Forecasting is a critical part of business operations. For example, being able to estimate future customer demand and sales numbers can help companies anticipate revenue. Similarly, technology departments have to predict ongoing storage needs, computing requirements, and other areas that fall in their purview while planning departments have to prepare for the number of products that may be needed to meet demand.
The ability to forecast accurately is often deemed critical. Without reasonable numbers, operational decisions may not align with what actually occurs over the next weeks, months, or years.
Often, companies rely heavily on people to make forecasts. However, as machine learning and artificial intelligence (AI) become more accessible, we are on the precipice of a forecasting transformation; one that has already started to take root.
If you are wondering how machine learning and AI are improving forecasting, here’s what you need to know.
While an artificial intelligence (AI) does not have its own personality, per se, that does not mean they are not affected by bias. Deep learning algorithms are designed to identify patterns and use them to make recommendations, come to decisions, or render conclusions. If any part of the learning process promotes bias, then the AI ultimately develops one. And, if an AI bias occurs, it can be incredibly hard to fix.
The Origins of AI Bias
AI bias can happen for a variety of reasons. While the most obvious source is the data used by the system, other issues can also result in bias.
For example, an AI is usually designed to help answer a specific question. If that question contains a subjective component, or a concept that is open to interpretation, the company creating the AI puts their own definition on the concept. If their viewpoint is biased (even if it is unintentional) or even just poorly defined, the AI could produce unintended outputs, creating a lack of fairness or other observable bias.
When data is collected, bias can show up in one of two ways. First, if the data collection method results in an inaccurate depiction of reality, that can create bias. Second, if the data reflects existing biases that are present in society, the AI then has them as well.
Finally, when data is prepared, bias can also creep in there, even if the source data was unbiased. For instance, the attributes selected for the AI to review could create a prejudice.
Why Eliminating AI Bias is So Challenging
Dealing with AI bias is actually incredibly difficult. In some cases, the introduction of bias is not very apparent, so the designer may not realize there is a problem until they begin reviewing outputs. When this occurs, retroactively finding the source of the issue is a daunting task.
Similarly, the subjective nature of some core questions can make it difficult to determine what an unbiased outcome looks like. Along the same line, defining fairness itself is not easy, particularly since it has to be examined in mathematical terms when designing an AI. Since social context can impact the definition of fairness, and that can vary dramatically from one location to the next, the challenge is even greater.
Dealing with AI Bias in the Future
While the AI bias problem could be considered vast, researchers are working diligently to find a solution to the problem. This includes developing new algorithms that detect potential issues, including hidden biases, and processes that hold organizations accountable for unfair practices.
Dealing with AI bias will take time. However, even if it will not be solved easily, a solution is in the works.
Do You Need Assistance Building Your Tech Team? Contact The Armada Group!
If you would like to learn more about AI bias and how it can impact business, the team at The Armada Group can help. Contact us with your questions or thoughts today and see how our deep learning expertise can benefit you.
Artificial intelligence (AI) and machine learning hold a lot of potential, providing technology that can change lives and business for the better. But, as any new technology emerges, certain professionals pay the penalty, often suffering job losses or pay decreases. While AI isn’t threatening every employee, certain positions are clearly at risk. Here is an overview of four jobs AI technology is threatening the most.
Thanks to the rise of chatbots, customer service professionals focused on tech support will likely see calls for the skills dwindle as these solutions become more sophisticated. Currently, these professionals are being specifically targeted by technology creators, especially for Tier 1 support issues.
In many cases, the majority of customer requests focus on simple matters that are resolved using repeatable processes. Chatbots can be designed to spot these problems and provide instruction based on tried-and-true troubleshooting methods not unlike the scripts many Tier 1 phone support workers use today. This means these standard issues won’t require human intervention, eliminating the need for some of these positions.
AI systems are already being designed with the ability to “think” creatively and improvise. While most of the public tests have involved competing against people by playing games such as Go, it isn’t hard to envision technologies being developed that are able to create and maintain software applications down the road. While this threat may be down the road, it is certainly viable, and something developers should keep an eye on.
