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Antitrust: Commission launches sector inquiry into the consumer Internet of Things (IoT)

aiuniverse — Sat, 18 Jul 2020 07:11:43 +0000

Source: europeansting.com

The European Commission today launched an antitrust competition inquiry into the sector of Internet of Things (IoT) for consumer-related products and services in the European Union.

The sector inquiry will focus on consumer-related products and services that are connected to a network and can be controlled at a distance, for example via a voice assistant or mobile device. These include smart home appliances and wearable devices. Knowledge about the market gained through the inquiry will contribute to the Commission’s enforcement of competition law in this sector.

Executive Vice-President Margrethe Vestager, in charge of competition policy, said: “The consumer Internet of Things is expected to grow significantly in the coming years and become commonplace in the daily lives of European consumers. Imagine a smart fridge making your grocery list, you pulling up that grocery list onto your smart device and order a delivery from a shop that sends the groceries to your door that unlocks automatically with a word. The possibilities seem endless. But access to large amounts of user data appears to be the key for success in this sector, so we have to make sure that market players are not using their control over such data to distort competition, or otherwise close off these markets for competitors.This sector inquiry will help us better understand the nature and likely effects of the possible competition problems in this sector.”

Despite the relatively early stage of development of the sector for the Internet of Things for consumer-related products and services in the European Union, there are indications that certain company practices may structurally distort competition. In particular, there are indications relating to restrictions of data access and interoperability, as well as certain forms of self-preferencing and practices linked to the use of proprietary standards. Internet of Things ecosystems are often characterised by strong network effects and economies of scale, which might lead to the fast emergence of dominant digital ecosystems and gatekeepers and might present tipping risks.

Therefore, through this competition sector inquiry, the Commission will gather market information to better understand the nature, prevalence and effects of these potential competition issues, and to assess them in light of EU antitrust rules.

The sector inquiry will cover products such as wearable devices (e.g. smart watches or fitness trackers) and connected consumer devices used in the smart home context, such as fridges, washing machines, smart TVs, smart speakers and lighting systems. The sector inquiry will also collect information about the services available via smart devices, such as music and video streaming services and about the voice assistants used to access them.

If, after analysing the results, the Commission identified specific competition concerns, it could open case investigations to ensure compliance with EU rules on restrictive business practices and abuse of dominant market positions (Articles 101 and 102 of the Treaty on the Functioning of the European Union – TFEU).

The inquiry complements other actions launched within the framework of the Commission’s digital strategy, in particular regulatory initiatives related to artificial intelligence (AI), data and digital platforms.

Next steps

In the coming weeks, the Commission will send requests for information to a range of players active in the Internet of Things for consumer-related products and services throughout the EU. The companies concerned may include, for example, smart device manufacturers, software developers and related service providers. Under EU antitrust rules the Commission can require companies and trade associations to supply information, documents or statements as part of a sector inquiry.

The Commission expects to publish a preliminary report on the replies for consultation in the spring of 2021. The final report would follow in the summer of 2022.

For further background, please see the sector inquiry website.

This sector inquiry follows a number of other antitrust sector inquiries carried out in recent years in fields including financial services, energy pharmaceuticals, and e-commerce. More information can be found on DG Competition’s sector inquiry website.

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The challenges of artificial intelligence

aiuniverse — Sat, 23 Nov 2019 06:10:05 +0000

Source:-euobserver.com

The parliamentary committee for internal market and consumer protection (IMCO) is working on one of the most fundamental principles of the European Union: the single market.

The entire Brexit saga shows once more how difficult it is to leave the single market once you have been a part of it. But also how beneficial it is to be a member of the EU.

After decades of living in a common internal market, many people seem to have forgotten how important it is, and how easy.

But when you travel to for example the United States, and you forget your adapter, you can’t even charge your mobile phone – a problem that never occurs when you travel on the European continent.

The internal market is not only about the free movement of goods and services. It’s also about standards, for products but also for consumer protection. That’s why worldwide people talk about “European standards”, as a label of global quality.

It is the task of the IMCO committee to make sure these European standards are upheld in every single space in Europe.

According to Petra De Sutter (Greens/EFA, Belgium), president of IMCO, “developing a long term strategy for better enforcement of Single Market rules” will be key for the committee’s agenda for the next five years.

Therefore, she says, it is “necessary to continue strengthening the internal market, in particular in a cross-border context, removing unjustified barriers and ensuring that the existing rules are properly and timely implemented and enforced.”

Next to the announced revision of the E-Commerce Directive or new Digital Services Act, the committee will need to have a lot of attention to what De Sutter calls “a single market fit for the digital age”.

