In a great leap forward for writers, ChatGPT is now able to auto-format the research it does for you into beautifully presentable, downloadable, .PDFs.
Now available to ChatGPT Plus, Team and Pro subscribers, the extremely helpful feature works with ChatGPT’s Deep Research.
The tool is an AI agent that can be prompted to do extensive research on your behalf and come back with a well-researched report, complete with link citations.
Observes Michael Nunez: The export feature enables users to “download comprehensive research reports with fully preserved formatting, tables, images, and clickable citations.”
Writers and researchers, for example, will be able to prompt ChatGPT Deep Research to create an extremely informative and artfully produced .PDF that will be presented by ChatGPT as a finished report – or ebook.
Bonus: The new export-to-.PDF feature works on both new reports and prior reports you’ve created with Deep Research.
Subscribers to ChatGPT Enterprise and Education accounts are promised to see the new feature soon, according to Nunez.
In other news and analysis on AI writing:
*ChatGPT Now Connects to Your Data Library on OneDrive or SharePoint: Writers and researchers with a wealth of data stored on MS OneDrive or SharePoint have a new, competitive advantage: They can now seamlessly integrate those databases with ChatGPT Deep Research.
The new feature, still in beta, enables users to prompt ChatGPT’s Deep Research tool to search those databases – which is especially handy if you know that the data you’re looking for is there, but you don’t know precisely where.
Other database platforms that also integrate with ChatGPT – at least in this beta application – are Dropbox and GitHub.
ChatGPT Plus, Pro and Team subscribers already have access to this extremely powerful new capability.
Access for ChatGPT Education and Enterprise subscribers is promised soon.
*Shoot-Out: ChatGPT Overall Best Choice for AI-Powered Deep Research: Writer Lance Whitney finds in his own tests that pound-for-pound, ChatGPT currently does the most in-depth, consumer-grade, AI-powered ‘Deep Research.’
Observes Whitney: “Though it took the longest to finish the job, its report was the most thorough, in-depth, well-written and interesting to read.”
Other chatbots Whitney tested against ChatGPT were Google Gemini, Perplexity AI and xAI’s Grok.
*ChatGPT Competitor New Contender in Deep Research: ARI Enterprise – a new Deep Research platform – has scored high results as an AI tool for examining a narrow topic for an extended period of time and then coming back with a finished report on that topic.
The tool, which can search the Web, your company’s internal database plus premium databases available via Internet access, scored 80% for accuracy.
Essentially, ARI analyzes over 500 sources simultaneously across public Web data — in addition to secure, private documents and premium databases.
This comprehensive research approach gives decision-makers the confidence that no critical insight has been overlooked, according to Richard Socher, CEO, You.com.
*Next Generation AI Agents for Research and Other Uses Will Bypass Your IDs and Passwords: Coders are furiously working on a next generation of AI agents – which can perform multiple tasks for you without supervision – that will simply bypass your ID and password when they need to access your various online accounts and apps – including your bank accounts.
Observes writer Steve Rosenbush: “The AI ecosystem is working on the ‘plumbing’ that will make such complex AI agents possible.
“The introduction of app stores in 2008 abruptly and broadly changed the norms by which people interact with the world.
“AI agents could be very close to triggering something just as big.”
*ChatGPT: Okay if We Ingest Every Detail of Your Life?:” In its quest to become your go-to AI buddy, shaman, know-it-all – and oh yes, writing tool – for all time, ChatGPT wants to be able to ingest every detail of your life, forever.
Observes ChatGPT-Maker CEO Sam Altman: ChatGPT’s ultimate AI capability will be able to “reason across your whole context and do it efficiently. And every conversation you’ve ever had in your life, every book you’ve ever read, every email you’ve ever read, everything you’ve ever looked at is in there, plus connected to all your data from other sources. And your life just keeps appending to the context.”
Granted, offering up all the details of your life to an AI corporation is a disturbing concept to many.
But given that many users of AI already see their AI chatbots as trusted – and sometimes, even romantic – companions, it appears certain that a significant percentage of users are ready for ChatGPT and similar AI to ‘know everything’ and become their most intimate confidant.