Maintenance tasks and security measures are critical in tech and form a large part of the sysadmins role. AI technologies are already being created to offer automated solutions for much of the work associated with supporting uptime requirements, addressing performance issues, and improving security. While these solutions might not entirely eliminate the need for sysadmins, a possible reduction in the number required is certainly plausible if these AI systems can do everything their creators are hoping they can.
The manufacturing sector has experienced technologically-related disruptions to their workforce numbers before, and AI is likely to have a similar effect. Advanced robotics can replace assembly workers while providing the ability to run 24/7 without having to worry about shift changes. While these technologies won’t entirely remove the human component from the floor, professionals looking to stay in the field will need to increase their skills to remain relevant as more machines become part of the production cycle.
As outlined above, AI certainly has the ability to be a threat to many professions. However, it is important to note that these systems require support too. Skilled tech workers are the ones who program these solutions and perform maintenance on automated systems. That means a career in AI can be especially lucrative and an excellent method for staying relevant even as the technology becomes more sophisticated.
If you are interested in learning more about the impact of AI or are interested in finding a new IT position, the professionals at The Armada Group can help. Contact us today to see what our services can offer you.
Artificial intelligence is getting a lot of attention in the business world, making its mark in almost every industry along the way. Information about new developments seems to pour in endlessly, creating a challenge when it comes to truly seeing what is happening in AI today. To help you see through the onslaught of news, here are some of the latest trends in the field and what they can mean for your company.
Almost every organization is watching the AI trend, but few have started the process of implementing solutions that take advantage of the technology. Much of this delay is related to the need for a highly specialized skill set to bring in these systems. Professionals with the required background aren’t readily available and obtaining the necessary skills isn’t a small task. However, there are new frameworks being developed that look to ease the burden associated with implementing and supporting these systems. Howdy’s Slack Bot and Facebook’s Wit.ai are both bringing point-and-click systems to developers, making the creation and customization of AI systems easier to manage.
Other tools also aim to simplify the implementation of deep learning models. Options like TensorFlow, Keras, and Bonsai are just some of those looking to bring more advanced AI capabilities to a wider market. Cloud platforms are also lightening the load on business eliminating internal infrastructure concerns. Collectively, this makes AI more accessible to all.
General purpose AI solutions are still something to look forward to in the future. Now, highly specialized systems are the standard, working to manage specific tasks or function in defined niches. While these targeted solutions aren’t viable across all industries, the cumulative efforts have a wide variety of sectors well covered. Organizations operating in such diverse areas as banking, healthcare, security, and production can all expect AI systems designed specifically for their needs, making them exciting developments for increasing the speed of business.
Data overload is a real issue for some companies, especially as they take advantage of the information provided through IoT and other mechanisms. While businesses want to harness the power of their data, overflowing amounts of information make it difficult to find value in the data. AI systems are being designed specifically to alleviate this issue, allowing for more efficient processing and parsing of information. Structured data extraction, natural language understanding, information cartography, and automatic summarization are all being considered for their information management capabilities and may make data overload a non-issue in the future.
As AI technologies become more robust, their ability to communicate with people improves. Developers are focusing attention on improving the emotional intelligence of systems, helping them interpret human speech more effectively based on word choice and even tone.
It is important to keep in mind that AI is still evolving, including in all of the areas above. Advances are being regularly made, but it will take additional time before these solutions can fully replace certain human interactions. Additionally, it takes a significant amount of IT talent to keep these systems functioning as they need to in order to provide the necessary value. If you are looking for professionals with AI skills to join your team, the recruiters at The Armada Group have the connections to find the ideal candidates. Contact us to discuss your needs today and see how our services can work for you.
Whenever a technology begins to get a foothold in an industry, fears generally arise regarding how the innovations will affect the availability of employment. But even if it does impact those working in specific positions, that doesn’t mean the number of jobs available actually decreases. Often, it just indicates a shift within the job market, and can even lead to more work being available than before.
AI has the potential to make workers more efficient, eliminating tedious and repetitive tasks and allowing professionals to focus on duties that require human input. So, instead of eliminating positions in IT, it is more likely to change the nature of those working in the field.
Here is an overview of AI in the workplace, and how it could create more jobs, not less.
