Artificial intelligence (AI) has become pervasive in this era of technological advancements. Enterprises are leveraging AI at varying degrees, triggered by pandemic-induced disruptions. AI has evolved from augmented intelligence using classical algorithms to responsible and explainable AI systems using advanced deep learning-based models. Businesses should move across three horizons to evolve as AI-first live enterprises.
Transformer architectures, multitask learning
Transfer learning, responsible AI
Less data, explainable systems
Deep neural network architectures help improve generalization and accuracy
Deep-learning algorithms promise higher accuracy and better generalization characteristics than classical algorithms such as SVM, Naive Bayes, and random forest. Enterprise-class problems can be aptly resolved through graphics processing unit (GPU) computing; accessibility of large, labeled data; and fast-paced innovations in deep-learning algorithms.
Transition from system 1 to system 2 deep learning
The current state of deep learning-based AI is referred as system 1 deep learning. For example, a person can easily drive in a known vicinity without consciously focusing on directions. However, the same person in an unknown vicinity would require logical reasoning and connections to drive to the destination.
Active learning for content intelligence from documents
Enterprises embed information in various types of documents, digital or handwritten, comprising research study documents, know-your-customer (KYC) forms, payslips, and invoices. Here, extracting and systematically digitizing this information is a huge challenge.
Speech processing through deep learning
In the past year, deep-learning models have taken over the majority of speech processing, replacing conventional models. These neural network models have substantially improved the quality of speech recognition, text-to-speech (TTS), speech diarization, among others.
Open-source models now comparable to commercial counterparts
Traditionally, speech processing models, backed by large speech-to-text (STT) and TTS corpora, dominated the market. Most of these models, offered via cloud services, belonged to large tech giants. However, open-source models are advancing at speed.
End-to-end conversational offerings in focus
Offerings that ease the deployment of speech processing with simultaneous services, such as STT, text synthesis, and TTS, are becoming widely available. With these prominent capabilities, businesses can deploy speech processing for multiple problems simultaneously and achieve faster results.
Image segmentation, classification, and attribute extraction through AI
Object detection, segmentation, and classification are the building blocks to address complex computer vision challenges. Object detection helps identify an object in the image, forms a rectangular boundary, and creates a bounding box to narrow down the object. Then, image segmentation dentifies the object with all curves, lines, and the exact shape.
AI and cloud power video insights
AI's application to videos offers interesting possibilities, such as generating video captions, video highlights, content moderation, brand coverage timings, surveillance, and people/object tracking. For applications like these, cloud computing is necessary for most inference tasks. In fact, object tracking and surveillance are far more powerful in the cloud than on devices, even with new advances in light detection and ranging technology on edge devices such as iPhone.
Edge-based intelligence to address latency and point-specific contextual learning
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 use local, natively deployed AI models to improve the response time. In the absence of a local AI model, the inference or prediction would be based on a remote server, and the experience would be suboptimal.
AI-powered technologies enhance data scientists' experience
Even today, many data scientists manually analyze data by using various techniques, with the need to apply various data cleansing activities. There is no standardized set of tools for data wrangling, analytics, feature engineering, and model experimentation.
Responsible data crucial for safe and sound AI development
Explainable AI through responsible data is still evolving. The bias on data can have devastating effects on business outcomes, causing serious ethical and regulatory issues. The application of responsible and ethical data policies in AI development is beneficial for businesses and societies.
AI-based tools enhance data-quality
Whether it is for decision-making by corporate executives, frontline staff, or intelligent ML models, any intelligent enterprise needs high-quality data to operate. However, data quality issues are widespread. AI-based data-quality analysis has become an integral part of the ML Ops pipeline.
AI ethics throughout the development lifecycle
Responsible AI concepts should be factored in from the beginning to ensure the business stays out of any AI ethics and bias issues. Explainability is one such critical concept. The design and development teams should be aware and informed of every step in the AI lifecycle to answer any related questions, providing all information AI users would seek to understand how and why the system made a decision.
Integrated AI lifecycle tools to drive industrialized AI
Enterprises cannot afford to take an artisan approach to AI and experiment with pilots and a handful of disparate AI systems built in silos. Without focusing on achieving AI at scale, data scientists created “shadow” IT environments on their laptops, using their preferred tools to fashion custom models from scratch and prepare data differently for each model.
From data scientist to data engineer with automated ML
Data scientists spend around 80% of their efforts on finding data rather than building AI models. Creating an AI model from scratch needs effort and investment for collecting datasets, labeling data, choosing algorithms, defining network architecture, establishing hyperparameters, etc. Further, the choice of language, frameworks, libraries, client preferences, etc., differs from one AI problem to another.
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