Infosys TechCompass #3 - Artificial Intelligence
September 7, 2022
Artificial intelligence (AI) has become pervasive with technological advancements happening around. AI has evolved from augmented intelligence using classical algorithms to responsible and explainable AI systems using advanced deep learning-based models.
In this paper, we have discussed key trends across eight AI subdomains to help companies evolve as AI-first live enterprises.
A large technology company developed an AI model for supervised transfer learning-based deep neural net architecture for vision and text. This helped it identify, classify, and isolate any toxic content arriving from user-uploaded forms.
The current state of deep learning-based AI is referred to as system 1 deep learning. For example, a person can easily drive through in a known vicinity without consciously focusing on driving. However, the same person in an unknown vicinity would require logical reasoning and connections to drive to the destination. These types of problems, which require a combination of reasoning and a sense of “on-the-fly decision-making”, are system 2 deep?learning.
Find more about these trends and their use cases here .
A large global seed manufacturer extracted various data points from intellectual property documents related to studies and details of various experiments spread across geographies in different shared locations, languages, and versions.
Find more about these trends and their use cases here .
A global airplane transcribed conversations between pilots and ground staff to boost operational efficiency. The model delivered high transcription accuracy, ran language insights to infer causes of flight landing delay and air accidents, and provided insights to improve ground staff and pilot training.
A large U.S.-based railroad company wanted to transcribe call center conversations to optimize operations, upskill the workforce, and improve customer satisfaction. The company partnered with Infosys to develop open-source custom models and frameworks. Using these technical calls, Infosys helped the railroad company transcribe audio files and perform text analytics to detect common reasons for calls. It also helped the company get better customer insights and identify workforce training requirements.
Offerings that ease the deployment of speech processing with simultaneous services, such as STT, text synthesis, and TTS, are becoming widely available. Popular models include Mycroft, SpeechBrain, ESPNet, and NVIDIA NeMo.
Find more about these trends and their use cases here.
A large global energy company optimized its cable diagnostics and repair operations to identify faulty cables based on the picture sent from the site, allowing them to take appropriate action. This helped the company to save costs and efforts.
领英推荐
As part of a prestigious global tennis tournament, Infosys extracted various game insights using CV-based algorithms. Event highlights, such as players waving to the crowd, extracting the score from the video feed, recognizing players, and determining the timeframe of a particular advertisement during the live telecast, were created.
Find more about these trends and their use cases here .
A large telecom company developed a robust video intelligence solution for smart spaces. The solution takes data through real-time streaming protocol (RTSP) from CCTV, runs deep learning and computer vision models to detect humans across feeds, and tracks motion/movement. It derives insights such as people density in an area, ingress/egress count, dwell time analysis, and wait time analysis.
Find more about these trends and their use cases here .
A Europe-based telco wanted to use customer data to enhance client retention. It built data sets that helped effectively predict customer churn. The telco reduced churn by 10-15% by developing a catalog of customized offers.
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.
An investment firm wanted to build a data pipeline on AWS for corporate customers. It involved identifying, ingesting, cleansing, and loading the existing data in its legacy IT ecosystem built on mainframes. The company improved the marketing campaign effectiveness by 70%, with effort savings of around 45% for the commercial sales line.
Find more about these trends and their use cases here.
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.
Find more about these trends and their use cases here .
A U.S.-based telecom player struggled with AI development, as it used multiple tools and had limited data for model development. It developed an industrialized AI system to build a pipeline for AI and ML developments over AWS. It realized returns on its AI and ML investments.?
A global investment firm’s data scientists were struggling to get the right data experience. The firm built an efficient solution to plan, prepare, and predict data. The solution reduced the time spent on identifying the right tools and data, boosting data scientists’ efficiency by up to 65%.
Find more about these trends and their use cases here .
Read our 2022 AI TechCompass to know more about the key trends.
To continue to receive Infosys Knowledge Institute newsletters, follow us on LinkedIn and subscribe to the newsletter. Please do share it with your teams and your professional network.
Senior Principal - Thought Leadership: Strategy Execution and Operations | Business Insights | FP&A
2 年Enterprises should be AI-ready to promptly sense the changing dynamics of customers, businesses, partners, and employees. Enterprises need to adopt AI-first strategy to build future-competent systems by creating competitive advantages with better products, services, and business models.?These key trends will help enterprises to adopt AI-first strategy.