Machine Learning Improves Enterprise Software

Machine Learning Improves Enterprise Software

Every business has goals it must meet, and enterprise software encompasses any programs and tools used by a business to reach its goals. Typically, programs that perform very specific functions, such as creating spreadsheets or graphs, are not considered enterprise software. Enterprise software is used to run and improve business operations overall. Until recently, enterprise software relied on human guidance to work effectively, and human error meant that the computer programs made errors too. With the rapid improvement of machine learning, however, enterprise software stands to refine itself until it is nearly perfect in the management of business operations.

Data Drives Business, and Machine Learning Improves Data Analysis

Many of the day-to-day concerns of any enterprise involve number crunching, inventory management, and human-driven predictions that sometimes miss the mark. When computers are given the datasets necessary to manage an enterprise’s calculations, they may potentially improve the accuracy and efficiency of ordering supplies, formulating work schedules, and even predicting sales numbers.

A Smarter Sales Representative Makes More Sales

One realm that machine learning has improved exponentially is that of customer service. Thanks to trend tracking software and behavior analysis programs, many businesses have been able to improve customers’ experiences by tailoring the shopping experience to individuals’ preferences. Machine learning enables computers to outline and predict a customer’s shopping and buying habits, and once those preferences are learned by the program, they may be passed to flesh-and-blood sales representatives, so the human element can zero in on the optimal experience for that customer.

More Data Means More Reading, So Let Computers Help

The relatively quick growth of industry over the past two decades has ushered in an age of huge data stores, all set aside in servers and other storage areas where they may be accessed by engineers and studied to determine patterns and trends. This process usually takes a long time and is prone to human error. Now, however, intelligent machines may use their algorithms to collate and interpret data faster than humans ever could. The quickened data analysis skills of AI programs enable the reams and reams of enterprise-related data to be collected and read faster and more accurately than ever before.

Scattered Storage Requires Constant Clean-up

The aforementioned exponential increase in data generated by companies necessitates more and larger storage facilities, and many companies opt to build large external storage devices to contain all their information. While this solves the storage issue, it creates a problem of fragmentation. Data is split and scattered in different locations, and it must be read and understood to keep the disparate datasets flowing together seamlessly. It would take a team of people working around the clock to synchronize a given company’s datasets, so letting intelligent machines perform these tasks is a great way to maintain data accessibility and integrity.

Running a successful enterprise demands all kinds of work, and proper management of information is a huge component of profitable business. Machine learning improves enterprise software and makes it easier to run your company with confidence and without hassles.

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

Why Retaining Machine Learning Engineers Should Be a Priority

Why Retaining Machine Learning Engineers Should Be a Priority

Good engineers are hard to find, and the specificity of a machine learning engineer’s job means that finding a good machine learning engineer is even more difficult. Once your organization finally finds the right talent it needs to progress your machine learning programs, keeping that talent on board is of the utmost importance. However, turnover rates of machine learning engineers have been reported at roughly 10%. This number may seem small at first, but it grows as you consider how many factors are affected by a valued employee leaving your company; the team loses morale, and productivity goes down. The cost can be quite staggering.

This Ripple Effect is Not a Good One

When most companies attempt to quantify the actual cost of losing a machine learning engineer, the numbers typically begin in the tens of thousands, before jumping to a number that rises as high as twice the engineer’s annual salary. With a bottom that is so high, it is no wonder that the monetary cost of losing a talented engineer is a concern for today’s top technology development companies. One reason for this high cost is the expense required to train a new engineer. When one engineer leaves, another takes their place, and the new engineer will need time – and by extension, money – to reach the skill level of the person they replaced.

The Bucks Don’t Stop There

While the aforementioned training costs are disheartening, they are not the only costs that you need to worry about when it comes to finding new machine learning engineers. In order to search for new employees, you and your company must put out advertisements, compose job listings, and begin a general recruitment effort that will use up additional time and resources.

There is also the cascading effect of a known and valued employee leaving your team. Other members of your team will wonder why that person left, and morale could decrease, all while increasing the chances of more engineers jumping ship for a new horizon. The best solution to the problem of potential talent loss is to prevent problems before engineers are hired in the first place. This means every aspect of hiring and onboarding must be reviewed to ensure your company’s processes make and keep machine learning engineers happy.

A Good Feeling from Start to Finish

When the talent is happy, everyone around the talent – and even those indirectly affected by them – will be happy as well. While in the process of recruiting new machine learning engineers, your company should be sure to honestly and accurately portray the overall culture of your team so potential disconnections and missed fits may be prevented before they happen. While introducing new engineers to your teams and processes, the utmost care and respect must be taken to ensure both the new employees and experienced employees are happy. The future happiness of new and current employees depends on this positive integration.

