dependency


What Will Shape the Future of Machine Learning in 2018?

@machinelearnbot

Any new technology is not successful until it is embraced and used to its potential. Machine learning is no exception to this rule and its success or to say its ability can be gauged by the trends that exist. Machine learning is already a hot technology at the moments and it seems to have a promising future. At present, it seems to be an evolutionary phase where remarkable developments are expected. This brings us to the thought of what the machine learning of future will be like.


GitHub to devs: Now you'll get security alerts on flaws in popular software libraries

ZDNet

Devlopers can view the security alerts in the repository's dependency graph, which can be accessed from'Insights'. Development platform GitHub has launched a new service that searches project dependencies in JavaScript and Ruby for known vulnerabilities and then alerts project owners if it finds any. The new service aims to help developers update project dependencies as soon as GitHub becomes aware of a newly announced vulnerability. GitHub will identify all public repositories that use the affected version of the dependency. Projects under private repositories will need to opt into the vulnerability-detection service.


How to get started with machine learning: Use TensorFlow

#artificialintelligence

Machine learning is still a pipe dream for most organizations, with Gartner estimating that fewer than 15 percent of enterprises successfully get machine learning into production. Even so, companies need to start experimenting now with machine learning so that they can build it into their DNA. Not even close, says Ted Dunning, chief application architect at MapR, but "anybody who thinks that they can just buy magic bullets off the shelf has no business" buying machine learning technology in the first place. "Unless you already know about machine learning and how to bring it to production, you probably don't understand the complexities that you are about to add to your company's life cycle. On the other hand, if you have done this before, well-done machine learning can definitely be a really surprisingly large differentiator," Dunning says.


building-a-natural-language-processing-library-for-apache-spark

@machinelearnbot

Check out David Talby's tutorial "Natural language understanding at scale with spaCy and Spark NLP" at the Strata Data Conference in San Jose, March 5-8, 2018. Registration is now open--save 20% with the code BIGDATA20. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. When I first discovered and started using Apache Spark, a majority of the use cases I used it for involved unstructured text.


Rise of the machines must be monitored, say global finance regulators

#artificialintelligence

Replacing bank and insurance workers with machines risks creating a dependency on outside technology companies beyond the reach of regulators, the global Financial Stability Board (FSB) said on Wednesday. The FSB, which coordinates financial regulation across the Group of 20 Economies (G20), said in its first report on artificial intelligence (AI) and machine learning that the risks they pose need monitoring. AI and machine learning refer to technology that is replacing traditional methods to assess the creditworthiness of customers, to crunch data, price insurance contracts and spot profitable trades across markets. There are no international regulatory standards for AI and machine learning, but the FSB left open whether new rules are needed. Data on rapidly growing usage of AI is largely unavailable, leaving regulators unsure about the impact of potentially new and unexpected links between markets and banks, the report said.


FSB say rise of the machines must be monitored

Daily Mail

Replacing bank and insurance workers with machines risks creating a dependency on outside technology companies beyond the reach of regulators, the global Financial Stability Board (FSB) said. The FSB, which coordinates financial regulation across the Group of 20 Economies (G20), said in its first report on artificial intelligence (AI) and machine learning that the risks they pose need monitoring. AI and machine learning refer to technology that is replacing traditional methods to assess the creditworthiness of customers, to crunch data, price insurance contracts and spot profitable trades across markets. Replacing bank and insurance workers with machines risks creating a dependency on outside technology companies beyond the reach of regulators, the Financial Stability Board (FSB) said. There are no international regulatory standards for AI and machine learning, but the FSB left open whether new rules are needed.


Why hasn't AI taken off yet in monitoring? – Breathe Publication – Medium

#artificialintelligence

There's a lot of talk about the applicability of artificial intelligence (AI) and deep learning to taming the vast quantities of data that modern Operations teams and their tools deal with. Analyst reports frequently tout AI capabilities, no matter how minor, as a strength of a product, and the lack of them as a weakness. Yet no effective use of AI seems to have emerged and claimed wide adoption in Network Operations or Server Monitoring. Why not? (Disclaimer: LogicMonitor does not currently have deep learning or other AI capabilities). Part of the issue is that AI is a soft definition.


GitHub aims to make coding more automated

ZDNet

GitHub has redesigned the Explore experience to connect developers with curated collections, topics, and resources from GitHub contributors. At its GitHub Universe conference this week, GitHub is announcing a series of automated coding features, demonstrating how machine learning and data science can be applied to software development. The new tools will leverage the intelligence aggregated on the online code sharing and development platform over its nearly 10 years in existence, helping developers track dependencies, keep code secure and discover new projects. GitHub has also redesigned the Explore experience to connect developers with curated collections, topics, and resources from GitHub contributors.


How to deploy Machine Learning models with TensorFlow. Part 2-- containerize it!

#artificialintelligence

Nowadays it is pretty common to pack such server and all its dependencies into a package, configure it and deploy this package as a whole. We need to create so-called Docker image, create a container on that image and run it. Corresponding to our case -- we should test that the container runs and the server provided by TensorFlow successfully starts, accepts requests to our model and responses to them. TensorFlow Serving provides Docker images, so we can clone the repository and use them.


Twitter Sentiment Analysis – Rahul Yadav – Medium

@machinelearnbot

First we have to register our account for the twitter API so for that login into your twitter account and open https://apps.twitter.com/app/new So after that you got registered for using Twitter API and you are now on your detail page .Click on the Keys and Access Token ….so these are very important for us to use twitter api . So now its time to install Dependency in our machine .For sentiment analysis we require only two dependency: