Created by OpenAI, ChatGPT has become quite a trend these days. While users are enjoying using the platform, the AI tool is giving sleepless nights to tech giants like Google and Microsoft, and also humans. It is considered as a threat to some human jobs. Firstly, what is this AI tool? ChatGPT is an AI chatbot that has been trained with data sets that help it to give correct and unique answers in real-time.

In a new era of rapid technical advancement, people are getting serious about using deep learning systems. A few years ago, we knew a good bit about deep learning as a science, but few people saw actual use cases for this type of technology. Fast-forward to today, and we have applications like Chat Generative Pre-Training (ChatGPT) and Dall-E from OpenAI (and other types of neural network engines from chatbots to advanced recommendation engines) working with human operators across the web to make art, tell stories and do all kinds of cognitive work that would have been unthinkable a decade ago. So what do these deep learning technologies really look like under the hood? This is one of the mot fundamental components of deep learning systems.

Comment The lucid ramblings and art synthesized by ChatGPT or Stable Diffusion have captured imaginations and prompted no shortage of controversy over the role generative AI will play in our futures. As we've seen with CNET and Buzzfeed, executives are no less dazzled by AI's creative potential to replace workers with profits. But one of things that's often missed in these conversations is the need to retrain these models regularly or risk them aging into irrelevance, particularly in rapidly evolving environments like the news. ChatGPT, Stable Diffusion, Dall-E-2 and the majority of generative AI today are trained on large datasets and then made available as proof of concepts or exported as a pre-trained model. Let's take Stable Diffusion as an example as it offers a glimpse at just how misleading the scope these models can be.

Making predictions about enterprise technology is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say with some degree of certainty whether the prediction came true or not -- with evidence to back that up. In this Breaking Analysis, we aim to do just that with predictions about the macro information technology spending environment, cost optimization, security – lots to talk about there – generative AI, cloud and supercloud, blockchain adoption, data platforms (including commentary on Databricks Inc., Snowflake Inc. and other key players), automation and events, and we even have some bonus predictions. To make all this happen, we welcome back for the third year in a row, Erik Bradley, our colleague from Enterprise Technology Research. As well, you can check out how we did with our 2022 predictions. Each year, tech vendor PR pros reach out to us to help influence our predictions. It starts as early as October.

Can generative AI ChatGPT really serve as a mirror into humanity? Mirror, mirror, on the wall -- humans are the brightest of them all! That isn't of course a proper quotation from the famed Snow White and the Seven Dwarfs, but I opted to leverage the contrivance for a handy purpose. The matter has to do with how humankind sees itself when looking in an all-seeing all-telling mirror. Are we the cat's meow? Do we stand tall above all else? The reason I bring this up has to do with a topic that at first glance might seem afield of the weighty matters underlying how humankind perceives its place in the cosmos. I am going to tie these big-time vexing questions about life, our existence, and humanity all told to the emergence of Artificial Intelligence (AI). Some are insisting that the latest in AI can serve as a mirror into the soul of humanity. Yikes, do we want this? Maybe we won't like what we see. On the other hand, perhaps we have to stiffen our resolve and use AI to see us as we really are. Like a bucket of ice-cold water, AI might be the right thing at the right time to shock us into realizing who we are and where we are going.

OpenAI, the company behind the chatbot ChatGPT, has ramped up its hiring around the world, bringing on roughly 1,000 remote contractors over the past six months in regions like Latin America and Eastern Europe, according to people familiar with the matter. About 60% of the contractors were hired to do what's called "data labeling" -- creating massive sets of images, audio clips, and other information that can then be used to train artificial intelligence tools or autonomous vehicles. The other 40% are computer programmers who are creating data for OpenAI's models to learn software engineering tasks. OpenAI's existing Codex product, launched in Aug. 2021, is designed to translate natural language into code. "A well-established company, which is determined to provide world-class AI technology to make the world a better and more efficient place, is looking for a Python Developer," reads one OpenAI job listing in Spanish, which was posted by an outsourcing agency.

OpenAI's chatbot, ChatGPT, debuted in November and has quickly become a viral sensation. And having seen its capabilities -- producing cogent and creative writing based on user prompts -- everyone from venture capital luminaries to my buddies at the bar seem to be saying the same thing: this is about to put knowledge workers like me out of a job. After all, Microsoft announced a multiyear, multi-billion dollar investment in OpenAI this week. So for this week's newsletter, I put the A.I. tool to the test. Keep in mind, ChatGPT warns you off the bat that it "may occasionally generate incorrect information" and "may produce harmful instructions or biased content."

As the breakout success of OpenAI's ChatGPT triggers a tsunami of excitement over artificial intelligence, Microsoft Corp. is positioning itself at the forefront of what some see as the next wave of technological innovation.

Originally published on Towards AI. Prophecies of the third AI winter early in 2022 -- or AI hitting a wall -- aged fast and poorly with DALL·E 2's announcement in April 2022, followed by many more text-to-image applications largely driven by Diffusion Models, a very productive area for Computer Vision research and beyond. The year 2022 in AI was defined by a strong upward trend. Moreover, large Language Models proved to be an even more fertile area with several papers significantly expanding on their capabilities: Retrieval Augmentation, Chain-of-Thought prompting, Mathematical Reasoning, Reasoning Bootstrapping. Language Models research is far from over.

Everybody seems to be enthralled with ChatGPT and the technology is indeed impressive. The chatbot is passing all kinds of graduate level exams, and educational institutions are rushing to devise policies to prevent students from cheating on assessments using the program. ChatGPT can even write scientific abstracts and research papers that fool scientists into thinking that they are real reports. Some users, however, have reported that the chatbot cited non-existent academic journal articles as source material. Amidst all this buzz, we recently conducted our own simple and non-iterative queries to test how the program fares with written texts, which are bread-and-butter products for the communications and PR industry. We noticed that prompts generated texts that included a few paragraphs at most and were short on e.g., persuasive arguments or supporting evidence.