Nature Journal Machine Learning- Nature Machine Intelligence provides original research and reviews on robotics, AI, and machine learning. It looks at how these topics affect many scientific fields, as well as society and business. The article talks about how artificial intelligence (AI) could make people better at many things, such as urban planning, healthcare, and scientific study. Because things are changing so quickly, the magazine wants to talk about these moral, social, and legal issues from different fields.

Through Comments, News Features, News & Views items, and Correspondence, Nature Machine Intelligence gives people a place to talk about these big effects. The magazine, like all Nature publications, is dedicated to strict peer review, on-time publication, editorial independence, and high editorial standards.

Artificial intelligence and machine learning are talked about in this magazine in terms of their social and moral effects. It stresses how important it is to think about social, legal, and moral issues when making and using AI systems. It also pushes people to talk about how these technologies might affect society.

Statement on Nature Machine Intelligence

Machine learning has made people want their studies to be open and free to everyone. As an example, the Editorial Board of the Machine Learning Journal quit all at once in 2001 so that the Journal of Machine Learning Research (JMLR) could be created as a new free open-access journal.

This is an excerpt from my 2001 departure letter:

“…journals should principally serve the needs of the intellectual community, in particular by providing the immediate and universal access to journal articles that modern technology supports, and doing so at a cost that excludes no one.”

Aside from JMLR, almost all major machine-learning journals do not charge any fees for paper access or publication. These include NIPS, ICML, ICLR, COLT, UAI, and AISTATS.

The researchers below have publicly said that they will not be submitting to, reviewing, or editing for the new closed-access journal “Nature Machine Intelligence,” which was recently launched by Nature Publishing Group.

It would be a step backward for us if this new magazine became the official record for the machine learning community. We need help finding a place for author-fee or limited-access printing in the future of machine learning research. In the fields of machine learning and artificial intelligence, on the other hand, we would like to see more free, open-access studies and conferences.

Researchers Boycott New Nature Journal on Machine Learning

Artificial intelligence (AI) or machine learning is the process of teaching robots to do things that humans can do, like learn new things and solve problems. Open-access journals are where most machine learning researchers share their work. Their new journal, Nature Machine Intelligence, on the other hand, has put out a call for studies. Even though Nature has a good image, people in the machine learning community are against the new magazine. Some have even signed a petition to stop reading it.

The goal of Nature Machine Intelligence is to cover a wide range of themes in robotics, machine learning, and AI. It also wants to start a conversation about how AI might affect business and society. The first one is set to go live in January 2019.

A lot of the time, scholars want to write in journals with a high impact factor, even if they need more open access. Higher significance is likely if the effect factors are better. The impact factor is based on how many times a piece from a journal is cited on average. Nature is a well-known scientific magazine, and many scientists hope to have their work published there. Because of this, the authors hoped that the scientific community would really like this new closed-access book.

Top Research Topics at Nature Machine Intelligence?

Artificial intelligence, deep learning, machine learning, artificial neural networks, and pattern recognition are just some of the fields that fall under the umbrella term “nature machine intelligence.” The journal often publishes papers that combine studies on computer vision and artificial intelligence. In the same way, it talks about deep learning along with computer biology and benchmarking.

The focus of the journal is on machine learning, but it also covers related areas like computer science. Its robotics study often looks at how people and computers can work together.

Articles about artificial intelligence (56.75%) make up the majority of the journal’s material. Articles about deep learning (23.69%) and machine learning (17.91%) follow. These numbers show the variety of studies and discoveries made in artificial intelligence and related areas. They show that the journal covers a lot of ground in these interconnected areas.

Why thousands of AI researchers are boycotting the new Nature journal

It can take a lot of work for new writers to get their work published. People can pay a large fee of up to $3,000 if they want all readers to see their work. Readers can also be charged to cover the costs of the business instead of this fee. Most libraries pay these costs by charging large fees every year. This is how academic writing works; it only happens to people who want to write fiction.

A scientist from Palermo, Sicily, named Giuseppe Piazzi, found a dwarf planet more than 200 years ago. In his message to his friend Franz von Zach, he wrote about his plans to publish. Von Zach picked up letters from scientists all over Europe every month and sent them to other astronomers. The Monatliche Correspondenz were leather-bound books of letters that astronomers used to keep up with the latest findings since there was no internet back then. Despite this, problems arose because the study’s spread took time. It was already dark outside when Piazzi released his data; the planet was now blocked by the sun.

Luckily, a reader from Gottingen, who is 23 years old, saved the day. Carl Friedrich Gauss used Kepler’s ideas about how planets move to figure out where the object we now call Ceres is located. Piazzi and Gauss, who later became Germany’s best scientists, were eager to share what they had learned, but they also knew that von Zach should be rewarded for his hard work. People who make this kind of release are called “closed-access publishers.”

Machine learning articles from across Nature Portfolio

Machine learning is the process by which a computer can get better at what it does by looking at old data. These methods let computers learn without being explicitly programmed. This opens up a lot of options, such as making data mining algorithms better.

Machine learning is the process of using data to find patterns that can help you make smart choices or guesses. Machines are always making their models and programs better, which makes them more accurate and productive over time. Machine learning is different from standard programming methods because it can change and grow based on experience.

There are many useful and important uses for machine learning. When it comes to data mining, machine learning techniques are very important for finding patterns and insights in big datasets. These strategies make it easier to analyze data by finding patterns and correlations automatically. This lets businesses get useful information and make smart choices.

Machine learning is also used in many other areas and industries besides data mining. Targeted advertising in marketing and e-commerce, illness diagnosis in healthcare, and predictive maintenance in production are just some of the many possible uses.

What is the nature of machine learning?

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.

Computers can learn from their mistakes and get better even if they are not explicitly programmed to do so. This is known as machine learning. Data mining methods can be made better, which is one of its many uses.

In biology, the way tissues work is affected by how cells connect with their surroundings. To figure out where single-cell RNA sequencing datasets are stored, you have to model how phenotype and microenvironment interact with spatial transcriptomics data. You can do this by using methods like covariance environment (COVET), niche representation, and environmental variational inference (ENVI).

Cell segmentation is usually done using special algorithms that are made for certain kinds of cells, tissues, stains, and imaging methods. An all-purpose algorithm has been made that can tell the difference between different types of cells and pictures taken with a microscope using a variety of imaging methods.

A new paper gave a machine-learning way to look into how mutations affect protein sensors that are commonly used in fluorescence microscopy. Data-driven analysis is used in this method to make it easier to find high-performance sensors.

What is the abbreviation for Nature Machine Intelligence journal?

Journal abbreviation

The correct abbreviation for abstracting and indexing purposes is Nat. Mach. Intell.

In its first issue, Nature Machine Intelligence talks about software (Q1), computer networks and communications (Q1), computer vision and pattern recognition (Q1), human-computer interaction (Q1), and artificial intelligence (Q1). Springer Nature Switzerland AG puts it out. The general rank of Nature Machine Intelligence is 182. This journal has a rank of 6.210 on the SCImago Journal Rank (SJR).

The SCImago magazine Rank shows how important a magazine is in the field of science. It takes into account both how many times a paper is cited and how important the journals that cite it are. You can use SJR instead of the Journal Impact Factor, which is the average amount of citations over the last two years. That number is 47 for this magazine. That’s Q1, which is the best in this book.

The ISSN for the Nature Machine Intelligence journal is 25225839. Each International Standard Serial Number (ISSN) has eight digits and is a unique code. It can be used to find journals, newspapers, magazines, and other periodicals in both Internet and printed forms. In the three years before 2022, 6247 works have cited Nature Machine Intelligence.

Nature Machine Intelligence’s Impact IF 2022 is 16.66, which was calculated in 2023 based on its description. Compared to 2021, Nature Machine Intelligence IF has gone down by a factor of 0.23, which is about a 1.36 percent change. This shows that the trend is going down. The impact IF, which is also called the Journal impact score (JIS), figures out how many times new academic papers are cited each year on average. The info came from Scopus.

How much does it cost to publish in Nature Machine Intelligence?

The difference is that when an article is accepted for publication, the author/s or funder/s pay an Article Processing Charge (APC). The final version of the published article is then free to read for everyone. The APC to publish Gold Open Access in Nature Machine Intelligence is £8890.00/$12290.00/€10290.00.

People who write or fund a piece of work pay an Article Processing Charge (APC) when it is accepted for publishing. After that, the published piece can be read by anyone for free. It costs £8890.00, $12,900.00, or €10,900.00 to publish a paper in Nature Machine Intelligence with gold open access.

The author(s) or funder(s) of a paper that is accepted will pay an Article Processing Charge (APC). This fee makes sure that everyone can see the final version of the published piece. For example, the APC to publish in Nature Machine Intelligence under Gold Open Access is £8890.00, $12,900.00, or €10,900.00.

Creative Commons CC-BY licenses are used to print open access (OA) articles, which means that they can be shared and used again. You can get them for free online. An APC is usually used to pay for printing costs by the author’s group or a funding agency.

Springer Nature offers open-access funding and support services, such as information on groups that fund research and pay for APCs.

Lastly, APCs are needed so that anyone can freely access study papers. They make sure that research that has already been released can be freely shared and used again. This helps more people learn about the research and encourages collaboration and scientific growth.

How many journals does nature have?

Nature Portfolio journals

Nature — the leading international weekly journal of science first published in 1869. 32 Nature research journals, published monthly, across the life, physical, clinical and social sciences.

Nature, a weekly science journal that is read all over the world, has been around since 1869. It has become a leader in its field and is generally known for being the best at publishing scientific research.

The 32 research journals that the Nature Publishing Group puts out every month cover a wide range of biological, physical, clinical, and social subjects. By printing reviews, critical commentary, and analysis along with original research, these journals give a full picture of the latest developments in many scientific fields.

Every month, the Nature Publishing Group also puts out 20 Nature Reviews books. The review material for these books is known for being important, easy to read, and reliable. Researchers and professionals can use the high-quality graphics and improved material to help them understand other topics and see how they relate to each other.

Overall, the Nature Publishing Group is dedicated to following the strictest rules for scientific publishing while also giving the world’s scientists new and interesting material.

What is machine learning journals?

The Journal of Machine Learning Research (JMLR), established in 2000, provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing.

Machine Learning with Applications is an open-access journal that publishes studies on machine learning and is reviewed by other experts in the field. This magazine writes about many different types of machine learning, such as computer vision, data mining, natural language processing (NLP), and more.

Since MLWA is a peer-reviewed journal, all of the papers that are published go through a very thorough review process by experts in the field. This method helps to protect the honesty and high quality of the study that is published in the journal.

The goal of MLWA is to give practitioners, academics, and researchers a place to talk about the newest developments and findings in machine learning. By making its content easy for many people to access, MLWA encourages the sharing of information and teamwork in the field, making sure that many people can access useful research.

Overall, MLWA makes a big difference in the progress of machine learning by encouraging creativity in research and development and making it possible for people to share their ideas.

There was some doubt in the research community, especially among people who study machine learning. Experts in machine learning think that research results should be available to everyone for free in order to move the field forward. Some people are worried that people who work for companies and can’t pay the subscription fees will not be able to use Nature Machine Intelligence.

There have also been concerns raised about the possible direction of the study, which will be discussed in the next article. In the beginning, experts are worried that “flashy” research might get more attention than more important contributions. Because of these worries, there was a petition against the journal’s creation, and people stopped buying the magazine. It has been signed by more than 2,900 machine learning scientists who promise not to write, review, or edit for this new publication.

The positions of these scientists show how important ideas like free access and keeping the quality of research in academic journals are. It shows that people in the scientific community are working together to keep rules in place that encourage diversity and truth.