Day: May 26, 2019

Job Boards Pitfalls for Privacy

Post author By Craig Hufford
Post date May 26, 2019

I receive dozens of calls and emails every week on the subject of whether posting your resume is safe or should send that job boards pitfalls and privacy issues in red flashing lights. Here are a few job search tips to help you choose for a career search. Do NOT post your name, email address, resume, phone number, current and previous employer, and education information for all to view on a job board.With a labor participation rate at the lowest since the mid-1970s, there are millions of “wishers” (the un/under educated, inexperienced, under/over qualified), that make up a large chunk of the resumes on job boards. You simply get lost in the shuffle.

Job Boards Pitfalls – Overexposing your Resume

There are three types of job boards. First is the “major” such as Monster, CareerBuilder, The Ladders, etc. Second are the niche such as oilandgaspeople who claims to have 5,843 Active Recruiters 163,926 candidates or MedRepCareers which focuses on medical services, medical devices, and pharmaceutical sales jobs. Third are the job aggregators such as Indeed.com and SimplyHired.com. If you choose to use the former’ let this be a warning : job boards pitfalls and privacy issues means you will likely to receive loads of emails about jobs related to “insurance and financial sales or analysts”, car sales, and “temporary full time jobs” offered by RPOs (recruitment processing outsourcing firms) or IT engineering services companies.

Job Boards Pitfalls of Suspect Privacy Issues

Never show WHO your current employer is; instead use “Fortune 500 Widget Manufacturer” or Mid-Cap Widget Vendor”. Mention type of degree – not the university.NEVER post your resume/CV to a job board. Avoid the job boards pitfalls by NOT posting your resume/CV itself as it is the worst one-size-fits-all presentation tool ever imagined. Instead learn to create a ‘confidential profile”. Do this in MS Word, but post it in PDF. Here is what a careers focused profile should look like:

Executive Summary – 200 word max overview of your experience and the top 2-3 accomplishments plus your career objective.
Education, Amount of Travel willing to do (%), and Work Authorization Status
Work Experience (always start with current and work backwards): type of company, your title, # years, and any promotions or special recognition
Product or Service Lifecycle experience and your accomplishments
Project or Leadership Roles (w/ team size/budget/sales volume/IP developed/problem fixed, etc..
Your depth of Relationships. You do NOT need names; rather titles, last date connected, if internal or external customer/vendor, and if sales please include quota vs. actual numbers AND the sales cycles.
Relocation Considerations
This last one is OPTIONAL. Desired Compensation – what you DESIRE to have in base and bonus and/or commission – NEVER mention what you are making now.

Now on the other side of job boards pitfalls in your careers search, we look at the executive recruiter side. Here’s the tricky part – everyone thinks they are an “A Player” – reality check is less than 14% of the entire workforce is that. Next in line are the “B Players”, who compromise 30% to 35% of the workforce BUT in fact produce 8 to 10 times LESS than “A Players”.

For the 55% or more of any workforce they are “C players”. They can/will be replaced by automation or better people when production in the role becomes vital to the organization.If you speak with a retained search firm where they do not have a current search ideal for you, make sure you reconnect with them regularly every few months to get updated on potential careers and opportunities.

Choose to Post on Job Boards or Work with a Recruiter?

When choosing whether to use job boards in your career search or respond to an executive recruiter’s posting, another good reason for choosing the latter is that if your public resume is public or you have “I am seeking a job” or “I am looking for a career opportunity” in the title of your social profile, a good executive recruiter likely won’t touch you with a 10 foot pole. If you have your resume posted on several job boards, most recruiters will not be interested, but be forewarned, many corporate HR departments utilize RPOs (recruiting process organizations) usually based in some 3rd world country who will search and find your resume sending it to literally hundreds of companies. Talk about overexposure – the read is “what;s wrong about this person?”.

If in the end of the job boards pitfalls vs executive recruiters choice, if you choose an executive recruiter, find a good one and then network using social media and offline events to develop a relationship with him/her to enhance careers search.

Tags Interview Tips

Articles Artificial Intelligence Internet of Things Wireless Ecosystems

Smart Objects: Blending Ai into the Internet of Things

Post author By Charles Moore
Post date May 26, 2019

It’s been more than a decade since the time when the number of internet-connected devices exceeded the number of people on the planet. This milestone signaled the emergence and rise of the Internet of Things (IoT) paradigm, smart objects, which empowered a whole new range of applications that leverage data and services from the billions of connected devices. Nowadays IoT applications are disrupting entire sectors in both consumer and industrial settings, including manufacturing, energy, healthcare, transport, public infrastructures and smart cities.

