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) …
In this work we introduced a method to search for simple neural network architectures with strong inductive biases for performing a given task. Since the networks are optimized to perform well using a single weight parameter over a range of values, this single parameter can easily be tuned to increase performance. Individual weight values can then be further tuned as offsets from the best shared weight. The ability to quickly fine-tune weights is useful in few-shot learning and may find uses in continual lifelong learning where agents continually acquire, fine-tune, and transfer skills throughout their lifespan . Early works connected the evolution of weight tolerant networks to the Baldwin effect .
As a data scientist, I am curious about knowing different analytical processes from a probabilistic point of view. There are two most popular ways of looking into any event, namely Bayesian and Frequentist . When Frequentist researchers look at any event from frequency of occurrence, Bayesian researchers focus more on probability of events happening.
This is a post related to my recent talk at PyLadies Vancouver. The talk is about how to use the EmoPy toolkit in Linux Ubuntu 16.04 with OpenCV Python to perform Emotion detection in images and videos. You can find my slides here. EmoPy is an open-source emotion detection toolkit developed by Thoughtworks and currently supports OS X. However, it has not been tested on a Linux OS.
Every stranger's face hides a secret, but the smiles in this crowd conceal a big one: These people do not exist. They were generated by machine learning algorithms, for the purposes of probing whether AI-made faces can pass as real. University of Washington professors Jevin West and Carl Bergstrom generated thousands of virtual visages to create Which Face Is Real?, an online game that pairs each counterfeit with a photo of a real person and challenges players to pick out the true human. Nearly 6 million rounds have been played by half a million people. These are some of the faces that players found most difficult to identify as the cheery replicants they are.
This article summarizes the lessons learned after two years of our team engaging with dozens of enterprise clients from different industries including manufacturing, financial services, retail, entertainment, and healthcare, among others. What are the most common ML problems faced by the enterprise? What is beyond training an ML model? How to address data preparation? How to scale to large datasets?
The scholars focused on combating the malicious use of AI by terrorists. Their findings were published in the journal Russia in Global Affairs. Much has been written on the threats that artificial intelligence (AI) can pose to humanity. Today, this topic is among the most discussed issues in scientific and technical development. Despite the fact that so-called Strong AI, characterised by independent systems thinking and possibly self-awareness and will power, is still far from reality, various upgraded versions of Narrow AI are now completing specific tasks that seemed impossible just a decade ago.
Datasets are an integral part of machine learning. Without high quality training datasets, machine learning algorithms would have no way of knowing how to conduct sentiment analysis, categorize products or understand foreign languages. This spreadsheet contains the ultimate list of open datasets for machine learning. Organized by industry and use case, this database contains a diverse range of 300 datasets to train machine learning models.
On the AI journey, automation is often the default, depopulated destination. We must consciously choose to empower humans via augmentation. "Airplanes are becoming far too complex to fly. Pilots are no longer needed. Donald Trump's tweet, a hasty response to the fatal Ethiopian Airlines crash on March 10, offers a typically visceral response to computerization when it is perceived to cause a catastrophe. Unfortunately, it ignores the subtle issues surrounding the adoption of technology to both support and replace human involvement in decision making. Early investigations into the cause of this and an earlier crash point to complex, computerized flight-control software -- the Maneuvering Characteristics Augmentation System (MCAS) -- introduced on the Boeing 737 MAX 8 to correct the angle of attack of the aircraft if it becomes too steep under certain flying conditions. A single source -- faulty sensor data -- has been blamed for the crashes. However, the algorithm also considers ...
Ken Weiner leads the engineering and product teams behind the industry's top computer vision platform for marketers and rights holders. As an active member of the ad tech community, Weiner is a guest columnist for VentureBeat and Forbes, a frequent speaker at conferences and a participant of industry groups such as IAB's OpenRTB Working Group. He also runs the LA AdTech Meetup which brings together ad tech engineers and product managers. Weiner was LA Business Journal's 2015 CTO of the Year, and in 2003, he was included in the InfoWorld 100 for his work on uPortal, an open-source web portal framework for the higher education community. Before coming to GumGum, Weiner was the Director of Engineering at the lead-generation platform LowerMyBills.com,