“Since emerging technologies, such as artificial intelligence and blockchain, are becoming key drivers for economic development and enhance the value of goods and services, we will be following with the utmost attention any developments in this area”, she said, adding that this is important for both business and consumers.

As a fourth major point for IMCO over the next five years, De Sutter mentions a “sustainable Single Market”. More concretely, she points out that “addressing the needs of a growing circular economy and the integration of environmental concerns into consumer policy will be a key priority.”

A politically-sensitive point that might interfere with the IMCO agenda is the free movement of people.

De Sutter fears that “member states may take disproportionate measures and apply administrative controls and procedures which make free movement more difficult and costly for SMEs going cross-border.”

But all in all, De Sutter thinks that the speedy growth of artificial intelligence will become the biggest challenge, needing “legislation linked to transparency, liability, safety and ethical rules for digital platforms, services and products”.

On the one hand, European companies need access to data for developing AI. On the other hand “consumer protection rules have to ensure that consumers have clear information on how to use AI-enabled products and services, that they have control over data generated by such products and services, and how that data is used”, she said.

IMCO coordinators are: Andreas Schwab (EPP, Germany), Christel Schaldemose (S&D, Denmark), Dita Charanzova (Renew, Czech Republic), Marcel Kolaja (Greens/EFA, Czech Republic), Virginie Joron (ID, France), Adam Bielan (ECR, Poland), Katerina Konecna (GUE/NGL, Czech Republic).

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Prometeo develops artificial intelligence platform to monitor firefighters’ health

aiuniverse — Sat, 23 Nov 2019 05:26:28 +0000

Source:-zdnet.com
The smartphone-sized device can alert in real-time if a firefighter’s health is at risk when they’re out fighting fires.

Barcelona-based startup Prometeo has developed an AI-based cognitive health monitoring platform in hope that one day it will be used to monitor the health of firefighters while they are out battling brutal wildfires.

Co-founder Salome Valero explained the development of the platform came about following concerns that were raised by one of their own team members who is a veteran firefighter.

“When the firefighters are fighting against fire, they are breathing in a lot of toxins which can be dangerous for their health … and some of the immediate effects they feel from breathing in smoke is getting headaches. But there is evidence they can suffer respiratory diseases, cancer, and stress disorders,” she told ZDNet, during IBM Cloud Innovation Exchange in Sydney last week.

“The problem is there currently isn’t a lot of data about firefighters’ vitals, but because we have this real-time capability, we are will be able to monitor them.”

Working primarily with firefighters from Catalonia, Prometeo, which was recently announced as the winner of IBM’s Call for Code 2019 competition, has developed a smartphone-sized device that straps on a firefighter’s arm.

It features sensors that measure temperature, humidity levels, and smoke concentration, and can alert in real-time if a firefighter’s health is at risk through colour coded signals of green, yellow, and red.

“We can use it to analyse if the situation of the firefighters is okay, or getting worse, or if it’s a red alert that indicates the firefighter should get out of the fire immediately because their health is in danger,” Prometeo co-founder Josep Rafols said.

See also: IBM announces the finalists of Call for Code 2019 (TechRepublic)

Rafols said even though a minimal viable product has been developed, Prometeo is now looking to improve the product, pointing out how the platform currently relies on Wi-Fi connectivity to transmit data from the senor, which may not be ideal in certain situations.

“Right now, our connectivity solution is using a mobile phone [to hotspot]. We’re aware that some places will not have GSM communication, so one of the things we need to start working on is thinking a little outside of the box of how we will transmit the data when GSM is unavailable,” he said.

At the same time, Rafols said the team recognises the current design needs to be ruggedised.

“The sensors are currently on an arm band that you can just buy from Foot Locker, and obviously that should not be the final solution for the firefighter,” he said.

“In fact, in the initial test, we concluded that having the sensor on the arm is a bad idea because when firefighters clash into a tree the sensor breaks, so we are thinking of a new casing, and the firefighters have said placing it on the chest might be the best approach.”

Eventually, Prometeo hopes through the platform, enough data will be collected in the next two to three years to be used as historical scientific evidence to track the effects smoke has on firefighters’ health.

“This will not prevent fires, this will not prevent natural disaster, obviously, but this will help prevent firefighters from the effects [of firefighting],” Valero said.

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Unlocking the potential of smart cameras with deep learning

aiuniverse — Fri, 22 Nov 2019 06:02:55 +0000

Source:-itproportal.com
This article will examine the obstacles involved in trying to detect moving objects and how smart cameras and deep learning can correct them.

An object in motion looks fundamentally different from an object at rest — especially to a computer. To get a better idea of this concept, let’s imagine a film strip of a sprinter running: The person and pose in one frame look drastically different from the next frame, right?

