After author Jane Friedman protested that five books listed as being written by her on Amazon were actually not written by her, Amazon pulled the titles from sale. The books were also listed on Goodreads which is owned by Amazon. Friedman believes that the books were written by AI.
Friedman said, “It feels like a violation since it’s extremely poor material with my name on it.” The author, who is from Ohio, has written a number of books about the publishing industry, and the fake titles imitated her legitimate work.
How to Write and Publish an eBook Quickly and Make Money and A Step-by-Step Guide to Crafting Compelling eBooks, Building a Thriving Author Platform, and Maximizing Profitability were some of the listed books. Friedman’s real books include Publishing 101 and The Business of Being a Writer.
A reader discovered the book listings on Amazon and emailed Friedman after thinking the listings were fake, which is how she first learned about the phony titles. After reading the first few pages, Friedman assumed the books were produced by AI since she had familiarity with AI technologies like ChatGPT.
According to Friedman, the books were “if not entirely generated by AI, then at least mostly generated by AI.” She immediately started looking for ways to have the books removed from Amazon and filled out a claim form. Friedman claims that Amazon informed her it would not take the books down because she had not registered a trademark for her identity.
By Tuesday, however, the books had been removed from both Amazon and Goodreads, and Friedman believes this was as a result of her addressing the problem on social media.
Friedman said, “This will continue; it won’t end with me, unless Amazon puts some sort of policy in place to stop anyone from just uploading whatever book they want and applying whatever name they want. They don’t have a process in place for reporting this kind of conduct, where someone is trying to cash in on someone else’s name.” She urged the websites to create a way to verify authorship.
A recent opinion by Analytics India Magazine (AIM) claimed that Sam Altman’s OpenAI, which is also the creator of the infamous ChatGPT, will go bankrupt by 2024 due to the decline in user base, astronomical operational costs, and unrealistic revenue expectations. However, on detailed analysis, the claims of AIM are found to be lacking expert insights, rendering the article to be perceived as mere sensationalism.
The article cited significant daily expenditures, notably around $700,000 per day (approximately ₹5.8 crore per day), dedicated solely to ChatGPT. However, the writert fails to consider the fact that such huge expenses are quite common for early stage startups. The report also mentions that ChatGPT user base has declined as users are making use of LLM APIs in their workflows. This assertion also seems to undermine the fact that APIs are also a major source of revenue for the startup.
Microsoft-backed OpenAI has projected an annual revenue of $200 million in 2023. According to the AIM article, OpenAI “expects to reach $1 billion in 2024, which seems to be a long shot since the losses are only mounting.” We must consider here that OpenAI is still in its initial operational phase and has several projects and resources to raise decent funding to stay afloat.
After the AIM opinion gained some traction, Ather Energy CEO Tarun Mehta took to Twitter to explain how the ChatGPT maker won’t go bankrupt despite various claims.
This post seems to be going around.
People are sharing that Open AI might go bankrupt next year because it burns through 5.8cr PER DAY!
Yeah, right.
5.8cr/day is ~2000cr/year = $250M/year.
Your friendly neighbourhood Swiggy, Meesho, Paytm, Ola, Flipkart have burnt through… pic.twitter.com/Nx4babff2F
Tarun Mehta of Ather Energy voiced confidence on Sunday that OpenAI will easily manage its predicament, despite the claims of AIM. Mehta cited well-known Indian startups that later became well-known corporations, such as Flipkart, Meesho, Ola, Paytm, and Swiggy, as evidence in favor of his claim. These companies, he noted, also experienced extended periods of significant financial burns.
In addition, he pointed out that many Indian companies have seen a comparable level of capital consumption during their peak moments, and many of them have been able to maintain stability. According to him, Uber, at its height, consumed ten times the capital for an extended period of time. “They will be fine folks,” he added.
