The latest version has been architected from the ground-up to meet the demanding requirements of both IT and business users.

Automation Anywhere, the global player in Robotic Process Automation (RPA), announced the most significant upgrade in the company’s history to its flagship Digital Workforce Platform, Automation Anywhere Enterprise. Building on a robust enterprise RPA platform recognized as the most widely deployed in the industry, version 11 delivers enhancements in scalability, security and cognitive automation. This latest version has been architected from the ground-up to meet the demanding requirements of both IT and business users in banking, finance, insurance, healthcare, manufacturing, logistics and other industries.

“Within the next five years, it is our vision that robotic process automation will be as ubiquitous and as essential as electricity,” said Mihir Shukla, CEO and co-founder of Automation Anywhere. “A digital workforce platform, therefore, must be just as reliable as the power grid and just as easy to deploy as flipping a switch. Automation Anywhere Enterprise version 11 is a major step in this digital transformation, enabling enterprises to automate business processes. This includes not only automating repetitive tasks, but using AI technology along with predictive analytics to deliver even greater business value. With input from our large customer base and partner community, we have engineered a new standard for digital workforce platforms with this latest release.”

Automation Anywhere is the only provider that offers RPA, cognitive and analytics technology to enable end-to-end automation of business processes for global, enterprise organizations. The Automation Anywhere Enterprise platform has already been deployed across 700 of the world’s largest companies, delivering 500,000 automated full-time equivalents that have saved hundreds of millions of hours of manual tasks. Read more

Your touchpad can listen to your keyboard.hundreds of laptops to remove hidden keylogger

If you bought an HP laptop anytime in the last five years, it could be tracking your every keystroke. Over the weekend HP revealed that nearly 500 of its notebooks dating as far back as 2012 shipped with a secret keylogger installed. Alongside the announcement, HP released driver updates to eradicate the software on affected laptops.

Security researcher Michael Myng discovered the keylogger when probing the Synaptics touchpad software on an HP laptop. HP’s security bulletin says the “potential security vulnerability” affects all laptops with “certain versions of Synaptics touchpad drivers”—not necessarily just HP models.

The keylogger is disabled by default, however. “A party would need administrative privileges in order to take advantage of the vulnerability,” the bulletin states. “Neither Synaptics nor HP has access to customer data as a result of this issue.” HP told Myng that the keylogger was a debugging tool.

How to remove the keylogger in HP laptops

The same security bulletin includes separate software update links for every HP laptop loaded with the keylogger. HP says you should install those updates “as soon as possible.” CSO counted them all up and found a total of 475 affected laptops, with 303 being consumer laptops. Spectre, Envy, Pavilion, Omen, Compaq—they all contain the keylogger

Read more

The parking optimisation company turned to Google to bring its third generation IoT platform to life.

Smart Parking is using Google’s new Cloud IoT Core platform to reinvent the parking experience through use of sensors and advanced data analytics.

The company helps customers including Transport for London, the City of Westminster and Hilton Hotels optimise parking at their sites through vehicle detection sensors that monitor the occupancy of spaces.

These devices generate a vast volume data that can provide increasingly sophisticated insights to businesses and drivers. To cope with the growing quantity of sensors and data and take advantage of the latest developments in data analytics, Smart Parking needed a management platform, and turned to Google’s Cloud IoT Core.

“Now we have a platform that is totally secure,” says John Heard, Smart Parking’s CTO. “It scales from zero to a billion devices on demand and we can place it in any region to regionalise the data that’s available.”

The IoT market

Cloud IoT Core was developed by Google to help businesses in sectors such as utilities, transport and logistics, oil and gas and manufacturing manage their expanding fleets of smart devices and the volume of IoT data they produce, securely, at scale and from a central location.

This data can then be queried using Google’s own range of advanced analytics services.

Smart Parking was one of the early adopters of the platform, which was launched to the public in September.

The result has made major efficiency savings for the company. “It would typically take two to three weeks of configuration and operational acceptance testing,” Heard says. “It’s now taking us about three to four days.

Read more

MoveFifty employee challenge united colleagues through movement and engagement to celebrate Capgemini’s 50th anniversary.

Capgemini employees around the globe got active to help celebrate the company’s 50th anniversary and raise 100,000 Euro to support three impactful educational projects through ‘MoveFifty,’ the special birthday challenge with a purpose. MoveFifty brought colleagues together in movement and engagement to raise money for three education-focused charitable initiatives: Ciudad Quetzal, Enlight andCap Sur Le Code. Capgemini employees moved a total of 415,696 kilometers.