Evolution of IoT Deployments

During this past decade IoT applications have evolved in terms of size, scale and sophistication. Early IoT deployments involved the deployment of tens or hundreds of sensors, wireless sensor networks and RFID (Radio Frequency Identification) systems in small to medium scale deployments within an organization. Moreover, they were mostly focused on data collection and processing with quite limited intelligence. Typical examples include early building management systems that used sensors to optimize resource usage, as well as traceability applications in RFID-enabled supply chains.

Over the years, these deployments have given their place to scalable and more dynamic IoT systems involving many thousands of IoT devices of different types known as smart objects. One of the main characteristic of state-of-the-art systems is their integration with cloud computing infrastructures, which allows IoT applications to take advantage of the capacity and quality of service of the cloud. Furthermore, state of the art systems tends to be more intelligent, as they can automatically identify and learn the status of their surrounding environment to adapt their behavior accordingly. For example, modern smart building applications are able to automatically learn and anticipate resource usage patterns, which makes them more efficient than conventional building management systems.

Overall, we can distinguish the following two phases of IoT development:

Phase 1 (2005-2010) – Monolithic IoT systems: This phase entailed the development and deployment of systems with limited scalability, which used some sort of IoT middleware (e.g., TinyOS, MQTT) to coordinate some tens or hundreds of sensors and IoT devices.
Phase 2 (2011-2016) – Cloud-based IoT systems: This period is characterized by the integration and convergence between IoT and cloud computing, which enabled the delivery of IoT applications based on utility-based models such as Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). During this phase major IT vendors such as Amazon, Microsoft and IBM have established their own IoT platforms and ecosystems based on their legacy cloud computing infrastructures. The latter have alleviated the scalability limitations of earlier IoT deployments, which provided opportunities for cost-effective deployments. At the same time the wave of Big Data technologies have opened new horizons in the ability of IoT applications to implement data-driven intelligence functionalities.

AI: The Dawn of Smart Objects using IoT applications

Despite their scalability and intelligence, most IoT deployments tend to be passive with only limited interactions with the physical world. This is a serious set-back to realizing the multi-trillion value potential of IoT in the next decade, as a great deal of IoT’s business value is expected to stem from real-time actuation and control functionalities that will intelligently change the status of the physical world.

In order to enable these functionalities we are recently witnessing the rise and proliferation of IoT applications that take advantage of Artificial Intelligence and Smart Objects. Smart objects are characterized by their ability to execute application logic in a semi-autonomous fashion that is decoupled from the centralized cloud.

In this way, they are able to reason over their surrounding environments and take optimal decisions that are not necessarily subject to central control. Therefore, smart objects can act without the need of being always connected to the cloud. However, they can conveniently connect to the cloud when needed, in order to exchange information with other passive objects, including information about their state / status of the surrounding environment.

Prominent examples of smart objects follow:

Socially assistive robots, which provide coaching or assistance to special user groups such as elderly with motor problems and children with disabilities.
Industrial robots, which complete laborious tasks (e.g., picking and packing) in warehouses, manufacturing shop floors and energy plants.
Smart machines, which predict and anticipate their own failure modes, while at the same time scheduling autonomously relevant maintenance and repair actions (e.g., ordering of spare parts, scheduling technicians visits).
Connected vehicles, which collect and exchange information about their driving context with other vehicles, pedestrians and the road infrastructure, as a means of optimizing routes and increasing safety.
Self-driving cars, which will drive autonomously with superior efficiency and safety, without any human intervention.
Smart pumps, which operate autonomously in order to identify and prevent leakages in the water management infrastructure.

The integration of smart objects within conventional IoT/cloud systems signals a new era for IoT applications, which will be endowed with a host of functionalities that are hardly possible nowadays. AI is one of the main drivers of this new IoT deployment paradigm, as it provides the means for understanding and reasoning over the context of smart objects. While AI functionalities have been around for decades with various forms (e.g., expert systems and fuzzy logic systems), AI systems have not been suitable for supporting smart objects that could act autonomously in open and dynamic environments such as industrial plants and transportation infrastructures.

This is bound to change because of recent advances in AI based on the use of deep learning that employs advanced neural networks and provides human-like reasoning functionalities. During the last couple of years we have witnessed the first tangible demonstrations of such AI capabilities applied in real-life problems. For example, last year, Google’s Alpha AI engine managed to win a Chinese grand-master in the Go game. This signaled a major milestone in AI, as human-like reasoning was used instead of an exhaustive analysis of all possible moves, as was the norm in earlier AI systems in similar settings (e.g., IBM’s Deep Blue computer that beat chess world champion Garry Kasparov back in 1997).

