TechAthena
Researches envisage the development of AI in following three stages;
Stage 1: Narrow AI —- Stage2: Broader AI —- Final or Stage 3: General AI
Narrow AI is where we are we are today, Broader AI is where we moving towards and General AI is where we aim to be.
AI, as we know it today, is being applied to language translation, speech transcription, object detection, and face recognition. This is Narrow AI which is applied to a single task or a narrow task.
Broader AI are where we start to reduce our heavy dependency on existing data, club knowledge with AI, and seriously start addressing issues like Ethics, Explainability, Fariness, Scale, Security, Lineage in AI
General AI is where we aim to be where machines think like human, evolve, adapt and learn by themselves. Unlike broader AI, research doesn’t expect general AI to arrive until after 2050.
Graph technologies are the scaffolding for building intelligent applications, enabling more accurate predictions and faster decisions. In fact, graphs are underpinning a wide variety of artificial intelligence (AI) use cases in our move towards broader AI.
Probabilistic graphical models (PGM) have emerged as one of the underlying pillars on which the science of Artificial Intelligence (AI) has been gaining its heights upon, in recent times. A core constituent of graph-based AI, PGMs have enabled modeling intelligence into concrete models represented as graphs, which in turn comprise of vertices (entities), and edges that connect pairs of vertices (relationships).
This talk provides a high-level introduction to a few important representations and techniques, that are fundamental to graph-based AI using PGMs.
Concepts of directed graphical models using Bayesian networks and undirected graphical models using Markov models are covered in this talk. Associated concepts such as Interdependencies, D-Separation, etc. are also explained.
Come Join our session to know more about “Graph AI” and share each others experience.
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