What most people don’t understand about AI and the the state of machine learning

AI is more than a buzzword, but right now, it’s closer to sea slug than an all-knowing machine.

Photo by Andy Kelly

Though the term was officially coined in the 1950s, Artificial Intelligence (AI) is a concept that dates back to ancient Egyptian automatons and early myths of Greek robots. Notable attempts to define AI include the 1956 Dartmouth conference and the Turing test, and passionate AI advocates persist to explain the concept to the world in a way that is distinguishable and digestible.

AI is a topic of mystery, wonder, and seemingly endless possibilities. However, it remains elusive to the general public, and is often portrayed negatively in predictions of its future. To combat the cycle of fear induced by Hollywood’s versions of AI, we need to understand, clearly, what artificial intelligence is.

How to know if it’s AI

In its most complete and general form, an AI might have all the cognitive capabilities of humans, including the ability to learn. However, a machine is only required have a minute fraction of these skills to qualify as an AI.

Artificial Intelligence

’. Put in one word: learning.

A common research subject is the sea hare (i.e. a mollusc or sea slug): specifically, scientists study the genes that define how its neurons fire. Depending on their genetic structure, two species of sea hares condition their behavior differently based on the same experience (i.e. data). Right now, machine learning operates at roughly this level; experts modify the program code of the learning algorithm (similar to the gene code of the sea hare), changing its abilities and predispositions to adapt to various experiences. The developmental state of machine learning is probably closer to invertebrates, like the sea hare, than to the advanced cognitive abilities of mammals or humans.

Photo by Johan Desaeyere

During the past two years, researchers started developing machine learning techniques that adapt autonomously to new tasks. However, methodologies are only in their infancy. To put it in the words of a DeepMind scientist,

recent work on memory, exploration, compositional representation, and processing architectures provides grounds for optimism.

In other words, we have reason to believe that reaching the goal of a more general AI might be feasible.

I’m afraid I can’t do that, Dave

The core message here: you cannot simply pour raw data into a general AI and expect something meaningful to come out — this kind of AI simply doesn’t exist yet. Furthermore, a machine can only learn suitable solutions if you provide a rock-solid problem definition. For a success story, you need good planning, a mathematically sound problem statement, sufficient training data, a lot of machine learning expertise, and software development capacities.

We will provide an article on the steps we use to create an AI shortly. Stay tuned.

Written by Alexander Hentschel
Edited by
Yasmine Nadery


What most people don’t understand about AI and the the state of machine learning was originally published in codeburst on Medium, where people are continuing the conversation by highlighting and responding to this story.

Gurupriyan is a Software Engineer and a technology enthusiast, he’s been working on the field for the last 6 years. Currently focusing on mobile app development and IoT.

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