Artificial Intelligence

AI

Overview of AI

For decades, researchers, scientists, and software companies have worked to advance Artificial Intelligence (AI). While key developments in machine learning and deep learning occurred decades ago, we are only now seeing the widespread benefits of AI. After an “AI Winter“, AI thrives in Natural Language Processing, Speech Recognition, Image & Video Analysis, and fraud detection. This resurgence, termed “AI Spring“, is driven by cloud storage, TPU/GPU processors, Big Data, analytics, and IoT. However, today’s AI remains narrow in scope. The future will bring Artificial General Intelligence (AGI) and intelligent robots capable of performing diverse tasks more efficiently.

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AI Landscape

AI is an umbrella term and is comprised of many technologies like:

1. Knowledge Reasoning

2. Planning and Insight

3. Problem Solving and Decision Making

4. Natural Language Processing

5. Computer Vision or CV

6. Speech to Text (STT) and Text to Speech (TTS)

7. Machine Learning

8. Humanoid Robotics

Our Expertise and Services in AI and Cognitive Computing

Pre-Built Model and Cognitive Services

Helping in AI-fication of apps and systems
Chatbot app Dev & test
VoiceBot skill dev & test
contact center modernization using cloud hosted contact center (AWS Connect)
Cognitive APIs – IBM Watson, Azure Cognitive Service

Citizen AI - No Code or Low Code AI

Data and ML pipeline build
Data preparation, validation, cleansing and wrangling
Machine Learning model build & test
No Code/Low Code tools like alteryx, rapidminer, KNIME, H2O.ai and Azure ML studio.
Cloud Big data & analytics platforms like Databricks

AI consulting

Data science business case preparation
Data science proof of concept
AI consulting and advisory
Data exploration, preparation, cleansing
ML consulting and advisory
DataOps and data pipeline consulting

Data Science – Building And Training Custom Models

Custom model built and testing using R & Python
Machine Learning libraries like scikit-learn, ML.NET etc
Deep learning frameworks like TensorFlow, Pytorch, Keras etc.
Building data and machine learning pipeline
ML Operations

AI, Machine Learning (ML), Deep Learning and Data Science

Artificial Intelligence (AI)

Artificial Intelligence encompasses technologies such as Natural Language Processing , Robotics, Knowledge Reasoning, Planning, Insight, Computer Vision , and Machine Learning. These technologies enable machines to do their tasks that require human intelligence, with ML being a critical subset that allows machines to learn from data.

Machine Learning (ML)

Machine Learning relies on data-driven algorithms rather than predefined rules. By using labeled data, ML algorithms build models to make predictions and infer insights. This iterative process helps machines adapt and improve, enhancing
their application potential.

Deep Learning (DL)

Deep Learning, a subset of Machine Learning, uses algorithms inspired by neural networks in the human brain. These Neural Networks process information in layers, recognizing patterns and making complex decisions. DL excels in image and speech recognition, surpassing traditional ML techniques.

Data science

Data Science covers the entire data lifecycle, including exploration, wrangling, preparation, analysis, and predictive analytics using Machine Learning. DataOps integrates these stages efficiently, ensuring accurate and actionable insights essential for successful data science projects.