This DIY machine mixes your favorite three-ingredient cocktail

Do you and your friends have a favorite cocktail? If so—and if it has three ingredients—then this Arduino-based cocktail machine from YouTuber “GreatScott!” may be worth checking out.

The device is capable of mixing three liquids, which in GreatScott’s case consist of vodka, cranberry juice, and grapefruit juice (also known as a Sea Breeze), in a drink size selected via a rotary encoder and LCD screen.

An Arduino Nano provides the brains for this operation, and each component is poured using a series of three peristaltic pumps. Meanwhile, a load cell underneath the glass holder ensures that the correct amount of liquid is dispensed.

The same setup could be used to make different three-ingredient drinks with a little programming work, or it could be expanded into a multi-drink unit with the addition of a few more pumps. You can see it in action below!

Arduino Blog

Why energy control room operators should travel back in time: Substation control with the energy time machine

When Britain’s energy infrastructure was first established, no one could have anticipated the increased demand for power that the future would bring. To measure the performance of today’s energy supply, power distributors largely rely on supervisory control and data acquisition (SCADA) software to gather insights from each substation.

Substations generate a plethora of data, from information on energy effectiveness to the lifespan and performance of machinery inside the facility. However, the majority are unmanned. Therefore, when power supply companies identify an anomaly in data, they will send a maintenance engineer to export and analyse the information manually. However, without witnessing an error occurring in real-time, pinpointing the cause of a problem in a substation can be tedious and time-consuming, says Jürgen Resch, industry manager for energy at COPA-DATA.

Travelling back in time

Hiring an engineer to supervise the substation full-time is not a feasible option. As an alternative, energy distributors should invest in substation automation software with process recording capabilities. Process recording can serve as a time machine for maintenance engineers, allowing the software to automatically record every process that occurs in the substation. Maintenance engineers can then replay the processes at a future date.

COPA-DATA’s industrial automation software, zenon, includes a Process Recorder module designed for this purpose. The module can help engineers identify errors in data and provide diagnostics. As standard, the module continuously records all processes and saves the recordings automatically. The recorded data can then be played back in detail in zenon’s simulation mode — in a similar format to a standard media player.

In an ideal environment, process recording would be provided as standard with any SCADA or automation software used in substations. Using process recording, maintenance engineers can review every single process in the substation. Therefore, when attempting to identify an anomaly in data, engineers can use the recordings to isolate the exact moment the problem occurred.

Consider this: an energy supplier has spotted an irregularity in the data from one of its substations. Using zenon’s Process Recorder, an engineer can replay the process in which the irregularity occurred. Let’s say that the process recording software determined that the change in data coincided with a power surge in the substation. With this knowledge, the engineer can investigate the problem with a more informed approach.

Jürgen Resch

In this instance, the engineer can find the cause of the power surge. For example, a piece of operational machinery overheating would cause the cooling fan to kick in unexpectedly, creating a spike in power. Considering the ageing equipment in some substations, this wouldn’t be an unlikely occurrence. With this insight, the engineer can provide necessary maintenance to the equipment before the problem escalates, potentially preventing the machinery from failing completely in the future.

Since it was first established in the late 1800s, Britain’s energy network has endured rapid industrialisation and a colossal rise in the nation’s demands for power. The infrastructure may be ageing, but new technologies are available to ensure that the existing network can cope with new challenges.

Energy distributors have already invested in SCADA software to better […]

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Spotlight on ‘Lawtech’: how machine learning is disrupting the legal sector

Ask a lawyer to describe their daily work, and you’ll probably hear the words ‘document-intensive’ in response. It’s an apt description. The legal profession is overburdened with paperwork. Legal rulings, past cases, contracts and myriad other papers contribute to a data proliferation that is hard to keep on top of.

Unfortunately, failing to keep on top of it is not an option. The thankless task of sifting, reviewing and summarizing often falls to the lot of new associates, who spend between 31 and 35 percent of their time conducting research, according to ‘New Attorney Research Methods Survey’, a study from the Research Intelligence Group.