AI Requires Support
While AI may take certain duties out of the hands of workers, the systems that use the technology will continue to need support. AI systems require human input to determine how the solution needs to react to certain variables. Additionally, issues can present within any system, making the need for troubleshooters a critical part of any AI implementation.
AI systems are not self-sustaining. Instead, they represent a part of overall IT strategy, and workers are needed to make any associated goals a reality. Skilled tech professionals are responsible for the creation and implementation of AI-oriented solutions, effectively creating new IT positions specifically designed to support these innovations.
AI Doesn’t Stand Alone
An AI system is only as powerful as the data with which it works, and that means people are still highly relevant to its operation. Additionally, employees are needed to oversee outputs and finalize conclusions or courses of action. Further, workers are responsible for taking outputs and turning them into meaningful information that can be used throughout an organization, a task that AI simply isn’t prepared to manage at this time. Experts in data analytics and engineering are needed to manage duties that require additional intelligence beyond what the system can provide.
Without the involvement of data professionals, the AI can’t perform its duties any better than a person who doesn’t have sufficient information to draw accurate conclusions or identify relevant patterns.
Pursuit of More Complex Objectives
Since a primary benefit of AI is the ability to remove repetitive administrative tasks from the hands of skilled professionals, companies have the capacity to refocus their goals in pursuit of higher level development objectives. Businesses will have the opportunity to invest more in the hiring of individuals with critical tech skills like coding.
While certain entry-level positions may become less available, more advanced positions might be created. This is essentially beneficial to IT workers who traditional pursue higher education to gain entry into the field, as companies can focus on hiring these individuals over those previously required for less technical tasks that support IT objectives.
Ultimately, AI isn’t going to eliminate workers across the board. Instead, it will change what kind of tech professionals are needed and how their daily tasks are managed. If you are interested in pursuing a new IT position related to AI or any other specialty, The Armada Group can help you explore your options based on today’s job market. Contact us to discuss your ideal job and experience how our services can benefit you.
Some might cite the cliché, "turnabout is fair play." For decades, workers in other industries have feared their jobs might be replaced by automation. Now, losing their jobs to computerization is one of the top fears of developers.
That's one of the findings in Evans Data Corp.'s survey of developers. To be sure, assembly language coding jobs disappeared when high-level languages were developed. But the role of the software developer didn't disappear; the skills still were needed, only the tools used changed. And in general, although the tech industry is an early and enthusiastic adopter of technology, programming languages linger. There are still jobs for Cobol developers out there.
New trends in artificial intelligence, though, are making developers uneasy. Previous applications of technology in programming, like the development of compilers, mostly automated the mechanics of software development. The cognitive capabilities of AI go beyond that, promising—or threatening—to co-opt the creative thinking parts of the software job.
Up 'til now, humans' cognitive abilities were unmatched. But new advances in machine learning mean software can make software design decisions or detect bugs as effectively as human developers. Code databases may let algorithms create applications that match requirements specifications. Those abilities could put development jobs directly at risk.
This is still mostly hypothetical, though; a worry for the future. Statistics show the number of IT jobs increasing, not decreasing, and salaries for these positions are well above median wages for other kinds of work. While developers do need to keep their skills up to date as technology trends change, there's still plenty of opportunity for skilled and experienced developers to work on challenging, exciting projects.
For companies that aren't ready to hire a robot as a programmer yet, and for developers who don't plan to retire any time soon, working with The Armada Group is an effective way to find a new hire or find a new job. With our deep database of jobs, deep pool of candidates, and deep understanding of the industry, we match opportunities and candidates based on education, skills, experience, and aspiration. Contact us to learn how we can help you hire or get hired.
While it has never been seen as a desirable trait in any industry, many information security experts suggest that a healthy dose of paranoia may actually be good for business. After all, a paranoid leader is a vigilant one. This state of alertness can actually improve the defenses of your organization, through regular improvements, scheduled maintenance, and increased awareness in your company. So should you look for a CISO with a paranoid streak? Consider the benefits before making your final decision.
1. Paranoid CISOs search out advancements.
Paranoid CISOs are ever-improving. Because they constantly suspect that their organization is under attack, they’ll always be looking for new, advanced ways to fortify their defenses and stay informed on new developments in the industry. There’s always room for improvement, so your company will have the most up-to-date information security system available with new, multi-layered controls. This valuable instrumentation and increased depth can help prepare for a threat or attack before you’re even aware it’s there.