Making sense of dynamic objects is taking on new importance as cities begin incorporating IoT devices like smart cameras to streamline municipal life. The town of Yuma, Arizona, is a great example of this. The city recently installed cameras on streetlights that can detect when cars, bicycles, and pedestrians travel through intersections, and it uses that data to optimise signal switching.

Athena Security is pioneering another interesting application of moving-video analysis: The company sells software that uses artificial intelligence to detect when people are fighting, fleeing, or lurking to determine whether crimes are being committed (or are imminent). Unsurprisingly, everyone from municipal police departments to Fortune 500 companies is interested in this AI application.

The applications are endless for IoT devices like smart cameras that analyse moving video. Fortunately, this technology has now reached a point where almost anything is possible.

Vulnerabilities in smart IP cameras expose users to privacy, security risks

Solving mysteries in moving video

Using computers to analyse video isn’t exactly a new concept. However, there’s one problem hampering the development of video analysis: Moving video is full of dynamic variables that can confuse even the smartest computers.

Objects look completely different in low light compared to bright light, for instance, which can lead to false analyses. Perspective offers up another challenge: Think about how different a car looks when it’s traveling parallel and then perpendicular to a relative point.

Other issues that might be confusing for a machine’s analysis of video include moving shadows, complex backgrounds, obscured objects, unexpected movements, and a camera’s technical limitations. For all these reasons, moving-video analysis has historically had a lot of potential — but not too many practical applications.

That’s all changing with advances in deep learning systems, which are often referred to as neural networks. Today, computing has advanced to a point where systems can learn from past data to get better at understanding future data.

The ability to learn and adapt is crucial for computers that need to make sense of the ever-changing data coming from moving video — and different combinations of neural networks could provide a solution. With convolutional neural networks, for example, computers model space in three dimensions to better predict the trajectory of objects within that space.

Deep neural networks can help cancel out background images so cameras can focus explicitly on moving objects. There are also recurrent neural networks that excel at pattern recognition. Each of these networks has strengths and weaknesses, but using them in the right combination makes moving-video analysis highly accurate in almost any setting.

Hackers could spy on you through your smart camera

Connectivity and the future of smart cameras

My company recently worked on a project that demonstrates how far connected devices like smart cameras have come, as well as the challenges they still face. For this particular project, a client in Israel asked us to develop a program to detect kicking motions in live soccer matches televised at 20 frames per second.

From the start, the project endured two obstacles: First, our team had to distinguish between an actual “kick” and a swinging leg motion that looked quite similar. Second, we needed to do that at 20 frames per second. That’s a higher resolution than most surveillance footage, and it’s packed with much more data to analyse.

Initially, we tried developing an algorithm that would create two “bounding boxes” around both a player’s foot and the soccer ball, and then register when those two boxes met. In practice, however, detecting kicks became extremely inaccurate when players were clumped together (and we know that happens a lot in soccer).

The solution? Tweak the deep learning element. We adjusted how the underlying neural networks were configured so we could accelerate object detection. Then, we created a data set using 500 frames taken from 20 seconds of a soccer match. Our team manually annotated this data to identify kicks and “non-kicks,” and we used it to “teach” our algorithms to make that distinction.

Our program eventually identified 58 per cent of real kicks; improving the numbers was possible through feeding the program data from more matches and different sets of players.

That’s because it proved that, with the right configurations, deep learning can make sense of all the complexities within the moving video within the connected device. While achieving these ends might take a ton of reference data, the technology has proved its usefulness and has finally made moving-video analysis a reality.

This kind of technology can be applied in many areas, from IoT surveillance systems to self-driving cars. And if one thing is certain, there’s not much further to go before moving-video analysis begins transforming our lives.

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Artificial Intelligence – a beginning

aiuniverse — Tue, 07 Aug 2018 05:49:33 +0000

Source – greaterkashmir.com

Artificial intelligence is a specialised branch of computer science, which constantly strives to build cognitive skills in machines, to make these intelligent enough to perform human-like tasks. It attempts to model and apply the intelligence of human mind with the simulation of intelligent behaviour in computers.

There are multiple versions for the definition of intelligence. Primarily, all human beings have a similar intellectual mechanism; the difference in level of intelligence amongst people arises from the prevailing conditions, which could be physical, biochemical, physiological, psychological or genetic in nature. Computers possess the advantage of uniform behaviour, coupled with vast amounts of memory and speed. This advantage can be harnessed for reliable and precise problem solving. However, the ability of computers to respond in an intelligent manner is sometimes limited to the understanding of the subject by the software developers who have plugged the subject matter expertise into their programs.

Artificial intelligence is about the capacity to learn and solve problems. Inculcating artificial intelligence in machines enhances their capacity for logic, understanding, learning, and problem solving. It enables machines to exhibit reasoning, knowledge, planning, learning, communication, perception, and the ability to move and manipulate objects.

The history of AI started with Turing test, which checked the ability of a computer to achieve human level performance in cognitive tests, sufficient to fool a human interrogator. Fundamentally, computers are better suited to carry out repeat instructions or computations in a very reliable and efficient manner. But, unlike humans, machines have trouble understanding specifics and adapting to new situations. AI moves computers from just performing monotonous tasks and computations to complex situation-based decision-making. Humans approach problem solving with an abstract thought, deliberate reasoning and pattern recognition. AI helps adopt these three behaviours to mimic human intelligence.

Today, AI is being applied in various fields, notably natural language processing, speech recognition, computer vision, image analysis, expert systems, robotics, machine learning and game theory. As an example, expert systems use a knowledge base of facts and Heuristics to assist experts for problem solving and decision making. Most of us are conversant with AI being used in smartphone personal assistants (Siri or Google Now), face recognition and GPS. AI is being extensively used for research on self-driven cars and medical diagnostics. We have started using AI applications in our daily chores pertaining to home security, temperature control, banking, digital cameras and customised news feeds.

AI has gained prominence recently due to big data, or the increase in speed, size and variety of data that businesses are now collecting. AI can perform tasks such as identifying patterns in the data more efficiently than humans, enabling businesses to gain more insight out of their data and enhance the capability of informed decision-making.

I would quote Prof Stephan Hawking, “In short, the rise of powerful AI will be either the best, or the worst thing, ever to happen to humanity. We do not yet know which.”

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DevOps and microservices driving container monitoring market growth

aiuniverse — Wed, 14 Mar 2018 05:30:36 +0000

Source – rcrwireless.com

Global container market to hit $706.2 million by 2022

The global container monitoring market is expected to swell from $169.6 million in 2017 to $706.2 million by 2022, at a compound annual growth rate (CAGR) of 33%, according to a report published by ResearchandMarkets.

Containers are a lightweight form of virtualization that has taken the telecom industry by storm.
According to the report, the demand for container monitoring is being drive by various factors, including increased use of microservices and the growth of DevOps.

Containers serve as a major component of microservices and DevOps. The former involves breaking down a monolithic application into a suite of modules designed for specific business functions, which can communicate with each other through a set of interfaces. The latter involves levelling barriers between software developers and operations team in order to accelerate time-to-market applications.

North America is anticipated to hold the largest container monitoring market size during the forecast period. The authors of the report said American organizations are more focused on providing new features using containerized applications. The authors added American companies have been making major investments in advanced technology in order to gain a competitive edge and improve business operations.

The European region, on the other hand, is expected to be the fastest growing region in the global container market during the forecast period. The authors of the report said this was on account of increased adoption of container monitoring solutions and services by small-and-medium sized enterprises (SMEs).

With respect to professional services, the report found the training and development segment is expected to grow at the highest rate during the forecast period. Per the report, “Container monitoring is used by developers and IT operators to monitor and manage containers to optimize the application performance. Although the container monitoring technology is rapidly growing and is simple to use, it is one of the new concepts among enterprises and needs professionals and experts to manage the solutions and services. Therefore, the training and development is expected to be the fastest growing segment in the container monitoring market.”

Among the major vendors the report found occupying the container monitoring market include: CA Technologies, AppDynamics, Splunk, Dynatrace, Datadog, BMC Software, Sysdig, SignalFx, Wavefront and CoScale. These vendors have helped boost their presence in the global container monitoring market through new product developments, partnerships and collaborations, mergers and acquisitions, and business expansions, according to the report.

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Google aims to make artificial intelligence easy to use for companies

aiuniverse — Fri, 19 Jan 2018 05:31:12 +0000

Source – livemint.com

For companies that are trying to figure how to take advantage of newer technologies like artificial intelligence (AI), but do not have adequate technical know-how or enough financial muscle to do so, Google Inc. believes its newly-launched automated tool, Cloud AutoML, might just provide an answer.

Machine Learning (ML) is a subset of AI, and Cloud AutoML, according to Google’s website, is a suite of ML products that enables software developers with limited expertise to train high-quality models by taking advantage of Google’s proprietary image recognition and other learning technologies.

“Currently, only a handful of businesses in the world have access to the talent and budgets needed to fully appreciate the advancements of ML and AI. And if you’re one of the companies that has access to ML/AI engineers, you still have to manage the time-intensive and complicated process of building your own custom ML model,” noted Fei-Fei Li, chief scientist, Cloud AI, and Jia Li, head of research and development (R&D), Cloud AI at Google in a joint blog post on 17 January.

Cloud Auto ML, according to Google, is aimed at helping businesses “with limited ML expertise” to custom-build quality ML models by using “advanced Google techniques like learning2learn and transfer learning”.

According to the blog post,learning2learn is the process of automating machine learning. Transfer learning, according to Lisa Torrey and Jude Shavlik of the University of Wisconsin, refers to the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned.

The process of taking AI and ML to a larger set of companies began in 2017, according to the above-cited blog post, when the company introduced the Google Cloud Machine Learning Engine to help developers easily build ML models that work “on any type of data, of any size”. They added that modern machine learning services, that is, APIs—including Vision, Speech, NLP, Translation and Dialogflow—could be built upon pre-trained models to quickly build business applications that can scale up for large use cases.

API is short for application programming interface, a common way for integrating different applications today, and NLP, which stands for natural language processing, is the ability of a computer program to interpret or understand human languages.

The two scientists also cited the examples of some businesses that are using or evaluating Cloud AutoML. While Mike White, the technology head of Disney Consumer Products and Interactive Media, said the company is using the technology to help it build vision models to “annotate our products with Disney characters, product categories and colours”, the team at Urban Outfitters—a US-based lifestyle retailer—has been evaluating Cloud AutoML to automate the product labelling process by recognizing “nuanced product characteristics like patterns and neckline styles”.

Google, of course, is not alone in pressing ahead with what many industry observers term as the ‘democratization’ of AI and ML. Other cloud computing providers such as Microsoft Corp. and Amazon Web Services Inc. (AWS) have also been active in promoting their own brand of AI and ML tools and services.

ML-as-a-service (MLaaS) is an umbrella definition of “automated and semi-automated cloud platforms” that covers most infrastructure issues such as data pre-processing, model training, and model evaluation, with further prediction, according to a 14 December post on Altexsoft.com. It noted that Amazon ML services, Azure ML, and Google Cloud AI are three leading cloud MLaaS services that allow for fast model training and implementation with “little to no” data science skill.

Usage of AI and ML by businesses around the world, as well as in India, has been discussed much in recent times with several estimates on AI’s potential economic impact. In December, for instance, Accenture LLP in a reportsaid AI has the potential to add $957 billion, or 15% of India’s current gross value in 2035. “The combination of the technology, data and talent that make intelligent systems possible has reached critical mass, driving extraordinary growth in AI investment,” it concluded.

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Recent trends in cloud computing fuel the need for DevOps methods

aiuniverse — Fri, 22 Sep 2017 07:59:05 +0000

Source – techtarget.com

Cloud services have transformed IT infrastructure, but the most recent trends in cloud computing signal a more fundamental shift that’s reshaping jobs. Newer cloud services and application design principals — such as microservices, serverless computing and function as a service — have important implications for both IT operations staff and developers.

However, understanding the difference between these services and how they affect application deployment can be confusing, especially since most cloud providers will simply tell you their service is best. Let’s review the characteristics that define each service and how they fit in with DevOps methods.

The rise of microservices

In 2011, the concept of microservice architecture was just beginning. By 2015, every developer was talking about it. Large companies were all in on microservices, touting the benefits of code reusability, mitigation of risk from upgrades and the speed at which teams could deploy new features. Microservices made it easy for developers to work in small teams while still contributing to a large-scale product capable of managing an outage to any single microservice.

There are three key ingredients to a microservice:

It is independently scalable and deployable.
Each service is responsible for the smallest possible task.
Services may work better together, but they will fail gracefully if one dies.

For example, Netflix employs several microservices in its overall product, including one for recommendations on videos to watch next. If that recommendation service goes down, the rest of the streaming platform continues on as if nothing happened.

Microservices helped lead to the launch of Docker, which allowed developers to further segregate their individual components via containerization. Docker helps developers deploy applications more quickly and in multiple parts — without having to worry about underlying hardware or even the OS.

The case for serverless computing

Among other recent trends in cloud computing is serverless, which stands on the premise that developers should not have to worry at all about underlying hardware. Google App Engine made serverless computing available before Amazon Web Services’ (AWS) Lambda made it popular. While Google App Engine was an amazing technology, it was too early for developers to give up control of the underlying hardware, and was not very well deployed.

There are three key elements that make something serverless:

There are no idle charges, meaning there is no cost for time that isn’t used.
There is no provisioning required. Infrastructure scales automatically.
You do not need to manage any OS, hardware or unrelated software.

Some providers may put in safeguards or limits to how much capacity you can use without manually requesting more. The point here is to ensure that — as scaling happens automatically — you don’t end up with an unexpectedly high bill.

