Self-learning Artificial Intelligence

What self-learning AI is?

Self-learning artificial intelligence is the simulation of human intelligence processes by computer systems.
SLAI as a child that can be trained, and it’s up to you what it grows up to be.

It involves data whose scale exceeds what humans can analyze, absorbing knowledge like a sponge, which can be used afterwards. SLAI can not only collect data, but also combine data in a unique way, creating new ideas and solutions.

Collect demonstration data
and train a supervised policy.
‍

A prompt is
sampled from our
prompt dataset.

Explain reinforcement learning to a 6 year old.

A labeler
demonstrates the
desired output behaviour.

We give treats and punishments to teach...

This data is used to
fine-tune GPT-3.5
with supervised
learning.

SFT

Collect comparison data
and train a reward model.
‍

A prompt and
several model
outputs are sampled.

Explain reinforcement
learning to a 6 year old.

In reinforcement learning, the agent is...

Explain rewards...

In machine learning...

We give treats and punishments to teach...

A labeler ranks the outputs from best
to worst.

This data is used to train our reward model.

Optimize a policy against the
reward model using the PPO
reinforcement learning algorithm.

A new prompt is
sampled from the dataset.

Write a story
about cats.

The PPO model is initialized from the supervised policy.

PPO

The policy generates an output.

Once upon a time...

The reward model calculates a reward for the output.

The reward is used to update the policy using PPO.

Who we are

We are a team of specialists who have developed the architecture of self-learning artificial intelligence.

We were united by the freedom to create and do something new, because people cannot want what they have never seen.

Our services

Our company provides services for the implementation of artificial intelligence architecture in the client's business processes. Depending on the task, we design and develop artificial intelligence for the client.
With our help, you can train a set of neural networks and implement it in your business processes.

Sounds

Images

Texts

Numbers

Preliminary preparation
of data received
from the client.
‍

Creation of software architecture,
selection and training
of neural network models.
‍

Post-processing of output dataset,
analysis and repetition
of machine learning until
the desired result is obtained.

Creating a user interface
and integrating our solution
into the client's
software architecture.

Tools

Our dedicated team leverages a robust technology stack to ensure we deliver the best results for our clients. Our carefully selected stack empowers us to tackle complex challenges, optimize efficiency, and drive innovation in every project we undertake.

Here's a glimpse into what our technology stack includes to provide exceptional solutions for our clients:

Image recognition

YOLO

DeepSORT

FastSort

StabbleDuffusion

DeepFace

FaceRecognition

Natural speech and text processing

spayCy

ChatGPT

SpeechRecognition

HuggingFace

Libraries and frameworks

scikit-learn

TensorFlow

PyTorch

Pandas

NumPy

Keras

NetworkX

SciPy

TensorBoardX

FastAPI

Environments

Linux

Windows

Anaconda

docker

GitHub

GitHub Actions

kubernetes

CI/CD

Database Management Systems

Nebula-Graph

neo4j

PostgreSQL

Possible areas of usage of AI technologies

AI technology is crucial for businesses as it processes data quickly and accurately, streamlines operations, and helps make data-driven decisions. It provides valuable insights, improves customer service, and enables companies to remain competitive in today’s business landscape. Here are some examples:

Computer vision in manufacturing

Computer vision has become an essential tool in modern manufacturing, providing a range of benefits to businesses. Here are some of the advantages of using computer vision in manufacturing:

Quality control: quickly and accurately inspects products for defects, helping manufacturers catch issues early, reduce waste, and improve product quality.
Increased efficiency: automates repetitive tasks like product inspection, freeing up workers to focus on more complex tasks. This increases production efficiency and reduces labor costs.
Improved safety: monitors manufacturing processes for safety hazards, preventing accidents and injuries at work.

AI technologies in medicine

AI technologies are transforming the healthcare industry by improving patient care, reducing costs, and increasing efficiency. AI algorithms can analyze vast amounts of medical data, such as patient records and medical images, to identify patterns and diagnose diseases more accurately and quickly. AI‑powered chatbots and virtual assistants can provide patients with personalized and 24/7 medical advice, reducing the workload on healthcare providers.

Additionally, AI technologies can help healthcare providers optimize treatment plans, predict potential health risks, and develop new drugs and therapies. As AI technologies continue to evolve, their applications in medicine will continue to expand, revolutionizing the way we approach healthcare.

AI algorithms in financial industry

AI technologies are increasingly being used in the financial industry to improve operations, reduce costs, and enhance customer experience. AI algorithms can analyze vast amounts of financial data, such as stock prices and market trends, to make informed investment decisions and predict financial risks. AI-powered chatbots and virtual assistants can provide customers with personalized financial advice and assistance, reducing the workload on human financial advisors.

Additionally, AI technologies can help financial institutions detect and prevent fraud, streamline loan approvals, and optimize risk management. As AI technologies continue to advance, their applications in finance will continue to expand, providing new opportunities for growth and innovation in the industry.

artificial intelligence