Industry Resources

How to Distinguish Hype Versus Reality Around Artificial Intelligence

With all the hype around Artificial Intelligence (AI), I’m reminded of a time when I was immersed in selling and building cloud solutions in 2009. A technology that was relatively new, “cloud” became the term du jour, finding its way into every offering and presentation. Does that sound familiar? Upon scrutinizing these presentations, it was apparent that the term “Web 2.0” was simply replaced with “Cloud,” previously the next big thing.

To educate potential customers about the cloud, our team developed a punch list of the five characteristics of cloud computing. This list was derived straight from the National Institute of Standards and Technology (NIST) and their definition of cloud computing. If an offering answered no to any of the characteristics, it was not cloud computing.

AI, tracing its roots back to the 1950s, presents a broader and more complex scenario than cloud computing. What we can do is attempt to create our own checklist of characteristics of AI, helping to discern whether a product that claims to be AI truly is.

Defining Artificial Intelligence

AI is fundamentally an emulation of human-like intelligence, with the capability to learn, adapt, and autonomously execute tasks within machine constructs. In essence, it weaves together various fields such as computer science, data analytics, and statistics to develop algorithms capable of mimicking human intellect. The ambition of AI is to create machines that can perceptively learn from experiences, adapt to new inputs, and autonomously perform tasks without human interference.

Mechanics of AI: A Glimpse into Machine and Deep Learning

Machine Learning (ML), a subset of AI, enables computers to refine their operations without explicit programming by learning from data. It uses algorithms to predict future outputs by analyzing historical data, forming a foundation for deep learning (DL).

DL, an intricate branch of ML, leverages artificial neural networks, often modeled to emulate human brain functionalities. These networks contain complex layers of interconnected nodes that meticulously process and transform data. Notably, DL has the ability to autonomously learn and enhance its performance from extensive data inputs, gradually improving its accuracy over time.

Navigating through applications, DL has carved substantial paths in fields such as image recognition, natural language processing, speech recognition, and recommendation systems. This technology permeates various industries, steering the evolution of innovations like self-driving cars, speech-to-text transcription, and personalized recommendation engines. Pioneering AI in Legal Recruiting

In the legal recruiting realm, emerges as a notable example of AI application. This AI-driven platform integrates publicly accessible data, esteemed industry legal rankings, and accumulated data from years in the lateral recruiting market, seamlessly matching candidates with premier firms., fueled by the aforementioned ML and DL techniques, scrutinizes data, identifying and correlating patterns of successful candidate placements to adeptly match candidates with suitable firms, thereby refining the recruitment process.

The merits of utilizing in legal recruiting are multifold:

An AI Checklist for Authenticity

To discern genuine AI applications, a checklist proves useful:

If a solution lacks any of these characteristics, it likely falls short of genuine AI.

Artificial Intelligence is a rapidly evolving field with transformative potential across various aspects of our lives and industries. Through its deployment of intricate algorithms designed to eschew bias and optimize legal recruiting, exemplifies the future trajectory of AI in legal spheres, driving efficient, accurate, and inclusive recruitment processes.