Big Data Can Yield Big Money – If You Master It

Big Data Can Yield Big Money – If You Master It

With more information streaming around the globe than ever before and even more on the way every year, it is no wonder that the big push in recent years is into big data. Companies can increase their profits easily if they figure out how to collect and properly utilize big data, but according to most researchers, this is a process that is escaping most companies. In order to avoid these pitfalls and get beyond data illiteracy, here are a handful of tips to help you move your company forward in the big data race.

Finding Good Data: What the Right Data Can Tell You

The first cornerstone of mastering the field of big data analysis is knowing how to find the information you seek, and this requires knowing data intimately enough to pull questions out of it. In essence, you and your employees must act as data detectives, reading all the data you have and deducing what sorts of answers you may derive from its myriad clues. Once you have figured out how to read and analyze data effectively, figuring out what the data can tell you becomes much easier. Once you know what the data is saying, you can begin to tackle the question of whether or not your new knowledge will lead to a well-researched decision; the more comprehensive your understanding, the more decisive you may be.

Funneling Data Properly Requires the Right Structures and Infrastructure

Before you can begin to analyze and decode your data, however, you must have a way of collecting and classifying your data. There are many machines and devices that collect data, including computers, phones, and all the tech inside them, such as email, apps, websites, click trails, and social media. Most of the technology we use has some way of recording data, and the better you are at collecting and sorting that data, the more streamlined your company’s data analysis will be. It becomes a matter of infrastructure and how you set up your data networks; if they are easy to navigate, the analysis process will go smoothly.

Communication Breakdowns Must be Avoided on the Personal Level

Data talks and clever employees listen to it, but that doesn’t mean people are talking about the data in a productive way. One major obstacle in the way of proper data leverage is the breakdown of communication between managers and data analysts; more often than not, the lines of communication between employees who collect and interpret the data and the leaders who decide what to do with the data become strained. This must not happen. In order for the best decisions to be made, data must be discussed openly and frequently. The more you know about your company’s data, the better your decisions will be.

Understanding Your Needs and How to Meet Them

Before any of these problems can be worked out, you must identify your company’s data needs. Once you have figured out what data your company requires and how to get it, you can start to accumulate the proper resources and knowledge to use your data effectively. The end goal is also the beginning; know where you want to go, then start going there.

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 Compelling Challenges of Engineering Computer Vision

The Compelling Challenges of Engineering Computer Vision

Of all the interesting obstacles slowing down the advancement of artificial intelligence, computer vision may be the most compelling. This is due to the multifaceted challenge of programming a machine with enough inductive reasoning to extrapolate information from observations and come up with plausible and accurate conclusions. Of course, this is the end goal of artificial intelligence research – endowing a computer with the power and ability to think, at least within reason. When it comes to translating flexible human thought processes into more structured machines, there are a handful of problems that slow down the computer’s mastery.

Quick Thinking: Classifying Sights at a Glance

While we move around the world and throughout our daily routines, we see an uncountable number of images that our brain parses through and then separates into different classifications. To us, this process of simplification and differentiation is done without conscious thought, but the trick of classifying images is a little more difficult for a computer. We have years of practice amid hundreds of contexts to fall back on, while a computer must decode all this input from scratch. Engineers and researchers are solving this problem by feeding computers a cavalcade of images to fine-tune their identification abilities, then programming the machines to scan images one set of pixels at a time until an accurate classification is made. The process is speeding up by the day thanks to creative engineers.

Knowing Where to Look: Object Detection

When we need to look for specific objects or types of objects, years of context and experience help us narrow down our search. Computers do not have these helpful shortcuts, so training a computer to detect certain things is a difficult process. Clever researchers and programmers have begun narrowing things down, however, by telling computers to look at region clumps – areas in images that are denser and more clumped together than others – thus indicating a space where objects are gathered. When a computer can narrow its search this way, the object detection speeds up, but it still requires a lot of power and time. Much testing and research is required to hone this process, so it is a promising area for machine learning engineers.

Inferring Predictions: Tracking Specific Objects

It is hard enough for an aware human to track one person or object through their visual field, and it is even harder for computers to do so. The aforementioned problems of classification and detection come into play as the computer must first identify what the operator wishes it to track, then it has to parse through multitudes of image layers to keep its sights trained on that one object. Researchers are using multi-layered image captures to help computers stick to their targets, and in recent times, heat tracking has helped computers stay accurate. There is still a lot of work to be done, and this is promising for aspiring AI researchers.