Over five months from June to October, Capgemini employees participated in MoveFifty to help raise the funds in two ways. First, it was through a physical challenge to accumulate as many kilometers as possible, in as many ways of movement as were possible to track (e.g. run, cycle, walk, jog), in support of a donation of up to 50,000 Euro to be shared among the three projects, proportional to the number of kilometers dedicated to each one of them.

Employees raised an additional 50,000 Euro for the three initiatives through engagement on social media. Team members shared, liked and re-tweeted posts across Facebook, Twitter, Instagram and Yammer using the 50thanniversary hashtag, #Capgemini50. The total number of social media engagements for the June, October timeframe reached 298,600. Read more

Amazon Web Services wants to bring machine learning to the enterprise and start-up masses, releasing a fully managed end-to-end machine learning service called Sagemaker and a video camera that runs deep learning models dubbed DeepLens.

Amazon Web Services wants to bring machine learning to the enterprise and start-up masses, releasing a fully managed end-to-end machine learning service called Sagemaker and a video camera that runs deep learning models dubbed DeepLens.

“Machine learning is so tantilising for most everyday developers and scientists. The hope and the hype here is tremendous. And you could argue with all the buzzwords we’ve heard in the 11 years we’ve been doing AWS, machine learning might be the loudest, and it’s absolutely the buzzword du jour today,” said AWS CEO Andy Jassy at the company’s Re:Invent conference in Las Vegas.

“Builders don’t want machine learning to be so difficult. They don’t want it to be so cryptic. They don’t want it to be black box. They want it to be much easier to engage with,” he added at the high-production keynote, complete with house band and tenuous musical segways.

The main steps to standing up a machine learning model in SageMaker begin with setting up a Jupyter notebook for data exploration, cleaning, and preprocessing your data. These can run on general instance types or GPU powered instances if required.

Users can then utilise any of ten common supervised and unsupervised learning algorithms and frameworks which are built into the product, or create their own. The training can scale to tens of instances to support faster model building.

By removing some of the big hurdles of building machine learning models, Jassy said the techniques will be within reach of businesses without the need to employ specialists.

“There just aren’t that many machine learning expert practitioners in the world. Most end up living at the big technology companies. And if you want to enable most enterprises and companies to be able to use machine learning in an expansive way we have to solve the problem for making it accessible for every day developers and scientists,” he explained.

Here’s looking at you

Jassy also launched a $245 high definition camera – DeepLens – which comes loaded with a set of pre-trained machine learning models to give developers ‘hands on experience’ in image detection and recognition.

Developers can also train their own models with SageMaker and run them on the camera.

“These models will help you detect cats and dogs, faces, a wide array of household and everyday objects, motions and actions, and even hot dogs. We will continue to train these models, making them better and better over time,” said AWS chief evangelist Jeff Barr in a blog post. Read more

As AWS looks to democratise machine learning with Amazon SageMaker, we delve into what is going on under the covers and how it stands out in an increasingly crowded market

Can AWS SageMaker democratise machine learning in the enterprise?

SageMaker is essentially a platform for authoring, training and deploying machine learning algorithms to business applications without much of the manual heavy lifting generally involved, such as provisioning infrastructure and managing and tuning training models.

As Randall Hunt, senior technical evangelist at AWS wrote in a blog post: “Amazon SageMaker is a fully managed end-to-end machine learning service that enables data scientists, developers, and machine learning experts to quickly build, train, and host machine learning models at scale.

“This drastically accelerates all of your machine learning efforts and allows you to add machine learning to your production applications quickly.”

How does it work?

Under the covers this means hosted Jupyter notebook integrated development environments (IDEs) for data exploration, cleaning, and preprocessing.

Then there is a distributed model building, training, and validation service where users can pick an AWS algorithm off the shelf, import a popular framework like TensorFlow or write and deploy their own algorithm with Docker containers, directly within SageMaker.

For training, you simply specify a location in S3 and the instance you want to use and in one click SageMaker spins up an isolated cluster and software defined network with autoscaling and data pipelines to start training. Then, when you are done it tears down the cluster.

HTTPs endpoints are used for model hosting, which can scale to support traffic and allow you to A/B test multiple models simultaneously. The algorithms can be deployed straight into production using EC2 instances with one click, after which it will be deployed with autoscaling across availability zones.

