If it’s your first few hours around the Remotasks platform, chances are you’ve discovered that what we’re doing helps companies develop better AI and smart technologies. We’ve mentioned that the Tasks you’re going to accomplish can help teams create better smart assistants like Siri, or even self-driving cars!
However, how exactly does this work? How can Tasks that involve drawing boxes or labelling objects possibly lead to self-driving cars? Thing is, all of our Tasks help teach what we call AI, or artificial intelligence. Nope, we’re not building our robot overlords - but we’re building smarter robots to make our lives easier.
What Is AI Technology?
When we hear the term artificial intelligence or AI, we often think of concepts in science fiction. These include androids as intelligent as humans that accompany space explorers (Data in Star Trek: The Next Generation), sentient AI hell-bent on destroying humanity (Skynet in The Terminator), and an AI who uses a simulation to control humanity (The Machines in The Matrix). However, while these examples can make AI seem like a confusing scientific concept, the idea becomes much easier to understand when we take it step by step.
Artificial Intelligence is simply humans trying to create an “intelligent machine.” Ideally, a successful AI is something that can “think” and “learn” similar to a human but possesses the sheer computing power of a machine. Theoretically, an AI can help pave the way to automated cars and systems that can learn and adapt based on what humans think and do.
What is Artificial Intelligence?
If we want perhaps the best definition of AI, we might as well look at the definition of the Father of Artificial Intelligence himself - Dr. John McCarthy. According to him:
- Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It’s related to the similar task of using computers to understand human intelligence, but AI doesn’t have to confine itself to methods that are biologically observable.
And in the context of AI needing to “simulate” human intelligence, McCarthy also explains this isn’t necessarily the case. As he states:
- We can learn something about how to make machines solve problems by observing other people or just by observing our own methods. On the other hand, most work in AI involves studying the problems the world presents to intelligence rather than studying people or animals.
Is there a simpler way to define AI?
If McCarthy’s definition sounds a bit complicated, that’s because it’s incredibly hard to even define what “intelligence” is. As such, AI research - which also involves us here in Remotasks! - are hard at work with maximizing current AI systems. For now, we can try to simplify our definition of AI specifically in two shorter ways:
- AI is a set of algorithms that try to unite thinking, perception, and action together. (Patrick Winston, MIT)
- AI is a computer system able to perform tasks that usually needs human intelligence. (Jeremy Achin, DataRobot)
In simpler terms - AI is a computer system with two components:
- AI has a set of instructions that allow it to accomplish tasks.
- AI has instructions that allows it to use its own results to discover more efficient ways to do its tasks.
What are the kinds of AI?
Wait - if we’re trying to help AI in Remotasks, are we paving the way for evil AI like Skynet (Terminator) that would try and rule humanity? Not necessarily! While that topic is another article entirely, not all AI are immediately “robots that think like humans.” There are two broad categories of AI that we need to know about:
- Artificial General Intelligence, or Strong AI: This is the kind of AI we usually see in pop culture. Data from Star Trek: The Next Generation or robots from Westworld are examples of strong AI. They can think like humans and apply their intelligence to solve problems much faster than regular humans. We’re not there yet, though!
- Narrow AI, or Weak AI: Contrary to Strong AI, Narrow AI pertains to AI within a limited context. They focus on performing a single task extremely efficiently and operate under a lot of limitations and constraints. Since they don’t have the kind of intelligence humans do, they need some sort of guidance from us. Examples of Narrow AI are self-driving cars, personal assistants like Siri and Alexa, or even Google Search.
How does AI work?
Without relying on extremely complex descriptions, we can try to summarize how AI works by analyzing some of its most essential components. Here’s the definition of these components:
- Machine Learning. Machine Learning (ML) is the aspect of AI that focuses on learning from experience. Its programming allows ML to use algorithms that not only can analyze data but also make predictions based on that data. This is what helps navigational apps find the best routes, or for streaming services to predict recommended movies based on recent viewings.
- Deep Learning. Deep learning is a kind of ML that uses artificial neural networks to learn via data processing. These neural networks try to mimic the biological neural networks that our brains use. Theoretically, this helps machines self-educate and learn on their own. Deep learning works by employing layers of artificial neural networks to track multiple inputs and determining a single output. Self-education within deep learning works through constant processing, which allows smart assistants like Siri to answer questions and assist with other tasks.
- Neural Networks. Neural networks make deep learning possible. As we’ve mentioned, artificial neural networks try to simulate the biological neural networks in human brains. In this case, artificial neural networks employ the perceptron instead of the neuron in humans. As such, from a physical point of view, information flows via perceptrons in neural networks. In terms of function, neural networks facilitate deep learning via training examples. These come in the form of data sets, which neural networks process multiple times to help them give meaning to unknown data. For instance, thousands of dog pictures can help neural networks answer, “Is this a dog?”