OpenAI is funded by multiple large companies and is at the forefront of Large Language Models as of now. Microsoft has invested about $10 billion in OpenAI and there is every possibility that it will continue to invest more. For years, Microsoft has been in a tug-of-war with Google. Now, partnering with and investing in OpenAI gives Microsoft a once in life-time opportunity to make Google eat dirt, and we doubt that it will let OpenAI go bankrupt.
To add to the narrative, OpenAI today announced that it has acquired a New York-based AI design studio called Global Illumination. Now, it is only sensible to ask why would a startup, which is on the very precipice of bankruptcy, spend such crucial capital on acquisition. It only implies two things, either Sam Atham is out of his senses, or OpenAI is not going bankrupt.
Considering all these facts, it is safe to say that OpenAI will not be going bankrupt, or at least at any rate not because of the reasons cited by the AIM article, and certainly not as early as 2024.
The expansion of the Digital India Programme, which includes a ₹14,903 crore boost for e-governance services, cybersecurity, and the use of artificial intelligence, was approved by the Union Cabinet on Wednesday.
Initiatives under the expanded Digital India programme would prioritize cybersecurity. The Information Security & Education Awareness Phase (ISEA) Programme will provide training on information security to about 265,000 citizens.
The Indian Computer Emergency Response Team (CERT-In), the government’s official organization for cyber forensics, emergency response, and cyber diagnostics, would be greatly expanded, according to Ashwini Vaishnaw, Union Minister for Communication, Electronics, and Information Technology. Along with developing cybersecurity technologies, the plan will also integrate more than 200 locations with the National Cyber Coordination Centre.
As previously stated, the government intends to construct three Centres of Excellence (CoE) for the growth of the nation’s ecosystem for AI research and innovation, under this program. These centers will concentrate on sustainable cities, agriculture, and health. Moreover, 22 official Indian languages will all be supported by the AI-enabled multi-language translation tool Bhashini, which is currently offered in 10 languages.
Under the National Supercomputer Mission, the government will also install nine additional supercomputers for AI modeling and weather forecasting. This will be in addition to the existing 18 supercomputers.
To enable digital delivery of services to residents, the Digital India programme was introduced in July 2015. The programme will now run for a total of five years, from 2021–2022 to 2025–2026. Over 1,200 startups from Tier-II and Tier-III cities will receive help from the government throughout the extended time.
Approximately 625,000 IT employees will receive new training and up-skilling for next-generation technologies like the Internet of Things (IoT), machine learning, data analytics, and more, as part of the second phase of the government’s digital push.
An AI startup called Global Illumination was acquired by OpenAI, the AI company behind the popular AI-powered chatbot ChatGPT. The AI startup Global Illumination, based in New York, uses artificial intelligence technology to create innovative tools, infrastructure, and digital experiences.
In a short blog post that was posted on its official site, OpenAI stated that the entire team from Global Illumination has joined OpenAI to work on our flagship products such as ChatGPT. “We are very excited for the impact they’ll have here at OpenAI,” the company said. In its almost seven-year history, this is OpenAI’s first public acquisition. The agreement’s terms weren’t made public.
Thomas Dimson, Taylor Gordon, and Joey Flynn founded Global Illumination in 2021, and since then, they have worked on a variety of initiatives. With the support of venture capital firms Paradigm, Benchmark, and Slow, Global Illumination’s team planned and built products for Instagram, YouTube, Google, Pixar, Facebook, and Riot Games early on.
Dimson played a key role in improving Instagram’s search algorithms while serving as the company’s director of engineering. He participated in the establishment of the teams in charge of IGTV, feed and Stories ranking, Instagram’s Explore tab, and general data engineering.
Biomes, an open source sandbox multiplayer online role-playing game (MMORPG) designed for the web that resembles Minecraft, is the most recent project by Global Illumination. It’s unknown what will happen to the game after the acquisition, although it is being assumed that the team’s work at OpenAI will be less focused on entertainment.
Despite the fact that OpenAI has resisted acquisitions up until now, the organization has been running funds and grant programmes for several years to support investments in start-up AI businesses and organizations. The company is backed by billions in venture capital from Microsoft and other significant VCs.
