

        stop=None,

        temperature=0.8,

    )

 

    recommendation = response.choices[0].text.strip()

    return recommendation

 

# Search for YouTube videos

query = “ChatGPT”

videos = search_youtube_videos(query)

 

# Generate a recommendation using GPT-3

recommendation = generate_recommendation(videos)

print(recommendation)

 

In Conclusion

GPT-4, like predecessor GPT models will revolutionize programming, making coding faster and more efficient. Freelancers, contractors, and entrepreneurs stand to gain significantly from this technology, quickly building prototypes and complete projects. Programmers must stay current with these emerging technologies to avoid falling behind.

Integrating GPT models and the latest, most advanced GPT-4 with APIs available on APILayer unlocks a world of possibilities for developers to create innovative and intelligent applications. By leveraging GPT-4 and APILayer’s wide range of APIs, developers can build AI-driven solutions that cater to various industries and user needs, offering enhanced user experiences and greater value. As AI continues to evolve, the potential for combining these technologies will only increase, creating new opportunities for innovation and growth.

 

 

 

 

 

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