Generative and Predictive AI - Artificial Intelligence

Generative and Predictive AI - Artificial Intelligence

Generative AI:

Generative AI (GenAI) is a type of artificial intelligence (AI) that can create new content.

GenAI learns patterns from training data and uses this knowledge to generate new outputs.

The new outputs have similar characteristics to the training data, but they don't repeat it.

GenAI can create a variety of content, including:

Images
Videos
Audio
Text
3D models
Music
Speech
Software code
Product designs

Here are some steps to build a GenAI:

Benefits of GenAI:

Makes informed decision
Content creation and inspection
Personalized experience
Improved efficiency
Automaton friendly
Virtual guidance

Disadvantages of GenAI:

Generative AI can raise ethical questions about privacy, intellectual property, and misinformation.
Generative AI models depend on the quality and diversity of their training data.
Generative AI can't be completely trusted due to Incorrect facts and biased information.
Generative AI is not meant to replace human intuition, inspiration, and ideas.
Generative AI can increase the likelihood of fraud, economic crime, and organized crime.
Generative AI outcomes may be limited in traceability and irreproducible, which could lead to bad or illegal decision making.

Predictive AI:

Predictive artificial intelligence (AI) uses machine learning to identify patterns in past events and make predictions about future events.

Predictive AI uses patterns in historical data to forecast future outcomes or classify future events.

Predictive AI models can be trained to predict:

Stock market trends
Customer behavior
Disease progression
Future needs or events of a company
Trends

Here are some steps to build a predictive model:

Benefits of Predictive AI:

Identifying the most valuable customers
Provides actionable insights and aids in decision-making

Disadvantages of Predictive AI:

Predictive can tell you when you should consider taking action, but it can't tell you what action to take. Thus, if you rely on gut feel or standard practices to make decisions, sometimes you'll end up selecting a bad response.
Due to the lack of current data, the outcomes can be inaccurate.

Comments

Scaled Agile Framework (SAFe)

The Scaled Agile Framework (SAFe) is a set of organizational and workflow patterns for implementing agile practices at an enterprise scale. The framework is a body of knowledge that includes structured guidance on roles and responsibilities, how to plan and manage the work, and values to uphold. Scrum is a simple, flexible approach to adopting Agile that's great for small teams. SAFe is an enterprise-wide Agile framework designed to help bring Agile beyond the team and into the company as a whole. Scaled Agile has built a comprehensive level that includes all the four layers called the team, program, large solutions, and portfolio level. 4 Layers: Portfolio - Strategy, Vision, Roadmap, Strategy goal, Decision making, Budget, Portfolio level metrics, Program - Align multiple teams towards a common mission, Bring together all the Agile teams, transparency, collaboration, and synchronisation, Scrum of Scrums, Product Owners to define the overall vision. Large Solutions - archite

Risk Register

A project risk register is a tool project managers use to track and monitor any risks that might impact their projects. Risk management is a vital component of project management because it's how you proactively combat potential problems or setbacks. Risk Description Impact Risk Response Risk Level Risk Owner Automation Testing Software licence delay Delay in starting testing and project schedule impact As we have one licence. Planned to start automation testing in 2 shifts. Planned to get one more licence in 2 weeks’ time. High IT team Frequent Disruption in dependency API services Delay in development of integration and unit testing Dependency API service is down, and the team is working on resolving the issue. Continuously working with API team High External Team/ Project Manager There is chance of new requir

Lessons learned from sprint retrospective meeting

Scenario: Team Missed Sprint Goals Challenge: A development team consistently missed its sprint goals, leading to frustration and a drop in morale. Team members felt overwhelmed by the workload and struggled to communicate effectively. Retrospective Insights: During the retrospective, team members openly discussed their challenges and frustrations. They identified bottlenecks in communication, unclear priorities, and unrealistic expectations. The team realized that individual workloads were not evenly distributed, causing burnout for some members. Lessons Learned: Effective Communication Matters: The team recognized the importance of clear communication. They committed to regular stand-up meetings, where everyone shared progress, blockers, and priorities. Balancing Workloads: The retrospective highlighted the need to distribute tasks more evenly. They decided to monitor workloads and adjust assignments accordingly. Setting Realistic Goals: The team acknowledged that setting achievable

High-Tech Yuga

Search This Blog