The Future of Industry: Making the Jump to Advanced Manufacturing

With technology rocketing forward, many industries are pulled into advancement, and manufacturing has benefited from the progression of machines and tools. Advanced manufacturing offers a wide array of job opportunities for competent mechanical engineers. Here are a handful of professions that have expanded thanks to advanced manufacturing techniques.

Keeping People Healthy: Medical Device Engineering and Manufacturing

Healthcare is and will be an important field for the foreseeable future, and this means that the design and manufacture of medical devices is more important than ever. There are opportunities for regulators and inspectors as well as engineers and designers. New devices must be tested rigorously to make sure they meet standards and specifications, so the job opportunities in this field are manifold.

Keeping the World Healthy: Sustainability Manufacturing

The exponential increases in consumption over the last few decades have caused our output of waste to increase exponentially as well, and this means that solutions to waste management and recycling are needed as soon as engineers can think of them. The opportunities for engineers and problem-solvers in the realm of sustainability manufacturing are numerous and exciting, given the literal world-saving impact of sustainability engineers.

Making Machines More Perceptive: Control Sensors Design and Development

As self-driving cars and autonomous cleaning machines become more advanced and more prevalent in our society, the need for control sensors grows. These are technical devices that allow machines to sense their surroundings, and with advanced machine learning algorithms, respond to those surroundings in a proactive and safe way. Getting the algorithms and sensors just right is a challenging task and one that is sure to test and excite engineers looking for a puzzle to solve and people to protect.

Constructing Advanced Building Crews: Welding Engineers and Lasers

Even construction techniques are moving forward thanks to advanced manufacturing, and new laser technology and electron beam capabilities are pushing the boundaries of a welder’s capabilities. The work is technical and involves serious number-crunching, but competent engineers can handle these calculations and make precision welding a possibility. The technology has moved forward, and skilled engineers have opportunities to move forward along with the technology.

Getting Shipments Where They Need to Go: Supply Chain Management

Clever engineers can design solutions to any sort of problem and managing an efficient supply chain is a challenge in and of itself. A good engineer with knowledge of supply chains and shipment schedules can design algorithms and programs that can help warehouses and shipping managers schedule pickups and drop-offs in order to streamline their supply chains. These techniques and programs can also help stores and offices maintain their supply lines, so they have what they need to do their jobs to the utmost efficiency.

Many industries are moving forward into the advanced manufacturing business, and technological advancements necessitate talented engineers to understand and utilize effectively. The fields mentioned above are using advanced manufacturing to improve their business models, and mechanical engineers are crucial to making these advancements happen smoothly.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

How to Give Great Customer Experiences Through Great Engineering

Even in the technology field, success depends upon delivering satisfaction to customers with a high rate of success. While some engineering jobs require nothing beyond the end result, the best engineering firms deliver end results that also please customers and ensure repeat business. These tried-and-true methods of guaranteeing customer satisfaction work well in any service-oriented field and will work well in the field of technology. Your teams of engineers only need to use these tips to bring your customers closer to complete happiness.

Product Knowledge from the Inside Out

The best way to answer a customer’s questions and satisfy their desires is to demonstrate your knowledge of what they want, and then educate them about that desire. Engineers are in a unique position when it comes to customer service thanks to their hands-on experience; as the testers and creators of the products they deliver to customers, engineers understand their end results and processes better than anyone. This means that engineers alone can deliver the product knowledge that customers desire, while also delivering a finely crafted product. Customer satisfaction may be delivered on all fronts thanks to the impeccable knowledge and craftsmanship of your engineers.

Making Technology Comfortable for Everyone

Thanks to the extensive and intensive knowledge your engineers possess, it is easy for them to fine-tune their tech in ways that make it more accessible to the public. This ultimately makes your products easy to use. When the engineers devote themselves to refining the product’s ease of use and when they create end results that are easier to use, the satisfaction of your customers rises and your enterprise may be called a success. By making products that are easy to use and desirable to your customers, your engineers enhance your public image and attract more business.

Communication is Crucial to Bringing Out Your Team’s Best

Of course, in order to make customers happy, engineers must first understand exactly what it is their customers want, then find a way to deliver it to the customer’s satisfaction. This means that the knowledge demonstrated and applied by your engineers must actually be twofold: the knowledge to listen to and understand a customer’s request, and the knowledge to make that request a reality. In other words, superb communication skills will enable engineers to deliver high-end results with the utmost care and craft. The ability to converse in-depth with customers about what they want and then to work in concert with their fellow engineers will ensure your engineers satisfy customers as often as possible.

