Cloud Hosting

Tags: Caspersen, data analytics, Homepage, Professors

March 2023 What are text mining and abstract miningand how are they useful tools in medical research?

These questions and many others were answered during a hybrid event featuring Drew Universitys Dr. Ellie Small, Norma Gilbert Junior Assistant Professor of Mathematics and Computer Science.

Hosted by Drews Caspersen School of Graduate Studies Data Analytics program, attendees had the privilege of learning about text mining, abstract mining, and new methods developed to identify novel ideas for medical research.

Data analytics has seen a surge in growth opportunities, largely due to the availability of data, the need to analyze the data in various ways, and the increased ability to store and analyze data.

The cost of computer storage has decreased, while computation power has increased, said Small, who specializes in data science and has completed research papers in networks and text mining.

Small explained the difference between various types of data analysis: text mining is analyzing text in documentsfrom one document to thousands of documentsand abstract mining is the ability to analyze multiple words or short phrases in documents.

Utilizing PubMed, a biomedical literature database, Small developed logic to extract frequently occurring phrases from the housed papers and cluster them according to the frequency of the phrases within the papers.

This application of data greatly simplifies medical research for students and the medical community at large.

Alex Rudniy, assistant professor of data analytics, also offered an overview of many usages of the data analytics tools and the industries that utilize these toolsfrom marketing, travel, health care, and beyond.

Link:

From Text Mining to Abstract Mining - Drew Today

The purpose of data analytics is to collect, analyze, and visualize data so businesses can make data-driven decisions about their operations

Fremont, CA: As organizations have become more automated and big data-driven, data integration and business intelligence have become increasingly important. Businesses must consolidate and process vast amounts of data from various sources, including internal systems, cloud-based solutions, and third-party sources. A central repository for analyzing data can be created through data integration tools that help businesses bring data from several sources together.

In addition to automation, the need for accurate and timely data has also grown. Data integration is necessary for businesses to run their operations efficiently.

Data integration

The data integration process combines data from different sources into a unified view. An easy way to access and analyze data is by converting and loading it into an easily accessible central repository or data warehouse. Integrating accurate and timely data is critical to making informed decisions for any data-driven organization. Integration of data from different sources involves a complex process. In many cases, enterprises integrate their data using ETL (Extract, Transform, Load) tools, which convert data from disparate sources into a consistent format, then load it into a centralized repository. In addition to improving data quality and reducing redundancy, data integration can also streamline data management methods. A few popular solutions for data integration include Informatica PowerCenter, Talend Open Studio, and Microsoft SQL Server Integration Services (SSIS).

Business intelligence

A business intelligence system analyzes and interprets data to provide valuable insights that can help in making decisions. The purpose of data analytics is to collect, analyze, and visualize data so businesses can make data-driven decisions about their operations.

Data is used for business intelligence to gain insights into business operations, processes, and performance. There are many steps involved in the process of business intelligence, including data warehousing, data mining, reporting, and analysis.

Benefits of data integration

Integrating data helps make decisions more informed because the data is accurate, consistent, and up-to-date. Businesses can reduce costs and save time by integrating data from multiple sources. Integrating data can make data management easier and faster, improving access and analysis speed. Businesses can gain deeper insights into their operations by combining data into a unified view.

Go here to see the original:

An In-Depth Examination of Business Intelligence and Data Integration - CIOReview

BUENOS AIRES, March 21 (Reuters) - Argentina's mining exports hit historic levels last year, the government said on Tuesday, powered by surging lithium income as the South American agricultural powerhouse targets profits from the metal key to meeting booming electric vehicle (EV) demand.

Even as Latin America's third-largest economy suffers triple-digit inflation and the fallout of a devastating drought afflicting top farmland, lithium exports helped push up the country's mining exports to $3.86 billion last year - the highest level in a decade, according to economy ministry data.

Argentina's lithium riches, like those in neighboring Chile, are extracted from brine in sprawling salt flats that use the power of the sun to concentrate the ultra-light metal in evaporation pools.

In 2022, lithium exports surged 234% from a year earlier, accounting for nearly a fifth of all Argentine mining shipments.

In a rare bright spot for the country's ailing economy, the trend shows no sign of slowing.

During the first two months of this year, exports of the white metal more than doubled, from a year earlier, with February shipments pulling in a record $58 million.

The ministry sees mining revenues of $6 billion this year, in part boosted by two new lithium projects set to launch as well as a pair of major expansions underway.

A scramble for the metal has caused its price to skyrocket, which in turn has motivated companies and investors alike.

Some of the world's largest mining companies have operations in northern Argentina, including China's Ganfeng Lithium (002460.SZ) and U.S. miner Livent Corp (LTHM.N), which will supply lithium for rechargeable batteries in BMW (BMWG.DE) vehicles.

Mining industry investment since 2020 totals some $11.3 billion, the ministry data showed, including $5.1 billion for lithium and $4.9 billion for copper, also heavily used in EVs.

Spanning Chile, Argentina and Bolivia, South America's so-called "lithium triangle" accounts for more than half of global lithium supplies.

Reporting by Lucila Sigal; Writing by Sarah Morland; Editing by David Alire Garcia and Jonathan Oatis

Our Standards: The Thomson Reuters Trust Principles.

Originally posted here:

Argentine mining exports hit 10-year high as lithium, EVs take off - Reuters

Every week it seems the world is stunned by another advance in artificial intelligence, including text-to-image generators like DALL-E and the latest chatbot, GPT-4.

What makes these tools impressive is the enormous amount of data theyre trained on, specifically the millions of images and words on the internet.

But the process of machine learning relies on a lot of human data labelers.

