ChatGPT and other large language models that can craft speeches, essays, software code and more will provide enhancements in education, healthcare, science and many fields, according to former Google CEO Eric Schmidt...

Schmidt said that while the tools can do a significant amount of good, they can also cause harm -- something he said he witnessed through his experience as Google's CEO...

https://www.techtarget.com/searchcio/news/252529683/Former-Google-CEO-outlines-dangers-of-generative-AI?utm_campaign=20230308_AI+news+update%3A+The+latest+on+ChatGPT+and+generative+AI&utm_medium=email&utm_source=MDN&asrc=EM_MDN_262370723&bt_ee=rt8xAocbGT1VAnbvpF1hRbwdC0l1GC3EvInAticivSbdI4TxYyFMcwvCv5G%2F0L4mV1tP2BQCRtlKoLCSzUtYAA%3D%3D&bt_ts=1678299525696

As generative AI models grow larger and more powerful, some scientists advocate for leaner, more energy-efficient systems...

Commercial firms are rolling out ever-larger artificial-intelligence (AI) models, with the goal of developing systems that can use reasoning to solve tasks. Some scientists think this ‘bigger is better’ approach will only suck up more electricity, and hope that mimicking aspects of the brain will help AI to become smarter and more energy-efficient...

https://www.nature.com/articles/d41586-023-00641-w

Training large language AI models at quintillions of operations on OCI

A core purpose of using artificial intelligence (AI) and machine learning (ML) is to make sense of large volumes of data by arriving at a trained model. Much like how an athlete needs to train for optimal performance during their upcoming competition, an ML model must be trained to perform optimally when deployed in production. A trained model can deliver accurate results with a small tolerance for failure, quickly, and cost-effectively on computing infrastructure...

A large language model (LLM) uses massive amounts of data, such as the data deployed on the internet over a period of a few years. It learns from this information to help us in a variety of ways...

Training AI applications, such as ChatGPT, using large language models requires powerful clusters of computing infrastructure that can process vast amounts of data. These superclusters contain hundreds or thousands of bare metal Compute instances connected by a high-performance network fabric. Collectively, these Compute instances deliver the capability to process training datasets at-scale. Meta AI estimates that these superclusters must be capable of performing quintillions of operations per second as models are becoming increasingly large, complex, and adaptable.

Oracle Cloud Infrastructure (OCI)’s AI infrastructure is already enabling customers, such as Adept and SoundHound, to process vast amounts of data for training large language models. We have made optimizations in OCI’s cluster networks to support ultra-low latencies using RDMA over converged ethernet (RoCE)...

https://blogs.oracle.com/cloud-infrastructure/post/training-llm-on-oci?source=:em:nw:mt::::RC_WWMK200429P00044C00142:NSL400281243&elq_mid=240596&sh=1726241824292331053612&cmid=WWMK200429P00044C00142

Google jostles with AI rivals as it releases a large language model API and other developer tools...

Google Cloud opened some of its AI-powered applications to developers, introducing the Pathways Language Model, or PaLM, API for language models and the Makersuite prototyping tool within it. The developer tools go hand-in-hand with Google’s new AI productivity tools in Google Workspace, which are in testing now. The productivity AI can write emails, job descriptions or other business documents on request. For developers, there are a few new ways to get their hands on these tools...

https://www.techrepublic.com/article/google-cloud-enterprise-ai-api-developers/

Hello Dr Ajay Pandurangji Shelorkar

The most obvious concern to me is that Chat GPT will help people (unfairly) write essays. How do we know this is their own thought and decision rather than something generated by a computer. It is incredibly easy to use GPT for this purpose.

https://www.zdnet.com/article/how-to-use-chatgpt-to-help-you-write/

Could ChatGPT help to democratise the research process?

Several researchers have already listed the chatbot as a co-author on academic studies, and some publishers have moved to ban this practice. But the editor-in-chief of Science, one of the top scientific journals in the world, has gone a step further and forbidden any use of text from the program in submitted papers...

