OEMs spend too much conducting user trials of their products. AI in human factor research can help in reduce the spend. A ML model for different personas can to be created by learning from questionnaire. The design imperatives and the HMI attributes based on Norman's Visceral, Behavioral and Reflective aspects can be quantified. The model will rate the product based on the HMI attributes.

The herding anaimals tend to move in a one particular direction. There are sheepdogs who are in charge of herding the sheeps. The dogs analyze the state of a given herd and determine the best action to keep the sheep together while herding. These sheepdog think algorithmically. Social media discussion could be considered as herding sheep. Each of the smaller groups have a direction, right, left, centre etc. The dog algorithm could be employed to influence the group behavior. Machine Learning could be used to identify the groups and its direction and analyse the influence of sheepdog algorithm.

Recently I approached my Wellness center, where I have have been doing my annual health checkup, for my historical ECG data of last 10 years. To my disappointment no ECG data was stored. The data could have helped me to understand the aging and heart related anomalies. A ML model could be created for the healthy heart and detect any deviation from the normal. This is true for not just heart, we could created a ML model for any bunch of vital parameters and detect anomalies which could accelerate medical diagnosis. Data is pure gold.

According to the father of Artificial Intelligence (AI), John McCarthy, AI is “The science and engineering of making intelligent machines, especially intelligent computer programs”. An AI framework is made out of an agent and its environment. The agents act in their environment. The environment may contain different agents. Machine Learning is a sub-set of artificial intelligence.

The role of AI has been evolved from the Computer science to Business integration, some examples are Algorithm based trading in stock market . Al based recruitment system.

Bias and variance are two major issues we try to manage in machine learning. We are always looking for a trade-off between the two. Bias has to do with the training data while variance has to do with the model output.

Good to know about this. Thanks, Chris, for bringing it up. I like to have more details so that the methodology can be applied in our day-to-day machine learning endeavours.

Machine learning can be described as a form of statistical analysis, often even utilizing well-known and familiar techniques, that has bit of a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data.

Thank you all for your support and cooperation.Regards,Ajit Singh

This article follows the first piece on machine learning describing how AI interacts with neuroscience, as well as how hardware and chips are getting created and modified to be more efficient for specific AI algorithm

I highly recommend reading the work of Numenta, which gives a new perspective on neuroscience and the biological approach to AI

Undergraduates Harry Berg (Mechanical Engineering) and Haron Shams (Design Engineering) have set up the Imperial College Machine Learning Society to get students involved in and inspired by technology that’s going to change the world.It is interesting that this society was planned by two undergraduate students and that the first event attracted 250 attendees, with over half of them being PhD students.

Do you want to do machine learning using Python, but you’re having trouble getting started?In this post, you will complete your first machine learning project using Python.In this step-by-step tutorial you will:Download and install Python SciPy and get the most useful package for machine learning in Python.Load a dataset and understand it’s structure using statistical summaries and data visualization.Create 6 machine learning models, pick the best and build confidence that the accuracy is reliable.If you are a machine learning beginner and looking to finally get started using Python, this tutorial was designed for you.https://machinelearningmastery.com/machine-learning-in-python-step-by-step/

Good tutorial Saroj, thanks for sharing it

Kaggle have just launched a set of free resources for learning Machine Learning, R, Data Visualisation and Deep Learning. They look like an ideal introduction for anyone wanting to get a start in these disciplines. Take a look here: https://www.kaggle.com/learn/overview