Marketplaces Meghan McCarty Carino spoke to Sarah T. Roberts, a professor of information studies and director of the Center for Critical Internet Inquiry at UCLA, about how this work is often overlooked. The following is an edited transcript of their conversation.

Sarah T. Roberts: In the case of something like ChatGPT and the engines that its using, its really going out and pretty much data mining massive portions of the internet. Now, we all know that the internet is filled with the best information and the greatest stuff all the time, right? So whats required for something like that is to have human beings with their special ability of discernment and good judgement, and sometimes visceral reactions to material and in the case of ChatGPT, to cull material out, material that users, or more importantly, companies, would not want inside of their products as a potential output. And so that means these data labelers, much like content moderators, spend their days working on some of the worst stuff that we can imagine. And in this case, theyre trying to build models to cull that out automatically. But it always starts and ends with human engagement.

Meghan McCarty Carino: What do we know about the people who are doing this really key work of data labelling?

Roberts: So taking a page from the content moderation industry, much of this work is outsourced to third-party companies that provide large labor pools. Often these data labelers are at great remove from where we might imagine the work of engineering these products goes on. They might be in other parts of the world. There was a great article by Billy Perrigo in Time magazine in January of 2023, about a place in Kenya that was doing data labelling. It was a really hard, upsetting job, and folks were being paid at most $2 an hour to be confronted with that material. Unfortunately, this is an industry that is reliant upon human intervention and human discernment, but once again, takes it for granted and pays very little and puts people in harms way.

McCarty Carino: Right, very similar to, you know, what weve learned about content moderation, which, as you said, happens in a similar sort of outsourced way where these people are sort of the front lines of everything that we dont want showing up in our end product, and it runs through these workers.

Roberts: Yeah, thats right. And for years, Ive been listening to industry figures and other pundits tell me that my concern about the welfare of content moderation workers was appropriate, but it was finite, and that in just a few years, AI technologies would be such that we could eliminate that work. In fact, whats happening is just the opposite. We are expanding, greatly expanding at an exponential pace, the number of people who are doing work like this. I think of data labelling frankly as content moderation before the fact, both in practice, but also in the material conditions of the work.

McCarty Carino: When we think about how these technologies are often described, or characterized by the companies that put them out or, you know, in the press, I mean, what is important to keep in mind as we think about this type of labor and its relationship to those products?

Roberts: I think what we have to remember is that AI is artificial intelligence leaning heavily on the artificial. And what its doing at best is imitating human discernment, thinking and processes, but it is only as good as the material that goes into it. You know, theres an old adage in programming, garbage in, garbage out, that goes maybe even more so for applications like these AI tools that weve been discussing. Emily Bender and her colleagues wrote a great paper called stochastic parrots, which is how she and her colleagues describe what ChatGPT is actually doing. And for those who arent familiar with that term, basically, what shes saying is that you can use ChatGPT, its incredible, Ive used it as well. But you have to keep in mind that what youre seeing as its output is at best mimicking humans in the same way that a parrot might copy our pattern of speech, a series of words or phrases, even our inflection, but really has no cognitive ability to necessarily understand what those things mean.

And in fact, I would give a parrot a better chance of having that kind of cognition than I would have machine. So in a way, Ive been thinking about ChatGPT and other tools like it really as vanity machines. Just as an example, I requested it to generate an annotated bibliography for me the other day in my own field. I picked something that I thought I would have some expertise in in order to evaluate the output. And it gave me about 10 answers. The first one it gave me was something I would have chosen as well, a book by a colleague. Perfect response. And then it started producing a bunch of new papers and books in my area of study that Id never heard of. And I really thought, Wow, have I really been underwater that much during COVID? Like, all this stuff is coming out and Im missing it? Turns out, those were fake citations, fake authors, fake books on legitimate presses, fake papers, but using legitimate journal titles with even page numbers given. Imagine if I hadnt had the expertise to know that those were bogus. Thats just one example of the way that this stochastic parrot or this mimicry might reproduce. And, of course, to be fair, I didnt ask it to give me real citations or truthful information. It gave me its best guess at what an annotated bibliography would look like in my field. But none of it was real.

McCarty Carino: What gets lost when tools like this are thought of as these sort of genius technological achievements without considering all of the human labor that went into them?

Roberts: They could have really chosen any model. They could have decided, you know, an infinite number of possibilities of how to set up that work and how to treat those workers. And I think it says something about tech companies. The actual intelligence that they are mining, the very essence of what makes these tools appear to have this human element in other words, mimicking the humans that work on the labeling, work on the moderation, work on these inputs are erased from the process. And I think the erasure of the humanity that goes into these tools is to all of our detriment if for no other reason then we cant really fully appreciate the labor that goes into creating them or the limits of the tools and how they should be applied.

A report from Grand View Research valued the global data collection and labeling market over $2.2 billion in 2022. Its a huge sector.

And its important to understand its not just this new generative AI that requires this kind of work. For example, my colleague Jennifer Pak reported a couple years ago on a data labeling center in China that contracts with big companies like Baidu and Alibaba.

One of the workers Jennifer spoke to said he was making twice the average salary in his local province, roughly $11,000 a year, plus commission.

The operation had workers labeling street data for an autonomous vehicle project basically, Thats a bike, thats a pedestrian, thats a baby stroller.

The same type of labor is used to label faces to train facial recognition software or to help robot vacuums navigate their way around your home.

Earlier this year, we spoke to MIT Technology Review reporter Eileen Guo about her story on how sensitive personal images taken by robot vacuums inside peoples homes ended up online.

Its a winding path, but it runs through a group of outsourced data labelers in Venezuela that iRobot contracted.

More here:

The human labor behind AI chatbots and other smart tools - Marketplace