If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial intelligence has long caused fear of job loss across many sectors as companies look for ways to cut costs, support workers and become more profitable. But new research suggests that even in STEM-based sectors like cybersecurity, AI simply can't replace some traits found only in humans, such as creativity, intuition and experience. There's no doubt, AI certainly has its place. And most business leaders agree that AI is important to the future success of their company. A recent survey found CEOs believe the benefits of AI include creating better efficiencies (62 percent), helping businesses remain competitive (62 percent), and allowing organizations to gain a better understanding of their customers, according to Ernst and Young.
BPU Holdings is a global company, headquartered in Korea that pioneers in the development of Artificial Emotional Intelligence (AEI). The mission of the company is to generate the most advanced, secure usable, and innovative Artificial Emotional Intelligence technology in the world. BPU has developed the first Artificial Emotional Intelligent (AEI) platform -- AEI Framework, which emulates how people think and feel. BPU improves the human condition by offering rigorous tools to improve emotional intelligence. Tracking and handling emotions enable the management of professional and interpersonal relationships, empathetically and judiciously.
A convolution layer provides a method of producing a feature map from a two-dimensional input. This is accomplished by running a filter over the input data. The filter is just a set of weights that must be trained to identify a feature in regions of the input data. These features can be things like edges, points, or more complex information. The filter will have dimensional constraints that indicate width and height, and it will scan over the input data.
I've spent the last few months preparing for and applying for data science jobs. It's possible the data science world may reject me and my lack of both experience and a credential above a bachelors degree, in which case I'll do something else. Regardless of what lies in store for my future, I think I've gotten a good grasp of the mindset underlying machine learning and how it differs from traditional statistics, so I thought I'd write about it for those who have a similar background to me considering a similar move.1 This post is geared toward people who are excellent at statistics but don't really "get" machine learning and want to understand the gist of it in about 15 minutes of reading. If you have a traditional academic stats backgrounds (be it econometrics, biostatistics, psychometrics, etc.), there are two good reasons to learn more about data science: The world of data science is, in many ways, hiding in plain sight from the more academically-minded quantitative disciplines.
NEC and the Japan Agency Marine-Earth Science and Technology (JAMSTEC) have developed a system that uses artificial intelligence (AI) imaging recognition techniques to automatically detect microplastics from seawater and sediment samples. The system, according to NEC, has been developed using its Rapid machine learning technology in combination with JAMESTEC's method for staining microplastics using fluorescent dyes in samples, before capturing videos of the dyed microplastics. The software then automatically extracts image data for each microplastic that appears in the video and uses AI recognition technology to sort microplastics based on sizes and shapes at a processing speed of 60 per minute, NEC said. The Japanese conglomerate touted the new system could improve the current method that is used to analyse microplastics to determine the impact plastic waste has on marine life. Typically, the process of analysing microplastics involves scooping seawater and sediment with a fine mesh, before using a microscope to pick and analyse each microplastic manually to determine the number, size, and types that exist in the ocean.
So you want to train a neural network to distinguish puppies from people. Maybe you'd like to train a system that opens the door when your little puppy arrives but keeps strangers out, or you are the owner of an animal farm where you want to make sure that only people can get into the house. In any case, you take your favourite CNNs (say, ResNet-152 for performance and AlexNet for good old times' sake) and train them on a puppies-vs-people dataset scraped from the web. You are relieved to see that each of them reach about 96-98% accuracy. But does similar accuracy imply similar strategy?
In one second, the human eye can only scan through a few photographs. Computers, on the other hand, are capable of performing billions of calculations in the same amount of time. With the explosion of social media, images have become the new social currency on the internet. Today, Facebook and Instagram can automatically tag a user in photos, while Google Photos can group one's photos together via the people present in those photos using Google's own image recognition technology. Dealing with threats against digital privacy today, therefore, extends beyond just stopping humans from seeing the photos, but also preventing machines from harvesting personal data from images. The frontiers of privacy protection need to be extended now to include machines.