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The Hands and Eyes Behind AI

The Hands and Eyes Behind AI


As we continue to dive deeper and deeper into integrating artificial intelligence into our everyday lives, it is becoming more common to accept that artificial intelligence devices and presence have been there all along. We willingly accept algorithms, machine learning, even self-driving cars as our approaching normal. As our daily lives become more automated, and we assume that machines and computers will continue to collect and learn from our data and behaviors, how often do we pause to ask ourselves, “how is this machine or computer adapting to my needs?”. While artificial intelligence demonstrates an abundance of potential, the truth is that it still needs a human touch to function correctly

The critical thing to understand about the artificial intelligence and human loop is that it is not a one-sided relationship. Big data, collected, compiled, and applied by humans, and artificial intelligence has a consistently reciprocal relationship. While artificial intelligence is still incredibly reliant on data entry, it plays a significant role in collecting and processing data. Furthermore, artificial intelligence and algorithm models help us to better process and analyze the trillions of data points that we are gathering at all times. The cycle continues concerning our desire to accumulate as much data as possible. Consider this, as our demand for collecting copious amounts of data only grows larger, the more data machines have to process and learn from, which in turn, makes them smarter.

The demand for data continues to grow, and our machines and AI continue to play a crucial role; however, human labor is still required to input, manage, and organize data points. As we continue, we will explore the relationship between humans and AI, as well as what artificial intelligence looks like from the perspective of those working in the fields of data entry and data labeling. As we continue to input data to fuel these systems and models, are we creating a future where data entry and labeling are no longer career options for humans? Instead, are we creating a future in which AI and automated learning machines collect and process the data for us?

The Life of Those in Big Data

Big data and the Internet of Things have completely transformed our lives in countless ways. Entry and labeling of data help businesses collect, analyze, and implement future strategies based on previous behavioral patterns. Big data is continuously collected to help organizations optimize their communication and experiences with their customers, clients, and users. Some argue that this collection and application of data is creating static consumer profiles with unseen blind spots. As we continue to learn more and experience how big data and artificial intelligence affect our world, it is imperative to have a comprehensive understanding of the data’s input and implementation.

Data Scientists

The high-level careers in big data are reserved for engineers, managers, and developers. Scientists, analysts, and statisticians also play an integral role in the collection, entry, and labeling. Entry-level positions for jobs in big data are usually referred to as data scientists or data administrators. Those in these positions perform several tasks, including:

  • Collecting large amounts of data
  • Analyzing large datasets
  • Using data to solve problems
  • Comminating results to business and IT executives
  • Paying attention to trends, relationships, and patterns within datasets
  • Converting data
  • Working with AI and machine learning technology
  • Organizing analytics
  • Data preparation

Predictable and possibly monotonous, data scientists and administrators’ daily activities maintain a healthy level of consistency. Imagine looking at massive sets of raw data that you must enter and organizing within a system. Data scientists require a vast collection of skills, including processing large amounts of data, making inferences based on datasets, and considering all perspectives of a single problem. A keen eye for detail and an analytical mind are necessary for those within this position.

Data entry, analyzation, and labeling is an incredibly task-driven career. While we may think turning data into information is a simple task, it is easy to forget those who input raw data and teach out computers and artificial intelligence to make sense of the input. A data scientist must be well-rounded and meticulous while on the job. Nearly all aspects of this career require an affinity for numbers, cross-disciplinary skills, and analytical problem-solving. Data scientists can bridge the gap between computer programming and business direction.

Data scientists spend most of their days identifying data analytics to create and offer valuable advice to their organization’s decision-makers. As they learn about datasets and variables through working with unstructured data, they discover and develop solutions and opportunities. To ensure accuracy and uniformity, data scientists spend time “cleaning” and validating the datasets. After applying models and algorithms to mine the data, they analyze their findings for patterns and trends to present to stakeholders for decision-making purposes.

