As artificial intelligence takes over the more mundane and data-crunching tasks inherent in marketing, experts see creativity and strategic vision as two areas where human expertise will continue to be in-demand regardless of how prevalent the technology becomes. Early adopters can benefit from deep AI-driven insights, but, at some point, the tech is likely to offer less of a competitive advantage as it saturates the market. Marketers worried about being replaced by a machine can get ahead right now by figuring out how their current job function integrates with AI. Looking toward the future, it will be important for marketers to build their personal expertise in the skills that AI technology will not be able to replicate as easily. "AI is fantastic at certain things, especially forecasting, automation, segmentation and optimization," Michael Godwin, creative director at artificial intelligence startup Noodle.ai,

Masayoshi Son, chief executive of SoftBank Group Corp., believes that within 30 years, artificial intelligence will be smarter than the human brain. The Wall Street Journal reports that Son made the prediction at Mobile World Congress, the telecom industry's biggest trade show, which is currently being held in Barcelona, Spain. Not only does Son think that super smart robots will outnumber humans in 30 years, but more than a trillion objects will be connected to the internet. "That is why I'm in a hurry to aggregate cash to invest," he said. SoftBank has made quite a few recent deals, including purchasing British microprocessor designer ARM Holdings PLC for $32 billion, as well as U.S. asset manager Fortress Investment Group PLC for $3.3 billion. Son plans to ramp up the research-and-development funding for ARM, which designs the basic architecture for chips in more than 95 percent of smartphones around the world.

Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence", having become a routine technology.

Chandrasekaran B.,Josephson J.R., Benjamins V. R., (1999) What Are Ontologies, and Why Do We Need Them?

In this post, we are going to introduce you to the Support Vector Machine (SVM) machine learning algorithm. We will follow a similar process to our recent post Naive Bayes for Dummies; A Simple Explanation by keeping it short and not overly-technical. The aim is to give those of you who are new to machine learning a basic understanding of the key concepts of this algorithm. A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes. SVMs are more commonly used in classification problems and as such, this is what we will focus on in this post.

Schedule and Syllabus Unless otherwise specified the course lectures and meeting times are: Wednesday, Friday 3:30-4:20 Location: 200-219 This syllabus is subject to change according to the pace of the class.

He's been there for three weeks now -- though he is quick to point out that he was out of the office for one of them so it's really only been two weeks -- but that's not stopping Quartz from launching one of the Bot Studio's first experiments, a Twitter bot to help attendees of the NICAR conference. People going to #NICAR17 can ask a Twitter bot. Keefe says the Bot Studio, which is funded by a $240,000 grant from the Knight Foundation, will focus on two main areas: How bots and artificial intelligence can help journalists do their jobs better and how news consumers can use them to access news and information. Keefe joined Quartz after spending 16 years at WNYC. We spoke about the lessons he learned from public radio, his goals for the Bot Studio, and what it'll take for bots to catch on.

The effect that information has on security prices has been at the heart of financial economics research for decades. The general consensus is that superior returns can be achieved with superior information. "Timely processing of structured and unstructured data and understanding the complex interrelationships embedded in such information is critical for the success of any active investment strategy." However, monitoring what fuels stock performance is a constant challenge for active investment managers. Active managers are constantly attempting to find inefficiencies in the markets.

That's a phrase none of us has uttered yet, but the day when voice assistants go beyond helping us simply complete transactions to facilitating deeper human-to-human relationships is rapidly approaching. In fact, Amazon and Google are rumored to be adding voice calling to Echo and Google Home. While today people might ask Alexa for their bank balance, voice is ultimately about human conversation, which might lead you to doing something like connecting with your financial advisor. The news that Amazon and Google are working on adding voice calling to Alexa and Google Home isn't a harking back to simpler times, nor is it a concerted effort to kill the home landline. Rather, it's an acknowledgement that while voice assistants are great at completing transactional tasks, people still want to have human conversations, such as getting someone's opinion or walking through a complex purchase.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey that reflects how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.