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) …
A number of weeks ago I solicited feedback from my LinkedIn connections regarding what their typical day in the life of a data scientist consisted of. The response was genuinely overwhelming! Sure, no data scientist role is the same, and that's the reason for the inquiry. So many potential data scientists are interested in knowing what it is that those on the other side keep themselves busy with all day, and so I thought that having a few connections provide their insight might be a useful endeavor. What follows is some of the great feedback I received via email and LinkedIn messages from those who were interested in providing a few paragraphs on their daily professional tasks.
Don't you look at the CapsNet architecture and wonder... Wouldn't it have been amazing if I had come up with this idea? I mean, it was visible to all of us that pooling seemed just way too convenient amidst everything else about CNNs; just selecting the maximum weight among a specific number of weights and using that in the upcoming layers. Pooling was probably the easiest thing to visualize and understand in the entire architecture, which seemed very crude. But still, only the Godfather of Deep Learning did it again and came up with something brilliant -- adding layers inside existing layers instead of adding more layers i.e nested layers.... giving rise to the Capsule Networks! Improvements in CNNs started in the direction of adding more and more layers, playing with parameters and gradually towards connecting distant layers to each other to make sense out of their outputs once they were concatenated, when it was observed that simply increasing the number of layers also eventually reduces the performance after a certain point.
"We cannot be conscious of what we are not conscious of." Unlike the director leads you to believe, the protagonist of Ex Machina, Andrew Garland's 2015 masterpiece, isn't Caleb, a young programmer tasked with evaluating machine consciousness. Rather, it's his target Ava, a breathtaking humanoid AI with a seemingly child-like naïveté and an enigmatic mind. Like most cerebral movies, Ex Machina leaves the conclusion up to the viewer: was Ava actually conscious? In doing so, it also cleverly avoids a thorny question that has challenged most AI-centric movies to date: what is consciousness, and can machines have it?
With 2017 almost in the record books, it's worth a review of the main topics as well as what we learned from them. Here's a look at the seven trends identified by ZDNet editors and a crib sheet to the broader video discussion. ZDNet takes a look back at very best tech stories and features of 2015. From the year's tech turkeys to products and services that get business done, we round up top gadgets, cloud highs, security lows -- and much more. At CES 2017, Amazon's Alexa platform dominated the headlines as the voice assistant was embedded in more devices.
Jeff Cole's fascination with media and communication technology began with television, which he maintains is still "the most powerful medium ever invented". For more than three decades, Cole's research in the field of emerging media and technology has seen him serve as Director of the UCLA Centre for Communication Policy, testify before Congress, and advise presidents. In June 1999, Cole helped set up the World Internet Project longitudinal study, which draws on more than a decade of user data to examine how online and digital technologies are transforming our social, economic and media lives. On his last visit to Australia, I chatted with Cole about the past impacts of internet and digital technologies and how small businesses in particular might navigate whatever comes next. Smarter Business: What can small businesses learn from the impact of emergent technologies on the media industry?
In just over three years, clocks and calendars will mark the beginning of the third decade of the third millennium. As the pace of change in business technology shows no signs of slowing down, we can be certain that the future of workbeyond 2020 will be very different how we lived and worked in 2010. This decade saw the rise of tablet devices (the first iPad went on sale in 2010). DVD/Blu-ray sales declined as download and streaming services such as iTunes and Netflix changed how audiences access television and movies. The taxi industry was outflanked by innovative technology in the form of a new app with a better business model.
Ignore the noise about a "Terminator scenario" in which machines become self-aware and seek to destroy their flawed human masters. Those of us who live and work in the "salt mines" of machine learning and artificial intelligence are almost universally unafraid. Still, a few well-known technical folk heroes continue to push this "sky is falling" narrative. The most prolific of them is Elon Musk, famed founder of Tesla and SpaceX. Not only do I think he's wrong, I think his own company, Tesla Motors, is a compelling proof point against his argument.
It hasn't been an easy couple of years for algorithms. Increasingly populated with content decried as'fake news', 'clickbait', today's highly personalized social media feeds are coming under increasing fire for being filter bubbles, culminating in Mark Zuckerberg's highly public apology to Facebook's users at the start of this month. These shifts in media mirror concurrent developments in spheres as diverse as customer service and support, financial trading, healthcare, and more. Today, though, tech is fighting back – thanks to AI. After partnering with Automated Insights in 2014 – a natural language generation start-up – the Associated Press became one of the earliest adopters of AI within the media space.
Previous techniques relied on massive amounts of data and has problems with training the machines to find their own patterns. Researched had a hard time dealing with mapping a low-resolution image to a corresponding high-resolution image and colorization (mapping a gray-scale image to a corresponding color image). Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs.
The era of Artificial Intelligence (AI) is upon us. More and more, computer systems will become able to perform tasks that previously required human intelligence, such as decision making, visual perception, and speech and language recognition. Marketing, like most other fields, will feel AI's impact in several areas, including database marketing techniques, search queries and search engine optimization (SEO), personalization, predictive customer service, sales forecasting, customer segmentation, pricing, and many others. Act-On recently asked several marketing experts and analysts for their assessments of the pros and cons of using Artificial Intelligence in marketing and what they foresee for the future. Question: Will Artificial Intelligence (AI) be marketing's friend or foe?