2. Paranoid CISOs never neglect necessary system maintenance.
Complacency is just as dangerous as an inherently weak security system. If your CISO isn’t taking the time to update and patch their managed program, they’re opening up channels for potential breaches. A paranoid CISO, on the other hand, constantly patches their program to ensure that no known weaknesses exist in the system. This regular maintenance might be neglected by complacent leaders, creating dangerous vulnerabilities in your organization.
3. Paranoid CISOs improve company awareness.
In their constant state of hyper-vigilance, a paranoid CISO will want to ensure that every member of your organization is doing their part to follow security protocols. This will help create a culture of data security to protect your company at every level. From data analyst to CEO, you organization will be more secure and less vulnerable to attack.
4. Paranoid CISOs develop a deep understanding of the company.
Not only will they understand the nature of each and every potential attack, but a paranoid CISO will also understand the potential consequences they may have on the company. Their deep-rooted knowledge of the business will motivate them to improve and monitor the system, specifically targeting the threats that may cause the most harm to the company.
So while paranoia is often the butt of office jokes, it may actually help the performance of a company’s security system. A paranoid CISO can do more for a business than a complacent leader. Embrace a healthy level of paranoia in your CISO for an improved system and better overall defenses against attacks.
As technology continues to advance rapidly, the machines we use are getting smarter. Machine learning is the technology of constructing “learning” algorithms that drive a broad range of smart technologies — and the new generation of this discipline, called deep learning, has the potential to power more advanced artificial intelligence capable of everything from sophisticated speech and image recognition, to self-driving cars.
What is deep learning?
Deep learning, also called deep structured learning or hierarchical learning, is a type of machine learning that uses high-level data abstractions, nonlinear transformations, and layered cascades applied to learning representations of data, in order to help machines “learn” tasks through observations and examples.
Algorithms with deep learning applied are often inspired by communication patterns found in neuroscience — the study of the human nervous system. For example, a deep learning algorithm might be based on the relationship between a stimulus and a neural response, which registers as electrical activity in the brain. This type of machine learning attempts to create neural networks for machines that “think” in ways similar to humans.
Following are a few of the applications currently being developed with deep learning algorithms.
Automatic speech recognition
Technologies such as Apple’s Siri are built on machine learning algorithms that work to recognize speech, including words and sounds. Deep learning has led to the advancement of automatic speech recognition using the TIMIT data set — a limited-sample database using 630 speakers and eight major American English dialects, each with 10 different spoken sentences — to large vocabulary speech recognition through DNN models that rely on deep learning algorithms.
Deep learning differentiates from other forms of machine learning through the use of raw features at a learning level, rather than pre-constructed models. With deep learning, speech recognition can be highly accurate using the true “raw” form of speech — waveforms, or visual representations of sounds using curves.
Similar to speech recognition, a limited size data set called the MNIST database has been the popular model for powering image recognition applications. This database includes 60,000 training examples and 10,000 test examples, composed of handwritten digits. However, MNIST relies on shallow machine learning for image recognition — and deep learning allows for more large-scale image recognition at a higher accuracy rate.
One practical example of deep learning algorithms applied to image recognition can be found in the automotive industry. A car computer trained with deep learning may enable cars to process and interpret 360-degree camera views, allowing for heightened “awareness” in self-driving or assisted-driving vehicles.
Many in the tech industry view deep learning as a strong step toward realizing truer artificial intelligence. In 2013, Google hired three DNN researchers tasked with not only dealing with the search engine giant’s constantly growing stores of data, but also to improve Google’s existing machine learning products, such as semantic role labeling and search results.
Facebook has also created an artificial intelligence lab, largely dedicated to the development of deep learning techniques that will improve the user experience. Automatic image tagging was developed in Facebook’s AI lab — a technology that is still being refined for greater accuracy using deep learning.
As machine learning continues to increase in sophistication, more companies will look to hire IT professionals interested in developing deep learning algorithms and improved artificial intelligence applications. Machine learning is an exciting field with a wide range of possibilities ahead.