Tracking Multiple Objects at Once: Image Segmentation

Finally, there is the trouble of identifying disparate parts of images and following them through their respective paths. This problem will require reams of research. It is clear that honing computer vision will take work, and this means the future for machine learning engineers is open and 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

The Two-Way Street: Driving Data to Become Data-Driven

The Two-Way Street: Driving Data to Become Data-Driven

All signs point to a future in which information builds and crashes larger than a tidal wave, and this means that big data is the next big push for most forward-thinking tech companies. However, while many companies are looking at more data than ever, they aren’t exactly using it in the most productive ways. To truly master the art of reading and using data effectively, companies like yours must become data-driven and be so in tune with data that they almost live and breathe it. Here are five tips for focusing more fully on the big data picture.

Build the Best Infrastructure: Acquire Proper Machine Learning Computers

The best machine learning engineers can collect, collate, and coordinate data, but much of this work requires building an effective machine learning model. A wide network of models open to every individual in your company would be best for compiling large chunks of data and assessing it. Analysts will be able to research the data you have collected, and technicians may use it to streamline your data collection networks.

Keep the Data Accessible to Everyone

If your company is going to move forward as a whole, every part of the company must become aware of the data and the ways it may be utilized. This means that each person in your company must have access to data, along with the knowledge to interpret and use that data to best meet their goals.

Teach Each Employee How to Read Data

It is one thing to have a book in your hands, and it is another to open it and read it with understanding. This is just a long-winded way of saying that while it will be good to open data access to all your employees, the data will not be useful to them and your end goals if they cannot read the data. Analyzing and interpreting data is a skill, and it is one that grows and progresses with practice. Once employees understand the metrics used to gauge information, they will be able to read the data you collect and make informed decisions based on their analyses.

Make All That Data Work for You and Your Business

The data is available and everyone can read it. Now you must turn it into goals and take actions to meet those goals. Once you know what you’d like your company to accomplish, it is easier to designate goals and track them with the data you collect. Using data to track goals and push teams ahead is crucial to the success of any data-driven business, and since data is the way of the future, mastering it to meet your business goals is necessary.

Know What to Do with The Data

The data tells you and your employees all sorts of things, and the next step is to respond to the information the data is relaying. Having goals helps you to move toward decisive action; the data is merely the middle step of this process, following one action and leading toward the next until you have met your goals. With smart data usage, your company will move into the future and benefit.

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 Know if Artificial Intelligence is Worth the Cost for Your Business

How to Know if Artificial Intelligence is Worth the Cost for Your Business

Although many analysts and naysayers discuss the potential effects artificial intelligence may have on jobs in the near future, one thing many people don’t think about is the actual cost of implementing artificially intelligent workers in a business. The ROI, or return on investment, is a real concern for any business, so figuring out the ROI for artificial intelligence and automated workers is an important aspect of any company’s business model. Here are a few ways to look at the ROI for your company’s prospective artificially intelligent workers.

AI Can Fill Gaps in Your Workforce

Sometimes, it is difficult to hire flesh and blood workers. This is a prime example of a time when automated workers are the perfect solution to a problem; if there are no people to work a particular shift or the work is potentially harmful to the human body, artificially intelligent machines may do this work with no complaints and no damage. This increases productivity in general and protects people from harm – a win-win, to be sure.

Training and Job Fatigue Can Take a Toll

In a similar vein to the point above, sometimes it is just too costly to hire and train a brand-new person. A machine is programmed to do the job the same way every time, so there is no training required. This cuts down on training costs and production losses due to the learning curve. Furthermore, it can increase profits because artificially intelligent machines are made to do tasks the same way every time. This will increase the accuracy of the task performed, thus increasing the quality of the product and the satisfaction of the customers. Many people become tired or bored of their work when they must do the same thing every day. Having a machine step in to handle these mundane tasks reduces worker fatigue and increases productivity.

Additionally, there are some jobs that just shouldn’t be done by people, such as lifting heavy objects, operating heavy machinery, or using chemical-based items such as paint or cleaner. It is better to have a machine perform these tasks, so nobody is in harm’s way at work.

Make Your Money Work by Double-Checking Your Specifications

A surefire way to lose money on an AI investment is to contract a group to build and install the new machine and to let the installation team just go. While many teams are professionals and will do the job well, it helps to speak with your AI construction crew and tell them what you need. It is crucial to let them know what work the machines will be performing and how those machines will fit in the overall scheme of your company. If you make sure the builders know what you need, they will build a better and more cost-effective machine.

Although many people are still afraid that artificial intelligence will take their jobs, this has not been proven. Instead, many companies report that AI increases productivity and the quality of human employees’ work because machines can handle the mundane or dangerous tasks and let humans do what they want to do. This benefits all parties involved.

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

Talent in Droves: How to Run a Successful Mass Hiring Campaign

Talent in Droves: How to Run a Successful Mass Hiring Campaign

Some industries require many workers to get the job done, and these industries often initiate mass hiring efforts in order to bolster their numbers and complete projects efficiently. Before you begin a mass hiring campaign, however, it is useful to know exactly what your company’s goals are, how much you’re willing to spend to reach those goals, and how long you plan on keeping new hires around after the initial job is done. With these details set in place, you may begin planning your mass hiring campaign.

Know What Skills and Knowledge Are Required for Each Position

Before you can hire a talented developer or technician, you must figure out which skills you’ll want employees to use on the job. When you know what to look for in a potential hire, you can tailor interview questions to each position you need to fill, and hand these selective interview requirements to each and every one of your recruiters. With the right team looking for hires and every team member prepared with the right questions, hiring will proceed smoothly and quickly.

Put the Word Out on the Right Channels

A successful hiring effort isn’t just about finding the right people; it’s also about knowing the right places to look and make your company known. Putting ads up everywhere might yield high counts of candidates, but it may clog your hiring systems with unqualified candidates. Make sure that the places boosting your hiring signal mesh with your company’s culture and its skillset, and you’ll attract the qualified and proper candidates your future projects require.

Expand Your Network for the Best Possible Results

They are called social networking sites for a reason, and they can help immensely when it comes to widening your pool of potential job candidates. The people you know can help you find the right workers for any job you need done, and this includes your colleagues as well as your friends. Use social media to spread word of your hiring needs, and don’t be afraid to ask your current employees for referrals; there’s a good chance your colleagues know people with the right skillsets who can do the work you need.

Targeted Job Fairs Attract Lots of Qualified Candidates

If your company has clout and you know exactly the sorts of workers you need to get your projects done, a job fair can help fill your hiring needs quickly. Job fairs are a great way to bring many candidates together at once and meet with these potential hires face to face. Reading resumes and making introductions in this setting helps create a rapport right from the start, and the effort that goes into job fair preparation ensures that each candidate you meet here is willing to work hard for your company.

Education Centers Are a Great Start

When you need plenty of entry-level talent, hiring from colleges and/or vocational schools is a great way to bolster your candidate pool. Targeting specific majors that align with your company’s work streamlines this process, and many students are eager to put their learning into practice; this cooperation of business and education is a boon for both the candidate and 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

Leaping Forward and Learning: Overcoming the Challenges of TensorFlow

Leaping Forward and Learning: Overcoming the Challenges of TensorFlow

When a new program is introduced to a community, it naturally takes some time to get used to. This is the case with TensorFlow, a relatively new program designed to assist machine learning engineers with their coding and programming. Thankfully, the community has begun to work out the kinks in the TensorFlow system, and things are looking up for engineers and programmers looking to try out the new software for themselves. Google’s very own machine learning team has chosen to move forward with TensorFlow, and this is a great sign. There are still some problems, of course, so here are a handful of the pros and cons of using TensorFlow.

Ease of Use Has Improved

Many features of TensorFlow have enabled machine learning enthusiasts to streamline their projects and keep track of critical developments. TensorFlow supports multiple GPUs, which makes multi-tasking much simpler once you have designated tasks to each GPU. You may set up queues for specific programs and operations so the workflow moves the way you like, and the TensorBoard feature allows you to visualize your project’s flows and cycles using graphs. Along with this visualization comes memory, as you may log outputs and workflow with the graphs. Finally, you can create checkpoints throughout your project’s processes in order to go back and try new things. The ability to test new ideas and tweak old ones makes TensorFlow great for problem-solving.

Documentation and Data Comparison Could Use Some Work

Many of TensorFlow’s example datasets come from academic sources. Although this isn’t such a terrible thing, it sometimes limits the lengths the software can go when processing real-world problems that research has not yet extrapolated. This also slows down the learning process when TensorFlow is taking in entirely new deep learning information, and it causes the program to struggle with deviation and data anomalies. Additionally, the documentation of the machine learning processes on TensorFlow is skewed to the extremes – with more simplistic conceptual models on the one hand, and cutting-edge, real-world models on the other – without the middle ground that connects these two realms. This means the learning curve drops off, and it can be difficult to get beyond the initial stumbling blocks. If more realistic data models and comprehensive documentation are added to TensorFlow, it will improve dramatically.

Sharing Space and Resources Is Difficult

TensorFlow likes to have things to itself at this time, so it attempts to utilize your entire GPU when it starts up. This is useful in certain contexts, but most of the time, it simply bogs down your machines and creates obstacles when you need to run several programs at once. In the same vein, the popular program Theano is incredibly helpful to many machine learning enthusiasts, but when it is used in conjunction with TensorFlow, they each fight for GPU resources and cause problems. It is possible to reroute Theano to CPU using Python, but it would prevent future headaches to get both programs to work together.

TensorFlow is not perfect, but what program launches without kinks? The improvements that have been made – and continue to be made – are fantastic, and working knowledge of TensorFlow is indispensable to current and aspiring machine learning engineers.

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

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