For now, we think that researchers should see ChatGPT as an aide, not a threat. It may particularly be an aide for groups of researchers who tend to lack the financial resources for traditional (human) research assistance: emerging economy researchers, graduate students and early career researchers. It is just possible that ChatGPT (and similar programs) could help democratise the research process...

https://www.universityworldnews.com/post.php?story=20230221094508396

Chat GPT can create conversational AI applications like chatbot to conduct natural conversations with customers and other stakeholders. This technology launched by the American artificial intelligence research laboratory Open AI in November 2022 to analyze various types of written text, like texts and spoken language, and provide accurate responses to frequently asked questions (FAQs) and customer queries @Yasobanta

Chat GPT stands for "Conversational Generative Pre-trained Transformer" and refers to a type of large language model based on the GPT (Generative Pre-trained Transformer) architecture developed by OpenAI. These models are pre-trained on vast amounts of text data and can generate human-like responses to natural language prompts.

Merits of Chat GPT in the research field include:

1. Improved Natural Language Processing: Chat GPT has been shown to produce high-quality natural language responses, which can be used to improve tasks such as machine translation, question answering, and sentiment analysis.

2. Increased Efficiency: Because Chat GPT models are pre-trained, they can be fine-tuned for specific tasks much more quickly and with less data than traditional machine learning approaches.

3. Potential for New Applications: Chat GPT models have the potential to be used in a wide range of applications, such as chatbots, customer service, and personal assistants.

Demerits of Chat GPT in the research field include:

1. Bias: Because these models are trained on large amounts of text data, they can reflect biases in the data, which can be problematic in certain applications, such as decision-making systems.

2. Lack of Understanding: Chat GPT models generate responses based on statistical patterns in the data, which means they may not always fully understand the meaning of the input.

3. Limited Generalizability: While Chat GPT models can generate high-quality responses, they may struggle with tasks that require complex reasoning or understanding of context, which limits their generalizability.

Dear respected researchers

We have worked on a paper on that particular issue (ChatGPT and Academic research)

Title: "ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples"

The article's link:

https://www.researchgate.net/publication/369576448_ChatGPT_and_Academic_Research_A_Review_and_Recommendations_Based_on_Practical_Examples

Or

https://journals.cspc.edu.ph/index.php/jemds/article/view/175

I hope you will enjoy the article and it will be helpful for academic people.

Regards.

Thank you professor, Ljubomir Jacić, for your valuable comments.

Regards.

Why open-source generative AI models are an ethical way forward for science

Researchers should avoid the lure of proprietary models and develop transparent large language models to ensure reproducibility...

https://www.nature.com/articles/d41586-023-01295-4

Ajay Pandurangji Shelorkar

Chat GPT is an artificial intelligence chatbot developed by OpenAI and released in November 2022. It is built on top of OpenAI's GPT-3.5 and GPT-4 foundational large language models (LLMs) and has been fine-tuned using both supervised and reinforcement learning techniques¹². Chat GPT can interact in a conversational way and provide detailed and articulate answers across many domains of knowledge¹²³.

Some of the merits of Chat GPT for the research field are:

- It showcases the potential and capabilities of large language models for natural language understanding and generation, as well as their applications in various domains such as education, entertainment, health, business, etc¹²³.

- It provides a platform and a dataset for researchers to study and evaluate the performance, behavior, and limitations of large language models, as well as to develop new methods and techniques to improve them¹²⁴.

- It stimulates innovation and competition in the field of natural language processing and conversational AI, as well as fosters collaboration and communication among researchers, developers, and users¹² .

Some of the demerits of Chat GPT for the research field are:

- It sometimes produces plausible-sounding but incorrect or nonsensical answers, which can mislead or confuse users or researchers¹²⁴.

- It is sensitive to the input phrasing or the prompt, which can affect its consistency and reliability¹²⁴.

- It is not fully transparent or explainable, which can make it difficult to understand or trust its decisions or actions¹² .

- It poses ethical and social challenges, such as the privacy and security of data, the bias and fairness of models, the impact on human communication and cognition, etc¹² .