It’s costly, time-consuming, and (probably) extremely dull. But this way of working is beginning to change. A new sector is emerging in response to the ever-growing burden of data and the resulting high price points for clients. ‘Lawtech’ is bridging the gap between this most traditional of professions and a hitherto untapped source of help: cognitive computing, machine learning, and artificial intelligence.

The convergence point: human expertise meets AI

Over the course of this blog, we’ll look at two broad use cases for cognitive computing, machine learning and artificial intelligence in the legal profession:

  •       Technology-aided review
  •       Smart document creation

Let’s be clear: the idea is not to replace human lawyers with search bots, but to set them free to do more interesting and high-level work by eliminating the miserable grind. Artificial intelligence is not good at writing briefs, appearing in court, or negotiating with clients. This work remains the proviso of highly skilled human beings, and rightly so. However, it is good at document review and data extraction, and has the potential to realize enormous benefits for the legal sector.

Let’s look at some use cases.

Technology-aided review (TAR)

Cognitive computing, artificial intelligence and machine learning are three pillars of something known as TAR – or technology-aided review. This is the process of extracting relevant data points from unstructured data sets, like legal documents or contracts.

It’s in the ability to work with unstructured data sets that AI and cognitive-powered tools come into their own. While it is relatively simple to mine a nice, orderly spreadsheet for information, it’s much more difficult to extract it from more convoluted or scattered sources. TAR tools are capable of ingesting vast data sets of any specification – including data from the Internet of Things. Even more significant, perhaps, is their capacity to learn. These tools go far beyond just programming – instead, they refine their skills and knowledge with each interaction or query, developing and learning as they go.

One such tool is ROSS Intelligence, built on the Watson cognitive computing platform. Its natural language understanding capabilities mean it can understand and interpret nuanced questions more accurately than a simple keyword search. To answer a question, ROSS sifts through multiple text documents until it comes up with the relevant information. The volume of data it can handle is staggering: up to a billion text documents per second. The sheer speed means huge benefits in terms of reduced labor and costs.

TAR tools like ROSS have multiple use cases. They could be invaluable for consultancy work – extracting data such as contract start and end points, or payment dates, from large numbers of documents. The tool then presents these findings in a handy dashboard form, where it provides the base point for human analysis, theorizing or negotiation.

It could also be used to analyze target companies in mergers and acquisitions deals. Machine learning can perform searches on particular companies and identify wording that differs from the norm in global sales contracts, to spotlight potential new deals.

Smart contracts and document creation

While research and review is the obvious use case, automation through machine learning has the potential to perform more complex tasks, too. One of these is document generation.

Contract generation software enables contracts to be automatically produced by asking a series of questions, such as: ‘when does the agreement begin?’ or ‘is the tenant undertaking work on the property?’ The questions generate a decision tree that determines the form the contract will take, ensuring all bases are covered. It’s a little like setting up formulae in an excel spreadsheet, to determine the rules by which the content therein will be governed.

Specialized document software of this kind can also enhance document organization by ensuring that all internal cross-references apply language consistently – even if multiple people have been involved in drafting them. This means consistent terminology across the document and minimized risk of misinterpretation. Document comparison tools can also check for undefined terms and identify missing conditions or clauses, to ensure a water-tight result.

Interestingly, the demand for solutions like these is creating a brand new ‘Lawtech’ sector, and new jobs with it. ‘Legal technicians’, or ‘legal engineers’, many of them ex lawyers themselves, have the job of designing automation solutions and helping law firms to implement them, without the need for a centralized IT system.

Software operating on a cloud-based platform is agile enough to grow and adapt to firms’ growing needs. Cognitive computing and artificial intelligence means the platform is ever-learning, self-improving and immune to the threat of becoming obsolete, because it is always updating its knowledge.

The future of ‘Lawtech’

The emergence of ‘Lawtech’ and its accompanying job opportunities marks an interesting convergence point between mankind and machine. Large organizations that have invested in automation software are already seeing significant returns, in the shape of reduced operating costs, swift, accurate data extraction and better opportunities for their staff to take on high level work.