Implications of AI and IoT Convergence for Smart Objects

This convergence of IoT and AI signals a paradigm shift in the way IoT applications are developed, deployed and operated. The main implications of this convergence are:

Changes in IoT architectures: Smart objects operate autonomously and are not subject to the control of a centralized cloud. This requires revisions to the conventional cloud architectures, which should become able to connect to smart objects in an ad hoc fashion towards exchanging state and knowledge about their status and the status of the physical environment.
Expanded use of Edge Computing: Edge computing is already deployed as a means of enabling operations very close to the field, such as fast data processing and real-time control. Smart objects are also likely to connect to the very edge of an IoT deployment, which will lead to an expanded use of the edge computing paradigm.
Killer Applications: AI will enable a whole range of new IoT applications, including some “killer” applications like autonomous driving and predictive maintenance of machines. It will also revolutionize and disrupt existing IoT applications. As a prominent example, the introduction of smart appliances (e.g., washing machines that maintain themselves and order their detergent) in residential environments holds the promise to disrupt the smart home market.
Security and Privacy Challenges: Smart objects increase the volatility, dynamism and complexity of IoT environments, which will lead to new cyber-security challenges. Furthermore, they will enable new ways for compromising citizens’ privacy. Therefore, new ideas for safeguarding security and privacy in this emerging landscape will be needed.
New Standards and Regulations: A new regulatory environment will be needed, given that smart objects might be able to change the status of the physical environment leading to potential damage, losses and liabilities that do not exist nowadays. Likewise, new standards in areas such as safety, security and interoperability will be required.
Market Opportunities: AI and smart objects will offer unprecedented opportunities for new innovative applications and revenue streams. These will not be limited to giant vendors and service providers, but will extend to innovators and SMBs (Small Medium Businesses).

Future Outlook

AI is the cornerstone of next generation IoT applications, which will exhibit autonomous behavior and will be subject to decentralized control. These applications will be driven by advances in deep learning and neural networks, which will endow IoT systems with capabilities far beyond conventional data mining and IoT analytics. These trends will be propelled by several other technological advances, including Cyber-Physical Systems (CPS) and blockchain technologies. CPS systems represent a major class of smart objects, which will be increasingly used in industrial environments.

They are the foundation of the fourth industrial revolution through bridging physical processes with digital systems that control and manage industrial processes. Currently CPS systems feature limited intelligence, which is to be enhanced based on the advent and evolution of deep learning. On the other hand, blockchain technology (inspired by the popular Bitcoin cryptocurrency) can provide the means for managing interactions between smart objects, IoT platforms and other IT systems at scale. Blockchains can enable the establishment, auditing and execution of smart contracts between objects and IoT platforms, as a means of controlling the semi-autonomous behavior of the smart object.

This will be a preferred approach to managing smart objects, given that the latter belong to different administrative entities and should be able to interact directly in a scalable fashion, without a need to authenticating themselves against a trusted entity such as a centralized cloud platform.

In terms of possible applications the sky is the limit. AI will enable innovative IoT applications that will boost automation and productivity, while eliminating error prone processes. Are you getting ready for the era of AI in IoT?

Tags Machine - Deep Learning

Articles Internet of Things

Industrial IoT Predictive Maintenance – a Killer Application

Post author By Charles Moore
Post date May 26, 2019

Industrial IoT predictive maintenance is expected to generate the large scope of B2B transactions that require data analysis. Indeed, IIoT is on such a growth pattern many of the billions of connected things in the coming years will be industrial assets, which will be deployed in settings like factories, agricultural, oil refineries and energy plants.

According to McKinsey the Industrial Internet has the potential to deliver up to $11.1 trillion on an annual basis by 2025 and 70% of this is likely to concern industrial and business-to-business solutions i.e. the Industrial IoT is expected to be worth more than twice the value of the consumer internet.

The Industrial IoT is at the heart of the fourth industrial revolution (Industry 4.0), which is driven by the interconnection of all industrial assets and the ability to collect and analyze data from them. In the scope of the Industrial IoT, assets are cyber-physical systems, which enable the control of physical devices through their cyber representations and the processing of digital data about them.

The applications of cyber-physical systems span a very broad range, including production control, process optimization, asset management, integration of new technologies (such as 3D printing & additive manufacturing), as well as various industrial automation tasks. Nevertheless, the most prominent application is the ability to continually monitor, predict and anticipate the status of assets, with emphasis on industrial IoT predictive maintenance using predictions about when a piece of equipment should be maintained or repaired.

