📬 07-Active Learning is the Future of Generative AI, AI Floods Amazon with Hundreds of Books Written by ChatGPT
Transforming AI with Active Learning, ChatGPT Floods Amazon with a Library of Books
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AI Floods Amazon with Hundreds of Books Written by ChatGPT
Have you noticed an influx of new books on Amazon? Well, you can thank generative artificial intelligence for that. Close to 300 books written or co-written by OpenAI’s AI software, ChatGPT, were listed on Amazon recently, ranging from fantasy fiction to self-help and non-fiction. The titles of the books include some intriguing ones such as ‘ChatGPT smarter than humans?’ and ‘Make more money with ChatGPT’.
Interestingly, some authors have even used ChatGPT alongside other generative AI software to create illustrated children’s books entirely produced by artificial intelligence through simple text prompts. Due to the nature of ChatGPT and many authors’ failure to disclose they have used it, it is nearly impossible to get a full accounting of how many e-books may be written by AI.
But this technology has ruffled some feathers among some of the biggest technology firms, prompting Alphabet and Microsoft to hastily debut new functions in Google and Bing that incorporate AI. Despite the potential benefits of AI-powered writing tools like ChatGPT, there are concerns about the authenticity and potential biases that could be programmed into the algorithm.
But this doesn't seem to deter many first-time authors and self-help gurus from turning to the software to create bot-made e-books and publish them through Amazon’s Kindle Direct Publishing arm. Although this trend raises concerns over the quality of books, the Authors Guild has urged for transparency from authors and platforms about how these books are created.
When asked for comment, Amazon did not address whether it had plans to change or review its Kindle store policies around authors’ use of AI or other automated writing tools.
So, what does this mean for the future of book writing? Will AI-powered writing tools eventually replace human authors altogether, or will it lead to a new era of literary creativity? Only time will tell.
Active Learning is the Future of Generative AI
Data is essential for training AI models. Yet, data remains one of the most significant roadblocks to success for enterprises and teams engaging in artificial intelligence (AI). For one thing, a lot of it is required to develop high-performing models. Furthermore, you require data that has been correctly labeled. While many teams begin by manually labeling their datasets, many are resorting to time-saving technologies, such as active learning, to partially automate the process. To comprehend active learning, you must first understand the distinction between supervised and unsupervised machine learning. There is a ground truth in supervised learning. We feed the machine correctly labeled data, and it learns how to anticipate the correct response for unlabeled data from those examples.
Active learning fits under what we call "semi-supervised learning". Whereas a fully supervised learning strategy will give the model a whole, labeled dataset, a semi-supervised active learning approach will give the model only a labeled portion of the dataset, with the notion that not all data is necessary or helpful for training. The active learning method involves prioritizing which data from the dataset should be labeled for training the model. Essentially, the model gets to choose the data it wants to learn from on its own.
Active learning transforms model training from a linear to a circular process with a strong feedback loop.
Why is active learning important for Generative AI?
Active learning is critical for AI-generated businesses for various reasons:
Quality assurance: Active learning can assist AI-generated businesses in ensuring the quality of their products or services. By labeling the most useful samples and adding them to the training set, the model can learn more rapidly and efficiently, resulting in higher-quality AI outputs.
Customer happiness: By offering more accurate and tailored outputs, AI-generated firms can boost customer pleasure. For example, an AI-generated customer care chatbot that has been taught via active learning can respond to consumer inquiries in a more accurate and helpful manner.
Efficient use of resources: Active learning makes better use of resources by using less labeled data to train an AI model. As a result, AI-generated businesses can save time and resources that would otherwise be spent classifying enormous amounts of data, making the process of producing AI outputs more cost-effective.
Innovation: Active learning can also assist AI-generated enterprises in innovating and staying ahead of the market by allowing them to swiftly react to new data and build new AI-generated products or services.
Overall, active learning is critical for AI-generated enterprises to maintain output quality, boost customer satisfaction, make optimal use of resources, and promote innovation in their industry.
Corner of Hot News
South Korean startup rival to Nvidia seeks $400 million valuation and plans new A.I. chip - CNBC
AI-focused VC, Sure Valley Ventures announces €30M fund to back Irish software startups - TFN
BackNexus Ventures Partners closes $700 million fund, to invest in AI, SaaS startups - Mint
Generative AI Startup Typeface Emerges From Stealth With $65M - Crunchbase News
Quantifind, a provider of AI-powered financial crimes risk management solutions, raised $23M in funding- Finsmes
Transmetrics, an AI platform for the supply chain industry, raised €2.5M in funding - The Recursive
Windows 11 update accelerates Microsoft’s big AI push - Microsoft
NeoSwap AI, an AI-powered NFT trading startup, secured $2m in pre-seed funding at a valuation of $15m - Finsmes
Must or Should, We Do Recommend
🎧 Podcast - Riding Unicorns: Riding Unicorns dives into the operational side of building tech unicorns through interviews with the best founders and investors in the venture space. A must-listen for any founder or aspiring founder.
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