MXNet review: Amazon's scalable deep learning

MXNet review: Amazon's scalable deep learning

Deep learning, which is basically neural network machine learning with multiple hidden layers, is all the rage—both for problems that justify the complexity and high computational cost of deep learning, such as image recognition and natural language parsing, and for problems that might be better served by careful data preparation and simple algorithms, such as forecasting the next quarter’s sales. If you actually need deep learning, there are many packages that could serve your needs: Google TensorFlow, Microsoft Cognitive Toolkit, Caffe, Theano, Torch, and MXNet, for starters.

I confess that I had never heard of MXNet (pronounced “mix-net”) before Amazon CTO Werner Vogels noted it in his blog. There he announced that in addition to supporting all of the deep learning packages I mentioned above, Amazon decided to contribute significantly to one in particular, MXNet, which it selected as its deep learning framework of choice. Vogels went on to explain why: MXNet combines the ability to scale to multiple GPUs (across multiple hosts) with good programmability and good portability.

Source: InfoWorld Big Data

8 big data predictions for 2017

8 big data predictions for 2017

Market research and advisory firm Ovum estimates the big data market will grow from $1.7 billion in 2016 to $9.4 billion by 2020. As the market grows, enterprise challenges are shifting, skills requirements are changing, and the vendor landscape is morphing. The coming year promises to be a busy one for big data pros. Here are some predictions from industry watchers and technology players.

1. Data scientist demand will wane

Demand for data scientists is softening, suggests Ovum in its report on big data trends. The research firm cites data from Indeed.com that shows flat demand for data scientists over the past four years. At the same time, colleges and universities are turning out a greater number of graduates with data science credentials.

Source: InfoWorld Big Data

Get started with Azure Machine Learning

Get started with Azure Machine Learning

Machine learning is fast becoming the go-to predictive paradigm for data scientists and developers alike. Of the many tools available for tapping neural networks, Microsoft’s Azure ML Studio offers a quick learning curve that won’t take deep data or coding chops to get up and running.

Microsoft Azure Machine Learning Studio is a cloud service for performing value prediction (regression), anomaly detection, structure discovery (clustering), and category prediction (classification). While my previous tutorial for TensorFlow revealed how Google’s open source machine learning and deep neural network library requires you to roll up your sleeves a bit before digging in, Azure ML Studio’s graphical, modular approach will have you testing machine learning models quickly, as you will see below.

Let’s get started.

Source: InfoWorld Big Data

Move over Memcached and Redis, here comes Netflix's Hollow

Move over Memcached and Redis, here comes Netflix's Hollow

After two years of internal use, Netflix is offering a new open source project as a powerful option to cache data sets that change constantly.

Hollow is a Java library and toolset aimed at in-memory caching of data sets up to several gigabytes in size. Netflix says Hollow’s purpose is threefold: It’s intended to be more efficient at storing data; it can provide tools to automatically generate APIs for convenient access to the data; and it can automatically analyze data use patterns to more efficiently synchronize with the back end.

Let’s keep this between us

Most of the scenarios for caching data on a system where it isn’t stored—a “consumer” system rather than a “producer” system—involve using a product like Memcached or Redis. Hollow is reminiscent of both products since it uses in-memory storage for fast access, but it isn’t an actual data store like Redis.

Unlike many other data caching systems, Hollow is intended to be coupled to a specific data set—a given schema with certain fields, typically a JSON stream. This requires some prep work, although Hollow provides some tools to partly automate the process. The reason for doing so: Hollow can store the data in-memory as fixed-length, strongly typed chunks that aren’t subject to Java’s garbage collection. As a result, they’re faster to access than conventional Java objects.

Another purported boon with Hollow is that it provides a gamut of tooling for working with the data. Once you’ve defined a schema for the data, Hollow can automatically produce a Java API that can supply autocomplete data to an IDE. The data can also be tracked as it changes, so developers have access to point-in-time snapshots, differences between snapshots, and data rollbacks.

Faster all around

A lot of the advantages Netflix claims for Hollow involve basic operational efficiency—namely, faster startup time for servers and less memory churn. But Hollow’s data modeling and management tools are also meant to help with development, not simply speed production.

“Imagine being able to quickly shunt your entire production data set—current or from any point in the recent past—down to a local development workstation, load it, then exactly reproduce specific production scenarios,” Netflix says in its introductory blog post.