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

It’s Alive: Guiding Your Deep Learning Project to Production

It’s Alive: Guiding Your Deep Learning Project to Production

In the field of machine learning, getting from point A to point B is never a totally straightforward journey. While designing a deep learning algorithm, there are a plethora of factors that will change over time and affect the way your potential product operates. The path to production will very likely involve all kinds of detours, backtracking, and changes on the fly. Luckily, there are ways to keep track of these changes and ensure that your project is ready for the changing variables that come along; the field is called machine learning for a reason, after all. Here are a few ways to teach your machines to adjust algorithms as they go.

Tinker and Test Until You Find the Best Version

With all the datasets you will be feeding to your machine learning model, there is going to be a veritable cornucopia of information coming your way. You must keep tabs on all the resulting changes and the performance trade-offs, as new and different information is given to your deep learning model. This will allow you to test the model over and over again until you find the best possible versions of it and figure out which variable changes affect which aspects of your model’s performance. This means you’ll be doing a lot of cataloging and recording, but the record-keeping will be worth it once you go to production.

Be Ready for Any Number and Kinds of Failures

All that testing and recording ensures you will have a decent number of working models saved in case of failures – and with all the new data coming into your model, there will be plenty of failures. These failures will be for a variety of reasons, and you must catalog them as you revert to older versions of your model and see how they respond to the same data. Sniffing out these failures and preparing to overcome them is a vital part of bringing your model to production, and you will want to have the strongest possible backup model ready in case your production model encounters a failure. Preparedness is crucial.

Multiple File Types Increase Machine Learning’s Complexity

Typically, machine learning models are written in several different code languages, and this makes for a more complicated challenge of record-keeping. Couple this multiple-language challenge with the many and varied datasets you use to test your models, and keeping track of everything accurately becomes a Herculean task. Saving each and every model iteration is important, and logging which datasets responded to which changes – and how – will be critical to success. Once again, preparedness is the key to ensuring a smoother route to production.

Once Production Begins, Things Still Move

Production does not stop your work. As your model heads into production, you may have to make changes to overcome failures or unforeseen alterations to real-world information. Going all out with changes will overwhelm the systems and your ability to track the changes. This means a gradual rollout of changes is the best strategy to ensure proper tracking of new machine learning and product quality. Take your time and keep your models as smooth as possible, and production will be a success.

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

Learning Right from Wrong: The Ethical Troubles of Artificial Intelligence Technologies

Learning Right from Wrong: The Ethical Troubles of Artificial Intelligence Technologies

Ideally, artificial intelligence can operate like we do, with intelligence and forethought. However, since we are the creators ushering artificial intelligence into the present and future, we invariably give AI programs some of our own biases and ethical problems. Here we will discuss the foremost of these ethical problems and provide solutions in cases where artificial intelligence crosses an ethical line.

Problems with Politics: AI Can Be a Double-Edged Sword

What one side of the political spectrum sees as innocent or harmless, the other side may see as a near-declaration of war. This means that ethics surrounding AI applications become muddy quickly, and it is up to us to appoint experts who can clearly delineate every side of an ethical dilemma in order to safeguard artificial intelligence firms from breaching ethical contracts and guidelines.

Even Intelligent Weapons May Do Harm

Many AI proponents believe that this technology should be used to advance our military and weapons capabilities to superhuman levels, and this is a big ethical problem. Luckily, many companies have already sworn off involvement with military agencies, but there are still researchers and businesses that sell their technology to the military and the government for defensive and offensive purposes. Clear guidelines and safeguards must be in place so artificially intelligent weapons systems are not used unless absolutely necessary.

Intelligent Law Enforcement May Still Use Force

Artificial intelligence has been used to ramp up the search capabilities of many law enforcement officers and allows for more sophisticated tracking of suspects and criminals. However, our history of racial bias and minority mistreatment means that the same biases may be woven into artificial intelligence frameworks inadvertently. Special care must be taken to ensure that AI technology is not used to perpetuate stereotypes, biases, and wrongful accusations.

The Watchmen Are Not Always Looking Out for You

Surveillance capabilities have improved exponentially thanks to enhanced facial recognition and tracking software, but this, too, is a double-edged sword. While the technology may be used to keep an eye on suspicious people and wrongdoers, it may also be used to keep tabs on innocent civilians. The authoritarian bent must be watched at all times, lest it be used to deprive us of our human rights.

Racial Bias and Privilege May Continue Through AI

The tracking abilities of artificial intelligence systems are not just for keeping tabs on dangerous individuals. They may also be used to profile people wrongfully and keep track of individuals deemed to be “less than” by the authorities. If you do something the government doesn’t like or vote the “wrong” way, for example, AI may be used to lower your credit score or stop you from getting a job you applied for.

Clearly, there are many ways that AI may be abused by those in power. This is why every institution must have a team of ethics advisers, and a code of ethics for AI must be written and followed. AI activity should be tracked and monitored in case of misconduct, and every individual should be trained in the ethical application of AI systems. Finally, in case AI does something wrong, remediation for wrongdoing must be made by law.

