What is GPT-4? Features, Price, Limitations – The AI Expert‘s Guide

So your friend keeps talking about this "GPT-4" thing, but you‘re not even really sure what it is. Don‘t worry, I‘ve got you covered! As an AI expert, let me walk you through the basics of what GPT-4 is, what it can do, who‘s using it, and what we might expect next. I‘ll try to keep the technical jargon to a minimum and make this as straightforward as possible!

Alright, what exactly is GPT-4?

Put simply, GPT-4 is the unofficial name for the next version of OpenAI‘s powerful AI system called Generative Pre-trained Transformer.

The GPT models are trained on massive amounts of text data to generate remarkably human-like writing. GPT-3, the previous version, was a huge leap forward when it launched in 2020. GPT-4 aims to push the boundaries even further.

While not formally announced yet, GPT-4 is the anticipated fourth generation release following GPT-3 and GPT-3.5. Given how the previous versions were named, GPT-4 is what most experts have pegged as the likely title.

The key point about GPT-4 is that it should be the most advanced AI system created by OpenAI to date. Based on what we know so far, here‘s a quick rundown of how it could improve on earlier versions:

• Much bigger model size – likely 100 trillion or more parameters, up to 1000x bigger than GPT-3!

• Trained on way more data – possibly up to 100x more text data than GPT-3‘s training set

• Multimodal – can ingest both text AND images, unlike GPT-3

• More accurate & less biased – higher quality responses overall

• Faster performance – better efficiency to enable real-time uses

So in a nutshell, GPT-4 takes what made GPT-3 so groundbreaking and cranks it up to 11! Keep reading for a more in-depth look at how it works and what we can expect.

Okay, how does this GPT-4 thing actually work?

GPT-4 is still in development, so we don‘t know the full technical details yet. But based on what OpenAI has revealed about their previous models, we can make some very educated guesses about how it likely operates under the hood.

The key things to understand are:

• GPT-4 uses a neural network architecture called the Transformer. This was invented by Google in 2017 and found to be really well-suited for language processing tasks.

• It‘s trained via a technique called unsupervised learning. This means GPT-4 isn‘t given labeled datasets, but rather learns patterns from unlabeled text on its own.

• The goal is to predict text statistically. Given any input text, GPT-4 will estimate the next most probable words to follow and continue generating from there.

Here‘s a high-level overview:

• GPT-4 takes in a massive dataset of text passages scraped from books, Wikipedia, internet sources, etc. This is its "training data."

• The Transformer architecture analyzes these text passages to learn the statistical patterns of how language works – things like grammar, word meanings, and how concepts relate.

• Once trained, GPT-4 models can take a text prompt as input, and leverage what they learned to generate a predicted output that continues the sequence in a human-like way.

• They don‘t actually "understand" the text per se, but are very good at producing something coherent!

The key advancement with GPT-4 is it can train on much larger datasets and absorb way more textual data. This allows it to become incredibly skilled at generating natural language.

Of course, this is vastly oversimplifying how these complex models work. But hopefully it gives you an idea of the basic approach! Next let‘s look at some of the cool new capabilities GPT-4 is expected to have.

What new features is GPT-4 likely to have?

Given how tight-lipped OpenAI is about GPT-4, we don‘t know exactly what new capabilities it will offer. But experts have provided some hints about the likely improvements they‘re targeting. Here are 5 of the biggest upgrades that could be coming:

1. Much Larger Model Size

One of the most important changes with GPT-4 will probably be significantly larger model sizes.

To put this in perspective, GPT-3 topped out at "only" 175 billion parameters. But early reports suggest the largest GPT-4 models may reach 100-200 trillion parameters – that‘s up to 1000x more than GPT-3!

In AI, model size basically corresponds to capacity – how much knowledge the system can store and use. So a trillion parameter model trained on massive datasets will be vastly more knowledgeable compared to GPT-3 or other previous AI.

This expanded capacity could potentially allow GPT-4 to handle extremely complex prompts, have more flowing conversational abilities, and draw on more contextual information to inform its responses.

2. Training on Much More Data

In addition to model size, GPT-4 is expected to train on exponentially more data than GPT-3.

While GPT-3 trained on hundreds of billions of words from books, websites, and text passages, GPT-4 may use up to 100x as much training data – perhaps trillions of words sourced from an even wider collection of documents!

With all of this additional information about how humans communicate and use language, GPT-4 is anticipated to converse even more naturally and provide highly accurate, nuanced responses tailored precisely to the prompt.

3. Multimodal Capabilities

One of the most exciting upgrades rumored for GPT-4 is the ability to process both text and images as inputs, instead of just text alone.

This opens up many possibilities! For example, GPT-4 may be able to:

• Generate captions or descriptions for images
• Answer questions by reasoning about visual elements
• Summarize the key points of an image into text
• Describe scenes, objects, and actions in images

By expanding beyond text, multimodal GPT-4 models could have a much richer understanding of the interplay between language and vision. This could make it useful for many more applications than text alone.

4. More Accurate & Less Biased

Since GPT-3 was trained on text sourced from public websites, it sometimes unfortunately exhibits offensive, biased, or factually incorrect outputs.

While it‘s far from perfect, GPT-4 is expected to employ improved training techniques to produce higher-quality, less biased, and more factually accurate responses overall.

Some methods that may help include using carefully filtered training data, expanding bias datasets, novel unsupervised algorithms, and reinforced feedback loops with human input.

Reducing undesirable model biases and errors would make GPT-4 safer for real-world applications across a much wider domain compared to GPT-3.