A Satisfying Solution, No Matter the Problem

No matter what sort of product or service your customers request, your engineers should be able to deliver it to the best of their abilities and with a smile. If customers experience problems with machines or performance, your team’s excellent knowledge will smooth out those problems and fix whatever is going wrong, much to the customers’ satisfaction. That satisfaction will keep customers coming back to your company.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

The Big Scoop: Handling Big Data

Data weaves its way in and out of our lives seemingly effortlessly these days, with our phones, tablets, and computers becoming the forefront of an information mega-wave. With all the data available to us at high speeds, the big question lurks in the background: how is all this data moving around so quickly and freely? The answer is multifaceted, and it has to do with a few different types of data and the ways they are handled.

Different Data, Different Processes

The most nebulous and difficult type of big data to deal with is called unstructured data, and it is comprised of all the data attached to social media, sensor systems, and log files. In many ways, this data type is the newest to emerge in recent years, and it requires newer techniques to control. These new techniques include unstructured text analysis, news analytics, and natural language processing, and the engineers looking to ride the exciting wave of the future would do well to learn and understand these new techniques if they wish to handle unstructured data efficiently.

Next comes data that is somewhat structured – or semi-structured, as it is known in the tech industry. Data from emails, call centers, and the burgeoning Internet of Things all count as semi-structured data; the processes used to parse this data are a mix of the new techniques mentioned above and traditional databases, such as metal servers and real-world storage.

Finally, there is streaming data, which is considered to be the cutting edge of big data due to its always-live, always-moving nature. The enhanced connectivity of the Internet of Things helps increase streaming speeds, but there is still a lot of work to be done to master the art of handling streaming data as fast as possible. This means that this field will be an exciting new place for an engineer to work.

Storage and Computing Should Go Hand in Hand

One of the biggest obstacles to data travel speed is the time it takes to send data freely from its source to other devices. So far, the best way to reduce data travel time is to place the origin of the data as close as possible to the devices that will ultimately parse the data in order to transmit it as quickly as possible. There is less latency this way, and when a cluster of computers and devices is located on-site, a close-knit network of machines would be the best way to ensure information travels quickly and freely throughout your workplace.

A Thorough Data Network is the Ideal Data Network

While an efficient network of machines transmitting data from one to the other sounds fantastic, it is often difficult to achieve. This is unfortunate because the best way to get the data moving is to fashion a systemic solution that goes from one end to the other as seamlessly as possible. Such a complete solution frees up kinks in the system and smooths out data transfer processes, but it requires teams that are competent in their areas of expertise and are adept at communication to keep the network open for mass data travel. Engineers often become the crux of these processes thanks to their knowledge, so the future for engineers is bright.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

Keys to a More Advanced Future: Machine Learning and Deep Learning

Ask anyone who knows a bit about technology, and you’ll more than likely hear them say a few things about machine learning and/or deep learning. These two concepts are catapulting artificial intelligence into strata never seen before, and sometimes it seems as though they are the same thing – but look closely and it becomes clear that they are two different beasts, and both must be tamed before artificial intelligence achieves its next breakthrough.

Machine Learning at a Glance

At its simplest, it is possible to describe machine learning as a process by which machines use algorithms to take in data, learn from that data, and use what they’ve learned to make more nuanced, informed decisions. This involves a great deal of data and usually brings machine and social networking into the picture. For example, music streaming services are capable of looking at what you listen to, finding other users who listen to the same music, and suggesting more music for you based upon the preferences of users similar to yourself.

This process is, at its core, still a process that is programmed. The machines are simply performing a programmed task, learning while doing that task, and improving their performance based on what they learn. These processes may become more nuanced when they advance to deep learning.

The Details of Deep Learning

To boil down the difference between machine learning and deep learning, it may be easiest to say that machine learning still relies on guidance from engineers and programmers. Machines follow routines and functions set forth by engineers and learn from these preset parameters. Deep learning goes, well, deeper than that.

A deep learning model does the same essential thing that a machine learning model does, but the difference is that a deep learning model is capable of making decisions for itself eventually. Given the proper data and ample processing power to analyze that data, a deep learning model could make better and more accurate predictions, thus teaching itself to parse data and make decisions without human guidance. It sounds a little crazy, but that’s because our own brains are crazy powerful, and deep learning models are based upon our brains.

The Importance of ANN

The network that is set up to power deep learning models is called an artificial neural network, or ANN. These artificial neural networks are made to mimic our own neural networks as best they can, and this means that artificial intelligence has the capability of making refined conclusions and to think more like we do. Even humans err, however, so ensuring that a deep learning algorithm doesn’t make incorrect conclusions is the next step in the evolution of artificial intelligence.