Some, but not all, serverless computing environments are also function as a service (FaaS). For example, AWS Lambda and Auth0’s Webtasks are both serverless FaaS. AWS CodeBuild and Google App Engine are serverless, but not FaaS.

Go on demand with function as a service

Amazon introduced AWS Lambda in 2015. Lambda thrust users into serverless computing, and it also introduced the concept of function as a service. AWS Lambda is both serverless — no managed provisioning, idle charges or hardware to manage — and a FaaS.

There are three key factors that define FaaS:

It executes code on demand (no idle executions).
It scales automatically.
It runs one specific function without worrying about OS, hardware, etc.

With FaaS, users are able to run on-demand code blocks that are lightweight, as well as easily created and torn down. Functions running in this environment need to have minimal runtime — typically less than five minutes — and are often best suited for applications that respond directly to user interactions. For example, a developer could write code for a FaaS that serves up a dynamic website or checks a user’s permissions to a given API. FaaS is often used as middleware to apply business logic rules for user interactions with a database. It is also commonly used for webhooks or other event-based triggers.

FaaS does not imply serverless. For example, Docker Functions requires you to run servers (or VMs) running Docker; but it allows you to quickly trigger a single container with a bit of code. FaaS simply means the code is executed only in response to an event. It does not require that the underlying infrastructure remain idle while waiting for a customer’s code.

Where platform as a service fits

Platform as a service (PaaS) is an older concept. While similar to FaaS in that it does not require any manual provisioning, PaaS often involves some idle runtimes and isn’t truly considered microservices. PaaS providers include Google App Engine and Heroku. These providers typically offer a framework, such as Express.js, or a custom Python framework, such as Google App Engine, and automatically scale the infrastructure — adding servers — based on application need.

There are two key conditions that define PaaS:

It’s a single, end-to-end platform that builds an entire application.
It requires no provisioning, no hardware, no OS and no other software.

Many developers have switched away from PaaS offerings in favor of FaaS, as the latter offers a higher level of abstraction without as much vendor lock-in. Google’s Firebase, however, is a PaaS that’s becoming more popular. Google Firebase started off as a simple database as a service, but has since morphed into a broader platform offering lots of connected parts. Firebase is unique in that it’s a fully fledged PaaS that provides FaaS as one of its offerings.

The many uses of software as a service

The highest level of abstraction, away from any user-management requirements, is software as a service (SaaS). Database as a service is a type of SaaS that offers services that enable developers to build better applications without managing databases.

In order to be SaaS, the software needs to meet the following criteria:

run without any installation on your part;
require no coding to get started;
be accessible from anywhere that has internet connectivity; and
automatically scale to your needs.

There are many types of SaaS, including products such as Salesforce and Gmail. Developers and IT operations professionals use SaaS-based tools for application performance monitoring, databases and security.

It’s important to note that developers need more than SaaS to create an application, and SaaS offerings cannot be joined to make a new application. Anything that requires coding to connect things together is not SaaS. Typical serverless applications will use something like a FaaS to connect to multiple SaaS offerings to prevent having to run any servers at all, such as running FaaS on AWS Lambda and connecting to Amazon DynamoDB — which is a database SaaS.

The DevOps connection

All of the as-a-service offerings can be considered cloud services. Any service that doesn’t require local hardware or even software installations is a cloud offering. Cloud services generally remove a lot of requirements for operations workers, who can focus on cloud service management rather than hardware.

As cloud computing becomes more popular, organizations require operational IT staff to switch from just managing hardware to learning development in order to manage virtualized hardware. This is where the term DevOps started. Operations staff needed to learn additional skills to survive in the new cloud age. In some organizations, operations teams transformed into DevOps teams, which require operations professionals to learn some coding in order to keep up and stay relevant.

Recent trends in cloud computing, such as microservices, serverless computing and FaaS, however, have introduced a new fundamental shift. Now that we’re moving more applications to serverless platforms, it’s the developers’ turn to learn a little bit more about operations. We can’t rely on operations staff to manage cloud resources if the cloud providers automatically handle everything for us. This doesn’t mean that serverless functions will immediately scale indefinitely. It’s not like with traditional architecture where operations teams can just add additional virtual instances or request more capacity. It’s important to know where bottlenecks exist. Developers need to be aware of limitations before they develop services designed for web-scale architecture.

For example, DynamoDB is a service that developers can provision to nearly infinite scale. However, practical scale is limited by the hash key. Hash keys are hard to change after an application is built, so developers need to understand these limitations before starting development. Otherwise, they end up rewriting code later.

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How artificial intelligence, robotics could transform jobs in 10 years

aiuniverse — Tue, 05 Sep 2017 10:38:05 +0000

Source – azcentral.com

You might not be in your dream job. Most likely, you don’t make as much money as you would like.

