Information Technology

Machine Learning Making Big Moves in Marketing


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.

IDC MarketScape names IBM Watson IoT a Leader in IoT Platforms


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).

The Whys and Hows of Becoming a Robotics Engineer


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.

Machine Learning For Virtual Machine Migration Plan Generation


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).

What Is Ray Kurzweil Up to at Google? Writing Your Emails


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.

Going deeper with recurrent networks: Sequence to Bag of Words Model


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.

Loving AIs: Bringing Unconditional Love to Artificial General Intelligence – Integral Life


We live in fascinating times. For decades we have seen an explosive exponential growth of technology, and the effects of this growth are only now beginning to surface. As a result, what seemed like science fiction even just a few years ago is rapidly becoming reality. Particularly when it comes to artificial intelligence, which has recently hit a new level of sophistication and usability, as seen in highly capable "digital assistants" like Siri, Cortana, and Google Now. It is an age of technological miracles, and the repercussions for the future are only beginning to make themselves known.

15 apps to power up your productivity

The Guardian

Important emails have a habit of arriving at inconvenient times. Boomerang is a plugin for Gmail, Outlook and Android that lets you temporarily dismiss messages from your inbox, to reappear in a few hours or days when you're better able to deal with them. You can also pause your inbox entirely, to suspend the torrent of interruptions while you're busy, and schedule outgoing messages to be sent at specified times. You get 10 free uses a month; after that, monthly subscriptions start at $5. The idea couldn't be simpler: you set up a list of possible dates and times and then your invitees drop by the Doodle website and tick the options that work for them. You'll quickly be able to see at a glance when everyone is available and since recipients don't need to create their own Doodle accounts, it's friction-free.

Why Cybersecurity Needs a Human in the Loop


A typical cybersecurity analyst is never short of work, a lot of which can be futile. According to a 2015 Ponemon Institute study, by the end of the year the average security operations center has spent around 20,000 hours just on chasing alerts that prove to be false alarms. Traditional security systems generate a lot of noise that needs to be waded through, which creates even more work. At the same time, a vast pool of security information is published across multiple media in natural languages that can't be quickly processed and leveraged by these systems. Cognitive security, or artificial intelligence, can "understand" natural language, and is a logical and necessary next step to take advantage of this increasingly massive corpus of intelligence that exists.