Tuning models is traditionally a trial and error exercise but SageMaker comes with what AWS calls ‘hyper parameter optimisation (HPO)’. By simply checking a box SageMaker will spin up multiple copies of the training model and uses machine learning to look at each change in parallel and tune parameters accordingly.

Democratising machine learning

The key message for AWS CEO Andy Jassy is democratising machine learning and AI. “If you want to enable most enterprises and companies to be able to use machine learning in an expansive way, we have to solve the problem of accessibility of everyday developers and scientists,” he said during his re:Invent keynote.

As a result, SageMaker will be fairly model agnostic, supporting all popular frameworks from TensorFlow and Caffe2 to AWS’ own Gluon library.

Jassy said that Google’s popular machine learning framework TensorFlow is already being run on AWS more than anywhere else, which will no doubt annoy the people at Google Cloud Platform. However, Jassy said the the general principle is “we provide all major solutions so you have the tools you need for the right job.” Read more

The cloud computing giant has finally launched support for Kubernetes, as well as a new fully managed container service called Fargate.

Amazon Web Services has launched two products to help customers deploy containers ‘as-a-service’ including a much-anticipated Kubernetes integration – Elastic Container Service for Kubernetes – and Fargate, designed to free customers from provisioning and managing infrastructure when deploying containers.

In a blog post introducing Amazon Elastic Container Service for Kubernetes (EKS), chief evangelist Jeff Bar writes: “We have a lot of AWS customers who run Kubernetes on AWS. In fact, according to the Cloud Native Computing Foundation, 63 percent of Kubernetes workloads run on AWS.”

Customers have been asking for better support for the popular open source orchestration platform Kubernetes for some time now. Google – where Kubernetes was incubated – and Microsoft Azure already support the container technology.

As Jon Topper, CTO of DevOps and infrastructure consultancy The Scale Factory told Computerworld UK before the event: “This container orchestration platform has become the de facto industry standard, and with both Google Cloud Platform and Microsoft’s Azure providing support for it, this may be the first example of Amazon being behind the curve in quite some time.”

Now with EKS, developers can deploy, run and manage Kubernetes on AWS infrastructure without the manual configuration previously required to do so.

Users of EKS will get the latest version of Kubernetes, it will work and integrate with what they are already running, and will automatically deploy Kubernetes with three masters across three availability zones so there is no single point of failure. Customers will also have access to automatic patches and version upgrades but with control over just when they may want to do that.

EKS also comes with AWS features out of the box like Elastic Load Balancing for load distribution, IAM for authentication, Amazon VPC for isolation, AWS PrivateLink for private network access, and AWS CloudTrail for logging.

Then there is AWS Fargate, which takes things a step further in terms of easing container consumption. As AWS CTO Werner Vogels tweeted: “As cool as EKS is, #AWS Fargate is a massive shift in making containers easier to use. No container cluster is easier to manage than no cluster at all!”  Read more

Google’s newest Android app might be its most useful of all. It’s called Datally, and it has one function: to stop apps from gobbling up your precious gigabytes of data.

google’s newest Android app might be its most useful of all. It’s called Datally, and it has one function: to stop apps from gobbling up your precious gigabytes of data.

The simple, intuitive app is designed to help you get a handle on your mobile data usage and stop rogue apps from surreptitiously using it up. So, if you get a message from your carrier about using an abnormal amount of data, you can use Datally to pinpoint the app that’s doing the most damage and shut it down.

datally setup screens IDG

Data trackers have been built into Android for a while, but Datally makes it drop-dead easy to use them.

There’s nothing necessarily new in Datally—data trackers have been built into Android for a while—but never has it been presented in such a user-friendly way. Many Android users don’t know to venture deep into the Settings app to see their mobile usage, so Datally pulls those features out of Settings > Network & Internet > Mobile network, and presents it in an easy-to-understand way.

After a brief setup, where you’ll need to allow Datally access to a VPN in order for it to work, you’ll be taken to a screen that clearly shows how much data you’ve used today. From there, you can dial into your weekly or monthly usage (via the manage data button), set up threshold alerts, and control which apps have access to your mobile data. There’s also a Data Saver kill switch that will shut down all mobile data at a tap. Read more

Cisco study shows that people around the world are ready to work alongside virtual teammates. In fact, adding a virtual teammate just might make workers happier.

