Little did OpenAI realize when it released ChatGPT last November that the advanced LLM (large language model) designed to uncannily mimic human writing would become the fastest growing app to date with more than 100 million users signing up over the past three months. Its success -- helped along by a $10 billion, multi-year investment from Microsoft -- largely caught the company's competition flat-footed, in turn spurring a frenetic and frantic response from Google, Baidu and Alibaba. But as these enhanced search engines come online in the coming days, the ways and whys of how we search are sure to evolve alongside them. "I'm pretty excited about the technology. You know, we've been building NLP systems for a while and we've been looking every year at incremental growth," Dr. Sameer Singh, Associate Professor of Computer Science at the University of California, Irvine (UCI), told Engadget.

Some software developers are now letting artificial intelligence help write their code. They're finding that AI is just as flawed as humans. Last June, GitHub, a subsidiary of Microsoft that provides tools for hosting and collaborating on code, released a beta version of a program that uses AI to assist programmers. Start typing a command, a database query, or a request to an API, and the program, called Copilot, will guess your intent and write the rest. Alex Naka, a data scientist at a biotech firm who signed up to test Copilot, says the program can be very helpful, and it has changed the way he works.

GPT-3 IS QUITE a beast. The Generative Pre-Trained Transformer 3, to give its full name, is a language model developed by OpenAI, a part-commercial, part not-for-profit artificial-intelligence (AI) laboratory in San Francisco. GPT-3 was trained on an unprecedented mass of text to teach it the probability that a given word will follow preceding words. When fed a short text "prompt", it cranks out astonishingly coherent prose written in a similar style. Your browser does not support the audio element.

GPT-3 IS quite a beast. The Generative Pre-Trained Transformer 3, to give its full name, is a language model developed by OpenAI, a part-commercial, part not-for-profit artificial-intelligence (AI) laboratory in San Francisco. GPT-3 was trained on an unprecedented mass of text to teach it the probability that a given word will follow preceding words. When fed a short text "prompt", it cranks out astonishingly coherent prose written in a similar style. Access to GPT-3 is restricted.

It can take years to learn how to write computer code well. SourceAI, a Paris startup, thinks programming shouldn't be such a big deal. The company is fine-tuning a tool that uses artificial intelligence to write code based on a short text description of what the code should do. Tell the company's tool to "multiply two numbers given by a user," for example, and it will whip up a dozen or so lines in Python to do just that. SourceAI's ambitions are a sign of a broader revolution in software development.

BROOKLYN, New York, Tuesday, August 21, 2018 - For decades, the cybersecurity community has devised protections to fend off malicious software attacks and identify and fix flaws that can disrupt the computing programs that are central to all aspects of life. Now, a team of researchers from New York University Tandon School of Engineering and Columbia University has received a grant from the National Science Foundation (NSF) to develop some of the first tools to bring those same protections to artificial intelligence (AI) systems. "There are ways to test and debug computer software before you deploy it and methods of verifying that your software works as you expect it to," said Siddharth Garg, an assistant professor of electrical and computer engineering at NYU Tandon. "There's nothing analogous for AI systems, and we're developing a tool suite that will lead to safer, more secure deployment of the systems used in autonomous driving, medical imaging, and other applications," he said. In addition to Garg, the research team includes NYU Tandon assistant professors Anna Choromanska, in the Electrical and Computer Engineering Department, Brendan Dolan-Gavitt, in the Computer Science and Engineering Department, and Suman Jana, an assistant professor of computer science at Columbia University School of Engineering.

Early in August, NYU professor Siddharth Garg checked for traffic, and then put a yellow Post-it onto a stop sign outside the Brooklyn building in which he works. When he and two colleagues showed a photo of the scene to their road-sign detector software, it was 95 percent sure the stop sign in fact displayed a speed limit. The stunt demonstrated a potential security headache for engineers working with machine learning software. The researchers showed that it's possible to embed silent, nasty surprises into artificial neural networks, the type of learning software used for tasks such as recognizing speech or understanding photos. Malicious actors can design that behavior to emerge only in response to a very specific, secret signal, as in the case of Garg's Post-it.

Early in August, NYU professor Siddharth Garg checked for traffic, and put a yellow Post-it onto a stop sign outside the Brooklyn building in which he works. When he and two colleagues showed a photo of the scene to their road-sign detector software, it was 95 percent sure the stop sign in fact displayed a speed limit. The stunt demonstrated a potential security headache for engineers working with machine learning software. The researchers showed it's possible to embed silent, nasty surprises into artificial neural networks, the type of learning software used for tasks such as recognizing speech or understanding photos. Malicious actors can design that behavior to emerge only in response to a very specific, secret, signal, as in the case of Garg's Post-it.

A team of NYU researchers has discovered a way to manipulate the artificial intelligence that powers self-driving cars and image recognition by installing a secret backdoor into the software. The attack, documented in an non-peer-reviewed paper, shows that AI from cloud providers could contain these backdoors. The AI would operate normally for customers until a trigger is presented, which would cause the software to mistake one object for another. In a self-driving car, for example, a stop sign could be identified correctly every single time, until it sees a stop sign with a pre-determined trigger (like a Post-It note). The car might then see it as a speed limit sign instead.