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 We Teach Machines to Learn: Tree Boosting and Adversarial Networks

How We Teach Machines to Learn: Tree Boosting and Adversarial Networks

No matter how you prefer to learn, you have probably heard someone mention something about your particular “learning style.” This seemingly simple phrase implies that there are many different ways to learn, and people can be better or worse at certain learning methods depending on personal preferences and circumstances. As it is with people, so we tend to make it for computers.

Machine learning is moving forward at a fast rate thanks to researchers and programmers figuring out how to optimize the ways through which machines learn new information. Two methods in particular, called tree boosting and adversarial networks, work in tandem to produce encouraging results. Read on to learn more about these two deep learning methods.

Making Well-informed Decisions: The Basics of Tree Boosting

Many of us have thought about it: one minor decision we make locking us into some indeterminate future that we seemingly cannot control, and the way we accept that there may not have been a choice at all. Then we start to think about all the choices we make on a daily basis, and these choices pile up. A basic version of this branching decision-making is the principle underlying tree boosting, otherwise known as gradient boosting.

To put it simply, to induce machines to learn, the computers make decisions, which are sometimes as simple as “correct” or “false” scenarios. When a computer makes the “wrong” choice, giving the “incorrect” answer, the result is called a loss. To minimize losses, engineers came up with a technique called tree boosting. Picture a tree being the initial problem, and the two possible answers branching from the tree. To minimize the number of losses the computer experiences in its learning, programmers created more trees that could move from the initial question and answer to give the machine another chance to get it right, thus correcting the loss. Eventually, the trees’ branches will lead to a correct answer; this is the basic gist of tree boosting.

Competing for Accuracy: Adversarial Networks Usher in Correct Identification

For computers to learn the best ways to utilize information, you must train them to identify and classify images and information correctly. A new method called adversarial networks streamlines and improves these classification processes by pitting two aspects of a computer against themselves; these programs are called the generator and the discriminator, respectively. The generator’s job is to generate images that look real, while the discriminator must determine which of those images is a fake.

Ideally, this game played between the generator and the discriminator increases the computer’s intelligence quotient, as one program must make better and better “fakes” while the other must get better and better at working out the “fakes.” Think of it as the way a friendly competition brings out the best in each competitor; the competing aspects of the computer improve each other until the computer is more intelligent.

It is clear that computers are improving much faster than we ever thought possible, and new methods of teaching AI to think, such as tree boosting and adversarial networks, bring us and our machines closer to realizing true artificial intelligence.

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

Harnessing Data: How to Leverage Big Data to Advance Your Company

Harnessing Data: How to Leverage Big Data to Advance Your Company

We are approaching a veritable information flood. In the next two years, bytes of new information will be created by the second, and the outpouring of new information will have increased exponentially. Naturally, there is a call to use this information to improve people and services. Tech-savvy companies are already looking at ways to extrapolate valuable customer data from these reams of information, and the future will likely be a happy marriage of the personal and the digital. This means that you and your company must look for ways to upgrade and revamp your business practices to increase your digital workflow while tightening your mastery of personal service. This will only increase profits.

Not So Much a Re-creation as a Redefinition

The best aspect of moving into a more digitized future is the groundwork that has already been established for information streams. Businesses don’t necessarily have to tear themselves down brick by brick just to rebuild for the future. Instead, they merely must integrate technology into the business model so every team and machine mesh seamlessly. This is, of course, more difficult than it sounds, but a proper combination of technology and humanity will only enhance your company’s output. Everyone from the ground up must learn to handle this new digital landscape. If the CTO and/or CDO can envision a way to educate their employees about digital networking and the IT team can build a smooth data pipeline, this new synergy will allow companies to satisfy customers like never before.

Transparency Enhances Understanding: Get to Know Your Customers’ Data

Nowadays, many people click on dozens of links a day, search for various products and services, and generally use the tiny computers in their phones to manage their daily rhythms and routines. All this computing accrues information about any given person, and the smart companies use this data to better understand that person, so they can pitch products in a smarter way. It goes beyond number crunching and pattern predicting, however. Data analysts may begin to understand customers’ feelings and preferences through sheer information, and it is this understanding that brings customers back to the same shops and services. The way of the future is one of comprehensive knowledge, and it is up to you to make sure your company strives to understand the data and satisfy the people with fantastic service.

Mastering New Technologies Doesn’t Happen Overnight

This new age of information may be daunting, but the trick to mastering it is not to panic or rush. All the new toys and programs may induce a sort of learning frenzy, and this might not be the best approach to growing a fresher, more solid business model. Engineers and analysts must take their time with new tech and new information in order to understand it to its fullest extent, then apply their newfound learning to business decisions. A whole cultural shift will be necessary to truly usher in this new age, and your company will be on the cutting edge as it responsibly learns the ins and outs of its customers and the ways they respond to information.

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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