Attorneys have more time to engage in the creative side of legal representation – keeping clients better informed throughout the legal process, and exploring strategies and outcomes fully with the benefit of trustworthy data. The value of cognitive computing in the legal sector is already apparent – and it’s not going anywhere.

Learn more

To find out more about how ROSS and IBM Watson are creating value for the legal sector, take a look at this blog post, or explore our other solutions for legal professionals.

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Digital Catapult opens doors on Machine Intelligence Garage

,Digital Catapult opens doors on Machine Intelligence Garage for UK start-ups

Digital Catapult has today launched Machine Intelligence Garage, a program to develop artificial intelligence and machine learning start-ups in the UK.

Digital Catapult is a not-for-profit body that focuses on helping UK businesses to scale up. Its latest initiative, the Machine Intelligence Garage program, previewed in the UK government’s Industrial Strategy last week, aims to support the ambition of making the UK a global centre for artificial intelligence (AI) development. It’s an industry that politicians hope will add £232 billion to the UK economy by 2030.

According to Digital Catapult’s own survey of around 10 percent of the UK’s AI and machine learning (ML) start-ups, more than half are currently held back because they lack access to computation power.

Read more: Digital Catapult to launch three new networks in UK

Garage programme

Digital Catapult’s Machine Intelligence Garage programme, due to begin in January 2018, aims to tackle this issue by giving small and medium-sized enterprises (SMEs) access to cloud-based and physical computational power for AI. The organization is also promising to host a series of investor pitch days, meet-ups and showcases to create a knowledge-sharing environment among start-ups in this area.

According to Digital Catapult, deep-learning techniques, which have reached human-level performances, incur extortionately high computational costs – a single training run for a ML system can cost upwards of £10,000. This can be a serious barrier for UK innovators and researchers.

Read more: Digital Catapult explains why it’s backing immersive reality

Valid endpoints

Clive Longbottom, analyst at IT advisory company Quocirca, said that when it comes to AI, ML and deep learning, it is very easy for huge amounts of resource to be required to reach a valid endpoint.

“If this is to be done on constrained equipment, then the results can take too long to come back. The costs of putting in place sufficient resource for real-time AI can be horrendous – therefore, it makes sense for a group to come together and enable a suitable environment for smaller start-up companies to access the resources they need,” he explained.

Longbottom suggested that, by sharing physical resources, risks can be shared too.

“For example, if there are 20 companies using a certain hardware system and a better one comes along, moving over to that new hardware is cheaper for everyone as a shared cost, rather than it all being down to a single player,” he said.

“Hopefully, the Machine Intelligence Garage will be a well-shared, multi-player environment with shared resources and shared risks across the board. Whether this will allow the UK to compete effectively against the massive R&D investments from the likes of Microsoft, IBM, AWS and Google in the US, or from low-cost or massively government-backed environments such as China, is something we will have to wait and see,” he added.

Read more: London Zoo turns to IoT to tackle global poaching menace

Partner network

Digital Catapult is collaborating with a number of technology companies, high-performance computing facilities and academics for the three-year programme. These include Amazon Web Services (AWS), Google Cloud Platform, NVIDIA, Graphcore, STFC Hartree Centre, EPCC, Newcastle University, the Alan Turing Institute, Bart’s Health Trust and Capital Enterprise. The programme’s initial funders are InnovateUK and ERDF.

Companies developing products or services that use ML or AI can apply to take part in the initiative and applications will be assessed based on a number of criteria. These include strength of  idea, technical implementation plan, availability of data, ethical use of data, and the immediacy of the need for computation power.

Applications for the programme will open every six weeks, with the first open call going live today. The first group of successful companies will join the programme on 23 January 2018.

Read more: Digital Catapult expands LPWAN program for councils and enterprises

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Amazon Web Services Announces a Slew of New IoT Services; Brings Machine Learning to the Edge

Amazon Web Services Announces a Slew of New IoT Services; Brings Machine Learning to the Edge

Amazon Web Services Announces a Slew of New IoT Services; Brings Machine Learning to the Edge

Today at AWS re:Invent, Amazon Web Services, Inc. (AWS), an company, announced six significant services and capabilities for connected devices at the edge.

