The artificial intelligence (AI) industry is worth $200 billion USDand is one of the biggest in the world today. Whether it’s robotics and automation or chatbots and search engines, this technology is central to the fourth industrial revolution. Despite years of discourse in the technology community and media, the scale of change is rapid and hard to believe.
As can be seen both in development and commercial news, the pace and scope of AI is now comprehensively manifesting. What’s more, we’re now also seeing the building blocks of change in products and platforms that build the speculative AI.
In this A3logics article we’ll cover key explainer points to help unpack and provide an understanding of the value of:
• How this AI works
• Why it’s important in the AI market and wider technology market
• What’s next for AI from here
Firstly, let’s discuss the headline characteristics of the Meta AI model, Segment Anything Model (SAM). Above all, it’s relevant to explain how Meta is operating as a company. Comparatively it’s also worthwhile to provide a brief 101 on how this relates to their original Facebook iteration.
As a matter of fact, most people still think of the company Meta as its former name Facebook. Undoubtedly this is because of Meta starting life as a social media platform. Since October 2021, Facebook Inc. has been known by its new name Meta Platforms Inc. Due to this change, the Facebook Artificial Intelligence Research (FAIR) laboratory became Meta AI.
Generally since then most people refer to all Facebook parent platforms as Meta. Be that as it may, their new AI model Segment Anything is “Research by Meta AI” as per official announcements. Accordingly, Meta AI research from Kirillov et al says this is, “a new task, model, and dataset for image segmentation”. Notwithstanding this Meta AI team research paper, the simplest way to understand Meta AI’s SAM is in terms of imagery.
• Picking out individual objects and components in images and videos in the Segmentation Process
• Provision of a dataset of image annotations to utilize in object recognition and computer vision technologies
• Doing this with text or click prompts
• Building upon existing Meta tools in use on Facebook for content checks, image tagging, and content flagging
Conclusively, Meta AI outline the Segmentation features in their announcement media. In essence, they say, “Segmentation — identifying which image pixels belong to an object — is a core task in computer vision and is used in a broad array of applications, from analyzing scientific imagery to editing photos.”
So, let us now cover the technical specifics of Meta AI’s SAM. In detail, Esther Ajao reports for TechTarget that this model “targets problem in computer vision market” that relate to accessibility. Further to this Ajao writes, “The research lab’s new model helps enterprises in the computer vision market access a well-labeled set of images without having a person manually tag different images.”
The concept of SAM is consistent with progressive AI developments. Furthermore early SAM uses suggest that this AI will be useful in Augmented Reality, Virtual Reality, and with other AI. In summary, Rashi Shrivastava reports for Forbes that SAM may be Meta AI’s “one-stop shop” for embedding AI solutions. Thereafter, Shrivastava expands on this that SAM may meet a broad range of work, life, and research needs. Explicitly this SAM future use might cover, “Editing photos, analyzing surveillance footage and understanding the parts of a cell.” At this point, SAM is of use for everyone — and especially those who don’t have AI infrastructure.
• Segment Anything Model (SAM) — Promptable model that segments and identifies objects
• Segment Anything 1-Billion mask dataset (SA-1B) — Providing an entire dataset of image annotations
As a result the whole SAM AI is currently a robust tool for use. Because the dataset is extensive, at 400x larger than the next biggest mask dataset, SAM is a highly trained AI. Overall, in this moment the SAM is impressive and promising as an AI development release.
Both of these options of SAM and SA-1B offer users a tool for multiple applications. As an illustration, in the next section we will explore the standout features of SAM. In detail, Meta AI emphasize their vision to reduce key pressures on people completing tasks and using AI. In fact, the SAM tool helps relieve pressure around task-specific modeling expertise, training compute, and custom data annotation.
Rather than just build specialty platforms for individual uses, Meta AI instead describes SAM and SA-1B as foundation models. Significantly, they say that the SA-1B dataset is for research and SAM is under an Apache 2.0 open license. Straightaway, this is best understood as a decision to encourage users to augment and build on top of SAM. Presently this is, “To enable a broad set of applications and foster further research into foundation models for computer vision”.
In order to thoroughly understand development model use, let’s discuss two industries that are already experiencing discussion.
