Utilizing Machine Learning technology is not so tough now. It doesn't require a deep understanding of the algorithm, statistics and probability. At present, it’s as simple as calling an API. Thanks to the magical invention done by OpenAI.
OpenAI is an Artificial Intelligence (AI) research and implementation company based in San Francisco, founded in late 2015. It started as a non-profit research organization with the mission of advancing Artificial General Intelligence (AGI) in the most beneficial way for humanity as a whole, free of commercial constraints. Its founders’ list comprises the who’s who of the modern tech world including Elon Musk, Sam Altman, Greg Brockman, and others. Later in the summer of 2019, OpenAI LP was announced as a new “capped-profit” company to rapidly increase its investments in computing and talent, continuing its mission.
OpenAI works on many products and applications. Its research focuses on reinforcement learning, the development of generative models, video game bots, and benchmarking. Among all, “Generative Pre-trained Transformer 3”, aka GPT-3 became very popular.
The GPT-3 (Generative Pre-trained Transformer 3) is a Deep Learning-based language prediction model. Deep Learning is part of a larger family of artificial neural network-based machine learning algorithms.
The first version of the GPT was released as a research paper and published on OpenAI’s website on June 11, 2018. It showed that the language prediction model could capture global knowledge. It proposed that a language model must be learned using unlabeled data, then, improved through samples of natural language processing tasks such as text classification, sentiment analysis, and word segmentation.
GPT-2 was first announced in February 2019. It was released as a limited demonstrative version to the public. GPT-2 was able to generate realistic text that was refused by OpenAI to be made open-source as the company was concerned about the development and spread of fake news.
On June 11, 2020, GPT-3 was launched as a beta version. The full version of GPT-3 has a capacity of 175 billion ML parameters. GPT-2 has 1.5 billion parameters that show how massively powerful GPT-3 is. The original paper introducing GPT-3 was presented by OpenAI engineers, who warned of GPT-3’s potential risks and called for research to reduce the risk.
On September 22, 2020, Microsoft and OpenAI announced a multi-year partnership and agreed to license GPT-3 exclusively to Microsoft for their products and services.
GPT-3 can generate various text content depending on the context and predict statements according to available sentences. Due to its context-based nature, it has incredible creative capabilities. The text predictor of GPT-3 processes all the text existing on the internet and can calculate the most statistically expected output. It can write poetry, blogs, PR content, resumes, and technical documentation.
The output quality is nearly the same as that of individual writing content. Besides this, it can also analyze the context in the provided text and based on that it can generate business idea pitches, fan fiction, memes, and so on. A startup can also be established by harnessing the power of GPT-3.
By seeing the result of how GPT-3 efficiently completes the sentence, it is known as an immense autocomplete algorithm. So far, it’s the most powerful and advanced text autocomplete program. It smartly spots the patterns and possibilities in huge data sets. By using that, it performs very amazing tasks that were impossible till now by an AI tool. According to The Verge, “The dataset that the GPT-3 was trained on was mammoth. It’s hard to estimate the total size, but we know that the entirety of the English Wikipedia, spanning some 6 million articles, makes up only 0.6 percent of its training data.”
GPT-3 has opened a distinct path for artificial text generation. It has huge potential to generate value for OpenAI as well as the end consumer of GPT-3. OpenAI has released an API for accessing AI models developed by them. The OpenAI API has a general-purpose ─ “text in, text out” interface that makes it possible to apply it to virtually any English language task — semantic search, summarization, sentiment analysis, content generation, translation, and more.
Currently, OpenAI APIs are in the beta phase and one needs to join the waitlist to get access to the OpenAI GPT-3 license. The new users receive $18 in free credit that can be used during the first three months and then move to the pay-as-you-go pricing model. The API is offered with various price points and capabilities. The fastest GPT-3 model is Ada, whereas Davinci is the most powerful.
The OpenAI API has numerous examples given in the documentation. The best way to start exploring the API is with Playground, where one can play around with the API and also train GPT-3 to produce the desired language output. The API is available as REST endpoints and has an official Python package to interact with the GPT-3. There are also community-maintained libraries for other programming languages.
Artificial Intelligence (AI) has witnessed remarkable advancements, and one of its prominent innovations, Generative Pre-trained Transformer (GPT), continues to evolve. As we look forward, several intriguing trends in the realm of GPT are shaping the future of AI.
Multimodal AI: Traditionally, GPT excelled in Natural Language Processing (NLP) tasks. In 2024, the trend is the expansion into multimodal AI. GPT models are being enhanced to understand and generate content that combines text with other modalities like images and audio. This holds enormous potential across diverse domains, from healthcare to entertainment.
Ethical AI: With the proliferation of AI systems like GPT, ethics takes center stage. The trend in 2024 revolves around ensuring that AI models are fair, unbiased, and responsible in their outputs. Developers and organizations are intensifying efforts to implement safeguards against the generation of harmful or misleading information.
Fine-tuning and Specialization: While pre-trained GPT models are powerful, they may not be perfectly suited for every specific task. A prominent trend involves fine-tuning GPT models for specialized domains. This adaptability enables developers to tailor AI for industries like legal, finance, or healthcare, expanding AI’s applicability.
Continued Scaling: GPT models continue to grow in size and complexity. GPT-4, GPT-5, and subsequent iterations are expected to feature even more parameters, enabling them to comprehend context and generate nuanced content. However, this trend raises concerns about computational resources and energy consumption.
Multilingual and Cross-Lingual Models: In our globalized world, multilingual and cross-lingual capabilities are gaining importance. Expect GPT models to advance in language understanding and generation across a wide range of languages, facilitating cross-cultural communication and comprehension.
Democratization of AI: A noteworthy trend is the democratization of AI and GPT models.
Enhanced Collaboration: Collaboration between humans and AI is flourishing. GPT is serving as a tool to assist professionals across various fields, from content creation to scientific research. This trend is poised to deepen as AI becomes an integral part of daily work and creative processes.
Personalization and Recommendation: GPT’s prowess in personalization and recommendation systems is noteworthy. It excels in understanding user preferences and delivering tailored content, products, or services. This trend is transforming e-commerce, entertainment, and more.
The world of GPT and AI is evolving rapidly, ushering in a new era of technological interaction. While the potential of GPT is vast, it brings challenges in ethics, bias, and resource utilization. These trends signify not only technological progress but also the ethical and societal considerations that will define the AI landscape.
Indeed, GPT-3 has shown the true power of Artificial Intelligence. So far, science-fiction movies were showing such possibilities but now it’s easily accessible. We are yet to see other competitive products that can show at par performance with the GPT-3. The largest language model before the GPT-3 was Microsoft’s Turing NLG, which was released in February 2020 and had a capacity of 15 billion machine learning parameters.
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