(1) Introducing ChatGPT. https://openai.com/blog/chatgpt.

(2) ChatGPT - Wikipedia. https://en.wikipedia.org/wiki/ChatGPT.

(3) ChatGPT Online. https://stablediffusionweb.com/ChatGPT.

(4) ChatGPT: What Is It & How Can You Use It?. https://www.searchenginejournal.com/what-is-chatgpt/473664/.

Why Japan is building its own version of ChatGPT

Japan’s government and big technology firms are sinking hundreds of millions of dollars into creating Japanese-language versions of ChatGPT. The AI chatbot and similar large language models (LLMs) “excel in English, but often fall short in Japanese due to differences in the alphabet system, limited data and other factors”, says machine-learning researcher Keisuke Sakaguchi. One LLM, which uses the Japanese supercomputer Fugaku, is expected to be released next year...

https://www.nature.com/articles/d41586-023-02868-z

How to stop AI deepfakes from sinking society — and science

Deceptive videos and images created using generative AI could sway elections, crash stock markets and ruin reputations. Researchers are developing methods to limit their harm...

Watermarking technologies could help, Pulverer adds. This idea has been explored previously for medical images — not to prevent AI alterations, but to protect privacy and prevent any kind of tampering. Pulverer says he plans to work with instrument makers and image-processing experts to discuss the feasibility of watermarking or provenance-tagging scientific imagery.

Beyond developing technological tools against fraud, he says, the key is to encourage efforts to reproduce data in science. Attempting to replicate results could be the ultimate way to catch out fakery...

https://www.nature.com/articles/d41586-023-02990-y

Approaching Artificial Intelligence and Open Research (OR) in Sync: Opportunities and Challenges

As scholarly publishing starts to come to grips with changes around OR, artificial intelligence (AI) and generative AI (GAI) have crashed onto the scene, with the launch of GAI products such as ChatGPT and Google Bard boosting applications and usage. AI is growing fast and has shifted from the R&D and prototype stage to the full commercial application stage — and from analytic AI that examines existing content to generative AI that generates new information. As its capabilities develop, AI and GAI should align with and be governed by human goals and values and be compliant with applicable laws...

There is a natural conflict between the principles of transparency that underpin the OR movement and the “black box” nature of existing AI models; for many people, it’s difficult to trust conclusions generated by a process they can neither see nor fully understand. Many AI researchers and providers are currently working on enhancing their models to be more transparent, accurate and their use more conscious of human rights, but this will take time...

https://scholarlykitchen.sspnet.org/2023/11/14/approaching-artificial-intelligence-and-open-research-in-sync-opportunities-and-challenges/?informz=1&nbd=6f03e560-5431-4744-8998-e00223ee7a82&nbd_source=informz

Bringing Generative AI to the Web of Science

Interested in becoming a beta tester? Contact info is at the end of the article.

Generative AI presents enormous opportunities to transform how scientific and scholarly research is carried out. Faced with the pressure to publish, a more complex research environment to navigate, and growing administrative demands, researchers need better tools available to them. To meet these challenges, Clarivate™ is developing cutting-edge AI technology that will remove roadblocks in the research and publishing process and ultimately contribute to the betterment of research. We previously shared plans to develop and embed market-leading generative AI, including conversational discovery, into our academic solutions, and are delighted to announce that the Web of Science™ AI Research Assistant will be available for beta testing this December...

https://clarivate.com/blog/bringing-generative-ai-to-the-web-of-science/?utm_campaign=EM1_RTL_LeadGen_Newsletter_Nov_AG_EMEA_2023&utm_medium=email&utm_source=Eloqua

There’s more to AI tools than just ChatGPT. Here are five others that can support your academic research:

• Consensus replies to questions n certain topic areas and its answers are in accord with the academic consensus.
• Elicit is useful for brainstorming ideas and summarizing papers.
• Scite gives detailed lists of genuine citations.
• Research Rabbit can learn about your interests and generate recommendations for papers to read.
• ChatPDF allows you to upload a paper and start asking questions about it...