In the rapidly evolving field of translation, computer-assisted translation (CAT) tools are indispensable for professional translators. These software applications help streamline and enhance the translation process, improving efficiency and ensuring consistency.
However, with a wide range of CAT tools available on the market, it might be challenging to pick the right one. Translators should do their research and pinpoint the essential features that their CAT tool must have to significantly enhance their productivity. This article explores CAT tools in detail and highlights six critical features that every translator should consider when choosing the right tool for their needs.
What are CAT tools?
Computer-assisted translation tools are software applications specifically designed to assist professional translators in their work. These tools provide a range of features and functionalities that streamline and enhance the translation process.
CAT tools typically incorporate a translation memory (TM) to store and reuse previously translated segments, improving efficiency and consistency. They also offer various style guide and collaboration features, as well as support for various file formats.
By leveraging these tools, translators can work more effectively, save time, ensure accuracy, and promptly deliver high-quality translations. As a result, CAT tools have become indispensable in translation, revolutionizing how translators approach their work.
6 essential CAT tools features that every translator needs
Translation memory
One of the fundamental features of CAT tools is translation memory (TM). TM stores previously translated segments, allowing translators to reuse them in future projects. This not only saves time but also promotes consistency in terminology across different translations.
A good CAT tool should have a robust TM database that is easily searchable and editable, enabling translators to locate and modify previous translations quickly. This feature is particularly beneficial for translators working on large projects or those who frequently translate content in the same domain.
Termbase
The termbase, alternatively referred to as a translation glossary, serves as a repository of definitions or specific guidelines for using pre-aproved, translated terms. They are similar to dictionaries employed with translation memories, allowing translators to search for significant terms for the organization they are translating for.
Termbases play a crucial role in upholding translation precision across various projects when utilizing a CAT tool by facilitating the consistent application of shared or specialized terminology pertinent to your project. They can ensure accuracy throughout your translations and contribute to maintaining linguistic consistency within your business context.
Style guide
Translation style guides encompass a collection of directives that serve as a handbook for faithfully translating your content into each target language while preserving its inherent meaning and purpose. Style guides are valuable in guaranteeing consistent communication of your brand’s distinct characteristics across different languages, cultures, and markets.
By outlining specific guidelines, a CAT tool with a translation style guide assists in upholding brand consistency throughout different languages. It ensures the precise translation of content while retaining its original essence, helping to maintain a cohesive brand identity across linguistic boundaries.
Collaboration and project management features
CAT tools with collaboration and project management features enable translators to work seamlessly with clients, project managers, and other translators. These tools often include real-time collaboration, version control, and task assignment.
As a result, translators can easily share files, communicate with team members, and track project progress. In addition, effective collaboration and project management capabilities ensure efficient workflow, minimize errors, and promote effective communication between all stakeholders involved in the translation process.
File format support
Translators often work with various file formats, from standard text documents to complex design files. This is why a CAT tool should support multiple file formats, including Microsoft Office documents, PDFs, HTML, XML, and more.
This ensures that translators can seamlessly import and export files without the need for manual formatting, preserving the original layout and structure. A CAT tool with comprehensive file format support simplifies the translation process and saves translators valuable time, enabling them to focus on the linguistic aspects rather than technical issues.
Linguistic QA capabilities
Translation quality assurance (QA), similar to the spellcheck and grammar check tools found in most text editing software, safeguards against errors infiltrating your translation endeavors while using the tool. These QA features can detect missing text or tags, deviations from authorized terminologies, numeric inconsistencies, and more.
The QA process can start before submitting a project for translation, persist throughout the translation and editing stages, and culminate in final checks even after completing the ultimate translation version.
By employing a CAT tool powered with linguistic quality assurance, you can foster confidence that your translated content is clear of errors and maintains the utmost quality on every occasion.