But let’s face it: Today’s employment market has improved substantially over the past several years. The nation already is nearly back to what economists call full employment, with a U.S. jobless rate easing to near 4 percent.

But that favorable trend masks a lot of pain, dislocation and disruption for people in certain occupations, with more coming. Robotics, artificial intelligence and other pressures are almost certain to alter the employment prospects for millions of Americans in the coming years, for better or worse. Here are some of the ways jobs and employment could change over the next five or 10 years:

Your co-worker: A robot

Robotics and automation already have made huge inroads, especially in manufacturing. Get ready for more changes ahead. Nearly half of American jobs, 47 percent, are at risk of being automated over the next two decades, according to a 2013 study by Oxford University’s Department of Engineering.

The researchers examined more than 700 occupations, examining the tasks workers perform, the skills required and the engineering obstacles currently preventing computerization. Tasks less at risk are those requiring creative and social skills.

Jobs in transportation, logistics and office administration are at high risk for replacement. Driverless vehicles, including big trucks, already are on the highways. While robots mainly have been utilized so far in manufacturing, millions of service jobs could be next, according to the Oxford report. Automation in service industries could be more significant, given that the service sector has a lot more jobs than manufacturing and agriculture.

A recent Ball State University study listed a number of occupations at risk of being automated. Among them: telemarketers, insurance underwriters, tax-return preparers, watch repairers and people who type in data. By contrast, occupations with a low risk of displacement include recreational therapists, social workers, mechanic supervisors, health technicians and hearing-aid specialists.

Technological advances are a double-edged sword. They will wipe out some jobs but create others.

In retail, for example, automation has resulted in self-service cashier lanes. But the pending adoption of computerized reading glasses or goggles will give shoppers the ability to walk down grocery aisles and spot foods with certain traits such as those that are gluten-free or vegan, said John Challenger, CEO of outplacement-firm Challenger, Gray & Christmas. Other emerging products or apps will allow you to detect and identify desired products more easily with your smartphone.

“Some of these new technologies will ultimately create jobs,” he said. “Workers with experience using augmented or virtual reality will see the most opportunities, as will those who can help guide customers in this new experience or train fellow staff.”

Robotics revolution

Eventually, the adoption of robots and automation will become national trends. But so far, especially for robots, the impact has been concentrated. The Brookings Institution recently mapped the prevalence of industrial robots and noted a heavy cluster in Midwestern states and those in the Upper South where the auto industry is focused.

More than half the nation’s 233,300 industrial robots are “burning welds, painting cars, assembling products, handling materials or packaging things in just 10 Midwestern and Southern states,” the report said. Michigan alone has 12 percent of the nation’s industrial robots, compared to 13 percent for all Western states combined. Ohio, Indiana and Tennessee also use robots extensively.

The increased use of industrial robots will eliminate some jobs, including dangerous, repetitive and physically demanding ones, but it could create new ones. In addition to engineers who will be needed to design these machines and technicians to maintain and program them, others eventually will work side by side with robots, said Heni Ben Amor, an assistant engineering professor at Arizona State University.

“In the past, there was a human/robot physical barrier because robots can be dangerous if you get hit by one,” he said. “The new trend will bring the two worlds together.”

For example, he said humans could do work requiring physical dexterity, such as attaching small screws, while robots do heavy lifting or more repetitive tasks.

Lingering unease

Ben Amor said he’s excited about the potential for job gains with advances in robotics. “It’s going to create way more jobs than the number lost,” he predicted.

Ben Amor considers driverless vehicles to fall under the banner of robotics, as both involve machines or systems perceiving changes in the environment and taking actions in response. He believes Arizona could have a bright future in the development of driverless cars and trucks, given that Uber, Alphabet, General Motors and Intel all have tested such vehicles on public roads around the Valley.

The unusual concentration of tests here has attracted the attention of a lot of smart students and young entrepreneurs who want to work for those companies or start their own, he said.

Still, the adoption of robots and artificial intelligence often causes public anxiety over job losses. This anxiety spills over into politics. “It is telling that the robot incidence in red states that voted for President Trump in November is more than twice that in the blue states that voted for Hillary Clinton,” Brookings noted in its report.

While Arizona has fewer robots and thus less robot-induced anxiety, the state lags in other respects affecting jobs, prosperity and employment.

For example, Arizona has a higher proportion of low-wage jobs, 27.8 percent, than the 24.2 percent national average, according to a report by Prosperity Now. The average pay of $49,700 in Arizona runs about $3,200 below the U.S. average, and a slightly smaller percentage of local employers offer health insurance to their workers.

