BOSS a New Reinforcement learning program that trains agents solve new tasks in new environment with LLM guidance.

Reinforcement Learning

This learning program has a large language models that will help machine to adapt and understand new languages. It also helps in building a skill library for tackling intricate tasks with minimal guidance. BOSS is much better in performing unfamiliar task as compared to other reinforcement learning programs. This innovation in machine learning is a giant step in making the learning skill process automatic.

BOSS has extends the skill repertories as it has a vast language models, guiding exploration and rewarding skill chain completion, yielding higher success rates in long-horizon task execution.

The ubnique feature of BOSS is that it has minimal human intervention and has the ability to acquire diverse skills. Through bootstrapping and Large Language Models (LLM) it combines various skills to perform complex tasks.

BOSS works in a two-phase framework. In the first phase it follows unsupervised reinforcement learning to set skills. In the second phase, skill bootstrapping employs LLM to guide skill chains and reward based on skill completion. This helps the machine to create complex behaviors from basic skills.

All these additions to the learning model makes BOSS stand out of the ordinary.

Unsupervised reinforcement learning and 4D scanning

Unsupervised reinforcement learning was used for 4D scanning g transmission electron microscopy for bimodal nonstructural analysis. The use of this type of learning module has optimized bimodal meaning spatial and reciprocal analyses of material nanostructures. The combination of 4D-STEM (Scanning transmission electron microscopy)   and unsupervised reinforcement learning has optimized a large amount of dataset that can help in microscopic and diffraction technique.

List of models that work without output labels

There are multiple models that do not require output labels to perform their task. The list is mentioned below.

K-Means Clustering

A popular unsupervised machine learning program that is used to group a set of data into different categories.

Hierarchical Clustering

This is a method which automatically determines the number of clusters.

Agglomerative Clustering:

This is the method in which the machine builds clusters in an bottom-up fashion. 

Divisive Clustering:

This is the method in which the machine builds clusters in a top- down order.

Density Based Spatial Clustering of Application with Noise (DBSCAN)

Gaussian Mixture Models (GMMs)

This works under the principle of expectation and sets the data on the basis of their probability scores.


This model encodes the input representation into a lower dimensional representation.

Visual Inspection AI

Evolution of AI-powered visual inspection 

The article talks about how the use of AI has reduced waste and also made easy to detect defects and anomalies in a product in less time. It also talks about how human-AI collaboratio0n has helped improve the productivity of production lines across many industries. The combination of human experience and fast detection making and detection by the AI has helped production line to generate less waste.

It also talks about self-training software and the changes it has brought along with it in the manufacturing industry.

Reducing of waste and time with AI automated inspection

The article talks about how IBM helped a renowned automobile manufacturer find defects in less time and also resolve the defects before it reaches the customer. It also says that it immediately reduced a significant amount of waste of waste in the production line of the automobile manufacturer.

The article also talks about trusting AI to enhance the performance level of the production line and also promotes IBM’s deep AI capabilities.

AI based Visual Inspection for spark plugs.

The article is about a semi-automated AI- based inspection is set up in the production line of spark plugs where the plug is rotated in the front of a machine vision camera to provide a complete 360 degree view of the plug and allow an automated inspection for any kind of defects in the plug before packing it. The company is using Viara’s AI based inspection system to increase the productivity of the factory outlet and to reduce waste.