Connected devices result in an increase in the volume of data flowing into companies. But, while companies are able to analyse the data, IT will struggle to maintain adequate application performance levels, writes WIMPIE VAN RENSBURG, Country Manager for Sub Saharan Africa at Riverbed Technology.
We’re all becoming pretty familiar with the idea of the Internet of Things (IoT). Often, when thinking of the IoT, the first things that come to mind may be a wearable fitness tracker or a smartphone app that can control a thermostat. In fact, adoption of the IoT is so widespread that Gartner predicts that by 2020, there will be over 20 billion connected things in the world. The IoT is not only having an effect on the consumer world. It is also driving rapid digital transformation in the realm of business. We’re already seeing IoT powering a wide range of applications across industries. For example, The William Tracey Group, one of the UK’s largest recycling management companies, is using the IoT to collect data from chipped wheelie bins, smart weighing arms on collection trucks and on-board computers. This data is then used to help enterprises protect the environment while creating new business opportunities.
The growing business case for connected things means that the volume of data flowing into companies’ data collections is increasing. However, whilst companies are able to analyse the volumes of data supplied by connected devices in order to improve decision-making processes and efficiency, IT will struggle to maintain adequate application performance levels as enterprises bring more connected devices online.
Implementing application performance monitoring (APM) establishes the end-to-end visibility IT needs in order to immediately identify what’s causing an application to perform poorly, so that the issue can be fixed before it escalates.
The challenges of IoT
There’s a lot that goes on behind the scenes in order to make the IoT come to life. While users may be launching a simple app on their smartphone, there are a number of factors that go into making that simple digital experience work.
By considering how a wearable fitness tracker works, we can understand the complexity that can be constructed by the IoT. The user interface is simple, but the wristband is always working to send and receive information via Bluetooth from a smartphone, upload that information to a cloud-based app that analyses a range of metrics, including activity levels, nutrition, sleep quality and heart rate. The application then supplies that analysis to its dedicated smartphone app, and possibly also to other mobile and web-based applications.
Users expect all of this to occur in real-time. In order to meet these expectations, network communication and interdependent application processes taking place on a grid of distributed environments need to perform to perfection. If just one piece of this application fails, so will everything else.
The complexity of this process is further amplified if we consider a company managing a fleet of delivery vehicles such as UPS. UPS has installed a variety of connected devices their vehicles to monitor mileage, optimum speed and overall engine health– all in real-time. This enables the company to ensure the driver is driving safely, automatically schedule maintenance, and provide immediate updates to customers. The operation becomes even more complex when scaled across an entire fleet of vehicles.
Achieving seamless app performance
With businesses storing information in the cloud as well as on local systems – creating what are known as hybrid environments – and enabling employees to access that data from an increasing number of connected devices, including smartphones, laptops and tablets, the number of things that can go wrong within applications as well as within the network increases.
Monitoring the performance of all the applications and systems that run across hybrid networks has become more and more difficult, costly and time-consuming for IT. This is why many organisations are seeking the help of technology in order to achieve real-time visibility to oversee the performance of massively distributed applications. By implementing the use of specialised APM tools, companies can:
1. Monitor distributed applications and the underlying networks: By achieving complete visibility over the organisation’s apps, IT can examine the type of information flowing through the network and map out how it is being collected and shared between devices, applications, cloud services and the analytics systems. IT can then quickly identify if there are any issues affecting the end user experience.
2. Pinpoint the causes of bottlenecks or errors: IT can then identify the causes of information bottlenecks, determine which are affecting business critical processes, and address these first.
3. Look for opportunities to improve performance: Because APM tools continuously monitor applications and information transactions, IT can amass a wealth of information that can then be analysed for patterns in order to identify minor bugs before they become severe, or to seek opportunities for performance improvement.
Business-critical IoT applications now span both physical, virtual, and hybrid environments and end-users’ expectations are continuing to grow. IDC predicts that within three years, 50 per cent of IT networks will transition from having excess capacity to handle the additional IoT devices, to being network constrained with nearly 10 per cent of sites being overwhelmed.
With this in mind, it’s now more important than ever to monitor the performance and availability of the business applications that employees and customers rely on so business productivity can increase. Companies need to be able to pre-empt an inevitable rise in the flow of data and ensure that they have adequate bandwidth to cope with this upsurge.
Application Performance Management tools can provide the end-to-end visibility and diagnostics necessary for identifying issues with complex networks and distributed applications as well as for taking action before issues escalate. Additionally, the detailed analytics provided by APM enables companies to not only take control of performance improvement, but to also evaluate the business impact of all applications in their network.
