I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...Full Bio
I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.
Changing Scenario of Automation over the years
468 days ago
Top 7 trending technologies in 2018
469 days ago
A Beginner's Manual to Data Science & Data Analytics
469 days ago
Artificial Intelligence: A big boon for recruitment?
470 days ago
Top 5 chatbot platforms in India
Artificial Intelligence: Real-World Applications
Levels of Big Data Maturity
Challenges of building intelligent chat bots
Chatbots' role in customer retention
How to Get Started as a Developer in AI?
- Sense: Identify and recognize meaningful objects or concepts in the midst of vast data. Is that a stoplight? Is it a tumor or normal tissue?
- Reason: Understand the larger context, and make a plan to achieve a goal. If the goal is to avoid a collision, the car must calculate the likelihood of a crash based on vehicle behaviors, proximity, speed, and road conditions.
- Act: Either recommend or directly initiate the best course of action. Based on vehicle and traffic analysis, it may brake, accelerate, or prepare safety mechanisms.
- Adapt: Finally, we must be able to adapt algorithms at each phase based on experience, retraining them to be ever more intelligent. Autonomous vehicle algorithms should be re-trained to recognize more blind spots, factor new variables into the context, and adjust actions based on previous incidents.
- Data Acquisition: First, you need huge amounts of data. This data can be collected from any number of sources, including sensors in wearables and other objects, the cloud, and the Web.
- Data Aggregation and Curation: Once the data is collected, data scientists will aggregate and label it (in the case of supervised machine learning).
- Model Development: Next, the data is used to develop a model, which then gets trained for accuracy and optimized for performance.
- Model Deployment and Scoring: The model is deployed in an application, where it is used to make predictions based on new data.
- Update with New Data: As more data comes in, the model becomes even more refined and more accurate. For instance, as an autonomous car drives, the application pulls in real-time information through sensors, GPS, 360-degree video capture, and more, which it can then use to optimize future predictions.