What To Look For When Hiring AI Researchers


By Naveen Joshi  |  November 5, 2018  |  CogWorld on FORBES

Is data the new oil? Considering oil’s sky-high demand by both industry and government organizations and the fact that oil only becomes more valuable post-refining and offers a competitive edge to those owning this commodity in surplus, I feel the analogy between data and oil is quite justified. Just as oil requires processing to become usable and is fed to engines to work and create value, data requires cleaning and is fed to algorithms to derive valuable insights and make decisions. However, that’s where the similarities between the two end.

While there's only one way to use oil - burning it in an engine to generate energy - data on the other hand can be used in myriad ways for a range of different purposes, from identifying simple statistical trends to training complex robotic AI for independent interaction with humans. The effectiveness and innovativeness of data-driven AI applications depends chiefly on the people working on those applications, such as AI researchers and data scientists, who are among the most sought-after people in the tech sector today.

Considering the shallowness of the global AI talent pool, finding good AI talent can be a hard task for organizations, unless the organizations happen to be up there with Google and Facebook. Before reading on to know more about hiring AI researchers, please note that when I say AI talent I am referring to AI researchers, who, contrary to popular belief, are different from data scientists.

What is the difference between data scientists and AI researchers?

The titles AI researcher and data scientist are often used interchangeably, which is excusable considering the extensive overlap in their domains and given the inclusion of machine learning as a common part of their expertise. Despite their shared interest in machine learning and focus on data, data scientists and AI researchers have a few fundamental differences. Data scientists are data-oriented engineers who perform tasks such as cleaning data, processing the data using algorithms and finding answers and solutions to pertinent problems through the analysis of data. AI researchers, on the other hand, conceptualize and explore new ways of leveraging data by developing new AI algorithms, i.e., they create and ask new questions that can be answered using AI.

While the result of a data scientist’s work is usually solutions to organizational problems created with algorithms, the result of an AI researcher’s work is an entirely new algorithm or an AI program or capability that enables novel and more effective ways of driving action through data. While the primary focus of data scientists is on the data itself, AI researchers focus on finding ways to analyze data in innovative ways for automated decision-making and action.

Simply put, data scientists are to AI researchers what engineers are to scientists. AI researchers explore new ways and create new systems of solving-problems, while data scientists tweak and apply these systems in real-world scenarios. Thus, a team of AI researchers can create an AI system that can potentially be used by hundreds of data scientists in different situations. However, it is important to note that the functions of AI researchers and data scientists are not always mutually exclusive and can vary across organizations.

For instance, the data scientist for an automobile manufacturer can use machine learning algorithms to gather and process data from the cars to monitor performance and driver behavior and to recommend performance and safety upgrades for them to improve the vehicle. Having a research facility with AI researchers can enable the car manufacturer to find groundbreaking ways of making vehicles autonomous, such as Google Deepmind’s development of humanesque grid-based navigation systems that can control driverless vehicles in the future.

Why do we need AI researchers?

The purpose of gathering and analyzing data, regardless of application, business or industry is to drive action. Data science and data scientists enable the mining of data and inference of logical conclusions to assist decisions that eventually lead to action. Meanwhile, AI enables unmanned systems to analyze quickly a large volume and variety of data to make decisions and act independently. Thus, AI researchers, as the name suggests, research novel forms of AI technology to create new applications that use data to drive independent actions. Besides having the knowledge of statistics, data science and programming, AI researchers are also skilled in advanced domains such as deep learning, deep neural networks and natural language processing. AI researchers are necessary to lead AI development and experimental projects and to extend the existing AI capabilities.

What to look for in AI researchers?

Although there's no rigid set of requirements other than the minimum educational qualification that needs to be satisfied to be hired as an AI researcher, the presence of a few acquired as well as innate attributes helps:

Acquired and innate attributesNaveen Joshi

 

 

 

 

 

 

 

 

 

 

 

 

 

  • AI programming skills: This one goes without saying, but coding skills is a given for any professional in the AI and data science domain. The best programming languages for AI development currently are Python, Lisp, Prolog, R, C/C++ and Java. Out of these languages, Python is most preferred by both tech companies and AI researchers themselves, possibly because of its ease of use.
  • Analytical thinking: Since artificial intelligence is closely intertwined with data analysis, analytical skills are necessary for potential AI researchers. Having good analytical skills translates into the ability to:

-make sense of data

-verify the validity of the data gathered

-identify connections between different variables, and

-form logical conclusions based on the available data.

These skills are required to help AI researchers create functional algorithms to analyze data to power AI systems.

  • Innate inquisitiveness: An innate propensity to ask questions and the ability to ask the ‘right’ questions is key for AI researchers, since asking questions is necessary to identify the different kinds and sources of data required for analysis. Asking the questions ‘why’ and ‘how’ enough helps in breaking down problems to their roots and devising effective solutions for the most complicated problems.
  • Creativity: Since AI research is innovative and exploratory by nature, creativity is a highly desirable quality in AI talent. Creativity and lateral thinking allow AI researchers to engage in the hypothesis of ‘what if’ scenarios, which can lead to breakthroughs and progress along entirely new avenues and can contribute to the expansion of AI capabilities to an unimagined extent.
  • Relevant interests and passions: AI researchers whose passions align with your industry of focus are necessary to lead successful AI projects that benefit your organization. For instance, if your organization operates in the healthcare industry, hiring AI researchers with a passion for and knowledge about the sector will be much more beneficial than ones without. The closer an AI researcher’s interests and industry experience aligns with expectations, the greater the effectiveness of your AI endeavors.

The AI revolution is happening and is increasingly gaining traction among organizations in various industries, regardless of whether you want it to happen or not and regardless of your preparedness for it. Just how the discovery of oil led to organizations quickly switching to oil-powered processes, the invention of AI has led to companies scrambling to gather AI researchers and data scientists to gain technological advantage over their peers and to not be left behind. So, what will you do during this period of rapid, revolutionary transition? Will you lead the change or risk being left behind?


Naveen Joshi is Founder and CEO of Allerin, which develops engineering and technology solutions focused on optimal customer experiences. Naveen works in AI, Big Data, IoT and Blockchain. An influencer with a half a million followers, he is a highly seasoned professional with more than 20 years of comprehensive experience in customizing open source products for cost optimizations of large scale IT deployment.


 

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