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) …
Machine Learning is (or should be) a core component of any marketing program now, especially in digital marketing campaigns. The following insightful quote by Dan Olley (EVP of Product Development and CTO at Elsevier) sums up the urgency and criticality of the situation: "If CIOs invested in machine learning three years ago, they would have wasted their money. But if they wait another three years, they will never catch up." I believe that this statement also applies to CMOs. Machine Learning-based personalization (SegOne Segment of One Marketing) is hotter than ever, especially when marketers select context-specific content to be presented to an individual consumer.
July 11, 2017 Written by: Chris O'Connor An IoT platform must connect devices, must collect data, must handle thousands of vendors, dozens of standards and must be able to scale to millions of devices sending billions of messages. To deliver true value beyond the basics, it must add cognitive, security, privacy, insight generation and close loop automation. Organizations in the midst of, or planning for, an IoT deployment understand the complexity of finding a solution that is holistic yet customizable to their own unique requirements. A solid platform is the linchpin in connecting endpoints, capturing meaningful data, and pulling all together into a central dashboard that allows you to glean actionable insights. I'm pleased to say that the IDC MarketScape has positioned the IBM Watson IoT Platform as a leader in the IDC MarketScape: Worldwide IoT Platforms (Software Vendors) 2017 Vendor Assessment (doc #.US42033517, July 2017).
In 2015, a poll of 200 senior corporate executives conducted by the National Robotics Education Foundation identified robotics as a major source of jobs for the United States. Indeed, some 81% of respondents agreed that robotics was the top area of job growth for the nation. Not that this should come as a surprise: as the demand for smart factories and automation increases, so does the need for robots. According to Nearshore Americas, smart factories are expected to add $500 billion to the global economy in 2017. In a survey conducted by technology consulting firm Capgemini, more than half of the respondents claimed to have invested $100 million or more into smart factory initiatives over the last five years.
If you ask ten data scientists, "Which machine learning tool is best?" you'll likely get many different answers. But slightly more surprising is that if you ask any one data of those data scientists the same question, you'll likely still get many different answers. Here's a quick preview of two key observations about what makes machine learning successful: Instead they usually keep three to five machine learning options in their tool box. Let's start with the first observation. Turns out that at any point in time many organizations have adopted a range of several machine learning tools.
Figure 1 depicts a flow diagram of a process for including parallelism when generating a virtual machine migration plan according to an embodiment. Exemplary embodiments relate to using machine learning for virtual machine (VM) migration plan generation. Embodiments can enforce both a colocation and an anti-colocation policy using colocation and anti-colocation contracts. A VM migration plan can be created by processing a first mapping of VMs to hosts along with a second mapping of VMs to hosts. Pre-processing can be performed followed by machine search techniques with heuristics and pruning mechanisms to generate serialized optimal paths from the first state (i.e., an origin state) to a second state (i.e., a goal state).
The AI win stunned the gaming community, because bots are generally considered inferior to expert human players. This one from Open AI -- a nonprofit artificial intelligence research firm known mainly for its backing by serial entrepreneur Elon Musk, of Tesla (TSLA) and SpaceX fame -- is a different story, and possibly a cautionary one. Open AI says its mission is to promote "responsible" AI development. Or, as Musk puts it, to ensure that AI doesn't grow unchecked and become the death of humanity. Musk said Saturday via Twitter that AI is "more [of a] risk than North Korea."
Then, there's what Tang calls the "reverse-engineered" version of the Markable technology. When a visitor clicks on a piece of clothing on AKIRA's shop, they'll be able to see if it has ever been modeled by a celebrity or fashion blogger. Click on a shirt, and you may see a photo of when that same shirt was previously worn by Taylor Swift. Shoppers then have the option of "completing the look" by buying the rest of T-Swift's ensemble.
Ray Kurzweil has invented a few things in his time. In his teens, he built a computer that composed classical music, which won him an audience with President Lyndon B. Johnson. In his 20s, he pioneered software that could digitize printed text, and in his 30s he cofounded a synthesizer company with Stevie Wonder. More recently, he's known for popularizing the idea of the singularity--a moment sometime in the future when superintelligent machines transform humanity--and making optimistic predictions about immortality. For now, though, Kurzweil, 69, leads a team of about 35 people at Google whose code helps you write emails.
Until the last 5 years or so, it was infeasible to uncover topics and emotions across the web without powerful computing resources. Engineers didn't have efficient methods to make sense of words and documents at a large scale. Now, with deep learning, we can convert unstructured text to computable formats, effectively incorporating semantic knowledge for training machine learning models. Harnessing the vast data troves of the digital world can help us understand people more directly, going beyond the limitations of collecting data points through measurements and survey results. Here's a glimpse into how we achieve this at MarianaIQ.
The eyes really do have it when people look for love, new research reveals. A study found that men and women rate a person's eyes more important than other facial features when seeking for a potential partner. Having attractive hair and lips is also an important factor in the beauty stakes. The least important facial feature seems to be someone's nose, researchers found. On average, people found eyes the most attractive, then hair, then the whole configuration then lips and finally nose.