Cisco surveyed workers in 10 countries as they wanted to know how people feel about advanced technologies in the workplace. This comes on the heels of our recent announcement of Cisco Spark Assistant, the world’s first enterprise-ready voice assistant for meetings.

Key themes and findings

The 52-question survey produced lots of intriguing data points. For instance, 94 percent said they dread meetings, yet 45 percent of innovators said they spend more than half the day in meetings. Clearly, anything we can do to make meetings more enjoyable will be a game changer.

Four primary themes predominated. They are:

  • We are optimistic. Most people think that technology advances will lead to more jobs, not mass unemployment. Plus they think that machines will free us from boring tasks and give us more time to focus on the bigger picture.
    Supporting findings:

    • Almost all workers (95 percent) said they believe AI can improve work tasks such as scheduling meetings, taking notes, or typing documents and emails.
    • Six in 10 workers expressed optimism, saying they believe technology advances will lead to more jobs.
    • When asked how a virtual assistant would benefit their team, more than half said it would increase productivity (57 percent) and focus (51 percent).
  • We are OK with machines being part of the team. Bots as co-workers? Bring it on. Across the board, people indicated they are OK with machines being part of the team.
    Supporting findings:

    • Six in 10 people said they want AI to do drudge work such as scheduling meetings and taking notes. Perhaps surprisingly, 39 percent of people who said they don’t trust AI indicated they would gladly hand over their least favorite tasks to AI.
    • More than half the people we surveyed said they have a human assistant at work; 82 percent of them said they would be more productive if they also had a virtual assistant. When asked how satisfied they were at work, half the people with human assistants said they were very satisfied. Only 32 percent of those with no human assistants said they were very satisfied. This suggests that giving workers virtual assistants could boost job satisfaction and even happiness.
    • We described a scenario in which a bot would attend a meeting, discern the topics discussed, and offer its analysis. Nine in 10 people expressed interest in or excitement about the idea. Very few said they were “terrified” or not interested.
    • We asked how they would feel if “the next time you walk into your office, your computer recognizes you, knows that you have a call starting soon, asks you: ‘Would you like me to join you to your call now?’ and then takes the action (assuming you say yes).” Fewer than 1 in 10 described it as “creepy” or “disturbing.” The rest chose terms such as “productive,” “cool,” “smart,” “savvy,” or “awesome.”
    • Eight in 10 people said they want bots to take an active role in conference calls by learning to tell the difference between a barking dog and the presenter and then removing noise.
    • Sixty-two percent of all workers expect talking to virtual assistants will eventually fully replace typing; 3 in 10 expect we’ll toss the keyboards in the next five years. Read more

MathWorks’ 5G Library for 3GPP radio technology development provides physical layer algorithms and link-level reference for upcoming 5G standard

Bangalore – (20 November 2017)MathWorks today introduced a 5G Library aimed at supporting wireless design exploration in advance of the release of the initial 3GPP 5G standard specification in March 2018. The 5G Library provides functions and link-level reference designs that help wireless engineers explore the behavior and performance of 3GPP new radio technologies. With the 5G Library, wireless engineers can conduct simulations to evaluate 5G enabling technologies and their impact on overall 5G system design.

The 5G standard will introduce advanced technologies to drive rapid innovation in mobile broadband, machine-to-machine, and connected vehicle applications. The 5G Library helps wireless system engineers explore and incorporate new 5G technologies before the standard is finalized. By using the library’s trusted MATLAB implementations of 5G algorithms and the 38.901 channel model, engineers can quickly evaluate the performance characteristics of new waveforms and coding schemes, and develop receiver algorithms.

“The ability to run simulations in MATLAB enables us to better engage with the various members of our 5G working group, as many of the companies we collaborate with also use MATLAB for simulation and data analysis,” said Lakshmi Iyer, link-level simulation lead at Convida Wireless. “In order to have an informed discussion with another member about a standards contribution, we need to be able to compare our assumptions and our results—and much of that discussion relies on simulations. Our MATLAB simulations with the 5G Library make it possible to move the dialogue forward.”

“Wireless engineers developing products for the new 5G standard face tremendous change and complexity,” said Ken Karnofsky, senior strategist, MathWorks. “The 5G Library lowers the learning curve for new 5G technologies with reliable, customizable, and well-documented software so engineers can create and verify designs that meet the specifications and performance goals of 5G.”  Read more