Before we begin, let’s get definitions straight — what is artificial intelligence (AI)? Or more specifically, what isn’t it? As you might expect, ask any technical person for a definition of something, and you’ll get a dozen different responses, and at least half of them will be, “It depends.”
In my humble opinion, AI is a machine or program that learns from its experience and adapts its behavior accordingly.
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As much as I love chatbots and voice interfaces, this excludes most of them, as they tend to have a static selection of knowledge and interactions. Their developers may learn from application logs and change the application over time, but that’s not the same. Current popular and widespread applications of AI include game intelligences (playing or running), self-driving cars, and object manipulation and perception.
AI is not new; I even took a handful of AI modules at university way back in the distant past of 2002, and it’s history stretches back much further than that. While techniques have existed for some time, recent demand and plunging hardware prices have created a surge of activity, interest, projects, and applications.
Many other technologies come under the general AI umbrella that you have likely heard about, including machine learning for training an intelligence, and a variety of technologies for processing inputs and outputs (language processing, image recognition, etc.). AI is a big topic to try to summarize, and in the words that follow, I am simplifying as much as possible, so bear that in mind. Okay, here we go!
Think of machine learning as (somewhat) similar to humans going to school. It’s when your application gains the initial knowledge it needs to be useful and builds upon as it learns from experience. Much like humans at school, it has the ability to make judgments and act, dependent on what it learns and how you taught it.
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If you give an application a disconnected, limited, or biased information set, then it may not be as effective. Getting the balance right for training an AI is hard, and (more than normal in the tech space), really depends on your use case. If you are creating an AI for a niche use case, with a smaller data set available, then it won’t need as much training as an AI for a broader purpose, or one where you can potentially pull from a lot of data sources.
Deep Learning and Neural Networks
A subset of machine learning, deep learning focuses on attempting to replicate the way the human brain works. That’s a mysterious statement, as we don’t even fully understand how the human brain works; it’s more that now computing power allows us to emulate better how we perceive the human brain works, and how it learns from experience.
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Neural networks are the method behind deep learning, multiple nodes that undertake tasks by considering examples and then sharing their experiences with other nodes in their network. If one node learns what success with a task equals, they can share that with all the other nodes, and move onto new experiments.
If you thought the definitions so far were broad, then prepare to be surprised, as there is no agreed definition of cognitive computing, so I will attempt my own.
If neural networks attempt to simulate the brain, then I think cognitive computing helps enhance that “brain” with useful sensory information. This information includes textual and aural language, images, heat, spatial awareness, and more. The stream of extra data supplied to the network helps it adapt and respond to changes, making decisions based upon them.
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While some may now consider it a part of cognitive computing, computer vision is more established with a longer history; I even remember image recognition being one of my favorite units at university.
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In an AI context, “vision” also includes images we aren’t used to seeing, as machines can also process other types of visual input that we can’t, such as x-ray or infrared.
Natural Language Processing (NLP)
As a writer, NLP is the aspect of AI most interesting to me. Again, it’s not a new discipline, but recent advances have pushed it further forward. NLP deals with interpreting written or spoken human language to understand its content, context, and intent, as well as responding to a human appropriately based on what its learned.
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Tools and Libraries
When it comes to tool and library recommendations, there are some specific to each section and others that span multiple categories. A lot of libraries in this field are aimed at Python developers, but I’ll try to include a few that support other languages, too.
Major cloud providers
IBM is pushing hard on their already infamous AI tools and platform; Watson has a service and library to suit most of the use cases above, often with an option to self-host or run in their cloud.
There are also plenty of self-installed open-source options, and a quick internet search results in dozens of options. Here’s a small selection of the common favorites.
- Keras, a high-level neural networks Python library that can sit on top of other deep-learning libraries aimed at making experimentation with models easier.
- MXNet, a new but already popular deep-learning library that supports multiple programming languages and deployment methods.
- Deeplearning4j is a JVM-based deep-learning library that also has an enterprise-friendly offering with built-in visual notebooks for experimentation.
- Spark MLib, if you are already using Spark for data streaming, then this additional library helps you do more with that data.
- OpenCV is a widely used (and supported) library for computer vision.
- SimpleCV is similar and snapping at its heels.
- NLTK is a Python library for processing and understanding natural language.
- And for the JVM users, OpenNLP is what you’re looking for.
Technically minded folks may wonder why I’ve added this point, but it’s something important to me, so I’m sneaking it in.
As we (as businesses and societies) become more reliant on AI to undertake an increasing amount of tasks for us, we need to be careful. I’m not a believer in sci-fi predictions of killer robots, but there are other real and more immediate issues related to AI.
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The lack of diversity in the tech industry is especially relevant when it comes to unsupervised automated systems making decisions based on training data. I don’t think engineers intentionally introduce biased data into their machine-learning models, but we are often unaware of our own subconscious biases, especially when there is no one different from us in our team to challenge what we do as “not right.” Remember…
Algorithms aren’t biased, but people are.
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