Why your first self-driving car ride will be in a robotaxi
Autonomous driving will take longer than we expect, and involve less ownership than the industry would like, writes Intel’s AMNON SHASHUA
As we all watch automakers and autonomous tech companies team up in various alliances, it’s natural to wonder about their significance and what the future will bring. Are we realizing that autonomous driving technology and its acceptance by society could take longer than expected? Is the cost of investing in such technology proving more than any single organization can sustain? Are these alliances driven by a need for regulation that will be accepted by governments and the public or for developing standards on which manufacturers can agree?
The answers are likely a bit of each, which makes it a timely opportunity to review the big picture and share our view of where Intel and Mobileye stand in this landscape.
Three Aspects to Auto-Tech-AI
There are three aspects to automotive-technology-artificial intelligence (auto-tech-AI) that are unfolding:
- Advanced driver-assistance systems (ADAS)
- Robotaxi ride-hailing as the future of mobility-as-a-service (MaaS)
- Series-production passenger car autonomy
With ADAS technologies, the driver remains in control while the system intervenes when necessary to prevent accidents. This is especially important as distracted driving grows unabated. Known as Levels 0-2 as defined by the Society of Automotive Engineers (SAE), ADAS promises to reduce the probability of an accident to infinitesimal levels. This critical phase of auto-tech-AI is well underway, with today’s penetration around 22%, a number expected to climb sharply to 75% by 2025.1
Meanwhile, the autonomous driving aspect of auto-tech-AI is coming in two phases: robotaxi MaaS and series-production passenger car autonomy. What has changed in the mindset of many companies, including much of the auto industry, is the realization that those two phases cannot proceed in parallel.
Series-production passenger car autonomy (SAE Levels 4-5) must wait until the robotaxi industry deploys and matures. This is due to three factors: cost, regulation and geographic scale. Getting all factors optimized simultaneously has proven too difficult to achieve in a single leap, and it is why many in the industry are contemplating the best path to achieve volume production. Many industry leaders are realizing it is possible to stagger the challenges if the deployment of fully autonomous vehicles (AVs) aims first at the robotaxi opportunity.
Cost: The cost of a self-driving system (SDS) with its cameras, radars, lidars and high-performance computing is in the tens of thousands of dollars and will remain so for the foreseeable future. This cost level is acceptable for a driverless ride-hailing service, but is simply too expensive for series-production passenger cars. The cost of SDS should be no more than a few thousand dollars – an order of magnitude lower than today’s costs – before such capability can find its way to series-production passenger cars.
Regulation: Regulation is an area that receives too little attention. Companies deep in the making of SDSs know that it is the stickiest issue. Beside the fact that laws for granting a license to drive are geared toward human drivers, there is the serious issue of how to balance safety and usefulness in a manner that is acceptable to society.
It will be easier to develop laws and regulations governing a fleet of robotaxis than for privately-owned vehicles. A fleet operator will receive a limited license per use case and per geographic region and will be subject to extensive reporting and back-office remote operation. In contrast, licensing such cars to private citizens will require a complete overhaul of the complex laws and regulations that currently govern vehicles and drivers.
The auto industry is gradually realising that autonomy must wait until regulation and technology reach equilibrium, and the best place to get this done is through the robotaxi phase.
Scale: The third factor, geographic scale, is mostly a challenge of creating high-definition maps with great detail and accuracy, and of keeping those maps continuously updated. The geographic scale is crucial for series-production driverless cars because they must necessarily operate “everywhere” to fulfil the promise of the self-driving revolution. Robotaxis can be confined to geofenced areas, which makes it possible to postpone the issue of scale until the maturity of the robotaxi industry.
When the factors of cost, regulation and scale are taken together, it is understandable why series-production passenger cars will not become possible until after the robotaxi phase.
As is increasingly apparent, the auto industry is gravitating towards greater emphasis on their Level 2 offerings. Enhanced ADAS – with drivers still in charge of the vehicle at all times – helps achieve many of the expected safety benefits of AVs without bumping into the regulatory, cost and scale challenges.
At the same time, automakers are solving for the regulatory, cost and scale challenges by embracing the emerging robotaxi MaaS industry. Once MaaS via robotaxi achieves traction and maturity, automakers will be ready for the next (and most transformative) phase of passenger car autonomy.
The Strategy for Autonomy
With all of this in mind, Intel and Mobileye are focused on the most efficient path to reach passenger car autonomy. It requires long-term planning, and for those who can sustain the large investments ahead, the rewards will be great. Our path forward relies on four focus areas:
- Continue at the forefront of ADAS development. Beyond the fact that ADAS is the core of life-saving technology, it allows us to validate the technological building blocks of autonomous vehicles via tens of new production programs a year with automakers that submit our technology to the most stringent safety testing. Our ADAS programs – more than 34 million vehicles on roads today – provide the financial “fuel” to sustain autonomous development activity for the long run.
