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Recent Advances in AI: From GPT-3 to Advancement in Video Content to Self-Supervised Learning

Bitcoinist
by Bitcoinist
2 years ago
·
Posted in Press releases
Trusted Editorial content, reviewed by leading industry experts and seasoned editors. Ad Disclosure

The future of technology seems quite promising with recent Artificial Intelligence (AI) developments that make life more efficient and convenient. The fascinating thing about AI is that it has developments for specific purposes like self-driving cars or medical diagnoses while still having developments that encompass multiple branches in one go.

 

This rapidly evolving field has made commendable strides that have positively altered our relationship with technology in the areas of computer vision, machine learning, natural language processing, and robotics. Some of the sectors of the economy that these technologies are completely transforming include healthcare, entertainment (video content), transportation, finance, and education.

We will examine three of the most recent advancements in AI and their use cases.

Generative Pre-trained Transformer 3 (GPT-3)

One of the most impressive AI language models developed to date, GPT-3, has taken the world by storm with its ability to generate natural language text that is virtually indistinguishable from text written by humans. It was created by OpenAI with the aim to revolutionize the creative space with over 175 billion parameters making it the largest and most powerful language model created to date compared to GPT-2, its predecessor that had 1.5 billion parameters.

GPT-3’s AI machinery can carry out Natural Language Processing (NLP) activities like summarization, translation, and answering questions. It achieves this through training on massive amounts of text data from articles, websites, and books to learn the relationship and patterns behind specific words and phrases.

There are a wide range of use cases where we can apply GPT-3 outside the natural language processing scope such as:

  • Content Creation – Generating content for different purposes such as articles, social media posts, among others
  • Customer Service and Chatbots – Creation of chatbots that can provide support and interact with customers
  • Business Automation – Automating specific business processes including but not limited to email management, scheduling, and data entry.
  • Education and Training – GPT-3 can serve as a valid tool for creating personalized training materials and educational content to help employees and students learn more efficiently and effectively.

Blockchain-AI in Video Content

AI is also positively revamping the entertainment sector in a number of ways by leveraging the strengths of another next-gen technology, Blockchain. Through the combination of Blockchain and AI, content creators, distributors, and advertisers are able to create new revenue streams, increase engagement, and provide a better user experience for viewers. One project that is leading this Blockchain-AI revolution in the video content space is AIWORK, a blockchain network facilitating a marketplace of crowdsourced AI Human Experts to help create, verify and validate AI data sets that make AI smarter

As a blockchain-based platform, AIWORK rises above the likes of YouTube with its ability to distribute content directly to audiences, without the need for intermediaries. While viewers enjoy easier access to the content they like, content creators will enjoy Blockchain’s ability to protect their video content against unauthorized use or distribution, creating a tamper-proof digital ledger of ownership and rights. Not only does this increase transparency between viewers and content creators but it also reduces costs and enables creators to retain greater control over their work.

AIWORK uses its AI computing machinery to take their platform to the next level, above the dominant video content platforms out there through the personalization of watchable content. With AI, video content can be personalized to the individual viewer, which improves engagement and retention. This sort of personalization or curation of video content helps users to discover new content that is relevant to their interests. The same feature can also help advertisers through changes in targeted advertising. AI will analyze user data and preferences so that advertisers on the AIWORK platform can serve ads to viewers who are most likely to be interested in the product or service being advertised. Working as a double-edged sword, this technique will improve the effectiveness of advertising campaigns and increase revenue for content creators.

Self-Supervised Learning

Self-supervised learning is a type of machine learning that lets machines learn from large amounts of unstructured data without the need for human oversight. In the past, this kind of learning involved labeled data, where each data point is labeled with the correct output, while today’s AI-aided unsupervised learning involves clustering or dimensionality reduction techniques.

The AIWORK team is working on incorporating this technology into their platform as a  model might be trained to predict the next frame in a video, or to fill in a missing word in a sentence when using the video platform search bar. When the model learns to perform specific tasks, it develops a rich understanding of the underlying data structure, which can be used to perform other tasks.

Other than showing great potential in fields such as natural language processing and computer vision, the AIWORK project is bringing this onto the video content ecosystem by incorporating it in a number of ways, which include:

  • Video Understanding – Training models to understand video content without focusing on explicit labels and/or annotations. A model could be trained to predict the next frame in a video, based on the previous frames, or to identify specific objects or actions in a video.
  • Video Captioning – Generating captions for videos automatically, based on the content of the video.
  • Video Editing – Using self-supervised learning to automate specific aspects of video editing like splicing different clips together or removing unwanted sections before a content creator uploads the video to your platform.
  • Video Recommendation – The tech can be used to improve video recommendation systems by training the AI models to predict which videos a user is likely to watch next, based on their viewing history and other contextual information. This makes recommendation systems become more accurate and personalized.

Summing it Up

These recent AI breakthroughs signify that next-gen technologies are continually evolving and hold great promise for the future – from GPT-3’s capabilities of natural language processing, to AIWORK’s advancements in ensuring security and preventing piracy in the video content scene, then finally witnessing the breakthroughs paving the way for unsupervised learning. As these technologies continue to evolve and become more sophisticated, we can expect to see even greater innovation in more divergent sectors of the economy in the years to come.

Disclaimer: This is a paid release. The statements, views and opinions expressed in this column are solely those of the content provider and do not necessarily represent those of Bitcoinist. Bitcoinist does not guarantee the accuracy or timeliness of information available in such content. Do your research and invest at your own risk.

Editorial Process for bitcoinist is centered on delivering thoroughly researched, accurate, and unbiased content. We uphold strict sourcing standards, and each page undergoes diligent review by our team of top technology experts and seasoned editors. This process ensures the integrity, relevance, and value of our content for our readers.
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