Those who, like this columnist, spend too much time online will have noticed a kind of feeding frenzy over the past two weeks. The cause has been the release of an interesting chatbot – a software application capable of conducting an online conversation. The particular bot creating the fuss is ChatGPT, a prototype artificial intelligence (AI) chatbot that focuses on usability and dialogue and was developed by OpenAI, an AI research laboratory based in San Francisco. ChatGPT uses a large language model built via machine-learning methods and is based on OpenAI's GPT-3 model, which is capable of producing human-like text when given a prompt in natural language. It's an example of what has come to be called "generative AI": software that uses machine-learning algorithms to enable machines to generate artificial content – text, images, audio and video content based on its training data – in a way that might persuade a human user into believing that its outputs are "real".

Artificial intelligence software programs are becoming shockingly adept at carrying on conversations, winning board games and generating artwork -- but what about creating software programs? In a newly published paper, researchers at Google DeepMind say their AlphaCode program can keep up with the average human coder in standardized programming contests. "This result marks the first time an artificial intelligence system has performed competitively in programming contests," the researchers report in this week's issue of the journal Science. There's no need to sound the alarm about Skynet just yet: DeepMind's code-generating system earned an average ranking in the top 54.3% in simulated evaluations on recent programming competitions on the Codeforces platform -- which is a very "average" average. "Competitive programming is an extremely difficult challenge, and there's a massive gap between where we are now (solving around 30% of problems in 10 submissions) and top programmers (solving 90% of problems in a single submission)," DeepMind research scientist Yujia Li, one of the Science paper's principal authors, told GeekWire in an email.

Something incredible is happening in AI right now, and it's not entirely to the good. Everybody is talking about systems like chatGPT (OpenAI), Dall-E 2, and Lensa that generate text and images that look remarkably human-like, with astonishingly little effort. These systems can be incredibly fun to play with. Take this example, generated by chatGPT by Henry Minsky (son of Marvin Minsky, one of AI's founders), who asked chatGPT to "Describe losing your sock in the dryer in the style of the declaration of independence": When in the course of household events, it becomes necessary for one to dissolve the bonds that have connected a sock to its mate, and to assume among the powers of the laundry room, the separate and equal station to which the laws of physics and of household maintenance entitle it, a decent respect to the opinions of socks requires that it should declare the causes which impel it to go missing. We hold these truths to be self-evident, that all socks are created equal, and are endowed by their manufacturer with certain unalienable rights….

As people tested the system, it asked them to rate its responses. Then, through a technique called reinforcement learning, it used the ratings to hone the system and more carefully define what it would and would not do. "This allows us to get to the point where the model can interact with you and admit when it's wrong," said Mira Murati, OpenAI's chief technology officer. "It can reject something that is inappropriate, and it can challenge a question or a premise that is incorrect." The method was not perfect.

Photo editing app Lensa grew massively popular over the last week as social media has become flooded with people posting AI-generated selfies from the app's latest feature. For $3.99, Lensa users can upload 10-20 images of themselves and then receive 50 selfies generated by the app's artificial intelligence in a variety of art styles. But, before you slam the purchase button, a word of warning: Lensa's privacy policy and terms of use stipulate that the images users submit to generate their selfies, or rather the "Face Data," can be used by Prisma AI, the company behind Lensa, to further train the AI's neural network. An artificial neural network like the one used by Lensa, or the popular text-to-image generator Dall-E 2, studies vast quantitites of data to learn how to create better and better results. To be able to convert simple sentences into surprisingly well-crafted images, Dall-E 2 was trained on hundreds of millions of images to learn the association between different words and different visual characteristics. Similarly, Lensa's neural network is continuously learning how to more accurately portray faces.

Daath AI Parser is an open-source application that uses OpenAI to parse visible text of HTML elements. It is built on top of FastAPI. It is ready to set up as a server, and make calls from any language. It is currently hosted in ai.kagermanov.com. You need to register a free account first.

Check out all the on-demand sessions from the Intelligent Security Summit here. Canva, the popular Australian-based graphic design platform, is boosting its efforts to target enterprise business users with today's release of Canva Docs, part of its Visual Worksuite that was launched in September. Generative AI plays a big role in the release: Canva Docs incorporates the company's recently-released text-to-image beta built on Stable Diffusion, as well as the newly-announced Magic Write, an AI-powered copywriting assistant built on OpenAI's GPT-3. Cameron Adams, cofounder and chief product officer of Canva, says that in 2015 the company started allowing users to create presentations in Canva, which "was a real pivotal moment," he explained. During COVID-19, he added, remote-working employees needed to communicate with colleagues differently and were looking for tools.

More than three years ago, this editor sat down with Sam Altman for a small event in San Francisco soon after he'd left his role as the president of Y Combinator to become CEO of the AI company he co-founded in 2015 with Elon Musk and others, OpenAI. At the time, Altman described OpenAI's potential in language that sounded outlandish to some. Altman said, for example, that the opportunity with artificial general intelligence -- machine intelligence that can solve problems as well as a human -- is so incomprehensibly enormous that if OpenAI managed to crack it, the outfit could "maybe capture the light cone of all future value in the universe." He said that the company was "going to have to not release research" because it was so powerful. Asked if OpenAI was guilty of fear-mongering -- Elon Musk, a co-founder of the outfit, has repeatedly called all organizations developing AI to be regulated -- Altman talked about dangers of not thinking about "societal consequences" when "you're building something on an exponential curve."

Anyone who's seen the show knows that this is not what happened. Instead, it's a somewhat humorous misconfiguration of the details--sorta like listening to a friend misremember a series that they haven't seen in awhile. In a different conversation, I asked ChatGPT how the TV show Gilligan's Island ended. In reality, Gilligan's Island was cancelled by its network, so there was no ending.

10 questions I asked AI-driven ChatGPT. Launched recently, OpenAI’s natural language processing tool is pretty impressive, but still has room for improvement.