AWS IoT 1-Click, AWS IoT Device Management, AWS IoT Device Defender, AWS IoT Analytics, Amazon FreeRTOS, and AWS Greengrass ML Inference make getting started with IoT as easy as one click, enable customers to rapidly onboard and easily manage large fleets of devices, audit and enforce consistent security policies, and analyze IoT device data at scale.

Amazon FreeRTOS is an operating system that extends the rich functionality of AWS IoT to devices with very low computing power, such as lightbulbs, smoke detectors, and conveyor belts. And, AWS Greengrass ML Inference is a new capability for AWS Greengrass that allows machine learning models to be deployed directly to devices, where they can run machine learning inference to make decisions quickly, even when devices are not connected to the cloud.

“The explosive growth in the number and diversity of connected devices has led to equally explosive growth in the number and scale of IoT applications. Today, many of the world’s largest IoT implementations run on AWS, and the next phase of IoT is all about scale as we’ll see customers exponentially expand their fleet of connected devices,” said Dirk Didascalou, VP IoT, AWS.

“These new AWS IoT services will allow customers to simply and quickly operationalize, secure, and scale entire fleets of devices, and then act on the large volumes of data they generate with new analytics capabilities specifically designed for IoT.”

“With Amazon FreeRTOS, we’re making it easy for customers to bring AWS IoT functionality to countless numbers of small, microcontroller-based devices. And, customers have also told us they want to execute machine learning models on the connected devices themselves, so we’re excited to deliver that with AWS Greengrass ML Inference.”


AWS IoT 1-Click: the easiest way to get started with AWS IoT (available in preview)

When considering IoT, many customers just want an easy way to get started by enabling devices to perform simple functions. These are functions like single-button devices that call technical support, reorder goods and services, or track asset locations. With AWS IoT 1-Click, enabling a device with an AWS Lambda function is as easy as downloading the mobile app, registering and selecting an AWS IoT 1-Click enabled device, and – with a single click – associating an AWS Lambda function. AWS IoT 1-Click comes with pre-built AWS Lambda code for common actions like sending an SMS or email. Customers can also easily author and upload any other Lambda function.

iRemedy is a healthcare e-commerce marketplace where healthcare consumers can buy medical supplies, drugs, devices, and technologies. “Back in August, we announced our plan to deploy 500 iRemedy NOW Internet of Things (IoT) buttons to our healthcare providers’ clients. Our customers simply click the button to order medical supplies and drug samples, or to request a call back from our service center,” said Tony Paquin, Co-Founder, President and CEO, iRemedy.

“The buttons are easy to use and drive down supply chain costs for our customers. AWS IoT 1-Click provides a fast and easy way to deploy more buttons, expand the types of actions we perform when the button is triggered, and also create simple reports.”


New AWS IoT services for managing, securing, and analyzing the data generated by large fleets of devices

Big DataAt scale, IoT solutions can grow to billions of connected devices. Today, this requires customers to spend time onboarding and organizing devices, and even more time integrating multiple systems to manage tasks like monitoring, security, auditing, and updates. Building solutions for such tasks is time consuming and easy to get wrong, and integrating third party solutions is complex and may introduce hard-to-detect gaps in security and compliance. Once a device fleet is operationalized, analytics is often the next challenge customers face. IoT data isn’t the highly structured information that most existing analytics tools are designed to process. Real-world IoT data frequently has significant gaps, corrupted messages, and false readings, resulting in the need for customers to either build custom IoT analytics solutions, or integrate solutions from third parties. AWS IoT Device Management and AWS IoT Device Defender simplify onboarding, managing, and securing fleets of IoT devices, while AWS IoT Analytics makes it easy to run sophisticated analytics on the data generated by devices.

