Unit 2 Current Trends in ICT

• Current Trends: Hyper-Automation

  • End-to-end automation accomplished by harnessing the power of multiple technology
  • Use of advanced technology to automate tasks that were once completed by humans.
• Key Component of Hyper-Automation: Robotic Process Automation (RPA)

  • Leverages technology like software bots to replicate repetitive human tasks
  • Typically works for tasks that are rule-based, have defined inputs and outputs, are repeatable, and occur often
• Key Component of Hyper-Automation: Business Process Management (BPM)

  • One of the most important components
  • Foundation of any successful automation strategy is built, monitored, and improved
• Key Component of Hyper-Automation: Artificial Intelligence (AI)

  • Method of making computers operate in ways that stimulate human intelligence
  • Used by organizations to carry out specific tasks without being explicitly programmed to do so
• Key Component of Hyper-Automation: Machine Learning (ML)

  • Often used synonymously with AI
  • Branch of AI that uses computer algorithms to allow systems to automatically improve over time
• Key Component of Hyper-Automation: Advanced Analytics

  • Offers organizations powerful analytical tools and capabilities
  • Helps organizations to access and analyze data that has traditionally been accessible to gain important organization level insights
• Benefits of Hyper-Automation: Flexibility

  • Organizations can move past the limited benefits of a single digital technology since hyper-automation relies on a multitude of automation technologies
  • Helps organizations to achieve scale and flexibility in operations
• Benefits of Hyper-Automation: Improved Employee Productivity

  • By automating time consuming tasks, employees are able to get more done with less resources and serve more valuable roles in organizations
• Benefits of Hyper-Automation: Integration

  • Organizations can integrate digital technologies across their processes and legacy systems
  • Stakeholders have better access to data and can communicate seamlessly throughout the organization
• Benefits of Hyper-Automation: Improved ROI

  • Boosts revenue and reduces cost
  • Organizations can optimize deployment of their resources with powerful analytical tools and capabilities
• Current Trend: Multi-Experience

  • Involves developing fluent customer experiences across websites, apps, and modalities of voice, touch, and text, irrespective of the channel
  • Business adaptation of technologies like AR, VR, MR, voice, chatbots, wearables, has led to the evolution of this
• Current Trend: Multi-Experience

  • Emergence of this development would help businesses fo beyond the traditional ways of connecting with users and develop voice, chat, wearable, and AR experiences in support of the digital business
  • Users would have the exact same experience with a business regardless of how they initiated the interaction like a true multi-experience scenario
• Benefit of Multi-Experience: Improved Operational Efficiency

  • Becomes easier than ever to streamline business processes with very process being present in one system
  • Enables enterprises to create efficient, faster, and valuable digital experiences
• Benefit of Multi-Experience: Minimize the Time to Market Apps

  • Enabling this will help brands significantly improve development time by as much as 10 times
  • All credit reusable code and streamlined designed processes
• Benefit of Multi-Experience: Enabled Controlled Deployment

  • A single cloud-based deployment that takes few weeks, can now be sent directly to cloud-based server providers
  • Giving better control and fast-tracked deployment
• Benefit of Multi-Experience: Remove Security Risk

  • Enabling this can help enterprises get a 360 view of their software landscape with all the applications feeding to a single platform
  • Eliminating all the potential security risks like Shadow IT
• Current Trend: Democratization

  • Providing people with easy access to technical or business expertise without extensive (and costly) training
  • Technical and business expertise should be accessible
• Current Trend: Democratization

  • Focuses on four key areas that is often referred to as "citizen access" which has led to the rise of citizen data scientists, citizen programmers, and more
  • Application development, data ana analytics, design, and knowledge
• Key Area of Democratization: Development

  • AI PaaS provides access to sophisticated AI tools to leverage custom-developed applications
  • These solutions provide AI-model-building tools, APIs and associated middleware that enable the building/training, deployment, and consumption of machine learning moderns running on prebuilt infrastructure-as-cloud services
  • These cover vision, voice, and general data classification and prediction moderns of any type
• Key Area of Democratization: Data and Analytics

  • The tools used to build AI-powered solutions, including AI infrastructure, AI frameworks, and AI platforms, were once catered to data scientists
  • The tools, AI platforms and AI services, are now targeting the professional developer community and the citizen data scientist
• Key Area of Democratization: Design

  • Low-code application development platform tools used to builf AI-powered solutions are themselves being empowered with AI-driven capabilities, automating the development process of AI enhanced solutions
• Key Area of Democratization: Knowledge

  • Non-IT professionals are able to access powerful tools and expert systems, enabling them to make full use of specialized skills and apply them well beyond their own knowledge and experience
• Current Trend: Human Augmentation

  • Field of research that aims to enhance human abilities through medicine or technology
  • Has historically been achieved by consuming chemical substances that improve a selected ability or by installing implants which require medical operations which can be invasive
• Current Trend: Human Augmentation

  • Augmented abilities have also been achieved with external tools such as eyeglasses, binoculars, microscopes, or highly sensitive microphones
  • AR and multimodal interactions technologies have enabled non-invasive ways to augment human
• Current Trend: Human Augmentation

  • Wearable technologies may act as mediators for human augmentation, in the same manner as eyeglasses once revolutionized human vision
  • Use of technology to enhance a person's cognitive and physical experiences
• Type of Human Augmentation: Physical Augmentation

  • Changed an inherent physical capability by impanting or hosting a technology within or on the body
• Type of Human Augmentation: Physical Augmentation

  • Automatic or Mining Industries - Wearable to improve worker safety
  • Retail and Travel - Wearables to increase worker productivity
• Type of Human Augmentation: Cognitive Augmentation

  • Enahnces a human's ability to think and make better decisions
  • Includes technology in the brain augmentation category as they are physical implants that deal with cogntive reasoning
• Type of Human Augmentation: Cognitive Augmentation

  • Exploiting information and application to enhance learning or new experiences
• Current Trend: Data Policing

  • Introduces cutting-edge technology that is changing how the police do their jobs and shows why it is more important than ever that citizens understand the far-reaching consequences of big data surveillance as a law enforcement tool
• Current Trend: Data Policing

  • Data driven technologies serve a similar function by collecting crime and other data, analyzing this data to determine crime trends, and using knowledge of these trends to make predictions about future crimes
• Current Trend: Data Policing

  • Data driven technologies serve a similar function by collecting crime and other data, analyzing this data to determine crime trends, and using knowledge of these trends to make predictions about future crimes
• Risks of Data Policing: Data Quality

  • The effectiveness of predictive software relies on the quality of input data
  • If input data is inaccurate, incomplete, or skewed, this will significantly affect the quality of predictive outputs made by predictive software
• Risks of Data Policing: Discriminatory Capacities

  • Use of predictive software can result in discriminatory outcomes
  • Evidence suggests that some police activity may disproportionately target members of marginalized groups and impoverished neighborhoods
• Risks of Data Policing: Privacy Harms

  • Use of data driven technologies requires the collection of large quantities of data raising questions about the police's contributions to mass surveillance
  • Such surveillance poses significant risks, include violations of privacy rights
• Current Trend: Machine Learning

  • Specific subset of AI that trains a machine how to learn
  • Science of getting computers to act without being explicitly programmed
• Methods of Machine Learning: Supervised Learning

  • Algorithms are trained using labeled examples
  • An input where the desired output is known
  • Commonly used in applications where historical data predicts likely future events
• Methods of Machine Learning: Unsupervised Learning

  • Used against data that has no historical labels
  • The system is not told the right answer
  • Algorithm must figure out what is being shown
  • Goal is to explore the data and find some structure within
  • Works well on transactional data
• Methods of Machine Learning: Semi-Supervised Learning

  • Between supervised and unsupervised learning
  • Use both labeled and unlabeled data for training
  • Typically a small amount of labeled data and a large amount of unlabeled data
  • Systems that use this method are able to considerably improve learning accuracy
• Methods of Machine Learning: Reinforcement Learning

  • Algorithm discovers through trial and error which actions yield the greatest rewards
  • Allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance