Machine learning (ML) continues to amaze us with its abilities and is set to transform the economic structure of many industries — from producers of widgets to financial analysts and health care providers. But many IT and operations practitioners are struggling to put the rapid advances in ML to work for their organizations.

To take advantage of ML in your environment, you should start with an understanding of the benefits you aim to achieve, such as improving efficiency, accuracy and safety, or cutting the cost of delivery of goods/services. Next, how will you get there? Roll your own? Integrate open-source code bases? Buy a product or service? Use a public cloud?

These decisions need to reflect the realities of the ML domain, your business needs and the skills you have available.

ML Is One Ingredient, Not The Whole Solution

Because ML is developing so fast, I believe solutions should be able to take advantage of the powerful techniques being made available in open-source. Ensure your solution is “ML agile” and able to upgrade to newer algorithms easily. An application architecture that allows you to plug in the major open-source frameworks could save years of effort and get your solution into production quicker.

The principal challenge with adopting any ML-based solution is the effort needed to build and tune models to your environment — which demands highly skilled engineers/data scientists. For that reason, insist that your integrators manage a solution throughout its life, and don’t sign off on a bespoke app until you have proof that it works. Remember that you may not know if a solution delivers significant benefits until you have seen it work in practice for long enough to establish the operational costs of false positives and false negatives.

The Power Of Open-Source

Today, the leading edge of innovation in ML algorithms and tools is to be found in open-source code bases that enjoy broad support. The community development model appeals to researchers, end users and developers – users can be sure they won’t be stranded with a dead-end proprietary stack, and developers can confidently invest time and effort into widely used code bases, developing skill sets that are portable across projects, employers and even clouds. The open-source ML tools are not only the leading edge of algorithm development but also embody the de facto work practice of many data scientists.

Worth noting is that the near-universal collaboration on a common set of open-source tools does not commoditize ML per se. Sure, the code is free, but there has been a sea change in the community development model: Leading researchers and practitioners pool their efforts to deliver a common code base of great value, freely available to all. It’s a fascinating trend that also serves the competitive interests of major contributors – like Google, Amazon and Microsoft – that gain advantage by ensuring competitors with proprietary solutions cannot keep up. For the cloud providers, ML-based workloads are a great way to monetize their cloud infrastructure, from storage through central processing unit, memory and graphics processing unit/tensor processing unit (TPU) resources.

Finally, note that the free availability of algorithms has not killed the value chain. Chip vendors, including Google with its TPU, NVIDIA and over 40 startups working on hardware acceleration for ML, aim to monetize the resource-hungry training and inference with proprietary acceleration hardware for clouds or on-prem devices.

What’s Next For ML?

Successful open-source projects attract developers and researchers, and successful ML open-source software projects become focal points of innovation for the industry, accelerating the state-of-the-art and delivering the power of collective contributions to all stakeholders. Contrast the strong community support for Google TensorFlow and the almost complete absence of a community around the proprietary IBM Watson. The integration of TensorFlow into a comprehensive set of consumer and enterprise solutions will build preference for Google services and TPUs, keep developers focused on Google technologies and give Google immense bragging rights – promoting itself in every ML success on the part of its community.

Cloud providers have massive marketing budgets and immense reach, and they already use ML to differentiate their packaged services – embedding AI smarts into applications they monetize via a subscription licensing model. This approach saves customers from having to understand the technology. Providers also win by building strong affinity with the user by incorporating ML-powered features that quickly make their way into SaaS apps.

Open-source ML is fuel for a race favoring execution and development efficiency. Winners will capitalize on the ready availability of powerful tools to deliver economic benefits quickly and at a reasonable cost. Those that adhere to the mantra of proprietary secret sauce may succeed tactically, but I believe they are doomed to eventual failure – slower adoption and less developer support.

Although the algorithms in major open-source frameworks form an immensely valuable community commons, it is the complete solution that is of value for your use case. There is plenty of room for proprietary innovation – delivering vertically integrated packages for specific industries, and infrastructure and packaging that makes these powerful technologies simple enough for non-data scientists to easily adopt and scale.

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For years, many healthcare organizations tended to be skeptical and resistant (if not outright hostile) to the idea of storing their data, particularly protected health information, in the cloud. IT and security decision-makers had deep reservations about stashing such sensitive data anywhere but their own on-premises servers, safe under their own watchful eyes.

But not too long ago that changed, and seemed to change quickly. To the surprise of many, over the past few years, it appears that many healthcare providers have been getting markedly more comfortable putting their trust in the cloud.

“If you had asked me in 2011, I would have predicted that healthcare would still be one of the slower moving industries,” said Jason McKay, chief technology officer of Logicworks, a managed hosting company that helps organizations in many sectors build and manage cloud infrastructure. “We were surprised at the uptake.”

Part of that is the obvious benefits of speed and agility that remote hosting has to offer. Partly it has to do with the recent attention paid to healthcare’s very specialized needs by giants such as Amazon Web Services, IBM, Google and Microsoft Azure, such as their willingness, only in the past several years, to sign onto HIPAA-compliant business associate agreements and gain HITRUST certification.

Yet another reason seems to be that, as the relentlessness and creativity of malware, ransomware, spyware and other cybersecurity exploits have ramped up in recent years, these major cloud players have been upping their own games – rolling out advanced artificial intelligence and machine learning capabilities to combat those threats to protect their clients’ hosted data.

Now the answer seems obvious to many: Who’s more likely to have a handle on the myriad threats to sensitive patient data? A small hospital with a dozen or so capable but overmatched IT staffers? Or a global hosting company with hundreds of security and AI experts, laser-focused on protecting information assets?

The trust level in the cloud has evolved to the point that Beth Israel Deaconess Medical Center CIO John Halamka, MD, put it: “I predict that five years from now none of us will have data centers. We’re going to go out to the cloud to find EHRs, clinical decision support, analytics.”

Automating detection of anomalous behavior

For his part, McKay – who, as CTO of Logicworks, works with major cloud companies – has some advice for hospital CIOs and CISOs looking to avail themselves of some of the recent AI-driven innovations in healthcare security.

It’s not just a matter of adopting new technologies such as Amazon’s Macie and GuardDuty, he said. As useful as those tools are in their approach to stopping threats, it’s important to have good processes in place for enterprise-wide infrastructure security, because often those capabilities will still require some tough decisions about the risks they sniff out.

With Macie, Amazon deploys artificial intelligence to automate the discovery, classification and protection of sensitive data in the AWS cloud. The tool can detect sensitive data such as protected health information or Social Security numbers and, with dashboards and alerts, offers visibility into how the data is being accessed or moved in the cloud. The technology looks out for anomalies and can issue alerts when it finds unauthorized access or data leaks.

GuardDuty, meanwhile, is described by Amazon as a managed threat detection service that scans continually for any malicious or unauthorized behavior. Its threat intelligence uses machine learning to find anomalies in the account and workload activity – looking for unusual API calls, for instance, or potentially unauthorized deployments that could point toward an account compromise. When a potential threat is detected, the service delivers a detailed security alert to the GuardDuty console and AWS CloudWatch Events to help make alerts more actionable.

Microsoft has also been innovating on its own cloud-based machine learning tools, of course, such as adaptive application controls in Azure Security Center, which can “analyze the behavior of Azure virtual machines, create a baseline of applications, group the VMs and decide if they are good candidates for application whitelisting, as well as recommend and automatically apply the appropriate whitelisting rules,” as Ben Kliger, senior product manager at Azure Security Center, explained in a blog post.

The tool helps surface apps that can be exploited to bypass a whitelisting solution, and provides full management and monitoring capabilities, through which clients change an existing whitelist alerted on violations of the whitelists, Kliger added.

IBM, meanwhile, updated its Resilience security platform last month with orchestration capabilities that combining machine learning and human intelligence to enhance incident response. And last week at its Google I/O 2018 conference revealed new security capabilities in artificial intelligence.

Human intelligence is a key first-step

When asked what he’d advise healthcare decision-makers to be thinking about when they consider cloud hosting and the automatic threat detection it enables, McKay’s first two suggestions have more to the gray matter of carbon-based life forms – clear communications between hospital employees and IT staff – than any artificial intelligence capabilities.

In his consulting work, “the first thing that comes up early – and if it doesn’t, we introduce it early – is a clear understanding of responsibility for all parties,” said McKay.

“Now you’re on a public cloud platform, so there’s at a minimum one extra player who is responsible for something that, when it was hosted in-house, was entirely in the purview of the institution hosting the data,” he explained. “So it’s important to understand the shared security model of either AWS or Azure.”

That means understanding who’s responsible for what, with regard to both day-to-day and long-term application level security components, he said.

“And then the other one we look for, and raise the red flag if we don’t have a clear understanding on the part of the customer, is a knowledge of the classification of your data,” said McKay. “That’s knowing what your sensitive data is, and then knowing where you’re putting it and how it’s used by your applications.”

For example, AWS has a business associate agreement that “stipulates which services can be used in what manner to comply with their BAA,” he said. “You could very easily run afoul of that if you don’t know where your data is and you haven’t classified it properly.”

With those people and process measures in place, it will be easier to enjoy the advances afforded by tools such as Macie and GuardDuty, said McKay.

Macie’s data classification alerting, based on the ability to “crawl the necessary bucket full of data and crawl and look for things” – social security numbers, say, or PHI, or credit card numbers for PCI enforcement – is “incredibly useful,” he said.

“And GuardDuty is an even more interesting one to me in that it’s a broader approach,” he added. “It’s a machine learning-based, security-focused anomaly detection engine. When it’s turned on for a given environment, they’re going to be looking both for things that are matching a security violation flag.”

For instance, McKay explained, “there’s known databases and botnet end-points; if it sees one of your instances communicating with a botnet in China, they can generate an alert about that. If they see patterns of access to resources falling outside of a norm – if it’s an outlier in some way – they can generate an alert.”

Of particular interest, he said, is that, “because it’s an AWS service, it’s very easy to tie that programmatically with other capabilities in AWS.”

As an example: “If you’re running an application where a potential security breach is more expensive to you than the loss of some application availability, you can do some things like, based on a GuardDuty classification of a compromised host speaking to a botnet, you could have that call a lambda function, which will take a snapshot of the instance for forensic purposes and then either shut it down or terminate it programmatically – meanwhile alerting those who need to know that this just happened,” said McKay.

Enabling programmatic action

It may sound complex but that sort of “programmatic security response” is one of the biggest selling points of cloud machine learning tools such as these, he said.

Once their ins and outs are sufficiently understood – and hospital IT staff are well-versed in how to use them the right way – they’re going to be “far, far better than a human being responding to that alert, doing diagnostics, taking some action,” said McKay. “Even if they’re well-trained, no one is going to do it as efficiently and quickly as a programming pipeline.”

When hospitals are doing classification with Macie, for instance, if misplaced data is detected – “let’s say an app developer made a mistake and they pushed some data that was supposed to go into a secured S2 bucket into an S3 bucket that was publicly available” – in a situation like that, “you might have your data identified by Macie as sensitive data in a public location,” he explained.

“You can take programmatic action to apply a security policy to that bucket to prohibit access. Rather than wait for the notice to come and someone to go make the change, you just automatically drop it off of the net.”

The same holds true for any misplaced credentials that might be found, said McKay. “AWS Trusted Advisor will scan things like public software repositories such as GitHub for things that match a regex of access keys into an environment. You can programmatically disable those keys when they’re found in a public space.”

Judgment calls to make

But the fact that the technology can easily do it doesn’t mean that the response is an easy call to make. And that’s why hospitals need to be thinking hard about their security processes beyond just whiz-bang technology.

“If your application was using those keys in such a way that disabling them breaks the application, that’s a decision the app owner has to make in terms of impact,” he said.

That downtime pose challenges for operations, “but it could be a multi-million-dollar compliance issue if you end up with a breach – a few minutes of downtime to prevent this breach might be worth it.”

For all the advances AI and machine learning have brought to cloud security, ultimately hospitals need to understand that it’s about making those sorts of judgment calls: programmatic action vs. impact on the application vs. avoidance of the cost of a breach.

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Cyber security companies are turning to artificial intelligence and machine learning tools to ward off growing number of attacks on networks, Finland- based internet security firm F-Secure said.

As the world is fast moving towards Internet of Things and connected devices, deployment of artificial intelligence (AI) has become inevitable for cyber security firms to analyse huge amount of data to save networks from infiltration attempts, F-Secure’s Security Advisor Sean Sullivan said.

Networks are persistently exposed to threats like malware, phishing, password breaches and denial of service attacks.

On a daily basis, F-Secure Labs on an average receives sample data of 500,000 files from its customers that include 10,000 malware variants and 60,000 malicious URLs for analysis and protection, Sullivan said.

For humans, it is a big task to go through such huge amount of data and machine learning tools and AI are lending a helping hand at this stage, he said.

Machine learning can be used to train logic designed to detect suspiciousness based on the structure of a file or its behaviour or both, another Security Advisor Andy Patel said.

Sullivan said any abnormal behaviour of a file is flagged by AI which helps in detecting threats at an early stage without much damage being done to the network.

Patel claimed behaviour models enable them to take preemptive steps to save their customers from ransomware attacks like ‘Locky’.

When asked if machine tools and AI can make people’s jobs in cyber security redundant, Patel said it is unlikely as attacks through malwares are designed by humans who think creatively to bypass automated security solutions. So, there is need of humans who can think creatively to defend networks from such attacks.

He also said AI and machine learning are at an evolving stage and there is a long way to go for widespread adoption of such tools in cyber security as only big players at present can afford building such systems and improving them every day.

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Staggering predictions were shared at the 2017 Gartner Symposium earlier this month. By 2021 Gartner predicts, artificial intelligence augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity. It is no wonder why companies are moving swiftly to embrace these emerging technologies to assist people and attain a competitive advantage.

Automated machine learning represents a truly transformational opportunity. Next generation business solutions powered by automated machine learning are an agent of change. Automated machine learning helps organizations better serve customers, improve business processes, solve complex problems and optimize outcomes when and where decisions are made. The goal isn’t necessarily to replace humans – it is to help them.

Decrypting the black box

Human resistance to change and decision automation is reasonable and rife. To ease fears and skepticism, start small with a well-known decision process that is not overly complex. Let the machine generate predictions and task humans with comparing actual results. In addition to validating predictions, delve into how the machine makes predictions.

Deciphering the secret sauce of black-box machine learning used to be challenging if it was at all possible. That problem is beginning to abate. Modern machine learning solutions today are becoming more transparent due to increased legislation such as the General Data Protection Regulation (GDPR). Innovations in this space include visibility into predictive model designs, data transformation steps, applied algorithms and triggered prediction data values. Revealing how machines make automated decisions fosters human trust and adoption.

Automated decision rules do not operate in isolation. Even though solutions such as DataRobot, H2O.ai Driverless AI, Tellmeplus, or open source auto-sklearn toolkit are simplifying the machine learning process with automation, humans are still essential. Machine learning, even when automated, still requires human evaluation, tuning, and monitoring. As these solutions interact with subject matter experts, the beauty of the human mind is combined with the amazing power of automated artificial intelligence.

Designing multi-layer intelligence

According to Internet of Things (IoT) subject matter expert John Soldatos, “Achieving optimal results when designing an intelligent business is not only a matter of deploying automated machine learning, it also requires understanding multi-layer intelligence.” Typically, organizations deploy automated machine learning technologies at one or more layers in the field, on the edge or in the cloud.

Field intelligence is used with smart machines, smart wearables and industrial robots where intelligent functions are embedded directly on field devices. With this approach, field devices become smart objects. Automated field-enabled decision processing saves bandwidth and can continue working regardless of network connectivity to enable real-time operations based on previous learning. Despite these benefits, field intelligence can be problematic due to the need to deploy complex data processing in CPU-constrained devices.

Edge intelligence describes artificial intelligence functionality that is installed on an edge server that controls one or more field devices. It is suitable for use cases involving fast or close to the field processing, involving multiple devices. Edge intelligence uses data from both smart objects and other passive or semi-passive devices such as sensors, and RFID tags but also from edge computers (such as routers). As a result, edge processing can still discover intelligence patterns in (near) real-time at a low latency and without any essential loss of bandwidth.

Cloud intelligence processing takes place in the cloud. It is ideal for the computing of massive datasets. Cloud intelligence typically uses data streams collected from multiple devices and edge servers, enterprise applications, supply chain management systems and other data sources. This level of automated machine learning configuration is commonly used for enterprise operations or industrial plant level management.

The best predictive models have little to no organizational value unless they are rapidly operationalized within the business. Deciding optimal placement of deployed automated machine learning functions across the layers of digital business architecture is a key success factor. The good news is that automated intelligence can take place at any layer or multiple layers depending on the timing, latency, bandwidth and processing constraints. Modern automated machine learning tools continue to simplify deployment across all layers.

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Artificial Intelligence is radically changing the way we think of technology. It is progressing rapidly, with key advancements ranging from virtual assistants (such as Apple’s Siri and Microsoft Cortana) to fraud detection. This emerging tech now plays a part in everyday life.

Another study performed by Forrester Research predicted an increase of 300% in investment in AI this year (2017), compared to last year. Tools and technologies play an important role in growth of any technology. Right software makes a huge difference in creating a complete AI experience.

So, here are the 10 Best AI and machine learning tools for developers,

1. Microsoft Azure

Azure ML is built on top of the machine learning capabilities of several Microsoft products and services. It shares many of the real-time predictive analytics of the new personal assistant in Windows Phone called Cortana.

Azure ML also uses proven solutions from Xbox and Bing. Outshining Nate Silver’s lauded FiveThirtyEight blog, Bing Predicts recently astonished many by correctly forecasting the results of more than 95% of the US mid-term elections. Thus, it might be worth checking out Azure ML to see what its powerful cloud-based predictive analytics can do for you.

2. ai-one

Claiming to be ‘biologically inspired intelligence’, ai-one lets developers create intelligent assistants within most software applications.

Ai-one’s ‘Analyst Toolbox’ provides a document library, building agents and APIs for developers. Ai-one can essentially turn data into generalised rule sets, enabling lots of in-depth AI and machine learning structures.

3. DiffBlue

Diffblue’s core AI builds an exact mathematical model of any code base. This model allows a very deep semantic understanding of what a program is trying to do. Founded by Daniel Kroening at the University of Oxford, DiffBlue is a dedicated code automation platform. And it’s a simple but extremely useful one at that.

Its aim is to locate bugs, refactor code, perform test writing and find and fix weaknesses in code, all done via automation.

4. Google’s TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

5. Amazon Web Services

At its re:invent conference in San Francisco last year, Amazon Web Services (AWS) announced three new artificial intelligence toolkits for developers.

AWS Rekognition uses AI to add image interpretation and facial recognition to apps, which is often used for biometric security features.

Polly uses AI to automate voice to written text across 47 voices in 24 languages.

While Lex is the open source engine behind Amazon’s personal assistant Alexa, allowing developers to integrate chatbots into web and mobile applications.

6. Protege

Although enterprise-focused, Protege has a suite of open source tools ideal for developers to create ‘knowledge-based applications with ontologies’.

Aimed at both experts and (somewhat) beginners, Protege lets developers create, upload, modify and share applications. Protege also houses an active community, making troubleshooting simple and collaboration optimised.

7. Apache Spark MLlib

MLlib is the machine learning library that is provided with Apache Spark, the in memory cluster based open source data processing system. It features a large database of algorithms focusing on classification, regression, clustering and collaborative filtering.

It is designed for simplicity, scalability, and easy integration with other tools. With the scalability, language compatibility, and speed of Spark, data scientists can solve and iterate through their data problems faster.

8. Nervana Neon

Nervana and Intel have joined forces to build the next generation of intelligent agents and applications and Neon is its open source Python-based machine learning library.

Founded in 2014, Neon lets developers build, train and deploy deep learning technologies in the cloud.

Neon has lots of video tutorials and a ‘model zoo’ which houses pre-trained algorithms and example scripts.

9. OpenNN

OpenNN is an open source class library written in C++ which implements neural networks. This open neural networks library was formerly known as Flood.

It includes lots of documentation and tutorials including an introduction to neural networks, although OpenNN is aimed at developers with lots of experience with artificial intelligence.

The package comes with unit testing, many examples and extensive documentation. It provides an effective framework for the research and development of neural networks algorithms and applications.

10. Apache Mahout

Apache Mahout is a library of scalable machine-learning algorithms, implemented on top of Apache Hadoop and using the MapReduce paradigm.

Once big data is stored on the Hadoop Distributed File System (HDFS), Mahout provides the data science tools to automatically find meaningful patterns in those big data sets. The Apache Mahout project aims to make it faster and easier to turn big data into big information.

The post 10 Best Artificial Intelligence (AI) and machine learning tools (ML) for you appeared first on Artificial Intelligence.

Microsoft, just like many of its competitors, has gone all in on machine learning. That emphasis is on full display at the company’s Ignite conference this where, where the company today announced a number of new tools for developers who want to build new A.I. models and users who simply want to make use of these pre-existing models — either from their own teams or from Microsoft.

For developers, the company launched three major new tools today: the Azure Machine Learning Experimentation service, the Azure Machine Learning Workbench and the Azure Machine Learning Model Management service.

In addition, Microsoft also launched a new set of tools for developers who want to use its Visual Studio Code IDE for building models with CNTK, TensorFlow, Theano, Keras and Caffe2. And for non-developers, Microsoft is also bringing Azure-based machine learning models to Excel users, who will now be able to call up the AI functions that their company’s data scientists have created right from their spreadsheets.

The Experimentation Service is all about helping developers quickly train and deploy machine learning experiments. The service supports all of the usual open source frameworks (PyTorch, Caffe2, TensorFlow, Cahiner and Microsoft’s own CNTK)and can scale from a local machines to hundreds of GPUs in the cloud (thanks to the use of Docker containers and the Azure Batch AI Training service). The tools also supports Apache Spark on Azure HDInsight clusters. The service keeps track of all the models, configurations and data (using Git repositories) to give developers full versioning for their experiments.

The Machine Learning Workbench is a desktop client for Windows and Mac (and yes, in this brave new world, Mac apps from Microsoft really aren’t a big deal anymore) that, in Microsoft’s words, is meant to be the “control panel for your development lifecycle and a great way to get started using machine learning.” It features integrations with Jupyter Notebooks and IDEs like Visual Studio Code and PyCharm and allows developers to build models in Python, PySpak and Scala.

As Microsoft’s Joseph Sirosh notes in today’s announcement, the most interesting feature here, though, may just be the tool’s ability to automatically transform your data so that the machine learning algorithms can work with it.

Like the Experimentation Service, the new Model Management service uses Docker containers to help developers and data scientists to deploy and manage their models to virtually anywhere a Docker container can run (including Microsoft’s own Kubernetes-based Azure Container Service).

The main takeaway from these announcements is that Microsoft continues to expand its toolbox for developers who want to build machine-learning based applications — both for their internal and external customers. What’s especially nice to see here is that these tools support a wide variety of non-Microsoft frameworks. A few years ago, that probably wouldn’t have been the company’s approach, but every one of these frameworks has its own advantages and disadvantages and thankfully Microsoft has understood that its focus shouldn’t be on excluding some frameworks but to offer a platform that supports all of them. The money here isn’t in offering open source frameworks, after all, but in providing the cloud services that developers will want to use to train, deploy and manage them.

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