What Is Artificial Intelligence? A.I. and Machine Learning …

Crazy singularities, robot rebellions, falling in love with computers: Artificialintelligence conjures up a multitude of wild what-ifs. But in the real world, A.I. involves machine learning, deep learning, and many other programmable capabilities that were just beginning to explore. Lets put the fantasy stuff on hold (at least for now) and talk about real-world A.I. Heres what it is, how it works, and where its going.

A.I. seeks to process and respond to data much like a human would. That may seem overly broad, but it needs to be: Developers are baking in human-like smarts into a wide variety of applications. Generally, A.I. falls within three categories which we would note there is still some disagreement as to what the exact definitions are, much less if theyre truly possible.

A.I. can also be classified by how it operates, which is particularly important when considering how complex an A.I. system is and its ultimate cost. If a company is creating an A.I. solution, the first question must be, Will it learn through training or inference?

As weve noted earlier, these definitions are only meant as a general guide (this Medium article is a great discussion on what weve just talked about), and some may have slightly different descriptions. But there are examples of current A.I. which are worth discussing.

Jan Schnorr/C2Sense

Voice assistants: Siri, Cortana, Alexa, and other voice assistants are growing more common, becoming the face of modern A.I. A growing subset here are chatbots, which manage messaging on websites and carry on online conversations.

Translation: This isnt just about translating language. Its also about translating objects, pictures, and sounds into data that can then be used in various algorithms.

Predictive systems: These A.I.s look at statistical data and form valuable conclusions for governments, investors, doctors, meteorologists, and nearly every other field where statistics and event prediction prove valuable.

Marketing: These A.I.s analyze buyers and their behavior, then choose tactics, products, and deals that best fit said behavior. There is a lot of crossover between these behind-the-scenes tools and voice assistants at the moment.

Research: Research A.I.s like Iris search through complex documents and studies for specific information, typically at higher speeds than Googles search engine.

Awareness: These A.I.s watch for and report unusual events when humans cant have an eye on them. One of the most complex examples of this is theft detection, which reports unusual behavior. A more exciting example, however, is self-driving cars, which use A.I. systems to scan for dangers and choose the appropriate course of action.

Editing software: These basic A.I.s look at pictures or text and locate ways that they could be improved.

Recently, neural networking expert Charles J. Simon recently opined on our pages about where he thinks A.I. is headed, which we recommend you read. While we wont cut and paste the entire article here, well point you to one specific section:

Most people look at the limitations of todays A.I. systems as evidence that AGI [general A.I.] is a long way off. We beg to differ. A.I. has most of AGIs needed pieces already in play, they just dont work together very well yet.

This is a key point. As weve noted, A.I. is getting better at least perceptually by the fact that developers are stringing together several narrow A.I. platforms. But the platforms dont talk with each other. For example, while Alexa might now be able to start your car, it cant use the current weather conditions to adjust your cars heater or air conditioning systems or start the defroster to make sure youre ready to go as soon as you get in. But Simon argues that we may have the computational and developmental capability either already and dont know it yet, or within the next decade.

Companies are spending massive amounts on money on A.I.right now, and as long as theyre willing to spend the billions (if not eventually trillions) to advance the technology, things are going to move quickly. But there are all kinds of roadblocks in the way whether it be a recessionary economy, computational challenges, and even moral and philosophical hurdles to overcome so the road to a real-world Skynet might be a long one.

While we keep coming back to the obvious Skynet references, its time for a bit of a reality check. A.I.s are long strings of programmed responses and collections of data right now, and they dont have the ability to makes trulyindependent decisions. That being the case, malice is definitely off the table for the time being. But thats not to say human error could make them so.

For example, if a predictive A.I. tells a team that storms will spawn on the East Coast next week, the team can send resources and warnings there in preparation. But if storms actually appearin the Gulf of Mexico and hit the coast there, that prediction was inaccurate and may have endangered lives. No one would think the A.I. is somehow personally to blame for this; instead, they would look at the various data inputs and algorithm adjustments. Like other types of software, A.I.s remain complex tools for people to use.

At least for now, A.I. is, for the most part, harmless and if anything helpful to the world at large. But that could change in the distant future, and at that time well need to have a serious discussion on just how much of our lives were willing to turn over to machines.

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What Is Artificial Intelligence? A.I. and Machine Learning ...