One caveat is that Hollow isn’t suited for data sets of all sizes—“KB, MB, and GB, but not TB,” is how the company puts it in its documentation. That said, Netflix also implies that Hollow reduces the amount of sprawl required by a cached data set. “With the right framework, and a little bit of data modeling, that [memory] threshold is likely much higher than you think,” Netflix writes.

Source: InfoWorld Big Data

AI is coming, and will take some jobs, but no need to worry

AI is coming, and will take some jobs, but no need to worry

The capabilities of artificial intelligence and machine learning are accelerating, and many cybersecurity tasks currently performed by humans will be automated. There will still be plenty of work to go around so job prospects should remain good, especially for those who keep up with technology, broaden their skill sets, and get a better understanding of their company’s business needs.

Cybersecurity jobs won’t go the way of telephone operators. Take, for example, Spain-based antivirus company Panda Security. When the company first started, there were a number of people reverse-engineering malicious code and writing signatures.

“If we still were working in the same way, we’d need hundreds of thousands of engineers,” said Luis Corrons, technical director at PandaLabs.

Instead, the company’s researchers created tools that do most of those jobs.

“That means that nowadays we only have to take a look at a tiny portion of the new malicious code that shows up every day—more than 200,000 new malware samples per day. I cannot imagine how we could do our main task, protecting our customers, without AI.”

Does that mean that hundreds of thousands of engineering jobs have been destroyed? Of course not, he said.

“Being realistic, no company could afford that,” he said.

In fact, AI has actually created new jobs, he said, including those of improving internal systems and creating news ones, and jobs for mathematicians applying AI to those systems.

“I get asked a lot by parents and college students about where they should be focusing, and security is where I think there are a lot of opportunities,” said Karin Klein, founding partner at Bloomberg Beta, Bloomberg’s venture fund that invests in early-stage tech companies.

There’s a great shortage of talent in the industry, and a growing need for security professionals, she said.

AI tools will put more power in your hands

AI promises to automate repetitive tasks and those that require the processing of large amounts of information.

But the industry needs that, since there’s too much for humans to process on their own.

“It’s more about augmentation rather than automation,” said Klein.

That’s been a common theme for the cybersecurity companies she’s been investing in, she said, adding that she is very optimistic about what the AI technology will bring.

“It’s going to help that over-stressed IT guy who is trying to manage everything,” said Dale Meredith, author and cybersecurity trainer at Pluralsight. “It’s going to help him have more time to look at what’s important for the company.”

AI is just another tool, he said.

“And it’s coming along at the right time,” he added. “Think of the amount of data we have now compared to just five years ago.”

New technologies, like the Internet of Things, promise to generate even more data, said Jason Hong, a professor in Carnegie Mellon’s School of Computer Science and an expert in AI and cyber security.

Peter Metzger, vice chairman and cybersecurity and business risk expert at DHR International

“Almost every aspect, every dimension of society now relies on computers, and the need for security keeps on growing,” he said.

That will allow individual analysts to do more than they can today, and do it more effectively.

“In the near term there are still plenty of positions and not enough professionals,” said Bryan Ware, CEO at Haystax Technology. “But over time, will AI will allow analysts to be more productive, automating low level tasks and intelligently alerting the analyst.”

For example, better AI will make it easier for security professionals to sort through mountains of noise to find actual indicators of compromise, said David Campbell, CSO at SendGrid, a Denver marketing company that suffered a breach last year.

“AI will help speed the identification and prediction of security breaches,” he said. “This will bolster career prospects for security professionals that are adept at divergent thinking, and limit career prospects for more traditional SOC analysts that respond to alerts without considering the larger picture.”

With AI automating out the horrible, routine, cutting-and-pasting jobs, most of the growth in the cybersecurity profession will be in forensic investigations, said Kris Lovejoy, CEO at security firm Acuity Solutions.

That may require additional training, she said—not necessarily a full university course, but something like a SANS training program.

“The security field currently requires lots and lots of manual labor,” she said. “You’ve got folks doing either very entry-level jobs, almost IT administration, and very sophisticated folks with lots of education spending 80 percent of their time waiting for something to load.”

That gets frustrating and burns people out. With automation, the jobs are going to become more interesting—and there might be less churn in the profession as a result, she said.

There will also be new job opportunities when it comes to properly deploying AI tools.