- Design an SDS with a backbone of a camera-centric configuration. Building a robust system that can drive solely based on cameras allows us to pinpoint the critical safety segments for which we truly need redundancy from radars and lidars. This effort to avoid unnecessary over-engineering or “sensor overload” is key to keeping the cost low.
- Build on our Road Experience Management (REM)™ crowdsourced automatic high-definition map-making to address the scale issue. Through existing contracts with automakers, we at Mobileye expect to have more than 25 million cars sending road data by 2022.
- Tackle the regulatory issue through our Responsibility-Sensitive Safety (RSS) formal model of safe driving, which balances the usefulness and agility of the robotic driver with a safety model that complies with societal norms of careful driving.
At Intel and Mobileye, we are all-in on the global robotaxi opportunity. We are developing technology for the entire robotaxi experience – from hailing the ride on your phone, through powering the vehicle and monitoring the fleet. Our hands-on approach with as much of the process as possible enables us to maximize learnings from the robotaxi phase and be ready with the right solutions for automakers when the time is right for series-production passenger cars.
On the way, we will help our partners deliver on the life-saving safety revolution of ADAS. We are convinced this will be a powerful and historic example of the greatest value being realized on the journey.
Professor Amnon Shashua is senior vice president at Intel Corporation and president and chief executive officer of Mobileye, an Intel company.
Sea of Solitude represents mental health issues through gaming
It’s a game that provides a tasteful visual representation of mental health issues. BRYAN TURNER dives into the Sea of Solitude.
Disclaimer: This review is based on four hours of gameplay.
Sea of Solitude, the latest adventure game by Jo-Mei Games and EA Games, takes a sobering look at loneliness. It represents this loneliness visually, using light and dark environmental changes, as well as creatures players must encounter. The main character, Kay, must make it through the sea without finding herself trapped in a sea of loneliness. She meets fantastical creatures along her journey, and she must help them solve their challenges while keeping herself in a sane environment.
The game is systematic in the way it represents its important aspects. It starts with a striking visual art style and a soft storyline, which gives characters a chance to absorb the beauty of the game. As one gets a hang of the controls and used to the art style, the story kicks it up a few notches to reveal the harrowing backstories of the creatures that reside in the sea Kay must travel.
In particular, it features a creature that keeps flying away from Kay. This was frustrating because the previous chapter of the game presents a backstory for the creature that was not only devastating to the main character, but also to the player. Once Kay meets this creature, players must be ready to cry. It’s a brilliantly crafted story and hats off to Jo-Mei Games for being great storytellers.
Cornelia Geppert, CEO of Jo-Mei Games, told EA: “Sea of Solitude centres on the essence of loneliness and tugs on the heartstrings of its players by mirroring their own reality. It’s by far the most artistic and personal project I’ve ever created, written during a very emotional time in my life. Designing characters based on emotions was a deeply personal achievement for our team and we’re so excited for players to soon experience Kay’s powerful story of self-discovery and healing.”
Generally, I steer clear of games that are metaphors about mental health issues because they tend to be crass in how they address mental health. Sea of Solitude is quite different because of its level of relatability. Other games about mental health tend to be about a specific disorder that not many people experience, while loneliness is something that so many of us experience. Additionally, the representation of how loneliness affects Kay in the real world is sharp but tasteful. The combination of relatability and respectful representation is what makes the game’s story so brilliant.
Another great aspect of this game is the music scoring. It uses sound and the absence of sound very carefully to invoke the right feelings expected from players. The game wouldn’t be as good with the sound off and subtitles on, so future players are recommended to turn up the volume or put on headphones.
The game is long for an indie game, at around three or four hours of gameplay until the end is reached. Several sources say there is a hidden ending, so players can look out for that in a second playthrough.
The game’s story isn’t perfect, though. The eventual sameness of creature encounters is a little disappointing. This may be down to the expectation of being extremely devastated by all the stories of the creatures, especially when one is less than devastated by the subsequent stories. One of the most affecting creature stories was also presented at the beginning of the game, which set the bar very high for the rest of the creatures.
One creature, in particular, tries very hard to have the greatest emotional impact, but this comes across as blunt and dampens the meaning of what it was supposed to represent.
While I didn’t mind sharp representation, the perception of themes like bullying, estrangement, and suicidal thoughts may vary in appropriateness from player to player. Prospective players with existing painful mental health issues should consult gameplay videos, like the one below, before purchasing the game, to gauge appropriateness.
Overall, the game is incredible at connecting with what it is to be human and what it means to be lonely. Dealing with issues as physical creatures is a great touch, as the main character tends to resolve the problems of the creature by understanding what the problems mean.