https://www.euronews.com/next/2023/11/12/best-ai-tools-academic-research-chatgpt-consensus-chatpdf-elicit-research-rabbit-scite

"The transformative capability of AI in hypothesis formation is reshaping the landscape of scientific research. But this is not possible without the collaborative partnership between humans and machines, having the potential to drive unprecedented progress. Thus, it is imperative to navigate the challenges associated with AI integration and embrace a symbiotic relationship between human intellect and AI; with which we can unlock the full potential of this dynamic collaboration and usher in a new era of scientific exploration and understanding."

https://www.enago.com/academy/ai-generated-research-hypothesis/

AI Can Help Democracy

Talking politics in a polarized society can get heated fast. Chatbots could help us keep our cool...

"Human facilitators have had some success mediating divisive online political conversations—but so many of these conversations are happening simultaneously all over social media that hiring a brigade of human experts to intervene would be expensive and difficult to scale. So social scientist Lisa Argyle and colleagues at Brigham Young University, Duke University, and University of Washington set out to see if artificial intelligence could help. For their recent study, the researchers developed an AI chat assistant using GPT (the same AI behind ChatGPT) and found it improved the civility of political discussions online, where they so often quickly fall apart..."

https://nautil.us/ai-can-help-democracy-465321/

Chat GPT is an AI-powered language model developed by OpenAI, capable of generating human-like text based on context and past conversations. It can also interact in a conversational way and follow instructions in a prompt. Chat GPT has many applications, such as conversational AI, content generation, text classification, and more. It can assist researchers and scholars with various tasks, such as writing, summarizing, finding information, and creating content. However, Chat GPT also has some limitations and risks, such as producing inaccurate, misleading, or harmful texts, depending on the prompt and the data it was trained on. It can also pose ethical, social, and legal challenges, such as privacy, security, bias, accountability, and ownership. Users should always verify the source and quality of the texts generated by Chat GPT and use them responsibly and appropriately.

What is ChatGPT and why does it matter? Here's what you need to know

"Despite looking very impressive, ChatGPT still has limitations. Users sometimes need to reword questions multiple times for ChatGPT to understand their intent. A bigger limitation is a lack of quality in the responses it delivers -- which can sometimes be plausible-sounding, but make no practical sense, or can be excessively verbose..."

https://www.zdnet.com/article/what-is-chatgpt-and-why-does-it-matter-heres-everything-you-need-to-know/

Study to look at ethical risks of AI in research

Project aims to help protect the privacy of those who participate in research...

"A new 10-month project led at the University of Strathclyde aims to help researchers and their institutions make informed decisions on how they use generative AI with participant data, in order to protect the privacy of those who participate in research...

The research aims to help guide research institutions, University Research Ethics Committees, regulatory authorities, funders, including REPHRAIN itself, data custodians, professional organisations, publishers, and advocacy groups in their early encounters with research involving Generative AI..."

https://www.researchinformation.info/news/study-look-ethical-risks-ai-research?utm_campaign=RI%20Newsline%2030-01-24&utm_content=Study%20to%20look%20at%20ethical%20risks%20of%20AI%20in%20research&utm_term=Research%20Information&utm_medium=email&utm_source=Adestra

Research assistance

"The ability of transformer models – the deep-learning architecture behind LLMs – to manipulate language has made LLMs superlative at summarising texts, changing style and correcting spelling and grammar.

Moreover, several other AI-related tools that assist in the research process have recently been introduced. For instance, research summarising tools (eg: Elicit, Perplexity and Consensus) can ‘find’ work (published but unknown to the scholar), ‘understand’ discourses, identify research gaps and assist with literature reviews.

Other tools can manufacture artificial data. Here, a scholar might give instructions regarding what a data set should contain, and, in the absence of this being available (or impossible to gather, for, say, ethical reasons), such data can be ‘created’ instantly..."

https://www.universityworldnews.com/post.php?story=20240207185336305