There are two problems facing marketers today. It can be overwhelming and time consuming to get through. That's where Datorama comes in. It's a SaaS-based, big data management platform for advertisers and ad agencies, which uses machine learning and artificial intelligence to make it easier to upload data, and to categorize it. The company closed 32 million in Series C funding, it was announced on Monday.

There's no denying that artificial intelligence is lightyears ahead of what it was just a few years ago. The technology continues to advance at an ever-increasing rate. But the ultimate goal of artificial intelligence researchers is to replicate human intelligence. So how do artificial intelligence and human intelligence measure up? If after all these years, human intelligence is still vastly superior to artificial intelligence, why do artificial intelligence researchers even bother?

Artificial Intelligence (AI) is one of the hottest areas of technology research. Companies like IBM, Google, Microsoft, Facebook and Amazon are investing heavily in their own R&D, as well as buying up startups that have made progress in areas like machine learning, neural networks, natural language and image processing.

There's no denying that artificial intelligence is lightyears ahead of what it was just a few years ago. The technology continues to advance at an ever-increasing rate. But the ultimate goal of artificial intelligence researchers is to replicate human intelligence. So how do artificial intelligence and human intelligence measure up? Why even try If after all these years, human intelligence is still vastly superior to artificial intelligence, why do artificial intelligence researchers even bother?

Recent accidents involving Tesla cars may have been a setback for self-driving cars, but Nvidia believes a fast computer under the hood could make autonomous cars and cabs truly viable. The company's new Drive PX 2 model is a palm-sized computer for autonomous cars that will marry mapping with artificial intelligence for automated highway and point-to-point driving. The computer's horsepower will help a car navigate, avoid collisions and make driving decisions. The Drive PX 2 could be attractive to companies like Uber, which want to deploy autonomous cars as taxis. The computer is also targeted at car makers looking to develop fully or partially autonomous cars, which would typically need human intervention.

Autonomous cars need a new kind of horsepower to identify objects, avoid obstacles and change lanes. There's a good chance that will come from graphics processors in data centers or even the trunks of cars. With this scenario in mind, Nvidia has built two new GPUs -- the Tesla P4 and P40 -- based on the Pascal architecture and designed for servers or computers that will help drive autonomous cars. In recent years, Tesla GPUs have been targeted at supercomputing, but they are now being tweaked for deep-learning systems that aid in correlation and classification of data. "Deep learning" typically refers to a class of algorithmic techniques based on highly connected neural networks -- systems of nodes with weighted interconnections among them.

Facebook's chatbot platform had too much hype. That's what David Marcus, head of Facebook Messenger, said on stage today at TechCrunch Disrupt in San Francisco. Facebook first released its chatbot platform in April, enabling developers to build robots that use artificial intelligence and natural language processing to let people talk with businesses just like they do with friends and family on Messenger. At the April launch, Facebook showed how a commerce company like 1-800-FLOWERS could use chatbots to help people order flowers and automate communication with customers, for example. But after TechCrunch reporter Josh Constine noted today that the product was "half-baked" when it first launched and asked what was missing, Marcus said that expectations for chatbots were a bit overdone.

As the title suggests, in this article, we will be taking a look at some of the deep learning models in R. Some of the pioneering advancements in neural networks research in the last decade have opened up a new frontier in machine learning that is generally called by the name deep learning. The general definition of deep learning is, a class of machine learning techniques, where many layers of information processing stages in hierarchical supervised architectures are exploited for unsupervised feature learning and for pattern analysis/classification. The essence of deep learning is to compute hierarchical features or representations of the observational data, where the higher-level features or factors are defined from lower-level ones. Although there are many similar definitions and architectures for deep learning, two common elements in all of them are: multiple layers of nonlinear information processing and supervised or unsupervised learning of feature representations at each layer from the features learned at the previous layer. The initial works on deep learning were based on multilayer neural network models.

Everyone should have an idea what machine learning is. Even because it is in the list of top priority areas of the companies that define modern digital industry: Google, Facebook and Amazon. The nature of this technology guarantees that it will change the technologies at the early stages of its development. Not to be left out of the cold, you should know how it works. In fact, it is teaching a computer to recognize objects as a human.

With students from a range of science, technology, engineering, and math (STEM) competitions from across the country looking on, U.S. President Barack Obama delivers remarks after viewing science projects at the White House Science Fair, at the White House, March 23, 2015 in Washington, DC. (Photo: Drew Angerer/Getty Images) It is already a given fact that gender inequality still continue to persist in the field of education. Despite the government's efforts to ensure that all students should have access to high quality education, gender gap remain notable, particularly in STEM (Science, Technology, Engineering And Mathematics)-related and CTE (Career and Technical Education) curricula. Fortunately, artificial intelligence (AI) has been considered as a powerful tool in bridging the gender gap in STEM education. That's why, Stanford has launched a tuition-free AI camp called SAILORS to encourage young girls, as well as "underrepresented minorities" to explore STEM-related fields. Initially launched on the summer of 2015, Stanford Artificial Intelligence Outreach Summer aka SAILORS was created by computer science professor Fei-Fei Li and Postdoc (postdoctoral scholar) Olga Russakovsky.