Jobs remain, but education needed

The pressures from robots, artificial intelligence and global outsourcing are serious, but the outlook isn’t entirely bleak. The nation’s economy is robust, resilient and innovative. New jobs will be created in new industries, as has regularly occurred in the past.

A recent study by the Georgetown Center on Education and the Workforce estimates that there are still 30 million “good jobs” out there for workers who lack college degrees. Such jobs offer median or midpoint annual pay of $55,000 (and a minimum of $35,000). Many are found in health care, finance and information technology.

Such positions have steadily replaced formerly good jobs in traditional blue-collar industries. For example, 25 years ago a machinist making $44,000 exemplified a good manufacturing job. Today, that description applies to a computer-support technician earning $60,000 a year. Other examples of good jobs cited in the report include financial managers, sales representatives and engineering technicians.

Still, the study noted that the educational requirements for good jobs are rising. While college degrees aren’t required, some higher education usually is. For workers with no more than a high school diploma, the number of good jobs has dropped by more than 1 million since 1991. By contrast, the number of good jobs for workers with an associate’s degree has climbed by 3 million over that span.

“To compete effectively, workers need some level of post-secondary education and training,” the report said. “In addition, a variety of non-degree credentials are sometimes necessary to get those jobs, or to advance in them.”

Reviving trade jobs

Higher education is important, but many well-paying jobs don’t require time spent in college classrooms. An estimated 10,000 or more unfilled jobs are in Arizona’s construction trades — plumbers, electricians, dry-wall specialists, carpenters and others.

The Brewer Companies, a large Phoenix plumbing company that includes Benjamin Franklin Plumbing on the retail side, is having such a hard time attracting workers that it has slowed its growth so that customer service and quality don’t suffer. Brewer, which is looking for candidates to fill 35 open positions, could have grown at least 15 percent this year, said the company’s CEO, Mike Brewer.

Brewer offers paid apprenticeships for people wanting to become plumbers. Prior experience isn’t needed, but applicants must be responsible and eager to work. “Will these people show up on time and work all day?” he asked. “It’s not rocket science.”

Promising, doomed occupations

Factors other than robotics, foreign competition and education affect jobs. So do industry strengths or weaknesses, customer demand and more.

Kiplinger.com recently sorted through 785 occupations to glean what it considers the 10 best and worst, based on current average pay and future growth prospects.

The 10 best are focused on technology and health care. In the tech field, promising positions include app developers and computer-systems analysts, while the health sector offers bright outlooks for nurse practitioners, physical therapists, health-services managers, physician assistants, dental hygienists and speech-language pathologists. Rounding out Kiplinger’s top 10 are market-research analysts and financial advisers.

The worst occupations are more varied but include many manufacturing positions such as textile-machine workers, photo processors, furniture finishers, metal/plastic machine operators and print binding/finishing workers. Robotics along with general technical obsolescence are dooming some of these jobs.

Other positions with poor prospects, partly because they are highly competitive or offer low pay, include radio/TV announcers, legislators, floral designers, gaming cashiers and door-to-door salespeople, according to Kiplinger.

STEM jobs — those in science, technology, engineering or math — enjoy especially good prospects. “The jobs of the future, no matter the industry or level, are no doubt going to involve at least a rudimentary knowledge of technology,” said Challenger. People who enter a STEM profession will have a leg up on the competition, he added.

Top STEM-focused jobs cited by his company for 2017 include computer-system analysts, statisticians, software developers, mathematicians and financial advisers. Median salaries in each of those fields already top $80,000, with unemployment rates below 2.5 percent.

Part-time work: An expanding option?

The assumption is that most people want full-time jobs with a range of benefits, but that’s not necessarily so. In fact, more than one in six U.S. workers currently labor part-time, and many of these people do so from home.

A part-time, remote job can be ideal for working parents, semi-retirees, individuals with health issues, military spouses and career changers, said Sara Sutton Fell, CEO of FlexJobs, a job-search website.

Companies of all sizes and across a range of industries hire part-time, remote workers, she noted, citing nurses, accountants and digital-marketing strategists as examples. So too for tutors, writers and editors, computer coders, interpreters and customer-service representatives.

With so many baby boomers in good health, part-time jobs remain an attractive option for young retirees — a way to remain socially engaged while generating extra income. In fact, 79 percent of workers polled recently by the Employee Benefit Research Institute said they plan to work for pay in retirement. However, just 29 percent of retirees, in the same poll, said they actually work or have worked for pay.

This survey has consistently found a wide gap between the expectations of current workers to stay employed and the proportion of retirees who are. But with looming job shortages in some occupations and increasing employer flexibility, part-time work for retirees might be more feasible in coming years.

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