5. Faster Performance

Here‘s a surprising fact – despite having what may be 1000x larger model sizes, GPT-4 could actually be faster at generating outputs than GPT-3!

Leveraging optimizations like sparsity and better parallel processing, OpenAI hopes to significantly reduce the response latency for GPT-4 models.

Lower latency would enable real-time applications for GPT-4 without frustrating lags, supporting more natural conversational workflows. This could be key for systems like Google‘s LaMDA to feel truly human-like.

So in summary, GPT-4 aims to make massive leaps in model knowledge, data understanding, multimodal abilities, output quality, and speed. This could take AI capabilities to amazing new levels!

Who is using GPT-4 now?

Given that GPT-4 remains largely confidential for the time being, access is very limited right now. However, OpenAI has reportedly provided some select partners with early access to GPT-4 models for evaluation and testing purposes.

The most notable is Microsoft, which invested $1 billion into OpenAI back in 2019. They clearly have very high hopes for integrating GPT-4!

Reliable reports indicate Microsoft is already testing GPT-4 features across a variety of products including Azure cloud services, Office 365, Bing search, Outlook, and Teams collaboration tools.

Several Microsoft job listings mention secretively that candidates would work on "a new OpenAI model", hinting at GPT-4 integration.

Beyond Microsoft, Google is also rumored to be kicking the tires on GPT-4 to potentially enhance natural language systems like LaMDA.

Apple, Meta, and other tech giants are likely running early GPT-4 evaluations as well to prep for integration into future products and services. And various AI startups will eventually gain access when OpenAI releases a commercial API.

For now, ordinary consumers probably won‘t directly play with GPT-4 until it begins rolling out in applications later on. And OpenAI will carefully limit access to prevent abuse.

But there‘s clearly tons of activity buzzing under the surface across the industry to tap into the immense promise of GPT-4!

What are the limitations and concerns with GPT-4?

Given how transformative GPT-4‘s capabilities sound, it‘s easy to get carried away with excitement. But it‘s important to keep grounded – as with any AI system today, GPT-4 will have significant limitations and challenges to work through.

No Factual Knowledge

One core limitation is that GPT-4 has no actual facts about the real world in its training data. It solely learns to predict words statistically, not ingest true knowledge.

So while responses may seem sophisticated, they could be logically flawed or objectively incorrect. It has no way to verify the accuracy of its generated claims.

Limited Reasoning

Similarly, GPT-4 won‘t be capable of complex logical reasoning or thinking through multi-step problems. Its predictions are based pattern recognition, not structured analysis.

This means it could struggle to break down and solve challenges that require deductive reasoning or planning.

No Common Sense

GPT models also lack basic common sense that even young children have through real experience. It has no inherent understanding of social norms, safety, ethics, physical laws, and human behaviors.

As a result, GPT-4 may sometimes generate troubling or nonsensical outputs without the ability to discern this, a limitation of its statistical training.

Bias Concerns

Despite mitigation attempts, biases around race, gender, culture and more could persist from the training data. Large models reflect these biases present in the data.

So while reduced, GPT-4 may continue to exhibit some undesirable biases that require ongoing monitoring and refinement.

Narrow Capabilities

It‘s also crucial to remember GPT-4 is narrowly focused on text generation tasks. It doesn‘t understand speech, make plans, reason logically, or learn outside of its training process.

GPT-4 has no aspirations approaching general intelligence – it seeks to optimize statistical language modeling, not replicate the remarkable breadth of human cognition.

So in summary, while GPT-4 appears extremely promising, managing expectations is important. Its skills are confined to producing human-like text, not comprehensively replicating the human mind.

What‘s next? Expecting GPT-5, and beyond!

GPT-4 signifies an exciting step forward in AI‘s progression, but it‘s just one milestone on a long roadmap. OpenAI has revealed they already have plans beyond GPT-4, and many experts believe GPT-5 development is already well underway.

In the near term, we can expect even bigger leaps as model sizes blow past the trillion+ parameter threshold, multimodal architectures expand, and unsupervised techniques advance.

But the challenges will compound too – the computational resources and training data needed will balloon, requiring novel efficiency innovations. Ethical concerns around potential misuse and programming of human biases will also demand increased scrutiny.

Further down the line, truly transformative opportunities could emerge if future models can learn symbolic reasoning and logic, obtain common sense and general real-world knowledge, and train on a broader span of modalities like images, video and audio.

Realizing this grand vision of Artificial General Intelligence that rivals humans across all domains remains many decades away, if achievable at all. But the rapid progress on models like GPT-4 suggests we‘re on an accelerating trajectory.

So while GPT-4 kicks off the next generation of language AI with an impressive array of upgrades, we‘re still just scratching the surface of what these systems may one day accomplish. But the future appears bright, if we innovate responsibly!

The Bottom Line on GPT-4

Let‘s recap the key facts:

• GPT-4 should represent OpenAI‘s biggest AI advancement since GPT-3 launched the large language model trend
• It‘s expected to be 1000x larger, train on 100x more data, and add multimodal capabilities
• Early users like Microsoft already appear eager to tap into its power
• But managing unrealistic hype will be important, as limitations remain around reasoning
• Future advances like GPT-5 will keep pushing boundaries of what‘s possible

While specific details remain scarce for now, the AI community is clearly buzzing with anticipation for what GPT-4 has in store! It may not change the world overnight, but represents an exciting milestone in our progress towards more human-like AI.

So hopefully this overview gave you a solid understanding of what this much-hyped GPT-4 technology is all about! Let me know if you have any other questions.