*Cherry-Picking AI Chatbots, Based on Need: One Writer’s Take: Writer Kelsey Piper offers an interesting, extremely in-depth rundown in this piece on her favorite AI chatbots, based on what she needs done.
Some interesting highlights:
–Best Overall AI Chatbot: ChatGPT. “ChatGPT gets you the most bang for your buck,” Piper observes.
–Best for Writing Fiction: ChatGPT-4.5: The downside is that at the ChatGPT Plus level, users are limited to sending 20 messages to ChatGPT-4.5 per month.
–Best for AI Imaging: ChatGPT-4o’s image creator. It’s “the best AI out there for generating images — by a large margin” according to Piper.
–Best at Being Your Friend: Claude 3.5 Sonnet. Observes Piper: “I’ve had far more fascinating or thought-provoking interactions with Claude than any other model. And it’s my go-to if I want to explore ideas rather than accomplish a particular task.”
*PhD in AI Cheating?: Student Plagiarism and Similar AI Fraud Now Rampant: A new study finds that student cheating with AI has essentially overtaken higher education.
Observes writer Mike Kaput: “For a growing number of students, using generative AI to complete assignments isn’t an exception. It’s the norm.
”From Ivy League halls to community college classrooms, students are increasingly offloading their cognitive labor to AI — including automating note-taking, summarizing readings, writing code, and even generating entire essays.”
The result?: “Faced with this tidal wave, many educators are in open despair, according to the report,” adds Kaput.
*Turbo-Charging Gmail With the Top Five AI Chrome Extensions: One Writer’s Take: Writer Doug Aamoth serves-up his top five favorite AI Chrome extensions for bringing Gmail to the next level.
His picks:
–Mailmeteor: A mail-merge tool that enables you to create a template email you want to send to say 100 companies, and then personalize each, individual email sent with data drawn from each individual company on your mailing list.
–Concisely: Auto-summarize your emails in a single sentence.
–Composte AI Extension: Offers suggestions for enhancing your email as you type.
–Inbox Purge: Automatically de-clutter and categorize your emails.
–Grammarly: A pioneer in AI editing/writing that now integrates seamlessly into Gmail with this Chrome extension.
*AI Big Picture: “Eric Schmidt: The AI Revolution is Under-Hyped”: In an unusual tongue-in-cheek pronouncement, Eric Schmidt – former Google CEO – characterizes the current AI tsunami as under-hyped.
Credited for transforming the fledgling, early 2000s Google into one of the planet’s top five tech powerhouses today, Schmidt and his perspective carries serious weight.
One of Schmidt’s most surprising revelations in this 25-minute, TED Talk video: In the fierce competition between major players like the U.S., China and other nations, one of those governments may not be willing to sit back and watch as a competitor lunges so far ahead in the AI race that they will leave the rest behind forever.
Instead, the envious competitor watching from the sidelines may simply decide to bomb the datacenter of the world’s AI leader, just to even things out.
Share a Link: Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.
–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.
The post Hey There, Good Lookin’ appeared first on Robot Writers AI.
Claire chatted to Lerrel Pinto from New York University about using machine learning to train robots to adapt to new environments.
Lerrel Pinto is an Assistant Professor of Computer Science at New York University (NYU). His research is aimed at getting robots to generalize and adapt in the messy world we live in. His lab focuses broadly on robot learning and decision making, with an emphasis on large-scale learning (both data and models); representation learning for sensory data; developing algorithms to model actions and behaviour; reinforcement learning for adapting to new scenarios; and building open-source, affordable robots.
The 2025 IEEE International Conference on Robotics and Automation (ICRA) will take place from 19-23 May, in Atlanta, USA. The event will feature plenary talks, technical sessions, posters, workshops and tutorials, forums, and a science communication short course.
There are three plenary sessions this year. The speakers are as follows:
Tuesday 20, Wednesday 21 and Thursday 22 will see a total of 12 keynote sessions. The featured topics and speakers are:
Join Sabine Hauert, Evan Ackerman and Laura Bridgeman for a crash course on science communication. In this concise tutorial, you will learn how to share your work with a broader audience. This session will take place on 22 May, 11:00 – 12:15.
The programme of workshops and tutorials will take place on Monday 19 May and Friday 23 May. There are 59 events to choose from, and you can see the full list here.
