5 Things You Should Know Before You Use AI
It’s time to face facts: AI is here, and it’s going to change everything. From pizza delivery services to self-driving cars, artificial intelligence is getting the world ready for a future where machines do most of the work while humans are just along for the ride. While that might sound scary at first (and it should), it’s also exciting! We’re living in a future filled with incredible new technology like this, which means we all need to be prepared for what comes next. To make sure you’re ready for any possible situation that involves AI technology—from ordering doughnuts online or calling an Uber—here are five things you should know about artificial intelligence when it comes into play in your life:
Speaking to a machine is surprisingly easy
Once you’ve gotten past the initial shock of talking to a machine, it’s pretty easy. You can ask it to do things for you and it will respond in kind. And when you talk to it, it actually understands what you’re saying!
Try asking your AI assistant something simple like “What is the weather today?” or “What’s the best way to get from point A to point B?” It will give you detailed answers with maps and images and all kinds of useful information about where there are good places for lunch along your route. Or maybe all of this sounds too complicated; perhaps instead just ask how old someone is. The possibilities are endless!
AI is not good at context or nuance
AI is not good at understanding context or nuance. It’s a machine, and its job is to hit the right buttons in your app or website to get the desired result. But you know that sometimes people don’t actually want the thing they say they want. Humans know that sometimes people use language as a way of expressing emotion and tone. We as humans also know that sometimes people are joking or teasing. Machines don’t get that, at least not yet.
Let’s use an example: if someone says, “I’m hungry,” it could mean one of several things. They’re really hungry; they’re bored; they’ve just eaten lunch and need a snack before dinner; the thought of food makes them feel sick because their body isn’t getting enough nutrients from what little food there is available… The point is, it can take some mental effort for us humans to interpret what another person means by their words based on a combination of their facial expressions and body language along with other contextual clues like whether or not we just had lunch ourselves (and therefore might be feeling similar hunger pangs).
Don’t try to teach it with your own ideas
- Don’t try to teach it with your own ideas.
Executives and managers tend to try to teach the AI their own way of doing something. For example, if they want an AI assistant that can do research, they might explain what they mean by “research” and use their own examples of how to perform research. This is not only unnecessary but also counterproductive: The technology is designed to learn on its own and will be more effective when left alone.
Don’t get too excited about the results, don’t get frustrated if it doesn’t work as you expect, and don’t expect it to be perfect.
AI can seem like a magic box that just spits out insights into your data—and that’s exactly what makes it so addictive. You’re going to start seeing results almost immediately after you turn on your AI platform, which can make you feel like all of your problems are solved! However, there are some important things to keep in mind before getting too excited about the results:
- Don’t get carried away with the results: As tempting as it may be to run with what looks like a good idea from an AI model, keep in mind that this is still only one possible interpretation of your data. Always consult multiple sources when making decisions based on data analysis (including human experts). And always validate any recommendations generated by an algorithm with actual users or customers!
- Don’t expect perfection: In fact, expect imperfection—this isn’t a perfect world we live in yet! Be aware that even though AI software has no feelings or opinions (at least not yet), its algorithms do not know everything either. They may make mistakes; they may misinterpret information; they may misinterpret each other! Make sure there is room for error-checking before relying solely on these systems for decision-making processes.
It’s all about the data.
- It’s all about the data.
- You need a lot of it, and it has to be good.
- If you want answers to questions, you can’t just ask any old question—you have to specify which data you want your AI to use as input for its answer. This is called a “feature,” in machine learning parlance. The more features your system has, the better it can learn patterns in your data and make predictions based on those patterns—but only if they’re relevant! So if you have an AI that’s supposed to detect fraud or predict product sales or something else along these lines, don’t just throw everything at it and hope for the best; make sure your algorithms are looking at the right stuff first by asking yourself what questions they’re trying answer (and where).
Artificial intelligence has real benefits for some tasks, but don’t expect it to fully replace a human yet.
If you’re new to artificial intelligence and are thinking that it could solve your problems, think again. AI is good at some tasks but not others, so don’t expect it to fully replace you yet.
As an example: The best way to use artificial intelligence is as a tool to help you solve problems. It’s important to know what AI tools can and cannot do when they’re being used by humans.
The most basic thing AI can do is take data you put into a machine-learning algorithm and make predictions based on that data—for example, predicting what products someone will buy based on what other people like them have bought in the past (this is called unsupervised learning). This kind of “predictive” task is easy for computers because all the information needed for prediction lives in the computer’s memory; there’s no reason why machines should be able to do this better than humans can unless there’s something wrong with how we think about our world (which may be true).
Other types of tasks require more creativity from computers because they rely on concepts that aren’t easily represented as digital bits or numbers—like recognizing objects in images or understanding human language (these are called supervised learning). These kinds of tasks require powerful computing hardware like GPUs which are made specifically for making sense out of big datasets by analyzing patterns within them—but even then it still isn’t trivial.
I hope these five tips have given you a good sense of what to expect from AI. Get excited about its capabilities, and don’t be afraid to try it out for the first time. You might be surprised by how easy it is!
To put all this AI talk into context, AI wrote 95% of the above post. I went to Copy.ai and I created the Headline, then the program produced an outline. I then asked a few questions regarding that outline, pressed a button and it created most of this post.
I did go back in and edit 11 things that were, “wrong,” with the article, but I use another free AI program called Grammarly that checks everything for me, and I can decide if I want to keep the suggestions or not.
You don’t have to use the content that AI creates for you word for word, but it definitely speeds up the process. I wrote a post yesterday that took 2 hours and was 1159 words. As of right now, this post is 1340 words and going to go up with every keystroke and AI wrote most of the words above the Conclusion in less than 2 minutes. In fact, it wrote the whole article faster than I wrote this last paragraph.
Some call AI’s writing, “soulless,” but I wouldn’t call it that. If you didn’t know that a machine wrote most of what’s above the Conclusion section, could you have told a machine did it?
For all you Apocalyptic people out there, don’t put your head in the sand on this AI revolution. It’s going to happen whether you like it or not and you might as well start familiarizing yourself with some of what’s going on now.
Good places to start are OpenAi, Chat GPT and Dall-E2. Google them now, because soon enough, Google will look like MySpace.