Monat: August 2017

From HAL to Watson: AI-driven models that boost efficiency

When I think of artificial intelligence (AI), I cannot help but think of the science fiction films that I grew up watching and the fictional AI computers they featured: HAL in “2001: A Space Odyssey” or Skynet in “The Terminator.” In those films people interacted with computers that could understand natural language and make decisions.
Today, in the real world, these fictional AIs have been surpassed by Watson and others – and thankfully are a lot less menacing. The art of the possible has progressed faster than my childhood self could have dreamed.
And yet, in IT operations, a lot of companies are still monitoring their environment in a traditional way. They are using static thresholds to alert them when something is anomalous. Some monitoring teams are actually still relying on customers complaints to make them aware of problems.
A better way to do alerts is to let software like Predictive Insights help manage the environment. Predictive Insights helps diminish the need for manual, time-consuming effort so teams can be alerted to the most significant problems first, and become more efficient.
You can gain efficiency by simply replacing one manual effort with another. That’s why we made Predictive Insights to be both configurationless and time series data-agnostic. This way teams do not have to expend any additional effort with tuning or configuring the system. Configurationless means that the software can learn automatically without human intervention. Time series data-agnostic means that it can take any time series data, such as key performance indicators, metrics, or measurements of something over time and show value. It can do this regardless whether the data came from IBM or a third-party source.
The second reason we do not require configuration is because it does not make sense for a machine learning-driven product to ask questions that it could better answer itself. For example, I have seen competitor products that require someone to select and configure the algorithm required to evaluate a metric. At best a data scientist could take an educated guess, and at worst create a random alarm generator. The answer depends on the data itself.
Predictive Insights is different. It has multiple algorithms assess the data, determine the algorithms that are best suited for each metric and attempt to build mathematical models that describe their normal behavior. The mathematical models must then pass a validation phase to ensure that they are accurate and that they do not overfit or underfit the data. If the mathematical model is not what the team needs, it will be sent back to the data for relearning. This validation step can occur many times, and models that pass will be used for anomaly detection. The best part is that this happens automatically, without disrupting the environment.
Predictive Insights can evaluate millions of models typically in less than one minute. It will perform three types of relationship discovery: correlations, granger causalities, and metrics that are frequently anomalous at the same time. Any of these algorithms alone requires trillions of calculations. And yet Predictive Insights will learn with them automatically, every day, on commodity hardware.
What once only existed in Hollywood’s imagination is now a reality with Predictive Insights. To learn more, register for our webinar on the value predictive insight brings to IT Operations.
Interested in how APIs can drive business insights for IT operations teams? Check out the first post in our IBM Operations Analytics series. And stay tuned for additional key learnings from our colleagues in coming weeks.
 
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Quelle: Thoughts on Cloud

Creating content with the help of AI

The vast majority of marketers use content marketing.
According to The Content Marketing Institute, instead of pitching products or services, brands are providing truly relevant and useful content to prospects and customers to help them solve their problems. Content should be at the core of marketing. The challenge is how to meet the growing demand for fresh, relevant content.
Marketers could turn to a content farm, also known as a “content mill,” where writers are sometimes paid just fractions of pennies, for inexpensive content. However, it will likely soon become apparent that organizations get what they pay for when search engines don’t rank their keyword-stuffed, low-quality content.
A better alternative is to put the power of artificial intelligence (AI) to work.
AI-assisted content creation
Articoolo helps writers create unique, proofread, high-quality content from scratch, simulating a human writer. Users can choose the topic and length, and an algorithm does the initial work. It helps writers do their jobs more quickly and cost effectively. Writers can get a head start on their content for as little as $1 per word.
Articoolo joined the IBM AlphaZone Accelerator program, which helps startups build leading solutions for the enterprise market. Using OpenWhisk on the IBM Bluemix platform ensures high availability and the flexibility Articoolo needs to meet changing demands. The Watson AlchemyAPI service performs powerful text analytics and natural language processing capabilities to fuel the algorithm that creates fresh, coherent content simulating a human writer.

A short lesson in content marketing
Articoolo generated some of the following content based on the phrase “content marketing”:
Know your demographic and what your audience cares about. Use keywords to target your audience. Conduct research with keywords. Create “expert” content that reflects the competence of your company. Invite people to write guest posts for your blog. Engage with social media followers. Cross promote your content on multiple social platforms to improve click through. Forty-four percent of online shoppers begin by using an internet search engine. SEO is important because your audience only sees a snippet of content in a search result and may never click past the first page.
See how an Articoolo customer in Japan uses AI to provide content for its blog or check out how this comedian got schooled by a robot.
Overcoming writer’s block
Not everyone wants an algorithm to create content, but content creators still might like a little help to get started. Articoolo offers other content-related tools and services for professional writers that can summarize or rewrite an article, generate a title, or find images or quotations. There’s also an API and a WordPress plug-in to make blogging much easier.
Quality content is not a commodity. Articoolo is not trying to completely replace human writers; it is primarily an ideation tool. Content marketers can try Writer’s Little Helper, a free service that offers inspiring ideas and relevant images to use as a starting point.
Read the case study for more details about the technology behind Articoolo.
The post Creating content with the help of AI appeared first on Cloud computing news.
Quelle: Thoughts on Cloud