Artificial Intelligence
“AI First” is not just a buzzword for us – we passionately believe that the software platforms that power Enterprises of the Future will be Intelligent at their Core. We also believe that the Intelligence is migrating towards the Edge, with more and more applications being able to “reason” and take “intelligent” decisions at the point of action. This, in our opinion, will fundamentally alter how commerce will get conducted. Our teams are well geared to help you on this transformational journey.
STRATEGY SUPPORT
- ML Strategy including roadmap definition
- ML Landscape definition
- Opportunity Identification
- Business Case Development
CORE MODEL BUILDING
- Identify the right framework for model development
- Explore various models and select the best one
- Model Tuning
ENGINEERING SERVICES
- Data Analysis
- Data Pipeline Build
- Model Testing
- Model Management
- Model Auditing
ANNOTATION SERVICES
- Image Annotation, detection and segmentation
- Natural Language Annotation
- Speech Annotation
- Enterprise annotation tool
We have experienced teams across the AI technology spectrum from SAS and MATLAB all the way to open source technologies like Spark, R and Python.
IOT
Over a million new IoT devices are connected to the Internet daily, and these numbers are accelerating. By the year 2020, experts predict there will be as many as 50 billion IP-enabled IoT devices. We notice an increase in hardware manufacturers creating new connected devices, applications and business processes – which is resulting in varied products, services and workflows.
The success of connected devices, or connected environment, catering to real time and efficient decision making is dependent on accessing, storing and processing of data.
- Connect edge hardware, access points and data networks to other parts of the value chain
- Design to store and process the data being generated from sensors, devices, gateways, machines, website, applications, customers, partners and other sources.
- Architect a massively scalable, real-time event processing engine
- Make the platform data format and product agnostic
- Automate the environment to handle ongoing management tasks and data visualization
- Generate a comprehensive and integrated perspective on customers
DevOps
n today’s world, the speed at which software is produced and distributed to customers often determines the amount of value delivered. However, delivering software at a fast pace is not the only goal. If speed is not balanced with the right level of quality, systems crash. Frequent crashes will eventually slow businesses.
In the cycle of build, test, deploy and support, we work on software continuously for new functionality. All the parts are changing all the time. We can help you accelerate this cycle by intelligent automaton of the processes, leveraging open source tools and our experienced teams. Use data collected through the entire cycle to visualize work, evaluate problems & risks and make the necessary changes.
- Development
- Source / Version control systems
- Build
- Testing
- Continuous Integration
- Deployment
- Collaboration
- Release Management
- Containerization
Need for Explainable
Artificial intelligence (XAI)
Explainable AI (XAI), Interpretable AI, or Transparent AI refer to techniques in artificial intelligence (AI) which can be trusted and easily understood by humans. It contrasts with the concept of the ‘black box’ in machine learning, where even their designers cannot explain why the AI arrived at a specific decision.
AI systems optimize behavior to satisfy a mathematically-specified goal system chosen by the system designers. The AI may learn useful general rules from the test-set. However, it may also learn inappropriate rules. Such rules may be undesirable if they are deemed likely to fail to generalize outside the test set, or if people consider the rule to be ‘cheating’ or ‘unfair’.
Cooperation between agents, in this case algorithms and humans, depends on trust. If humans are to accept algorithmic prescriptions, they need to trust them. For that reason, interpretability and explainability are posited as key goals.
In this context, there arises a need to interpret the complex AI-driven decisions, processes, patterns, techniques, models and data. Here comes the explainable artificial intelligence (XAI), for making the impossible possible. With XAI, humans will be able to understand the reason and logic behind every decision. It aims to produce more explainable models and techniques, while maintaining and ensuring prediction accuracy.
Explainable (XAI) helps in explaining and comprehending the useful parts of an algorithm’s working, that improve human trust. This includes:
- Auditing the data used for training the machine learning (ML) models – to ensure that the ‘bias’ is understood
- Understanding the decision paths for the edge cases (like false-positives and false-negatives)
- Understanding the robustness of models; specifically in relation to adversarial examples
How can we help?
Being amongst the very few organizations that are specifically focusing on XAI, we can help you with the following:
Before Model Building
- Define an explainable AI (XAI) strategy
- ROI and use case preparation
During Model Building
- Understand and define the type of explanations expected out of the model
- Design the architecture of the learning method, to give intermediate results
After Model Building
- Detect ‘bias’ in the data type used for training the model
- Use method like ‘LIME’ to provide local interpretability of features to output
- Game theory-based models like ‘SHAP’ to interpret target models
- Understand ‘robustness’ of neural networks using adversarial techniques
