The digital AI-first transformation enables organizations to create competitive advantages and develop brand-new products, services and business models. With the ability to sense changing employee, partner and customer dynamics, the enterprise of the future will use AI intelligently and at scale.
Improve generalization and accuracy with deep neural network architectures
Adoption of deep learning-based solutions to solve enterprise-class problems is driven by some key factors, such as availability of graphics processing unit computing (GPU), availability of large labeled data, and fast-paced innovations in new deep learning algorithms.
Transition from System 1 deep learning to System 2 deep learning
The current state of deep learning-based AI is referred as System 1 deep learning, and it can be best illustrated with an example of a person driving a car in a known vicinity while talking on the phone or with a passenger, and is able to automatically drive through, without consciously focusing on driving.
Image segmentation, classification and attribute extraction
Object detection, segmentation and classification are the building blocks to address several complex computer vision challenges. Object detection helps to identify an object in the image, forms a rectangular boundary and creates a bounding box to narrow down the object.
There are several interesting possiblities emerging from applying AI to videos, such as generating video captions, video highlights, content moderation, span of brand coverage, surveillance, and people or object tracking.
Adoption of neural machine translations and transcription-based systems to mine conversational insights
Historically, translation systems have been implemented using Statistical Machine translations primarily using count-based models. They were best suited for short sentences with standard nouns and phrases, importantly they are lightweight models.
Speaker-based authentication and verification is another key trend that is getting adopted as an augmented biometric method in addition to those already deployed by enterprises, such as using thumbprint or facial recognition. With the COVID-19 situation, this has gained more relevance.
Derive content intelligence from forms extraction, document attributes and paragraphs
Enterprises have information embedded in various types of documents and in the form of digital or handwritten content. These include research study documents, Know Your Customer forms, payslips and invoices. Extracting key information points and systematically digitizing this information are key problems and the driving pattern across various industries.
Address latency, point-specific contextual learning with edge-based intelligence
Smart Reply, auto suggestions for grammar, sentence completion while typing on a phone, voice recognition, voice assistants, facial biometrics to unlock a phone or an autonomous vehicle navigation system, robotics, augmented reality applications — all of them use local, natively deployed AI models to improve the response time to user actions.
Integrated AI life cycle tools to drive enterprisewide standardization
The AI life cycle involves various stages, from data collection, data analysis, feature engineering and algorithm selection to model building, tuning, testing, deployment, management, monitoring and feedback loops for continuous improvement.
Model sharing and usability through model exchanges
Creating an AI model from scratch needs a huge amount of effort and investment for collecting datasets, labeling data, choosing algorithms, defining network architecture, establishing hyperparameters, etc. Apart from this choice of language, frameworks and libraries along with client preferences, etc. differ from one problem space to another.
Adherence to AI ethics as a underlying principle to build AI systems
With the adoption of AI systems increasing in critical decision-making systems, the outcomes rendered by these systems become critical. In the recent past, there have been examples where the outcomes were wrong and impacted important human issues, some of the examples being an AI hiring algorithm found to be biased against specific races and gender.
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