Once we figure out how to minimize the chances that a machine draws incorrect conclusions from its data, we can reach the next level of artificial intelligence. Machine learning is the foundation, and deep learning builds upon it. As people and machines take in more data, we only become smarter and more capable.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

How to Advance Your Company’s Machine Learning Prowess for Future Success

As we advance our machine and technological knowledge, our machines and technology advance their own capabilities. It initially sounds like a science-fiction horror scenario, but it is actually quite helpful; when our machines have the capacity to learn and improve, they simply get better at solving problems and processing the data we wish to understand. This means we are advancing our ways to work with machines, and tech fields will rapidly increase their collective knowledge as the machines and people master new processes and procedures. This also means that tech managers have a few big components to consider when approaching machine learning endeavors.

Keeping and Accessing Big Data

Thanks to a steadily decreasing cost in data storage over the past several years, having the means to store and process larger and larger amounts of data has become less expensive and more accessible to companies of any size. Solid-state storage makes big server centers possible and placing these close to your company’s devices significantly decreases the amount of time it takes to transmit the important data you need for all your projects. RAM has also become relatively inexpensive, so building up an effective data storage-and-transmission network should be one of your priorities in this new age of machine learning.

Processing All That Data at Super-High Speeds

With so much data being stored and transmitted among your company’s premises, your managers also need a way to process all of the information zipping through your machines’ cables and clouds. Thankfully, advances in GPUs over the last decade have increased the rate at which data may be parsed and processed, so your team’s computational powers can only grow and improve from here. There is also a new technology called Brainwave that promises to endow machine learning devices with even more processing power than even GPUs can afford. The technology for processing data is constantly improving, so your team’s machine learning capabilities can only increase as you come to grips with new and better technology and techniques.

Sensational Software to Use and Apply Your Data to Learning

With all those high-powered machines storing, transmitting, and processing reams of data, there remains one more thing to do to facilitate a successful machine learning endeavor: get your machines to learn something. This is where software comes in. New programs for implementing and executing machine learning models are being developed, and they allow engineers to test their ideas more quickly and efficiently than before. The data collected from these faster, more widespread tests helps engineers to smooth out their processes, so both the engineers and the machines become faster and more knowledgeable over time.

Technology and the people who use it are only getting smarter and faster as time moves forward, and machine learning is the next big step on the journey toward a higher understanding of data and its applications. The future is bigger and faster, but it is also filled with more understanding; helping your managers to master the art of machine learning processes advances your company’s knowledge and relevancy, which is always a good thing.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

The Road to the Future: Automation and Artificial Intelligence

With more data being produced and networked every day, the new challenge is to figure out what to do with the data. Clever engineers and developers are working on that challenge right now, finding more efficient ways to collect and store data so the data can be used to propel machine learning and deep learning forward. Understanding the potential and importance of machine learning and deep learning is crucial to mapping out a future for your company, so here is some information about the two processes.

A Solid Foundation: Machine Learning Begins the Learning Process

Every machine and device today is designed to take in data of some kind, and smart developers decided that there should be a purpose for that data. Given enough information and time to think about it, we humans learn and make informed decisions regarding the information given to us. Machine learning is the process by which machines take in data and learn from it to make informed decisions. The machines are not yet autonomous, though; machine learning requires the guiding hand of a good data scientist or engineer.

This is because machines can still make mistakes. When a machine or device is given information but still yields a result that is incorrect, engineers and data scientists are there to correct the error and guide the machine toward a better understanding of the data it has digested. Machine learning requires human guidance and help as machines are nudged along the way, and deep learning is the process that should move the whole science forward.

Building Toward the Sky: Deep Learning Further Augments Machine Minds

While humans use machine learning to guide our devices and machines on the road to understanding data, deep learning pushes the process a step forward by endowing machines with the power to learn and think for themselves. This is an exciting yet scary thought; machines that can think like us will be incredibly powerful. This power is what companies desire so that data interpretation becomes a more seamless, automated process. The core that makes all this possible is the artificial neural network, or ANN, a neural network for machines that is similar to our own organic neural networks.

With the ANN, machines can better process information and use it to make more accurate and informed decisions. As the machines take in more data, they become more capable of analyzing and interpreting data, and the hope is that they will be able to do all these things automatically in the long term. When machines can do certain crucial tasks without human guidance, our workload gets lighter and we become more productive as a result. Many minds make light work, after all.

Machine learning is the first important step on the path toward true artificial intelligence, and deep learning is the giant leap that should get us and our machines there. With a team of machine learning engineers and deep learning engineers on your side, your company can move ahead to artificial intelligence and automation with confidence.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS