How can you combine computer vision with other AI domains?

Computer Vision (CV) with NLP can solve the biggest data problem enterprises have. 80% of enterprise data is unstructured and has been difficult to read. But by combining CV to read the data from docs combined with NLP, it’s becoming a game changer to free up and read the data. You can see the power of this within ChatGPT with GPT vision. Upload any document - a bank statement, a receipt, or even an invoice and see how ChatGPT can use the power of NLP and CV to extract the data for your analytics or automation!

Imagine asking AI, "Who's in this photo?" or "What's happening here?" It's no trivial task—the system needs to comprehend the natural language query while also analyzing the visual content to provide an accurate response. Combining computer vision with NLP magnifies the capabilities of AI. Take image captioning, for instance. Identifying the objects in a picture, and giving context to those objects in a way that humans understand—through language. You're essentially teaching the machine to 'see' and 'describe,' a confluence of visual cognition and linguistic skills. And identifying text on any surface in the real world—be it road signs or product labels—and converting it into something machines and humans alike can understand.

Computer Vision and NLP are currently in use for Video Surveillance Forensic Investigation. Think "person in a red shirt with a backpack". There are a multitude of use cases for CV in the physical security space. And it can all be done without capturing personally identifiable information or biases.

A good example of this is using Computer Vision to identify objects within an area and then using Natural Language Processing to describe the are, textually or verbally. For example, a grocery store may use Computer Vision to examine store shelves for stocking levels, verbally notify the appropriate stocker to replenish 'this' product on 'that' shelf, and then text the store manager that stocking levels are low for 'this' product. Ultimately, the humans in this scenario still carry the decision making power (how and when to restock the shelf, when and how much to reorder), with Computer Vision and Natural Language processing aiding the decision processes.

Few good usecase we have seen for NLP along Computer Vision in Education technology is for generating captions for images, understanding visual content, understanding relation of objects. I was helping in recent implementations of Custom LLM, our use case expected users to ask question for an image - Like what character is displayed and then the application respond based on visual analysis. Another use case is where a Cancer research institute is doing image analysis to detect potential tumors, their sizes, shapes and locations in the image. It aids in early detection of tumor, that might be missed by human eye.

???????????????????? ?????????????????? : Integrating computer vision with medical AI allows for more accurate and rapid diagnosis through image analysis. For instance, using computer vision to interpret medical images (X-rays, MRIs) and combining it with natural language processing for generating comprehensive reports.

We have used Speech recognition in Edtech, particularly for Oral Fluency Assessments. The approach we took here is to evaluate language proficiency. Our AI powered evaluation system, helped converting spoken language to text, along with raw data for subsequent analysis. For one of the assignments, identification of phonemes in speech was designed, critical for assessing pronunciation accuracy.

The combination of speech recognition and computer vision represents the convergence of audio and visual data for tasks like lip reading, speaker identification, and emotion recognition. This integration involves complex synchronization and feature extraction from both modalities, with the potential to enhance various applications in human-computer interaction and emotion-aware AI systems.

Combining computer vision with speech recognition is a dynamic process. It starts with acquiring data through microphones and cameras, followed by preprocessing, feature extraction using Mel spectrograms, and synchronization. The fusion of audio and visual features leads to the development of specific machine learning models designed for tasks such as speaker identification, emotion recognition, and lip reading. Rigorous testing ensures accuracy, and the final system is deployed for real-time applications, unlocking capabilities for tasks that demand a synergy of audio and visual inputs. This integration has far-reaching applications in diverse domains.

Computer Vision with Speech-recognition AI technologies are fused in a range of assistive technologies where classification, detection, segmentation, 3D reconstruction are combined with, speech recognition to solves many speech2text and text2speech conversion problems for visual or speech impairments. Apparently the OpenAI has released CLIP for image-text modelling and Whisper for speech-text modelling, are great examples of CV + Speech Recognition combined in use.

Combining reinforcement learning and computer vision empowers machines to handle tasks like navigation, manipulation, and exploration. This integration involves merging visual perception with decision-making, presenting technical challenges in data quality, model complexity, and system evaluation. Balancing the strengths and limitations of both domains is key to effective solutions.

We designed an application using AR where students interact with 3D models and simulations using gestures. The RL model ensured that application adapted content based on students progress and interaction.

Integrating computer vision with reinforcement learning unlocks profound potential, particularly in autonomous systems. This synergy empowers machines to interpret visual nuances and make informed decisions, refining tasks like navigation and manipulation. However, it's pivotal to address inherent challenges such as data quality, model scalability, and system evaluation to fully harness this combined capability. Educating and Empowering users to actively feedback to AI is essential to drive the AI learning much up-to-date and "growth" with the real world needs.

Autonomous vehicles uses computer vision based reinforcement learning techniques deeply. Starting from object detections, environmental understanding for the driving conditional parameters, accident avoidance circumstances, traffic-light behaviour, day or night visions and many more. All high-end mobile phone uses AI based reinforcement learning along with computer vison to improve focal points, capture excellence, and many more features of the ever growing camera capabilities. Reinforcement Learning from Human Feedback (RLHF) for Computer Vision is often used by Google to develop the Captcha technologies, RLHF + CV is used for image segmentation and radiology for image recognition and analysis.

Vision-Guided Reinforcement Learning: Advancing Autonomous Decision-Making - Objective/Claim: Merging computer vision with RL enhances autonomous decision-making in machines, boosting real-world applications, while facing challenges like data scarcity and model complexity. - Most Compelling Evidence: Real-time decision-making in autonomous vehicles, navigation and manipulation in robotics, and intelligent adversaries in gaming. - Limitations/Counterpoints: Data availability, model complexity, integration, and evaluation hurdles. - Implications: The integration could yield more autonomous, adaptable AI systems, necessitating careful design, thorough evaluation, and ethical deliberation.

The Application we designed, also helped educators at large by collecting data on student interaction, progress and challenges. The educators were interested to see where students may need more guidance and even identify topics that were proving to be more engaging.

Other considerations include the use of automation, and workflows to kickoff the start of a process. Solutions such as Robotic Process Automation can pair neatly with computer vision technology in the event of integrated systems with cross functional management/stakeholders.

Combining computer vision with other AI domains enhances the capabilities of AI systems. Whether integrating it with natural language processing for text analysis, reinforcement learning for visual decision-making, or medical AI for diagnosis, these synergies create more versatile and powerful AI applications. This interdisciplinary approach often requires experts from various fields and adapts AI models to handle visual data, providing a more comprehensive understanding of the world for AI systems.

Cross-Domain Synergy in AI - Objective/Claim: Merging computer vision with RL, speech recognition, and NLP enhances AI capabilities for real-world applications, with challenges like data availability and model complexity. - Most Compelling Evidence: Real-time decision-making in autonomous vehicles, enhanced audio-visual user interaction, and autonomous robotics navigation. - Limitations/Counterpoints: Data availability, model complexity, integration hurdles. - Implications: Fosters autonomous, adaptable, user-friendly AI systems, requiring meticulous design, ethical considerations. - Future Questions: Addressing data, integration challenges? Managing ethical implications in autonomous systems?

When combining computer vision with other AI domains, it is important to consider the following: Data: Computer vision systems need to be trained on large amounts of data. This data can be expensive and difficult to collect. Computational resources: Computer vision systems can be computationally expensive to train and run. This can be a challenge for deploying computer vision systems on mobile devices or embedded systems. Privacy: Computer vision systems can collect sensitive data about people and their surroundings. It is important to design computer vision systems in a way that protects the privacy of users.