Industrial IoT Predictive Maintenance Key to Industry 4.0

Maintenance and Repair Operations (MROs) are at the heart of industrial operations, as they involve repairing mechanical, electrical, plumbing, or other devices as a means of ensuring the continuity of operations. Nowadays, the majority of MRO operations are carried out on the basis of a preventive maintenance paradigm, which aims at replacing components, parts or other pieces of equipment, prior to their damage that could catastrophic consequences such as low production quality and cease of operations for a considerable amount of time. However, in most cases preventive maintenance fails to lead to the best usage of equipment (i.e. optimal Operating Equipment Efficiency (OEE)), as it maintenance is typically scheduled earlier than actually required.

In industrial IoT predictive maintenance (PdM) alleviates the limitations of preventive approaches. PdM is based on predictions about the future state of assets, with particular emphasis on anticipating the time when an asset will fail in order to appropriately schedule its maintenance.

PdM is empowered by models that estimate when the cost of maintenance becomes (statistically) lower that the cost that is associated with the risk of equipment failure.

Based on an optimal scheduling of maintenance, PdM leads to improved OEE, enhanced employee productivity, increased production quality, reduced equipment downtime, as well as a safer environment where failures are anticipated and repairs proactively planned. McKinsey & Co. estimates that the economic savings of predictive maintenance could total from $240 to $630 billion in 2025.

Nevertheless, there are still many industries that dispose with preventive maintenance, since they have no easy way to integrate and analyze data sets from thousands of heterogeneous sensors that are typically available in their plant floors. As a result only a fraction (i.e. 1% according to McKinsey & Co) of the available data is used, which is a serious setback to unlocking the potential of predictive maintenance applications, such as maintenance as a service, on-line calculation of OEE risk, maintenance driven production schedules and more.

The advent of Industrial IoT predictive maintenance is gradually unlocking the potential of PdM technologies facilitate the collection and integration of data from thousands of different sensors, while at the same time providing the means for unifying the semantics of the diverse data sets. Furthermore, IoT analytics technologies (notably predictive analytics) facilitate the processing of IoT data streams with very high ingestion rates based on machine learning and statistical processing techniques that can predict the future condition of components and equipment.

In several cases, IoT data are processed by Artificial Intelligence based techniques such as deep learning, in order to identify hidden patterns about the degradation of assets. Deep learning techniques are capable of leveraging (multimedia) data from multiple maintenance modalities such as vibration sensing, oil analysis, thermal imaging, acoustic sensors and more. Moreover, advanced deployments of industrial IoT predictive maintenance are not limited to deriving predictions about the future state of assets. Rather, they are able to close the loop down to the plant floor, through for example changing configurations in production schedules, altering the operational rates of machines or even driving automation functions.

Rise of Industrial IoT Predictive Maintenance Products and Services

PdM is looming as one of the killer applications for the Industrial IoT, which is evident not only on its potential savings but also on the rise of relevant IoT-based products and services. Most vendors have been recently releasing IoT-based solutions for PdM. In addition to empowering data collection and analytics, vendors are striving to enhance their products with added-value functionalities that help them stand out in the market. For example:

IBM predictive maintenance solution is able to perform root cause analysis in a holistic way, including predictions about where, when and why asset failures occur.
Software AG’s solution for industrial IoT predictive maintenance integrates with ERP and human resources systems to automatically plan the optimal allocation of tasks to technicians.
SAP integrates predictive maintenance information with business information (e.g., CRM and ERP systems) and enterprise asset management (EAM) systems. To this end, it benefits from its strong presence and installed base in the ERP market.
Microsoft offers PdM solutions over its Azure IoT suite in a way that offers preconfigured solutions (templates) for monitoring assets and analyzing their usage in real-time.

Recently, the DataRPM platform has been also established by a consortium of different vendors and manufacturers. DataPRM claims ability to deliver Cognitive Predictive Maintenance (CPdM) for Industrial IoT, based on the use of Artificial Intelligence for automating predictions of asset failures and closing the loop to ERP, CRM, and other business information systems.

Other major players in industrial engineering and automation, such as SIEMENS and BOSCH are offering their own platforms, while all major IT consulting enterprises have relevant services in their portfolio. Nevertheless, it is indicative of the market momentum of PdM and its positioning as one of the most prominent applications in the growing Industrial IoT predictive maintenance market.

Tags Industrial IoT

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