Artificial Intelligence

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.

Evolve to an AI-first live enterprise

Adapting to market dynamics: the three horizons
Show all horizons

H3

RESPONSIBLE, TRANSPARENT

Generative AI, Explain, Digital Brain

Key Patterns

  • Video insights
  • Feedback loop, self-learning
  • Generative networks – music, video generation
  • AI on edge – MobileNet, etc.
  • AI governance – AI ethics, explainable AI
  • System 2 DL, hybrid AI

H2

DEEP LEARNING

Higher Accuracy and Predictability

  • Object detection
  • Speech recognition
  • Facial recognition
  • Entity extraction
  • Speech transcriptions
  • Speech insights
  • Neural networks

H1

CONVENTIONAL AI

Augmenting Intelligence

  • Prediction
  • Recommendations
  • Logistic regression
  • Classification
  • Regression
  • Rules-based
  • Expression-based

Key trends across AI subdomains

AI algorithms and architectures

Adoption of deep learning and transfer learning architectures will drive accuracy, performance and speed

Trend 1

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.

Trend 2

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.

Computer vision

Graduation from mere object identification to deep learning-based video insights

Trend 3

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.

Trend 4

Video insights

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.

Speech

Context-specific models will drive enterprise adoption of speech-based experiences

Trend 5

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.

Trend 6

Speech biometrics

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.

Natural language processing

Shift from extraction of isolated entities to abstractive reasoning

Trend 7

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.

AI on the edge

Elevation from on-device intelligence to federated intelligence

Trend 8

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.

AI life cycle tools

Shift from fragmented to integrated, managed and monitored pipeline tools

Trend 9

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.

Trend 10

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.

AI governance

Shift from black box to interpretable systems

Trend 11

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.

Download Insights

Ask Domain Experts

Amit Gaonkar

Amit Gaonkar

Associate Vice President

Kamalkumar Rathinasamy

Kamalkumar Rathinasamy

Principal Technology Architect

Dr. Puranjoy Bhattacharya

Dr. Puranjoy Bhattacharya

Senior Principal

Dr. Ravi Kumar G V V

Dr. Ravi Kumar G V V

Associate Vice President

Sudhanshu Hate

Sudhanshu Hate

Senior Principal Technology Architect

Swaminathan Natarajan

Swaminathan Natarajan

Associate Vice President

Subscribe

To keep yourself updated on the latest technology and industry trends subscribe to the Infosys Knowledge Institute’s publications

Infosys TechCompass