Apple Launches Open-source, Dedicated Machine Learning Journal

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machine learning journal

Apple launched a website, dubbed the Machine Learning Journal, that is purely dedicated to its study of Artificial Intelligence.

Apple’s latest move comes as a surprise since the company has been known for its secretive research studies and technologies. Nevertheless, they are continuing to make good on their promise of more open-source efforts.

For years, Apple is one of the leading contributors in the field of technology and innovation. The company has shared some significant open source projects like the WebKit and the Swift to help developers around the world.

However, a site that will contain Apple’s research papers on artificial intelligence is something new to the eyes of the public. Apparently, the new Apple website will adopt some basic blog functionalities like sharing updates but on a grander scale. The site’s opening post read:

“Here, you can read posts written by Apple engineers about their work using machine learning technologies.”

The simple statement includes an invitation for researchers, students, and developers to share their feedback.

#Apple just raised its #AI game by launching its very own online journal!Click To Tweet
Machine Learning Journal
Apple’s Machine Learning Journal

Machine Learning Journal: Apple’s Take on AI

The Machine Learning Journal was announced by Apple’s director of AI research, Ruslan Salakhutdinov on his Twitter account.

For many years, critics have been talking about how behind Apple is in the field of machine learning. It seems that the tight secrecy surrounding every Apple research project is what lead to the belief that the company is lagging behind its competitors like Google and Facebook.

It was reported that Apple was not keen on letting its AI engineers publish scientific papers. However, a few research papers from the company’s engineers were spotted on ArXiv, a website that collects academic-style papers.

It is a known fact that in the field of research studies, a journal is a strong indication of an institution or person’s strong commitment to an area of study. With the launch of the Machine Learning Journal, experts speculate that this is a sign of Apple’s growing interest to be seen as an active player in the field of machine learning research.

Also, many believe that this new website will serve as one of Apple’s recruiting tools. In the past, Apple’s tight secrecy policy hinders them from hiring the best engineers and researchers. Apparently, great people of science wanted to make their research public and share it with everyone–unfortunately, this kept them away from Apple.

Now that the company is starting to open up a bit, it is expected that they will start receiving applications from respected individuals in the field of AI.

Going back to the Machine Learning Journal, the first post that people will get to read is about synthetic images.

The research study entitled Improving the Realism of Synthetic Images tackles the problem that comes with training machine learning models using synthetic images.

Learning from Simulated and Unsupervised Images through Adversarial Training
Learning from Simulated and Unsupervised Images through Adversarial
Training | {a_shrivastava, tpf, otuzel, jsusskind, wenda_wang, rwebb}@apple.com

The post is rich in information and offers some excellent insights including a solution to the problem.

However, it is quite odd that the post–which is based on a paper posted months ago on ArXiv–does not mention nor give credit to any Apple researcher who did the work.

Upon checking with ArXiv, the authors of the paper are Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb under Apple Inc.

Right now, no one knows how often Apple will update its journal.

Only one thing is sure: Apple is serious about machine learning and the company is willing to go beyond its boundaries to harness, develop and integrate AI into its latest technologies.

Do you think Apple’s latest move will lead to greater AI discoveries? Let us know in the comment section below!

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