  • AWS IoT Device Management (available today) makes it easy to securely onboard, organize, monitor, and remotely manage IoT devices at scale throughout their lifecycle—from initial setup, through software updates, to retirement. Getting started is easy; customers simply log into the AWS IoT Console to register devices, individually or in bulk, and then upload attributes, certificates, and access policies. Once devices are in service, AWS IoT Device Management allows customers to easily group and track devices, quickly find any device in near real-time, troubleshoot device functionality, remotely update device software, and remotely reboot, reset, patch, and restore devices to factory settings, reducing the cost and effort of managing large IoT device deployments.
  • AWS IoT Device Defender (coming in the first half of 2018) continuously audits security policies associated with devices to make sure that they aren’t deviating from security best practices, and alerting customers when non-compliant devices are detected. AWS IoT Device Defender also monitors the activities of fleets of devices, identifying abnormal behavior that might indicate a potential security issue. For example, a customer can use AWS IoT Device Defender to define which ports should be open on a device, where the device should connect from, and how much data the device should send or receive. AWS IoT Device Defender then monitors device traffic and alerts customers when anomalies are detected, like traffic from a device to an unknown IP address.
  • AWS IoT Analytics (available in preview) is a fully managed analytics service that cleans, processes, stores, and analyzes IoT device data at scale. Getting started is easy: customers simply identify the device data they wish to analyze, and they can optionally choose to enrich the device data with IoT-specific metadata, such as device type and location, by using the AWS IoT Device Registry and other public data sources. AWS IoT Analytics also has features for more sophisticated analytics, like statistical inference, enabling customers to understand the performance of devices, predict device failure, and perform time-series analysis. And, by using Amazon QuickSight in conjunction with IoT Analytics, it is easy for customers to surface insights in easy-to-build visualizations and dashboards.

At Philips Healthcare, the focus is to look beyond technology to the experiences of consumers, patients, providers, and caregivers across the health continuum. “We’re launching new health IoT services that we believe will dramatically improve our scale and capabilities. Part of the Philips solution involves managing connected devices that doctors and hospitals will rely on so they can deliver first class healthcare services,” said Dale Wiggins, Vice President and General Manager, Philips HealthSuite Digital Platform.

“We chose AWS IoT services to ensure we can meet our customers’ scale and reliability requirements. We have expanded these services to ensure that data from these devices is appropriately routed, devices are updated to the latest firmware, and are monitored to ensure they function properly in the field. Using AWS IoT Device Management, we were able to quickly develop the capabilities we need in the market.”

Best known for its GPS technology, Trimble integrates a wide range of positioning technologies including GPS, laser, optical, and inertial technologies with application software, wireless communications, and services to provide complete commercial solutions. “Trimble’s commercial solutions are used in over 150 countries around the world, and we use AWS IoT as a gateway for our next-gen IoT devices,” said Jim Coleman, Senior Engineer, Trimble.

“AWS IoT Device Management has helped streamline our device onboarding, which has enabled us to meet our planned production throughput for connected devices. With AWS doing the undifferentiated heavy lifting for our IoT platform, we can spend more time on our customers than on our infrastructure.”

iDevices is making IoT accessible to everyone in the smart home industry with its line of Wi-Fi and Bluetooth-enabled products. “The IoT analytics game is a race from raw data to actionable insights. Everyone has data, but it’s the insights from that data that are of real value to our customers,” said Eric Ferguson, Chief Software Architect, iDevices.

“The tools provided by AWS IoT Analytics to ingest, filter, transform, and analyze our data sources cut out a lot of the undifferentiated heavy lifting for our team, enabling them to focus on the enrichment activities in the pipeline and the downstream machine learning models, rather than the mechanics of the pipeline itself. This gets us to the insights we need with much less effort and allows us to really focus on market differentiation.”

iRobot is a global consumer robotics company that designs and builds robots for use inside and outside the home. “At iRobot, we rely on IoT services because connecting robots to the Internet to help them do more and better things is key to how we innovate,” said Ben Kehoe, Cloud Robotics Research Scientist, iRobot.

“With AWS IoT Analytics, we gain insights into our IoT data about device performance and usage patterns so we can empower our customers to do more both inside and outside of the home.”

