How GPT-4 Will Further Improve AI Applications like ChatGPT
Unless you've been living under a rock, you've either heard of ChatGPT, used it or know some who's used it. Just in case you missed the boat on this, you for sure have heard of Artificial Intelligence (AI). ChatGPT is a chatbot that uses AI to provide answer to most questions, in a human like manner. When OpenAI released ChatGPT, it amassed a huge following and user base. Tech giants like Google and Microsoft see ChatGPT and the technology that powers it, as a threat and/or an opportunity.
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Ok, I get it, what is GPT anyway?
OpenAI created the deep learning language model GPT (Generative Pre-training Transformer). This particular artificial neural network has been trained to produce writing that resembles that of a person. A sizable body of material created by humans, including books, articles from the internet, and other sources, is used to train the GPT model. When a model has been trained, it may produce new text that is similar to the text it used to train.
If you want to get really technical, then read this: "How Does GPT Work?"
But wait, are there predecessors of GPT-4?
Yep, there sure are. It all started with GPT, and as the model learned to do new things, OpenAI, kept on releasing new versions of it. ChatGPT uses GPT-3, which is the third version of the GPT language model.
To simplify things, AI models uses parameters to define a learning process and provide an output. Therefore, the greater the number of parameters, the more predictable the outcome. For instance, GPT-1 had 117 million parameters at the time of its release in 2018. A year later, GPT-2 was launched with 1.2 billion parameters, and GPT-3 increased that amount to 175 billion parameters.
There is a drawback as well; Because of the enormous amount of processing power needed, training and optimizing a model gets more expensive, the more parameters it has. Consider the text-generation model Megatron-Turing NLG, developed by Nvidia and Microsoft, which includes more than 500 billion parameters. Despite its size, MT-NLG cannot match the performance of GPT-3. In other words, greater does not always equate to better.
Read: Why AI Written Content May Eliminate the need for Human Written Content
Is there a cost to GPT and ChatGPT?
The cost of utilizing the GPT model depends on the particular API (Application Programming Interface) price plan that is selected. Access to GPT is often offered through a cloud-based API. The GPT API is available with a variety of price tiers, including a free tier with restricted usage, according to OpenAI, the company that created GPT.
Developers who are investigating the potential of the model or creating modest applications can use the free tier of the GPT API, which has a monthly API request cap. OpenAI provides premium API subscriptions with higher use limitations and more functionality for more complex or large-scale applications.
All caught up now, tell me about GTP-4?
According to Andrew Feldman, creator and CEO of Cerebras, a business that collaborates with OpenAI, in an August 2021 interview with Wired, GPT-4 will contain roughly 100 trillion parameters. This would provide GPT-4 a 100-fold increase in power over GPT-3, a quantum jump in parameter size that has rightly sparked a lot of excitement.
Having learned from MT-NLG that "bigger is not always better", for now, the focus of OpenAI will probably move to other factors, including algorithms and alignment, that affect the performance of the model.
It's possible that GPT-4 is the first sizable AI model with sparsity at its foundation. Since certain neurons in the AI model are not always active, sparse models employ conditional computation to lower computational expenses. The model is easily scalable to more than a trillion parameters without incurring significant processing expenses.
Additionally, because they may maintain a lot more options for the "next word/sentence" dependent on the user's input, sparse models are able to interpret context better. Sparse models are hence more comparable to genuine human thought than their predecessors.
What does the startup and investor landscape look like?
There has been a wave of startups that are leveraging OpenAI's API's to create all sorts of solutions. When there are startups, there have to be investors. According to statistics from PitchBook, venture capital investment in generative AI has surged 425% since 2020 to $2.1 billion this year, despite a drop in the overall technology industry.
Venture capitalists (VC) are flocking to invest in AI start-ups as surging enthusiasm around "generative AI" fills the gap left by failing bitcoin and blockchain businesses. Jasper, which promotes itself as a "AI copywriter" for marketers, got $125 million at a $1.5 billion valuation, while Stability AI, one of the businesses behind image-generation tool Stable Diffusion, raised $101 million at a $1 billion.
OpenAI itself is raising funds (via soft tender) and now valued at $27B. Going setup further, OpenAI announced an OpenAI Startup Fund of $100 million, to fund a select group of early-stage firms in industries where AI may have a revolutionary impact, such as health care, education, and climate change, and where AI technologies can empower people by enabling them to be more productive.
To date, OpenAI has invested in 4 companies - Descript, Harvey AI, Mem, and Speak, which OpenAI believes have enormous potential to significantly alter legal services, education, productivity, and creativity, respectively.
Read: How Can Startups Grow And Scale Using Artificial Intelligence In 2023? Our 3 Recommendations
So, What Next?
The consumer sentiment and ease of use have proven product/market fit for AI. The only place left now is start building offerings that will leverage the GPT techonology, both for capital gains and to have a positive impact on society. Game on then!
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