There will be three forums as part of the programme, one each on Tuesday 20, Wednesday 21 and Thursday 22.
Wednesday 21 May is community building day, with six events planned:
You can find out more about the other sessions and event at the links below:
In our high-speed era of a fast and furious digital lifestyle, the terms “automation” and “Artificial Intelligence (AI)” are drivers. While at first glance they appear to speak of the same things robots doing things with little human intervention, they are actually distinct technologies and have different jobs and impacts.
Knowing the main differences between automation and AI is vital, particularly with businesses and society becoming more reliant on them. This article discusses the difference between automation and artificial intelligence, challenges, and applications on industries and employees.
Automation means applying technology to perform tasks with little or no human intervention. The overall goal of automation is to create efficiency, consistency, and speed. Through automation, we can definite procedures, rules, or processes, which are performed by equipment without having to “think” or “learn.”
Artificial intelligence, however, is the simulation of human intelligence on machines. AI allows systems to learn through experience, adapt, and make decisions based on sophisticated algorithms instead of pre-programmed rules.
While automation only gets to do things according to the rule, AI gets to handle uncertainty, solve issues, and even mimic such high-level thinking as learning and solving problems.
With automation and AI doing the repetitive jobs, many of the jobs, especially those in sectors like manufacturing and retail, are disappearing. This is supporting more stress on low-skilled workers and can widen the gap between the poor and rich.
AI needs large amounts of data to operate optimally, but getting all that data is a direct threat to privacy. Tools like facial recognition can track people without their permission, overstepping on basic rights and freedoms if unregulated.
AI decides on black processes, but even to those who create it. However, when something goes wrong, like an incorrect medical diagnosis, it is unclear who is responsible.
As deals with data, AI systems can be hacked with disastrous effects. For instance, autonomous vehicles might be tricked by bogus information, or AI might be employed in cyberattacks. Strong defenses must be constructed to make these systems safe and secure.
As we increasingly depend on AI to make routine decisions, there’s a chance we’ll begin losing our own capabilities. If we let the machines do all the thinking for us, we’ll be forgetting how to make decisions, solve problems, or even perform our work efficiently without them.
True potential is not either-or, automation vs. AI, but mastering how to use them together. Used correctly:
These companies that capitalize on this synergy will be able to innovate, compete, and build strong futures.
The expense of building AI can be prohibitive, here are some reasons why it is so costly:
1. Research and Development
It is expensive to recruit skilled AI researchers, data scientists, and engineers. They are in-demand individuals and get compensated well. The finest AI talent usually comes from academia or leading tech companies, so it is competitive and usually pricey to recruit them.
2. Data Collection and Labeling
AI models need huge amounts of high-quality data to learn from, especially for healthcare applications, where data must be carefully curated and anonymized. Collecting, cleaning, and labeling such data is labor-intensive, which reduces costs.
3. Computational Resources
Training advanced AI models like large language models or computer vision requires enormous computational resources. That entails high-end GPUs or TPUs, which are extremely costly to buy or rent from cloud providers. The power consumption also commands a significant portion of ongoing operational costs.
4. Infrastructure and Maintenance
Building and maintaining AI infrastructure, including servers, storage, networking, and monitoring software, requires long-term investment.
5. Testing and Safety Measures
AI development involves a lot of testing, including model verification, bias identification, and safety checks. For self-driving cars or medical diagnostics, this testing must be highly specific, sometimes to the extent of requiring real-world tests and regulatory approval, and both are expensive.
6. Legal and Compliance Costs
AI development must meet regulatory requirements and adherence to law in data protection (e.g., GDPR) saves costs significantly.
7. Deployment and Scaling
Migrating an AI model means adaptation and interfacing with other systems. Scaling AI to numerous regions, languages, or platforms adds additional expense.
AI and automation are change drivers with inherent strengths and potential. Where automation works by speed through inflexible, fixed principles, AI is gifted with learning, growth, and decision abilities. Rather than setting the two against each other as new technologies, they are better placed to be put side by side as complementary technologies. They revolutionize the way of living, working, and existing with the world entirely together.
Connect with USM Business Systems, the best AI development company, to bring your dreams into reality.
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