A Proper CAT Tool Is a Translator’s Best Friend
CAT tools have revolutionized the translation industry by providing translators with powerful features that enhance their efficiency and quality of work. Translation memory, termbases, style guides, collaboration and project management, file format support, and quality assurance are essential features that every translator should consider when selecting a CAT tool. By leveraging these features, translators can streamline their workflow, maintain consistency, improve accuracy, and deliver high-quality, timely translations.
The establishment of the generative artificial intelligence center of excellence (CoE) at the Indian Institute of Technology (IIT), Delhi, was announced on Wednesday by Wipro. The teams at Wipro center of excellence will work on solutions based on artificial intelligence, machine learning, and other technologies.
The center will concentrate on research and development (R&D) projects and evaluate the commercial viability of research-based ventures undertaken by Yardi School of AI students at the institute. Wipro will provide financial assistance through the CoE to IIT Delhi’s generative AI research initiatives, including both fundamental and applied research.
According to a joint statement released by Wipro and IIT Delhi, the company’s $1 billion ambition to create an ecosystem of services in the field of AI, known as the “Wipro ai360” ecosystem, includes the formation of the generative AI CoE at the institute.
Professor Mausam, Dean of the Yardi School of AI at IIT Delhi said, “Students will gain valuable insight into problems of relevance to industry and will learn first-hand how their technical know-hows transfer to commercial environments with the help of the facility.”
The move is being taken as experiments and investments in generative AI continue to rise at every IT services company in the nation. During the company’s June quarter post-earnings press conference on July 12, K. Krithivasan, the recently appointed chief executive of Tata Consultancy Services, stated that the company is currently working on more than 50 proof-of-concept (PoC) projects and about 100 opportunities in the generative AI field.
OpenAI has declared that the company does not use client data given via its APIs to train its large language models, such as GPT-4. Sam Altman, the CEO of OpenAI, took to Twitter to reiterate the same amid confusions surrounding the decision. On March 1, 2023, OpenAI modified its terms of service to reflect this new commitment to user privacy, putting into effect the company’s shift in policy.
seeing a lot of confusion about this, so for clarity:
openai never trains on anything ever submitted to the api or uses that data to improve our models in any way.
Altman said, “Customers clearly want us not to train on their data, so we’ve changed our plans. We will not do that.” Altman claimed that OpenAI hasn’t been using API data for model training for a while, implying that this official statement just formalizes an already-accepted practice.
The decision made by OpenAI has broad ramifications, especially for the companies that it serves as clients, including Microsoft, Salesforce, and Snapchat. Because these businesses are more likely to use OpenAI’s API capabilities for their operations, the shift in privacy and data protection is more important to them.
The new data protection regulations, however, only apply to clients that use the company’s API services. According to the most recent version of OpenAI’s terms of service, the company may “use Content from Services other than their API”. So, unless the data is shared over the API, OpenAI may still use alternative types of data input, such as words inputted into ChatGPT.
A turning point in the current discussion concerning data privacy and AI has been reached with OpenAI’s decision to forgo using consumer data via API for training. Ensuring user privacy and upholding trust will probably continue to be at the center, as OpenAI pushes the limits of AI technology.
A new prototype of an analogue AI chip that functions like a human brain and executes intricate computations for a variety of deep neural network (DNN) applications has been announced by the tech company IBM. According to IBM, the cutting-edge chip can significantly increase artificial intelligence’s efficiency while reducing battery consumption for computers and cellphones.
The completely integrated circuit has 64 AIMC cores that are coupled via an on-chip communication network, the company stated in a blog introducing the chip. Additionally, it uses extra processing and digital activation functions that are used in each convolutional layer and long short-term memory unit.
The 64 analogue in-memory computation cores in the new AI chip were created at IBM’s Albany NanoTech Complex. In order to bridge the analogue and digital worlds, IBM claims that it has incorporated small, time-based analog-to-digital converters inside each tile or core of the chip. These converters are modeled after the main characteristics of neural networks that operate in biological brains.
