Google recently announced through a blog post that its Google Lens technology, which has been on the market for a year, can discover billions of products. When the AI camera feature is released, it can only recognize 250,000 objects.
Google Lens algorithms are fed by hundreds of millions of search terms into image searches as a basis for training the algorithm. It also collects data from images captured with smartphones and can expand product recognition capabilities through Google Shopping, while the database of items recognizable by Google Lens is large. But nowadays it’s often impossible to recognize a rare and cheerful object like vintage cars or 1970s stereos.
Google Lens has a number of practical applications. A person’s picture can be converted into a search term to show all relevant information to the image through machine learning and AI algorithms.
Lens also uses TensorFlow, Google’s open source machine learning framework. TensorFlow helps better deliver descriptive images. For example, if a user takes a picture of a friend’s PlayStation 4, Google Lens will connect the image with the words PlayStation 4 and game console.
The algorithm then links these stickers to Google’s Knowledge Graph with tens of billions of facts on the Sony PlayStation 4, and this helps the system know that the PS4 is a gaming console. It is clearly not perfect. Similar things can be confused.
Lens offers the desired image, as well as thousands of returned images per search term. These related images help train the algorithm to obtain more accurate results.
Google’s Knowledge Graph helps reveal tens of billions of facts on topics ranging from “pop stars to tiny dog breeds.” This is what tells the system that Shiba Inu is a breed, not a smartphone brand.
The system is often easy to deceive. We take photos from different angles, in different backgrounds or different lighting conditions. All of these factors can create images that differ from the database provided in Google Lens.
There is still work to be done to make Lens smarter, which is why the Google team is expanding its Lens database by inserting images captured via smartphones, and Google Lens can also read texts from menus or books.
The lens makes these words interactive so that the user can perform actions on them. Users can also save ingredients from cookbooks and import them to their shopping list.
Google Lens teaches how to read without a bone, Google developed an optical character recognition (OCR) engine, and the company combines it with a practical understanding of the language from Google Search and a knowledge graph. It teaches machine learning algorithms through different letters, languages, and fonts, and is inspired by sources like Google Books Survey.
Lens also has a Pattern Find feature that offers suggestions for similarly styled items. Therefore, if users point the camera at clothing and home decoration, they will be advised of similar stylish items. Lens will display product reviews for products related to the model.
Our smartphones are getting smarter with each passing day. We rely on it a lot due to its overall portability and ease of use. This is what Google realized long ago. We focus heavily on improving service on smartphones. Google Lens is just one example.
Deep learning algorithms used in Google Lens may soon be used for medical diagnoses. There is great potential for detecting signs of diabetic retinopathy just by looking at the shape of the eye, at present, the lens can only be used on smartphones.
Diabetic Retinopathy (DR) is the fastest growing cause of blindness, with nearly 415 million people at risk of developing diabetes worldwide. If the disease is caught early, it can be cured. If not, it can lead to irreversible blindness. Unfortunately, medical professionals capable of detecting the disease are absent in many parts of the world where diabetes is common, and we believe that machine learning can help clinicians identify patients who need it, especially amongst disadvantaged populations.
In recent years, many of us have begun to question whether there is a way Google technology can improve the DR scanning process, particularly by leveraging the latest advances in machine learning and computer insight in “development and verification”. The need for a deep learning algorithm to detect diabetic retinopathy in retinal images, published today in JAMA, we present a deep learning algorithm that can interpret DR signals in retinal images, which may help clinicians screen more patients in a location with limited resources
One of the most common ways to detect diabetes in the eye is for a specialist to examine the back of the eye (Figure 1) and assess the presence and severity of the disease. The severity depends on the type of lesion present (such as a fine aneurysm, bleeding, solid secretions, etc.) which indicates bleeding and fluid leakage in the eye. Interpreting these images requires specialized training, and in many regions of the world there are no primary students qualified to examine anyone at risk.
Working closely with doctors in both India and the United States, we have created a development dataset of 128,000 images, each evaluated by 3-7 ophthalmologists from a panel of 54 ophthalmologists. Deep nerve network for indicating diabetic retinopathy. We then tested the effectiveness of the algorithm in two separate clinical validation sets, combined into approximately 12,000 images, with most decisions made by a faculty-certified ophthalmologist. Seven or eight US directors serve as benchmarks. Ophthalmologists were chosen for the highly consistent validation kit from the original set of 54 physicians.