Valmet is a leading global developer and supplier of technologies, automation, and services for the pulp, paper, and energy industries. “Valmet is building industry leading Industrial Internet solutions for pulp, paper, and energy customers, and we are using the AWS cloud environment as part of our platform,” said Juha-Pekka Helminen, Director, Valmet Digital Ecosystem.

“AWS is continuously improving and adding tools into their portfolio. We look forward to utilizing the new AWS IoT Analytics service for our customers’ benefit.”


Amazon FreeRTOS lets customers easily and securely connect small, low-power devices to the cloud

Today, countless devices are already capable of connecting to the cloud, and the number continues to grow dramatically. Many of these devices contain enough onboard computing power (CPU) to take advantage of AWS IoT services. However, a large number of other devices—from lightbulbs and conveyer belts to motion detectors—aren’t big enough to house a CPU and possess a microcontroller (MCU) instead. The most popular operating system used for these devices is FreeRTOS, an open source operating system for microcontrollers that allows them to perform simple tasks. FreeRTOS wasn’t designed specifically for IoT, so it lacks functionality to help devices connect securely to the cloud.

Amazon FreeRTOS extends FreeRTOS with software libraries that make it easy to securely connect small, low-power devices to AWS cloud services like AWS IoT Core, or to more powerful edge devices and gateways running AWS Greengrass (a software module that resides inside devices and gives customers the same Lambda programming model as exists within the AWS Cloud).

With Amazon FreeRTOS, developers can easily build devices with common IoT capabilities, including networking, over-the-air software updates, encryption, and certificate handling. Developers can use the Amazon FreeRTOS console to configure and download Amazon FreeRTOS, or go to or GitHub. Several microcontroller manufacturers and AWS Partner Network (APN) Partners support Amazon FreeRTOS, including Microchip, NXP Semiconductors, STMicroelectronics, Texas Instruments, Arm, IAR, Percepio, and WITTENSTEIN.

Arm defines the pervasive computing shaping today’s connected world. Realized in more than 100 billion silicon chips, Arm architecture is the de-facto standard for embedded applications. “As we’ve seen the Arm-based microcontroller ecosystem grow over recent years, FreeRTOS has played a key role in enabling embedded developers,” said Rene Haas, EVP and President, IP Products Group (IPG), Arm.

“We are pleased to see AWS extend the FreeRTOS kernel with increased connectivity, while adding additional security features. Amazon FreeRTOS running on Arm-based processors is an important milestone toward improving hardware, software, and networking security for the industry.”

Allegion is a provider of security products for homes and businesses. “Amazon FreeRTOS makes it easier for Allegion to rapidly innovate new features for our connected products, such as our Schlage electronic locks, and to move easily between hardware platforms,” said Todd Graves, Senior Vice President of Engineering and Technology, Allegion.

“We can focus on our core strengths, developing innovative safety and security products, knowing that Amazon FreeRTOS will make integration reliable and predictable.”

Hive (Centrica Connected Home) is a market leader in connected home products that helps its customers manage their energy usage in the UK, Ireland, and North America. “Amazon FreeRTOS is an exciting leap forward for our business and our customers,” said Seb Chakraborty, CTO, Hive (Centrica Connected Home).

“Dev teams can now focus their energy on the application and not the plumbing, messaging or security. Instead, they choose the board, the chip, and connect to AWS IoT seamlessly.”


New AWS Greengrass feature brings machine learning to the edge (available in preview)

Machine LearningAWS Greengrass ML Inference is a new feature of AWS Greengrass that lets application developers add machine learning to their devices, without requiring special machine learning skills. IoT devices frequently collect and forward large quantities of data, which can be used to automate real-time decision making through machine learning. To do this, customers build, train, and run machine learning on their IoT data in the cloud. However, some applications are highly latency sensitive and require the ability to make decisions without relying on always-on network connectivity.

With AWS Greengrass ML Inference, devices can run machine learning models to perform inference locally, get results, and then make smart decisions quickly, even when they’re not connected. Using Amazon SageMaker, or any machine learning framework, customers build and train their machine learning models in the cloud and then – with just a few clicks – use the AWS Greengrass console to transfer the models to devices they select.

More details available at

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