- Cognitive Computing. Aside from machine learning, cognitive computing is another essential aspect of AI. Its main purpose is to help improve the interaction between machines and humans. Through a computer model, cognitive computing hopes to create the closest replica of the human thought process. In this context, cognitive computing comes to life by image recognition and analysis, as well as language comprehension.
- Natural Language Processing. Natural language processing or NLP enables computers to produce, recognize, and interpret human language. At its core, the main purpose of NLP is to help systems facilitate communication between humans and machines by helping them analyze context in language and give logical responses. For instance, Skype Translator can interpret multilingual speech and translate them for audiences in real-time.
- Computer Vision. Computer vision is a technique specifically designed to interpret and identify the contents of visual data. Through computer vision, machines can analyze the context and content of pictures, tables, graphs, and even pictures and videos. We see computer vision applied in various fields, such as in medicine where computer vision have begun evaluating x-rays and other scans.
Why Do We Need AI?
Given how “advanced” and how very “sci-fi” the concept of AI can be, it’s a reasonable question to ask just why we need AI in the first place. What exactly is the goal of AI in terms of improving our tech, and our lives in general? Here are some important things that AI can do that can greatly help us in our daily lives:
- AI maximizes the use of data. In our society today, we provide and make use of a lot of information. Thanks to AI, they can sift through that information and provide the best information we need based on what we’re asking. This is why Google and even our smart assistants can provide us with very useful information even if we ask them very vague or very specific things.
- AI can analyze volumes of data. Aside from helping us find answers to questions, AI can analyze data that have many complex layers. This allows AI to identify fraud in transactions or even create statistical models that can give insights to the economy, natural disasters, and simulate things that can help us prepare for the future.
- AI can help with complex problem solve situations. As AI and neural networks become capable of analyzing volumes of data, it becomes more capable of solving problems and applying its programming in other problem solving situations. Applications of AI for problem solving can include creating complex simulations for physicists and even weather engineers that ordinary computers won’t be able to solve. Thanks to AI, we can now prove or disprove mathematical and scientific theories, and we can create relevant predictions regarding various industries.
- AI can add intelligence in products. “Smart” technology exists today thanks to AI. Thanks to smart algorithms, we have things such as smart air conditioners and smart appliances that we can program to help us feel more relaxed and comfortable in our own home. This explains how we can just speak to our television or our speakers and they know what to do.
Where Do We Use AI?
When it comes to AI and its applications, this is where Remotasks comes in. Thanks to our platform, we can help machine intelligence and neural networks perform a specific task properly. With our expert system, our human insights can pave the road to more advanced AI development in order to create more functional systems we can use in everyday life. Here are some examples of AI uses that we’re already seeing in real life:
- We use AI in Self-Driving Cars. Us Remotaskers also help AI study how to manage self-driving cars. Autonomous vehicles have sensors that constantly take note of their surroundings, and they can distinguish between various cars and elements on the road thanks to our assistance and their computer vision. It’s because of our help and analysis that self-driving cars can get on the road safely and lead us wherever we want to go.
- We use AI in Healthcare. Thanks to AI, we have made significant strides in a lot of aspects in healthcare. With machine learning, some AI can help pathologists make accurate diagnoses through intricate tissue analysis. Meanwhile, Atomwise uses AI to study compounds and proteins in order to lead to the discovery of new drugs.
- We use AI in Robotics. While we don’t have extremely intelligent “robots,” we do have devices and machines that can “think” and solve problems to a limited capacity. Examples of these are the smart Roomba vacuum cleaners, or even Olly from Emotech, which is a smart assistant with an evolving personality. Unlike the likes of Alexa or Siri, Olly can gradually adopt the mannerisms of its owner via machine learning.
AI And Remotasks: What’s The Point?
As we’ve mentioned about Narrow AI, these AI don’t have the thinking capabilities of humans to be able to perform basic tasks. Rather, they use algorithms or programming to accomplish duties depending on what their programming dictates. However, instead of putting millions of lines of code manually, AI can “learn” by what’s known as machine learning and deep learning.
While they’re different in extremely specific ways, machine learning is simply the act of feeding data to a machine and using statistics to help it “learn” how to accomplish a task. Machine learning makes use of supervised learning (using labeled data sets) and unsupervised learning (unlabeled data sets) to teach an AI how to do a task.
This is where Remotasks comes in. Thanks to our Tasks, we provide valuable data sets that companies can use to teach their growing AI. This also explains why we need our Tasks to be as accurate as possible, as AI needs the best examples of data so it can learn properly.
That’s Cool! Sign Me Up!
If you’re interested in becoming a part of the Remotasks effort to help AI change more lives in the future, then you’re more than welcome to join us! Just go to the Remotasks website and sign up for free! You’ll get onboarded in less than an hour and have a front seat in helping AI make a change in the world. And as a plus, you’d get paid for it!