��ɫ��Ƶ

As 2023 began, many of us found ourselves working alongside a new kind of colleague:��artificial intelligence��(AI).

While not exactly new, AI has broken out in the past year as an overt force in the workplace to automate simple tasks and help optimize overall efficiency in a variety of industries. Most notably,����had everyone talking when a free preview was released in December 2022.

Many more artificial intelligence tools are available today besides ChatGPT, which is a chatbot linked to a language model called��generative pretrained transformer, and the months ahead are certain to see the release of still more besides.

While it can be tempting to dive headlong into the brave new world of automation that artificial intelligence offers, the technology is still new and fraught with both��flaws and unanswered questions. In the past few months, we’ve seen numerous examples of artificial intelligence creating problems for its creators and users at work, including����and��. And��.

So, it’s important to consider the risks and benefits of this emerging tech and��apply it wisely at work. Here’s what you should know before using AI-powered tools and systems at your job.

What is artificial intelligence?

Artificial intelligence, simply put, is a combination of computer science and large data��sets capable of making decisions based on algorithms.

While the field of AI implicitly includes any program that simulates human thought, the term today refers mostly to��algorithmic learning, where programs or robotics can analyze data, make decisions and create output without being explicitly told how to make each decision. The methods by which these algorithms autonomously analyze data (text, images, numbers) and discern trends or patterns within them to make decisions are collectively known as����(ML).

Machine learning algorithms have been gaining in sophistication for several decades now. They became common in��big-data business applications��since at least the dawn of the social media era, particularly in AI-powered marketing applications, such as advertising and content personalization, where complex networks make split-second decisions about which content to load for a given user based on available data about the individual.

If you’ve ever been creeped out by the uncanny coincidence of content in your social media feeds displaying ads for things you’ve just been looking at or talking about, that’s machine learning at work — and it’s been going on for years. The last three tech companies on my resumé were focused primarily on machine learning-driven marketing applications like these, and their use in the broader business market has only grown in recent years.

Up to this year, most AI applications required a lot of technical effort to use, with specialists in data science and various other tech fields having to run complex queries against the machine learning models to get usable output.

What’s new is the emergence of��relatively intuitive interfaces��for ordinary people to ask robotics to perform useful tasks based on what they know from their large data sets. ChatGPT can generate text content in response to typed prompts. Other tools, such as��, can generate custom graphics based on typed input.

So, instead of needing someone with a PhD in data science to run queries for you, you can now type in what you want using plain English (or a variety of other languages) and get something useful back, at least some of the time.

The general term for the type of artificial intelligence that ChatGPT and DALL-E2 belong to is��, so called because these tools don’t just parse data, they actually��generate output in novel ways. ChatGPT doesn’t just spit out information it has collected in its databases; it generates new text algorithmically, using the data it has collected and increasingly sophisticated methods of writing content.

This means you can, for instance, give it your grocery list and ask it to render that list as a Shakespearean sonnet. DALL-E2 doesn’t just put existing pictures from its database into an image; it generates a new image from scratch based on your request. So if you ask it for a picture of Albert Einstein playing a guitar while riding a unicorn, it’ll figure out a way to create that picture, pixel by pixel. (I’ve tried both these examples personally, and the results are entertaining.)

1.���� Do your own research

2.���� Be wary of infringement

3.���� Protect your own data

4.���� Do the final edit yourself

Even if you do generate a whole work of content with artificial intelligence, it’s important to give the output��a final review with human eyes and expert analysis. Edit it, in other words, before pushing it out into the world. This will help you avoid embarrassing artificial intelligence errors.