Author: Emily Curtis

What exactly is Artificial Intelligence?

Artificial intelligence is really starting to shape the world as we know it. The field of AI includes everything that has anything to do with the “intelligence” of a machine; and more specifically, that machine’s ability to imitate a human’s thought process and reasoning abilities.

Artificial IntelligenceWhile artificial intelligence develops programs to help solve problems, the patterns needed for solving a problem via AI is a lot different from the way a human would solve it. In a general sense, these programs that are developed are often designed to interpret, sort through, and provide insight from a vast amount of data. We want these AI programs to handle this data because it can process far more than a human brain ever could.

Four AI abilities

There are four abilities that contribute to artificial intelligence; and without them AI would not be what we expect.

Ability to sense

object-recognitionThe first, the ability to sense, correlates directly with object recognition. In this case, object recognition is the picking out and identification of objects from different inputs such as videos and digital images. Natural Language Processing (NLP) also contributes to the ability to sense, meaning the ability to read text and make sense of it.

Ability to converse

Thinking robotThe second, which is the ability to have a conversation, is the foundation to develop the ability to think. Predictive Analytics sums this up by identifying the likelihood of future outcomes based on historical data and algorithms (machine learning).

Ability to act

Working robotThe third, the ability to act, refers to taking action based on thinking. This is also known as “Prescriptive Analytics,” and determines the best solutions/outcomes among various choices, with known parameters.

Ability to learn

Learning robotThe fourth and final ability, the ability to learn, includes automatically occurring self-improvements. Not only do these improvements need to happen, but we also need to understand how these improvements were made as they occur.

More than sci-fi robots

SophiaThroughout the advancement process of AI, the technology industry has made AI an essential part of its work. The advancement of this field has caused debate over whether AI is a threat to humanity or not. Artificial intelligence is NOT something to fear; and it IS more than just sci-fi robots taking over.

Of course, it’s easy to understand why some may think AI and robots are one in the same, getting some things mixed up. Pop culture can be blamed for this, because robots are often portrayed in such a way that may cause humans to worry about what exactly they may become. In reality, robots are physical machines created to carry out a specific task and artificial intelligence is used to develop programs to solve problems. When AI and robots are integrated, autonomous robots are born.

Practical uses today

Believe it or not, but artificial intelligence systems are seen every day. Interesting Engineering came up with a list of everyday applications of AI, which can be separated into two categories: consumer-focused and enterprise-focused.

Some consumer-focused applications include smart cars, video games, smart homes, and preventing heart attacks.

Examples of enterprise-focused applications are customer service, workflow automation, cybersecurity, and maintenance predictions.

With the increasing advancements in the field of artificial intelligence, we are destined to see more and more practical uses.

Orzota can help!

The Orzota BigForce Docu-AI Solution helps automate document workflows for insurance and finance use cases. It uses sophisticated AI techniques to parse documents (image files, PDFs, etc.), extracting information and key insights while providing instant search and analysis capabilities.

To find out more, please contact info@orzota.com.

The topic of data science has been on the rise within the tech industry. Often, we see techies conversing and sharing articles about data science on social media and we hear professionals discussing it as part of their business plan. By now, most of us are aware that it exists and have an inkling about what it does. But can you answer the following questions?

Data Science

Do You Need a Data Scientist?

In the past, it has been known that larger, technologically advanced companies used data scientists (Facebook, LinkedIn, Google, etc.). However, we are seeing non-technology type businesses hire data scientists. For example, retailers are using data science for everything from understanding customers to managing inventory. Data science allows companies to gain insights from data in many fields and ultimately improve forecasting and decision making.

What Does a Data Scientist Do?

What Does a Data Scientist DoAccording to Dr. Steve Hanks, there are three major capabilities that data scientists must have: (1) They must understand that data has meaning, (2) They must understand the problem that you need to solve and how the data relates to that, and (3) They must understand the engineering.

A data scientist, in very general terms, looks at and investigates a set of data and then comes up with different ways to answer previously posed questions. Along the way, the data scientist may consider historical data analysis to develop analytical models and dashboards that provide insights and improve decision making.For example, a data scientist for a large retailer like Macy’s may look at not just past seasons’ data, but current economic and weather conditions to make predictions for their upcoming season. Retail executives make use of this to improve things such as sales, revenue, marketing strategies, advertising efforts, etc.

How Do You Build a Strong Data Science Team?

Choosing people that are aware and skilled in areas that fit your company’s need is essential. An article from Datafloq says, “The team needs to take the data and understand how it can affect different areas of the company and help those areas implement positive changes.” Not all the skills of a data scientist can be taught; it is important to have a natural affinity for data analytics, and the drive to produce beneficial insights to answer your company’s needs.Data scientists are not only computer scientists and statisticians, but must have a solid understanding of the business as well.

Should You Outsource Your Data?

Because this field of work is both complex and intimidating, there is a shortage of skilled professionals in the industry. Advanced analytics require a certain skill-set to develop and run machine learning models. Instead of spending the money and putting in the efforts to develop a team with the necessary skills internally, you can speed up your path to data science and outsource. For small-to-medium businesses, it can be cost-prohibitive to have their own data science team. There is work in the field of data engineering that must be done before a data scientist can develop models. This may not be an efficient use of resources for a small-to-medium business to hire both data engineers and data scientists.

Shanti Subramanyam, CEO at Orzota says, “Deciding to outsource reflects the core competency of your business. If you don’t have the financial resources or the capacity to focus on it, outsourcing is a faster and more efficient way to stand up a capability.”


If you’re overwhelmed by these questions, don’t be. Although the idea of data science and big data may seem complex, it is important to understand at least the basics. If you can articulate your business pain-points, it will be easier to answer these questions and find the best solutions to fit your company’s needs. Orzota is here to explain further, answer your questions, and offer services to help you feel comfortable with understanding and fulfilling your data science needs.