As Josh Wills once said,
“Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”
Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. In fact, Mathematics is behind everything around us, from shapes, patterns, and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives.
Predictive maintenance is an increasingly common sight in industrial facilities around the world, but the ability for AI to detect when machinery is about to fail relies upon a steady stream of data. One would imagine the data provided by Google Street View might not be up to the job therefore, but new research from RMIT suggests otherwise.
The authors propose using data from Google Street View to spot when road furniture needs replacing. The authors highlight the often manual and laborious task of monitoring street furniture and traffic infrastructure.
With convolutional neural networks and state-of-the-art image recognition techniques it is possible to make old movie classics shine again. Neural networks polish the image, reduce the noise, and apply colors to the aged images.
The first movies were created in the late nineteenth century with celluloid photographic film used in conjunction with motion picture cameras.
The healthcare industry has generated plenty of data. The new method of data collection, such as sensor-generated data, has helped this industry to find a spot in the top.
What if this data can be used to provide better healthcare services at lower costs and increase patient satisfaction? Yes, you heard it right. It’s actually possible by applying machine learning (ML) techniques in the healthcare industry.
The fourth industrial revolution, or Industry 4.0, dawned upon the enterprise sector faster than any of the prior industrial eras (steam, electricity, and computers). Yet, excess wastes haven’t escaped the balance sheets. The zest to predict industrial waste and keep it to a minimum is one of the promises that industry 4.0 — driven by predictive analytics — holds.
Despite the automation hangover (Industry 3.0), manufacturers are proactively attuning their processes into smarter ecosystems. According to the PWC Global Industry 4.0 Survey, industry 4.0 could potentially bring cost reduction up to 3.6% p.a., amounting to USD 421 billion globally.
In this article, I’ll show how to build a natural language interface for a typical home light switch so that you could turn the lights on and off with simple commands like Turn off all the lights, please, or Get the lights on in the kids bedroom.
We’ll concentrate on Natural Language Interface (NLI) part, and I’ll leave speech-to-text and the actual light controlling outside of the scope of this short blog. You can easily add speech-to-text with WebSpeech, if necessary, while Arduino/HomeKit can provide simple API to control the lights in your house.
Machine learning refers to the process of enabling computer systems to learn with data using statistical techniques without being explicitly programmed. It is the process of active engagement with algorithms in order to enable them to learn from and make predictions on data. Machine learning is closely associated with computational statistics, mathematical optimization, and data learning. It is associated with predictive analysis, which allows producing reliable and fast results by learning from historical trends. There are basically two kinds of machine learning tasks:
Supervised learning: The computer is presented with some example inputs, based on which the desired outputs are to be formed. The computer is made to learn general rules of converting inputs to outputs.
Every developer understands there are multiple interfaces for end-users to interact with technology — keyboard, mouse, touch-pad, touch-screen, pointing device, VR, AR, mixed reality, and voice. With the popularity of devices like Amazon Echo, voice-interactive experiences have enhanced how many engage technology within the home or office. In recent months, the Amazon Alexa team announced a new feature that provides access to the geolocation of participating Alexa-enabled devices. What does this mean? It provides an opportunity for end-users to interact with Alexa when away from home and allow custom experiences based on location.
From a developer’s perspective, this could raise multiple questions:
As the impact of fake news has grown, so too have attempts to detect and remove it. I wrote recently about an AI-driven approach developed by the University of Michigan, which is able to accurately spot fake news stories around 76% of the time.
A second system, developed by a team from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute (QCRI), are attempting to do likewise. Their approach focuses less on the reliability of individual claims and more on the general reliability of the news sources themselves.
Microsoft recently published a blog post announcing a new competition for data scientists. It calls for participants to use machine learning to predict, given the current state of a device, when (or if) it is likely to get infected with malware.
"The competition provides academics and researchers with varied backgrounds a fresh opportunity to work on a real-world problem using a fresh set of data from Microsoft," the blog post states. "Results from the contest will help us identify opportunities to further improve Microsoft’s layered defenses, focusing on preventative protection. Not all machines are equally likely to get malware; competitors will help build models for identifying devices that have a higher risk of getting malware so that preemptive action can be taken."