Data Engineers

Data engineers are another essential piece of the big-data-artificial-intelligence puzzle. Their primary role is to design, manage, and optimize how data flows throughout an organization. Engineers are imperative in the designing of systems that utilize data inputs to make decisions. This is where AI and big data start to grow closer together. Both artificial intelligence and software engineers are critical in new software developments and engineering.  Think of data engineers as funnel creators. They collect data from fragmented sources and compile them into a single set that is sent to a data scientist for analyzation and interpretation. Concerning AI, data engineers help detect anomalies within data, create flow and pipelines, and structure the data. This is helpful as you approach deeper AI levels, such as deep learning, experimentation, and analytics.

Data engineers need to possess many skills to complete their jobs effectively. Important skill areas include:

  • Open frameworks
  • Software engineer and architect skills
  • Programming
  • SQL
  • Foundation software design and architecture
  • Visualizations and dashboards
  • Cloud platforms
  • Analytics
  • Data modeling

A primary function of a data engineer’s workload is to find new ways to automate data. These strategies and implemented into machine learning and AI to help streamline processes. Through sophisticated analytics programs, data engineers support machine learning and statistical practices. Aside from developing new models for machine learning, data engineers perform an abundance of data and task-driven responsibilities. They establish and maintain architectures, acquire new data, and align software architecture with business plans and requirements. They must find new ways to improve data reliability, efficiency, and quality to ensure that artificial intelligence models are functioning correctly and accurately.

Data engineers use data to discover tasks that cannot yet be automated, highlighting potential and improvement areas for future AI systems and devices. Ultimately, data engineers and data scientists’ teamwork is crucial to the success of AI and machine learning.

Data Analysts

To complete the big data triangle, data analysts play another crucial role in collecting and implementing data for artificial intelligence. Data analysts examine data and create reports and visualizations to help others better understand the finding of large data sets. How does this apply to artificial intelligence? There are several applications of data analytics to AI and machine learning. Analytics can help organizations and companies determine how to allocate their artificial intelligence resources, assist in identifying biases and flaws in machine learning and AI systems, and help artificial intelligence systems learn more about human behavior and how to satisfy requirements best. Data analysts are the closest thing we have to fortune-tellers. They help executive and business leaders determine the best path forward.

Data analysts are typically required to possess many different skills regarding programming and statistical tools, such as SQL, R Programming, Hadoop, Python, SAS, and Tableau. These processes and programs help data analysts share their stories with other disciplines, both within and outside their organizations. Like many different positions within AI and data science, analysts require an incredibly task-driven approach and likely spend their days performing the same responsibilities over and over again. These can include:

  • Interpreting data
  • Analyzing finding through statistical techniques
  • Developing and implementing databases, collection systems, and analytics
  • Create strategize that optimize statistical productivity and quality
  • Acquire data from multiple sources
  • Develop and maintain databases
  • Identify, examine, and decipher data trends and patterns
  • Clean data through reviewing reports and performance indicators, which help to identify coding issues
  • Work with executives and management to address business and information needs
  • Locate and outline improvement opportunities

As data analysts retrieve and collect data to organize it and use it to reach new conclusions, they spend most of their time serving the goals of those in other departments. So, depending on the purposes of the departments they are working with, their daily tasks and responsibilities may differ. Overall, data analysts provide invaluable insights that are shaping the way artificial intelligence and machine learning evolve.

What About Data Labeling?

Concerning machine learning, data labeling is the means of identifying raw data and adding significant or informative labels to provide context for machines to use. The raw data, in this case, usually refers to images, videos, and text files. This is the point in which human labor and interaction are most crucial for machine learning and successful artificial intelligence models and systems. Data labeling is imperative for many reasons, including computer vision, speech recognition, and natural language processing.