Specifically, Meta AI says that there are broad applications for use. To explain, they hypothesize this as everything, “From analyzing scientific imagery to editing photos”.
Additionally this Meta AI development shows an intersection between using the technology for AI and AR/VR. Not only does SAM segment images to allow for isolating specific areas, but also it is usable with other AI. For example, this overcomes AI barriers in recognition as software integrating SAM can recognize or interpret unfamiliar or unknown objects. Afterward this could lead to comprehending data more accurately or in line with human intelligence. Equally, SAM’s use of interactive segmentation shows that the AI is capable of learning and even automating over time. Consequently, it could achieve the outcome of continually refining and improving results thus fulfilling the vision of generative AI. All in all, these are certainly early days for the technology and general market use is now undergoing testing.
Even though Segment Anything is useful in high-level technology settings, its use is likely to be widespread. For that reason, the expectation is that everyone from professionals to ordinary people will use it. As a matter of fact, Meta AI say that the model’s development is about making segmentation technology accessible. Furthermore, their official Segment Anything Model AI development announcement says,
“We aim to democratize segmentation”.
Therefore we can identify that SAM use will likely include the following user groups:
Overall, Meta AI’s goal in sharing the research and dataset behind the SAM is progress. To that end, this includes uses in:
In summary, the SAM use is likely to keep evolving. Both the extensive scale of the dataset and the interface flexibility indicate that the SAM automatic features offer great promise. Moreover consider Meta AI’s statement, “It is a single model that can easily perform both interactive segmentation and automatic segmentation.” Whereas existing image segmentation AI is more time-consuming and not as comprehensive, SAM advances this AI area.
Undoubtedly, the Segment Anything Model is an AI development that is usable in a variety of formats. While it is currently early days, initial speculation suggests that the SAM generative AI is widely usable.
In reality, Kirillov and Ravi say that their object detection tool can be used for “gaze-based” detection of objects. Markedly, these input prompts show the SAM utility in cases such as wearable AR/VR technology through to medical imaging. For the most part these technical characteristics show us that the SAM is going to increase precision and accessibility.
Concurrently, exact usage is still entirely in a flux state. Because AI development is changing rapidly and continually, the precise and longer-term SAM uses are unpredictable. But we can be fairly certain that Meta AI’s SAM is a crucial advancing of AI development technology.
Undeniably, the Segment Anything Model is making a market splash. Due to its sophistication, scale, speed, and accuracy, the SAM is a groundbreaking generative AI. On the other hand, Shrivastava outlines that both “object recognition and computer vision technologies have been around for years”. Additionally, usage is common in security and military products such as surveillance cameras and drones. Up to the present time, the technology is effective enough to use.
With this in mind, the landmark use case for Meta AI’s SAM is how it combines these two development areas:
Straightaway organizations are discussing using the SAM in experimental and practical ways.
For example, current reports show speculation for immediate if not forthcoming use in:
Thereafter we can say that early use helps streamline and improve operational tasks. In the meantime, Meta AI making the technology available shows us how computer vision is advancing and improving. Analogous to other emerging AI — as we will cover below — all development is part of innovating machine learning and automation.
Overall it’s unclear if Meta is using the Segment Anything Model in their products. Nonetheless comparison are understandably possible between existing photo technology and leading Meta social media platforms Facebook and Instagram. Concurrently Meta is already using internal computer vision products to flag inappropriate content and automate photo tagging.
Even if Meta is already using the SAM as a tool, it’s likely that it’s part of training the AI. Comparatively, as far as we know, widespread use is still under controls. Later it may be in use however at present it’s difficult to conclusively say. At the same time, Meta AI emphasizes their hope that the SAM leads to “more general image and video understanding”. Evidently this overlaps with their existing image and video products so integrating the SAM into UX or backend is possible.
Whether the Segment Anything Model will affect the other Meta products is a leading question. After all in the last 18 months, Mark Zuckerberg continually asserts the move towards the Metaverse. Another facet to this is Reuters’ Katie Paul reporting Meta’s intention to integrate AI development technology into their products. Paul references, “Chief Executive Mark Zuckerberg has said that incorporating such generative AI “creative aids” into Meta’s apps is a priority this year.” Additionally specific reports say that this generative AI is for advertisements on Facebook and Instagram. Undoubtedly this supports the speculation that Meta might use SAM for this.
