We were all impressed by ChatGPT. The model was amazing at providing answers and surprising us with its human-like conversational ability. But once you tested it a few times, you could clearly see that one of the major limitations was that it created its own answers, or, in industry speak, "hallucinated" responses that were not based on the truth. Case in point: its description of me, as seen below.
It first mentioned my co-founder as the CEO, and then it mentioned I previously worked at SAP and Google (both amazing companies, but unfortunately not true 😊) among multiple other mistakes, including not mentioning one of the co-founders. So, the first thing I wanted to check on the GPT-4 release today was, of course, the same question.
As you can see, the response is a lot more concise and accurate. It did not hallucinate or create any new information. So let's read a bit more about what GPT4 is and how it is transforming an ever-changing industry even further.
Brief History of GPT Models
As we know, the Generative Pre-trained Transformer (GPT) models have come a long way since their inception. The development of the GPT (Generative Pre-trained Transformer) models has its roots in the fields of natural language processing and machine learning. The original GPT model was introduced by OpenAI in 2018, which built upon the success of the Transformer architecture developed by Vaswani et al. in 2017. The Transformer architecture enabled better handling of long-range dependencies and more efficient training compared to previous models, such as LSTMs and RNNs.
GPT-2, the second iteration, was released in 2019, with substantial improvements in text generation capabilities. This model, GPT-2, had 1.5 billion parameters. In June 2020, OpenAI released GPT-3, the third and most advanced iteration to date. With 175 billion parameters, GPT-3 demonstrated remarkable language understanding and generation capabilities, thus outperforming its predecessors. The model was trained on a dataset called WebText, which includes a wide variety of internet sources. This was then updated with GPT 3.5, or as we all know it, ChatGPT. This, surprisingly, was based on fewer parameters but was better tuned for conversations. And now, GPT-4 is set to redefine the AI landscape once again. Though there were rumors around GPT-4 being close to 10 times larger than GPT3, it has been publicly stated by Open AI that this is not true and it is estimated that GPT-4 has around 175B-280B parameters. Even though the models are similar in terms of the number of parameters, we can clearly see differences in responses.
You can see some interesting examples here GPT-4 (openai.com)
Key Advancements in GPT-4
The biggest differences between GPT 3.5 Turbo (ChatGPT) and GPT 4
You can also see that GPT-4 can now solve tasks that ChatGPT could not. For example, if you tried to get ChatGPT to help you with your kids' math homework, you would have learned that it wasn’t exactly great. And see the results now.
As shown by the benchmarks and the quick descriptions below, GPT-4 significantly outperforms previous transformer models, especially on topics like reasoning and conciseness. These advancements have led to an AI that can generate more human-like responses, understand complex conversations, and adapt to a wide range of tasks.
Benefits of GPT-4 for Enterprises
Enhanced Customer Service
GPT-4-powered chatbots finally look more like what an enterprise needs and can provide businesses with the ability to deliver personalized, efficient, and most importantly, accurate customer service. As anyone who has worked in this field knows, accuracy and compliance are critical for any enterprise application. Now, with the new privacy changes introduced by OpenAI coupled with GPT-4, the potential of these applications is quickly going from "this could be interesting" to "we need this for our support". But before you get carried away, it is very important to have a complete view of what your needs are and how you can automate transactions to supplement this technology (check out our blog about how to make ChatGPT Enterprise). Ready to read more about this?
Streamlined Internal Discovery
Internal company-based search has been a major issue, and today knowledge management is a multi-billion dollar industry. Unfortunately, even with these billions of dollars being poured into this field, employees struggle to find what they are looking for on a day-to-day basis. Finding the right information is almost like a Sherlock Holmes mission where you need to follow the bread crumbs of past wiki authors, speak to your colleagues, or sometimes just post and request information in Teams groups as a last-ditch effort. Finally, with GPT-4, you can aggregate all your information and just tell it what you are looking for. (Important Caveat: You cannot use GPT-4 directly on your technology; you need a connector like DocBrain by Enterprise Bot or similar to enable the use of these connectors.)
Improved Business Analytics and Decision engines
GPT-4's advanced natural language processing capabilities enable businesses to gain deeper insights from their data. By analyzing large volumes of unstructured text data, GPT-4 can help enterprises make better-informed decisions, uncover hidden opportunities, and also assist in decision-making, where you could ask for recommendations on new marketing campaigns, cash flow transactions, or even insights on high-churn clients, and by linking your BI tools, allow GPT-4 to give you insights you never thought possible before.
Concerns and Ethical Considerations
While GPT-4 brings numerous benefits to enterprises, it also raises concerns and ethical considerations that businesses must address. One of these is Data Privacy. As GPT-4 processes vast amounts of data, ensuring data privacy becomes crucial. Enterprises need to establish strict security measures to protect sensitive customer information and comply with data protection regulations.
Misinformation and Bias
GPT-4's text generation capabilities can potentially contribute to the spread of misinformation or perpetuate biases present in its training data. To mitigate these risks, businesses must closely monitor their AI chatbots' output and incorporate mechanisms for detecting and addressing such issues, including combining the OpenAI APIs with a moderation API that ensures compliance and blocks harmful or non-compliant content.
Conclusion
GPT-4 is undoubtedly going to transform the world of AI chatbots for enterprises, offering a plethora of benefits across various industries, from enhanced customer support to streamlined internal communications and improved business analytics. We cannot wait to help make the future more productive and engaging and bring about the next industrial revolution that will take humanity forward.
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