How it works is relatively simple, a human takes a piece of raw data, such as a photo, and applies labels that accurately describe what the picture contains. If it’s a photo of the New York City Skyline, you may use tags such as city, building, New York, skyscraper, traffic, cityscape, etc. This allows artificial intelligence and computer systems to accurately identify the photo’s context and deliver to another computer or human searching for a similar image. Of course, there are many approaches to data labeling. It can be as simple as presenting a photo to a large group of individuals and asking whether or not a particular object is present within the picture.

Once the raw data has been assigned, the appropriate labels, machine, and computer learning systems can be trained to produce the correct information when prompted. However, it is crucial that humans provide an objective standard, called the ground truth, to maintain a model’s accuracy.

There are many different types of data labeling, all of which include a human touch. Computer vision relates to labels, photos, pixels, and critical points. Photographs can be labeled based on the quality type, specific content, or pixel level. Natural language processing requires a person to identify significant sections of text manually. Human interaction and development are specifically crucial to this form of labeling as this form of data are used for sentiment analysis, optical character recognition, and entity name recognition. Audio processing is another form of labeling which converts sounds like speech, wildlife, buildings, cities, and weather and turns them into a structured format. Audio processing begins with the manual transcription of sound into written text. Adding tags and categorizing the audio empowers users to uncover in-depth information.

What Data Scientists Think of Their Careers

Data science is an emerging and rapidly growing field, which attracts the eyes of those looking forward to a sustainable and rewarding career. However, this career path is not for everyone and is hardly as wonderful is as it may seem. Research suggests that those within the data science disciplines spend over half of their time completing tasks that they do not like doing, such as cleaning and organizing large sets of data. While data labeling is one of the most important aspects of artificial intelligence and machine learning, nearly half of those in data science careers agree that this of one of their least favorite tasks.

The unfortunate truth is that data scientists and those in related positions rarely get the chance to work on the more cerebral tasks that allow them to exercise some creativity and critical thinking within their field. Their time is consumed with the dull and monotonous tasks they would prefer to do less. Another significant challenge for those in data science is the abundance of poly-structured data. This includes multimedia, image files, texts, and other forms of data that lack traditional structure. As the digital and virtual experiences become richer, the organizing of content becomes more difficult.

What Does the Future of Data Science Look Like?

Here is where the big-data and artificial intelligence loop starts to get a bit messy. Those working in data science are absolutely essential to the success of AI and machine learning; however, if, how, or when will they be replaced by the models and systems they helped create? While this is not an absolute and promised outcome, it can’t help but be questioned and addressed somehow.

So what is the likelihood that AI will take over data science jobs? Let’s consider the facts. AI has can currently perform similar tasks to data scientists and engineers, such as preparing data, cleansing, identifying outliers, checking for accuracy, and emptying records. AI can also perform self-service systems and automate the deployment of models into production. Furthermore, artificial intelligence can automate predictive features, generate a copious number of models, build basic models, and detect which models are becoming less effective.

This information seems alarming; however, there is plenty of evidence to support the theory that AI and data scientists work best together! A dynamic, multi-faceted approach to data and machine learning improves the quality of the work and the success of the outcomes. As things change within this space, so too will how we employ humans to service this industry. If careers within data science become less effective, lower-level positions such as data engineers will likely feel the negative impact first. This doesn’t mean that engineers will become eliminated or obsolete, but it may signal required education advancements or career modifications.

While big data and data science have a reputation for being boring, monotonous, and tedious, it is absolutely beneficial for those in and around the industry to improve and develop a general literacy for big data, analytics, AI, and machine learning. These technologies are not going anywhere any time soon. Data collection and analysis are changing the very fiber of business, marketing, machine learning, automated systems – really, everything. There is no reason to be scared of this new tech wave, but we do need to understand how its implications affect human lives and society as we know and understand it.

The opportunities within data analytics, machine learning, and artificial intelligence are dynamic, diverse, and continue to expand. Analyzed data is becoming a new form of storytelling. Whether or not this is harmful, we do not yet know. But those who embrace data and take steps to educate themselves in data science and analytic disciplines will have an edge above the rest regarding career advancement and longevity.





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