In the long run, it’s highly likely that Meta will use their AI development technology to enhance and level up. What’s more, Zuckerberg says that Meta is currently putting together a new product team to work on generative AI tools. Since this announcement in February 2023, it’s possible this ties in with the SAM.
In any case it’s clear that Meta sees its long-term prospects in AI development. All things considered, this is a logical development strategy as they work to make their Metaverse a reality. Altogether this fits together due to how Meta AI links the SAM to its AR/VR hardware products. “SAM can take input prompts from other systems, such as in the future taking a user’s gaze from an AR/VR headset to select an object.” Following this, Meta AI shows SAM generative AI footage using their “open sourced Aria pilot dataset”. In other words, it’s safe to bet that the model’s AI will integrate into existing and new products.
In view of Meta’s AI development push, let’s look at how the Segment Anything Model compares to other major platforms. Now that we’re in the ‘gold rush’ AI era, companies are announcing products and tools seemingly every single day. Obviously each AI development is different however it’s worth understanding how each works and how it compares to SAM.
Together with our previous discussion points, we can now look at what’s next for AI development. Since this market is moving quickly, we can expect that there will be developments in response to this Meta AI. Because of the importance of visual data in our current digital world it’s logical that vision technologies keep progressing.
Of course, two major technology companies to watch are Apple and Amazon. Neither the Apple Siri product nor the Amazon Alexa product have AI updates in line with those of other companies. Whether this will happen in the near future remains to be seen. Even so, many are expecting the release of Apple’s mixed reality device at some point in the coming year. By comparison, Amazon does already use some AI in their shopping user interface that might begin integrating Meta’s SAM.
Lastly, Google’s own vision technologies are advancing as they develop their AI-rich image recognition. For example, Lifewire writer Sascha Brodsky describes the Google Single Shot Multibox Detector as object detection similar to the SAM. Regardless, Brodsky reinforces, “But observers said that SAM is unique as an open-source model.” From here we can see that deep neural networks for visual detection and utility are likely to keep diversifying. After all, both Google and Meta are platforms that use images and video in a range of ways.
In conclusion, this development in computer vision is a history-making moment in AI development. Unquestionably Meta AI’s SAM tool arguably takes us closer to a realizable Metaverse. On the positive side, seeing that Meta’s vision is to democratize segmentation, it’s optimistic to experience a robust, agile model. On the negative side, we need to acknowledge that the SAM is an AI with only two-dimensional image training. Unfortunately Shrivastava says this creates limitations for both professional and entertainment use. Furthermore, “This means it wouldn’t accurately identify non-standardized objects through an AR/VR headset or it wouldn’t detect partially covered objects in public spaces if used by an autonomous vehicle manufacturer.”
Finally, present analysis shows that the leading AI development model is still the paid OpenAI product ChatGPT Plus. Whether Meta AI progresses to a paid, professional-grade version of the Segment Anything Model is still awaiting confirmation.
The Meta AI Segment Anything Model (SAM) is a computer vision AI that is generative and humanoid. Comparable to many other leading AI releases, this technology is:
Furthermore the SAM AI development is able to segment images, identify elements with text or click prompts, and generate imagery.
There are many different uses for the SAM AI technology. Specifically, Meta AI nominates that industries that may particularly benefit are agriculture and biology. At the same time there is significant scope for businesses to use the SAM in AR/VR. Additionally, the SAM is usable in image and video editing or other creative industries such as advertising.
As the SAM is open source and for hardware, software, and mobile applications, there are no limits to use.
There are no additional costs to using the Segment Anything Model as it is open source. In short, Meta offers the technology, including the dataset, for use in any development. Overall this is integrable in coding in the Python development language.
There are no specific costs to using the Segment Anything Model. Presently using cutting-edge AI is already part of developing and deploying powerful software products. Most important is the strategy and quality of the development plan from conceptualization and testing to launch and maintenance. Contact expert development A3logics team to learn more and start your software product development.