According to the blog post from IBM, each tile (or core) also has compact digital processing units that carry out straightforward scaling and nonlinear neuronal activation operations. Future computers and phones could run advanced AI apps on IBM’s prototype chip instead of the ones that are now used.
IBM says that a lot of the chips being created right now separate their memory and processing units, which slows down computing. This indicates that AI models are often kept in a separate location in memory, and computational operations necessitate the frequent rerouting of data between the memory and processing units.
When comparing the human brain to conventional computers, Thanos Vasilopoulos, a scientist at IBM’s research facility in Switzerland, told BBC that the former is able to achieve remarkable performance while consuming little power. He claimed that because of the IBM chip’s improved energy efficiency, large and more complex workloads can be executed in low power or battery-constrained environments.
The New York Times has taken proactive steps to prevent the exploitation of its material for the development and training of artificial intelligence models.
The NYT changed its Terms of Service on August 3rd to forbid the use of its content, including text, pictures, audio and video clips, look and feel, metadata, and compilations, in the creation of any software programme, including, but not limited to, training a machine learning or artificial intelligence system.
The revised terms now add a restriction prohibiting the use of automatic technologies, such as website crawlers, for accessing, using, or gathering such content without express written consent from the publication. According to the NYT, there may be undefined fines or repercussions if people refuse to abide by these new regulations.
Despite adding the new guidelines to its policy, it doesn’t appear that the publication has altered its robots.txt file, which tells search engine crawlers which URLs can be viewed. The action might be in response to Google’s recent privacy policy update, which disclosed that the search engine giant may use open data from the internet to train its numerous AI services, such as Bard or Cloud AI.
However, the New York Times also agreed to a $100 million contract with Google in February, allowing the search engine to use part of the Times’ content on its platforms for the following three years. Given that both businesses would collaborate on technologies for content distribution, subscriptions, marketing, advertising, and “experimentation,” it is probable that the modifications to the NYT terms of service are aimed at rival businesses like OpenAI or Microsoft.
According to a recent announcement. website owners can now prevent OpenAI’s GPTBot web crawler from scraping their sites. Numerous large language models that power well-known AI systems like OpenAI’s ChatGPT are trained on large data sets that may contain content that has been illegally stolen from the internet or is otherwise protected by copyright.
Stability AI, the pioneering generative AI startup behind Stable Diffusion, has unveiled its first Japanese Language Model (LM), known as Japanese StableLM Alpha, in a key step towards improving the Japanese generative AI market.
As the company claims their language model to be the most effective publically available model catering to Japanese speakers, this historic debut has drawn attention. Accordingly to the company, thorough benchmark assessment against four other Japanese LMs supports the assertion. With its design of 7 billion parameters, the recently unveiled Japanese StableLM Alpha is a tribute to Stability AI’s dedication to technological development.
The well-known Apache Licence 2.0 will be used for the commercial distribution of the Japanese StableLM Base Alpha 7B iteration. This specialised model was painstakingly created after prolonged training on a massive dataset that included 750 billion tokens of both Japanese and English text that were carefully collected from web archives.
The Japanese community of Stability AI created datasets by utilising the knowledge of the EleutherAI Polyglot project’s Japanese team. The use of EleutherAI’s GPT-NeoX software, a key component of Stability AI’s development process, in an expanded form, greatly facilitated this group effort.
The Japanese StableLM Instruct Alpha 7B is a similar model that represents yet another outstanding achievement. This model was created primarily for research purposes and is only suitable for research-related applications. Through the use of several available datasets and a advanced approach known as Supervised Fine-tuning (SFT), it demonstrates a unique capacity to follow user instructions.
EleutherAI’s Language Model Evaluation Harness was used to conduct thorough evaluations that served to validate these models. The models underwent scrutiny across various domains, such as question answering, sentence classification, sentence pair classification, and sentence summarization, emerging with an impressive average score of 54.71%.
According to Stability AI, this performance indicator clearly places the Japanese StableLM Instruct Alpha 7B ahead of its rivals, demonstrating its strength and supremacy.
