Machine Intelligence in Ad Tech
Advertising is a field that is ripe with unstructured and high-dimensional data. Advertising edicts, bylines, articles, reports, jingles, ad copy, ad creatives, social sentiments, user generated data, brand guidelines, audio and video content are all forms of unstructured data. From a dimensionality perspective, the number of products and categories in the advertising space and feature of each product and the utility it provides contributes to dimensionality explosion. Added to this, the demographic, behavior, geography, consumption habits, social bias, cultural make and content consumption channels of an individual consumer explodes the dimension of data beyond comprehension.
Work With Parallel Database Streams Using Custom Thread Pools
By default, parallel streams are executed on the common ForkJoinPool where they potentially might compete with other tasks. In this post we will learn how we can execute parallell database streams on our own custom ForkJoinPool, allowing a much better control of our execution environment. Speedment is an open-source Stream ORM Java Toolkit and Runtime Java tool that wraps an existing database and its tables into Java 8 streams. We can use an existing database and run the Speedment tool and it will generate POJO classes that correspond to the tables we have selected using the tool. One distinct feature with Speedment is that it supports parallel database streams and that it can use different parallel strategies to further optimize performance.
How to avoid becoming a part of a DDoS attack?
The scale of this attack, and the fact that it used devices we’re normally not taking care of, makes it a real wake-up call for IT administrators, but also for various IoT device users in general. Think not only about the flaws in your patch management strategy at work, but more about the complete lack of patch management strategies that exist at the homes of most, if not all your coworkers, friends, and family. Do they run vulnerability scans regularly? Manage and deploy patches to all nodes under their control? ... While defending against a DDoS may be beyond the capabilities and capacities of many of us, we can at least ensure that we are not contributing to the problem, so here’s a list of things all of us can do to help.
What Trump’s Win Means for Cybersecurity
Security and foreign policy analysts warned that it would only embolden the Russian hackers who injected chaos into the presidential campaign and the Democratic party. Election day itself got a taste of alt-right hacking, as an anonymous poster on 4Chan appeared to target a Clinton get-out-the-vote phone bank—but inadvertently hamstrung both Democrat and Republican calling efforts. Edward Snowden and other privacy activists warned that the surveillance powers expanded under Obama could be abused by Trump and called for Americans to use encryption tools to protect themselves. And WIRED offered a primer on how Trump will reshape national security policy, including his likely support for the Syrian regime of dictator Bashar Al-Assad.
Clearing the fog: a vision of security for hybrid clouds
Perhaps the biggest issue that IT teams face is that using hybrid clouds can put data and business applications beyond their traditional IT security controls, which don’t typically touch the cloud – especially public cloud environments. At the same time, the number of cyber threats and breaches are increasing. Once an environment is breached, attacks are able to spread laterally within the cloud infrastructure and even extend externally outwards from the cloud to on-premise networks. ... It all adds up to an enlarged, complex and blurred attack surface for organizations, so they need a comprehensive solution to bridge security gaps and extend protections, visibility and control from data centers to the cloud in a way that works with the cloud’s elasticity and automation.
What E-Commerce Business Owners Need to Know About Artificial Intelligence
"Companies integrating deep learning into their eCommerce site will drastically improve user's search capabilities," says the AI expert Akash Bhatia, cofounder and CEO of Infinite Analytics. "For example, a woman could take a picture of a dress that she likes, upload the photo into the search bar of an eCommerce site and, using AI, the site would immediately analyze the image, understand the patterns, fit, style, color, brand, and other attributes to identify the dress. Voila! That consumer is able to convert right away." Other experts agree with Bhatia. Ryan BeMiller, inbound marketing expert focused on the ecommerce sector, writes, "Photos alone cannot be expected to provide a full understanding of the product. The array of products on display should have distinct and clear product descriptions.
The Current State of Machine Intelligence 3.0
The danger here, unlike the mobile app explosion (where we lacked expectations for what these widgets could actually do), is that we assume anything with a conversation interface will converse with us at near-human level. Most do not. This is going to lead to disillusionment over the course of the next year but it will clean itself up fairly quickly thereafter. When our fund looks at this emerging field, we divide each technology into two components: the conversational interface itself and the “agent” behind the scenes that’s learning from data and transacting on a user’s behalf. While you certainly can’t drop the ball on the interface, we spend almost all our time thinking about that behind-the-scenes agent and whether it is actually solving a meaningful problem.
What Is the Future of Data Warehousing?
While the concept of BI is not necessarily new, traditional BI tactics are no longer enough to keep up and ensure success in the future. Today, traditional BI must be combined with agile BI (the use of agile software development to accelerate traditional BI for faster results and more adaptability) and big data to deliver the fastest and most useful insights so that businesses may convert, serve, and retain more customers. Essentially, for a business to survive, BI must continuously evolve and adapt to improve agility and keep up with data trends in this new customer-driven age of enterprise. This new model for BI is also driving the future of data warehousing, as we will see moving forward.
An Artificial Intelligence Definition for Beginners
“Artificial intelligence is a computerized system that exhibits behavior that is commonly thought of as requiring intelligence.” Or more technically speaking, AI is a “system capable of rationally solving complex problems or taking appropriate actions to achieve its goals in whatever real world circumstances it encounters.” In a way, artificial intelligence is about understanding – then recreating – the human mind. And AI is not just about designing computers that mimic how we think, learn and process information, but also how we perceive and feel about the world around us. Understanding the world of AI only begins with a simple artificial intelligence definition. There’s a whole universe of terminology we need to explore in order to understand the domain before we can invest in it.
Key KPIs Across Agile Methodologies
The accelerated timeframes of Agile sprints dictate that KPIs guide processes towards remaining on course with project goals and objectives. KPIs guide Agile teams in essential software features and primary functionalities. Sprint planning meetings must include key indicators of performance goals and achievements which tie into business needs. KPIs standardize code development and simultaneous automated testing to ensure that shippable releases remain in compliance with planned objectives. To better adhere to KPIs, cross-functional Agile teams, in which developers, QA, and IT contribute to all three disciplines, provide expertise that best ensures quality functionality from each perspective.
Quote for the day:
"Your mind is your prison when you focus on your fear." -- Tim Fargo
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