Will JavaScript type annotations kill TypeScript?
The creators of Svelte and Turbo 8 both dropped TS recently saying that "it's not worth it".
Yes: If JavaScript gets type annotations then there's no reason for TypeScript to exist.
No: TypeScript remains the best language for structuring large enterprise applications.
TBD: The existing user base and its corpensource owner means that TypeScript isn’t likely to reach EOL without a putting up a fight.
I hope they both die. I mean, if you really need strong types in the browser then you could leverage WASM and use a real programming language.
I don’t know and I don’t care.
Software Development

Computer Vision Modeling Unlocks New Use Cases

Teaching a computer to recognize and analyze the content of images can streamline manual processes and reduce the time to make decisions, as well as unlock powerful applications and innovative new ways to engage with customers.
Jul 13th, 2022 10:37am by
Featued image for: Computer Vision Modeling Unlocks New Use Cases

Clément Stenac
Clément Stenac is a passionate software engineer, Chief Technology Officer and co-founder of Dataiku. He oversees the design and development of Dataiku, software that’s making the use of data and AI everyday behavior for everyone. Clément was previously head of product development at Exalead, leading the design and implementation of web-scale search engine software. He also has extensive experience with open source software, as a former developer of the VideoLAN (VLC) and Debian projects.

Computer vision is a powerful data science and machine learning area that uses deep learning models to understand the content of images and videos.

Teaching a computer to recognize and analyze the content of images can streamline manual processes and reduce the time to make decisions, as well as unlock powerful applications and innovative new ways to engage with customers. These cutting-edge techniques have been unavailable to many companies, but with recent innovations, more organizations and users can drive value with computer vision techniques.

Traditionally, deep learning, including computer vision models, has been the domain of expert data scientists using advanced frameworks like TensorFlow or PyTorch and custom code to create models. The custom nature of these models means they can take months to develop and can be challenging to update and maintain, often taking weeks for the most simple changes.

The training of computer vision models also requires annotated images showing the various items or conditions from which the system can learn. People prepare these training images by reviewing each image and then identifying the relevant information. This process can be time-consuming for data science teams to find experts, provide images and annotation instructions, and then review and format the relevant information. These challenges prevent many companies from taking advantage of computer vision techniques, limiting how they can improve processes and customer experiences.

Insurance Case Study

One example where deep learning, including computer vision and natural language processing techniques, are successfully used today is the review of insurance claims. Traditional auto insurance claims go through a manual review that can take days or weeks to determine the appropriate course of action. For consumers who have just been through a traumatic car accident, delays in processing a claim only make things worse and can cause customer churn and create a negative sentiment towards the company.

A well-known insurance company has streamlined auto claims processing to provide near real-time responses using Dataiku. The insurance provider used to take days or even weeks to process claims. But today, when customers call about claims, the claims agent on the phone can now immediately tell them if their claim will result in a total loss or a repair of their vehicle and take them to the next step right away.

Teaching a computer to recognize and analyze the content of images can streamline manual processes and reduce the time to make decisions, as well as unlock powerful applications and innovative new ways to engage with customers.

Machine learning models help drive this nearly instant process in the background by reading and processing the claim documents and reviewing any images of the damaged vehicle. The combination of structured information provided by the customer in the claim form, unstructured text that describes the accident and vehicle condition, and vehicle images are enough for the machine learning model to determine if the damage is too severe for repair.

This change in processing time has converted the people handling claims from form-fillers who navigated customers through a complex and sometimes painful claims process into customer heroes who could help them immediately and provide concrete next steps.

New Computer Vision Capabilities Enable New Users

Dataiku is a data science and machine learning platform that companies worldwide use to create, deploy, and manage machine learning and deep learning models. Dataiku is known for enabling various technical and non-technical users to take on machine learning projects with a collaborative environment that supports everyone from full code to no code users.

Announced in the recent Dataiku 11 release are new computer vision modeling capabilities that allow non-technical users to build models using a visual, no-code interface. Users can point the system to training images, and the AutoML engine takes care of the rest. To help create the training images, Dataiku has also introduced a new managed labeling system that allows data science teams or program managers to assign labeling tasks to groups of subject matter experts and oversee labeling progress and quality.

Users can apply computer vision modeling to various use cases across industries where real-time image processing can save time and money. For example, computer vision models can improve quality control in manufacturing, speeding processing of everything from computer chips to jewelry. This helps quickly recycle defective items and reduce issues for customers. Construction companies use computer vision to track site safety, comply with OSHA regulations, and limit downtime and equipment loss by tracking equipment and ensuring that workers wear safety equipment.

Computer vision used to be the domain of experts, but projects could take a long time to develop and were challenging to maintain. However, with changes in the technology landscape, computer vision is rapidly becoming an area more companies can take advantage of to deliver better customer experiences and lower costs.

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TNS owner Insight Partners is an investor in: Enable, Dataiku.
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