“AI isn’t free,” said Haystax’s Ware. “Many techniques require significant algorithm training, data mark up, and testing that has to be done by humans.”

The care and feeding of AI also involves ensuring that the AIs have highly available, highly secure infrastructure on which to run, said David Molnar, IEEE member and senior researcher at Microsoft.

“Highly available infrastructure because if the AI stops, the business suffers,” he said. “Security, because if the AI gets bad data or the AI is hacked, then the business makes bad decisions.”

The CSO’s job will increasingly be about protecting the AI’s role in business, and understanding the processes around the AI.

And the CSO might also need to act as a mediator between the AI and the rest of the company.

“To establish legitimacy for an AI driven decision, the CSO must help the rest of the business leaders advocate and explain that process to the world,” he said. “It isn’t going to be easy, but it will put the CSO at the heart of every business.”

Finding ways to apply AI to a business will also require a different way of thinking.

“A successful AI strategy requires very multi-disciplinary skills,” said Hossein Rahnama, CEO and founder at Flybits, and a visiting scholar at the Human Dynamics group at the MIT Media Lab.

“Many AI experts are very much siloed in the past, and lack the experience of communicating business use cases. Translating AI research into business value is something very important.”

To get training in this area, he recommends looking at programs that combine a foundational understanding of AI with an understanding of public policy implications.

“There are a number of universities working on programs directly addressing those needs,” he said. “Stanford is looking there, and there are some interesting initiatives at MIT.”

There are also learning opportunities available beyond traditional colleges and universities and training institutes, said Kunal Anand, CTO and co-founder at security firm Prevoty.

He recommends attending conferences around machine learning and data science, and subscribing to blogs and mailing lists.

“And look at open source projects,” he added. “The best way to learn is to build.”

Branching out

Security analysts typically don’t have to write new code at their jobs. But there could be more opportunities to do that in the future.

“Learn to code,” said SendGrid’s Campbell. “Professionals seeking careers in security will need to be able to code in order to be successful.”

He suggested languages like Python, Ruby and Node.js.

“Being able to code and interpret these languages will help career prospects differentiate themselves and provide greater value for organizations looking to automate security tasks,” he said.

On a higher level as well, security professionals can help improve their companies’ software. Automated tools can spot common vulnerabilities, but it takes a human to understand logical flaws, said Giovanni Vigna, co-founder and CTO at Lastline.

“For example, the fact that a coupon in an e-commerce application should be applicable only once is something that is immediately obvious to a human,” he said.

That might not be, strictly speaking, a technical vulnerability, but it is a security issue, and requires human judgment, and imagination, to understand.

“No amount of AI would allow a program to understand what a program does in every case,” Vigna said. “It’s actually a fundamental theorem of computer science, called ‘The Halting Problem’.”

Computers will also lag behind in leading and innovating, said Peter Metzger, vice chairman and cybersecurity and business risk expert at DHR International, an executive search firm.

“We’re still going to need people to lead, decide, and get things done,” he said.

Providing business value

As the routine tasks get automated, humans will be able to focus on making strategic, values-driven decisions.

That will require a true understanding of the business, and how that is intertwined with technology, said Diana Kelley, global executive security adviser for IBM Security.

“I recommend that cybersecurity pros beef up their 360-degree skills,” she said. “Get an understanding of the business, an understanding of the stakeholders in their work.”

That could involve in working closely with the legal department and understanding what they do, or helping with media outreach or marketing.

“This is extremely challenging and difficult,” she said. “But to be valuable, you need to understand how people are interacting with their technology. Cybersecurity is a very fascinating area that is very horizontal, it goes through all the areas of a business.”

Another area that cybersecurity pros can look at is that of education.

“Humans are great at explaining things to other humans,” she said. “That is something that we see at IBM. Someone who can explain things in a clear way that someone else can understand can be very valuable, not just for other security professionals, but also for a general audience, too.”

And if the education task involves teaching the AI systems how to do cybersecurity, InfoSec experts shouldn’t be worried that they are a traitor to humanity, she added.

“You’re a helper to humanity,” she said. “There is so much data and it’s so hard to keep up with it that this is about throwing out that life jacket, helping people to float. It’s not about getting rid of humans. It’s about making our existing humans super-human.”

This story, “AI is coming, and will take some jobs, but no need to worry” was originally published by CSO.

Source: InfoWorld Big Data