Instagram Hits 3 Billion Active Users And Reveals Growth Strategy - 1

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Instagram Hits 3 Billion Active Users And Reveals Growth Strategy

  • Written by Andrea Miliani Former Tech News Expert
  • Fact-Checked by Sarah Frazier Former Content Manager

Instagram announced on Wednesday that it has reached the milestone of 3 billion monthly active users. The company says it will continue focusing on video and improving its algorithm.

In a rush? Here are the quick facts:

  • Instagram announced it has reached 3 billion active users.
  • Adam Mosseri said the company will optimize its algorithm and give users more control.
  • In 2022, Zuckerberg disclosed that Instagram had 2 billion monthly active users.

According to CNBC , Meta’s CEO, Mark Zuckerberg, revealed that Instagram had surpassed 3 billion active users. The last time Zuckerberg disclosed Instagram’s user base was in 2022, during an earnings call, when he reported it had reached 2 billion monthly active users.

“What an incredible community we’ve built here,” said Zuckerberg in a post. The milestone represents significant growth in the past few years.

According to TechCrunch , Instagram had 1 billion users in 2018. Adam Mosseri, Head of Instagram, reflected on the platform’s journey, achievements, and strategies behind its rapid growth.

“Messaging, reels, and recommendations have driven most of our recent growth,” said Mosseri in a post on Instagram. “So, over the next few months, we’re going to better organize the app around those features.”

He added that, as part of Instagram’s upcoming strategy, the company will optimize its algorithm and give users more control.

“We’ll soon start testing a way for you to tune your algorithm by adding and removing topics based on your interests, starting with reels,” added Mosseri.

This year, Instagram has been applying new strategies to boost user engagement. A few weeks ago, Meta introduced a new AI-powered translation tool to translate videos in English and Spanish, allowing content creators with more than 1,000 followers to “speak” in another language with its AI-dubbing system.

Meta is also developing AI-driven ad automation , which will allow businesses to create and target ads automatically by 2026.

In May, Meta also announced that the messaging platform WhatsApp reached 3 billion monthly active users during an earnings call.

New MIT AI System Makes Image Segmentation Faster And Easier - 2

Image by Keith Tanner, from Unsplash

New MIT AI System Makes Image Segmentation Faster And Easier

  • Written by Kiara Fabbri Former Tech News Writer
  • Fact-Checked by Sarah Frazier Former Content Manager

MIT researchers have developed an AI system that helps medical experts to accelerate their research through rapid image analysis of medical data.

In a rush? Here are the quick facts:

  • Manual segmentation often takes hours and limits research progress.
  • MultiverSeg learns from user clicks and scribbles to improve accuracy.
  • Unlike other tools, it doesn’t need large presegmented datasets.

The tool, called MultiverSeg, allows scientists to mark specific image areas, by simply clicking or scribbling, and the system uses this information to generate predictions for upcoming results.

MIT explains that the initial and most labor-intensive process in clinical research requires medical image annotation, also known as segmentation. For example, to study how the hippocampus in the brain changes with age, researchers must manually trace it across various scans.

“Many scientists might only have time to segment a few images per day for their research because manual image segmentation is so time-consuming. Our hope is that this system will enable new science by allowing clinical researchers to conduct studies they were prohibited from doing before because of the lack of an efficient tool,” said Hallee Wong, lead author and graduate student in electrical engineering and computer science.

Unlike previous systems, MultiverSeg does not require researchers to train it with large presegmented datasets. The system creates a “context set” from past segmented images and uses them to improve future predictions. The researchers explain that the system requires almost no user interaction as time progresses.

The researchers tested MultiverSeg against state-of-the-art tools, and found it required fewer clicks and scribbles, and produced more accurate results. Indeed, the AI system required only one or two manual segmentations of X-rays before it could make accurate predictions for the remaining areas.

“With MultiverSeg, users can always provide more interactions to refine the AI predictions. This still dramatically accelerates the process because it is usually faster to correct something that exists than to start from scratch,” Wong explained.

The team plans to test the system in clinical settings, with hopes that it could also improve efficiency in areas such as radiation treatment planning.