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) …
There's only one constant in business – and that's that things change. And that change has been accelerating in recent years. Businesses have had to adjust to new ways of doing things, most of them related to the digital transformation that business, and the world, has experienced in recent years. From artificial intelligence (AI), to blockchain and the Internet of Things (IoT), new digital technologies are having a major impact on business – and that impact will only grow in 2018. And companies can't afford to ignore the trend.
Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
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?
What are neural networks, but more important, how are they trained in practice? How can data scientists design an optimal neural network when a single training run can take 2 weeks? In this Data Science Central webinar we will start from the foundation of what deep learning is then fast forward through what it takes to train a production quality neural network. You won't be able to train a network when this talk is over, but you'll understand enough basics to start smart conversations about our customers' practice of deep learning.
One intuitive way to make forecasts would be to refer to recent time points. Today's stock prices would likely be more similar to yesterday's prices than those from five years ago. Hence, we would give more weight to recent than to older prices in predicting today's price. These correlations between past and present values demonstrate temporal dependence, which forms the basis of a popular time series analysis technique called ARIMA (Autoregressive Integrated Moving Average). ARIMA accounts for both seasonal variability and one-off'shocks' in the past to make future predictions.
This is a graphics card created for the PC. VentureBeat's Blair Frank said "The new Titan V card will provide customers with a Nvidia Volta chip that they can plug into a desktop computer." Thursday marked its debut, positioned as "the world's most powerful GPU for the PC." CEO Jensen Huang did the introduction. The announcement took place at the annual AI gathering, the NIPS (Neural Information Processing Systems) conference. It can carry massive amounts of power and speed AI computation.
The ability to truly democratize the process is perhaps the most important element of any enterprise machine learning platform. DataRobot automates the entire modeling lifecycle, enabling users to quickly and easily build highly accurate predictive models. The only ingredients needed are curiosity and data -- coding and machine learning skills are completely optional!
Once upon a time, the very concept of Open Source was absurd, and only its proponents ever thought it could be other than marginal. Important software could only be built and supported by sophisticated businesses, an expensive industrial component whose blueprints -- the source code -- was extremely valuable. It became clear, to no historian's surprise, that once knowledge is sufficiently distributed and tools become cheap enough, distributed development by heterogeneously (and heterogeneously motivated) people not only creates high-quality software at zero marginal cost; because it only takes a single motivated individual to leverage existing developments and move them forward regardless of its novelty or risk, it's inherently much more creative. Open Source developers can take risks others can't, and they begin from further ahead, on the shoulder of other, taller developers. What's more adventurous than a single individual toying with an idea out of love and curiosity?