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"But the answer to how he was chosen is actually an algorithm, a computer program that crunched through reams of data, looking at how much each passenger had paid for their ticket, what time they checked in, how often they flew on United, and whether they were part of a rewards program. The algorithm likely determined that Dr. Dao was one of the least valuable customers on the flight at the time."

"PhotoGuard works by altering select pixels in an image such that they will disrupt an AI's ability to understand what the image is. Those "perturbations," as the research team refers to them, are invisible to the human eye but easily readable by machines. The "encoder" attack method of introducing these artifacts targets the algorithmic model's latent representation of the target image - the complex mathematics that describes the position and color of every pixel in an image - essentially preventing the AI from understanding what it is looking at."

"This figure compares a traditionally trained algorithm to Aarabi and Guo's heuristically trained neural net."

"The founders of Predictim want to be clear with me: Their product-an algorithm that scans the online footprint of a prospective babysitter to determine their "risk" levels for parents-is not racist. It is not biased. "We take ethics and bias extremely seriously," Sal Parsa, Predictim's CEO, tells me warily over the phone. "In fact, in the last 18 months we trained our product, our machine, our algorithm to make sure it was ethical and not biased. We took sensitive attributes, protected classes, sex, gender, race, away from our training set. We continuously audit our model. And on top of that we added a human review process.""

""As expected, we found evidence of a performance improvement over generations due to social learning," the researchers wrote. "Adding an algorithm with a different problem-solving bias than humans temporarily improved human performance but improvements were not sustained in following generations. While humans did copy solutions from the algorithm, they appeared to do so at a lower rate than they copied other humans' solutions with comparable performance." Brinkmann told Motherboard that while they were surprised superior solutions weren't more commonly adopted, this was in line with other research suggesting human biases in decision-making persist despite social learning. Still, the team is optimistic that future research can yield insight into how to amend this."

"The role of AI writing systems as drafting buddies is a big departure from how writers typically get help, yet so far it is their biggest selling point and use case. Most writing tools available today will do some drafting for you, either by continuing where you left off or responding to a more specific instruction. SudoWrite, a popular AI writing tool for novelists, does all of these, with options to "write" where you left off, "describe" a highlighted noun, or "brainstorm" ideas based on a situation you describe. Systems like Jasper.ai or Lex will complete your paragraph or draft copy based on instructions, and Laika is similar but more focused on fiction and drama. "

"No prominent developer of AI foundation models - a list including companies like OpenAI and Meta - is releasing sufficient information about their potential impact on society, determines a new report from Stanford HAI (Human-Centered Artificial Intelligence). Today, Stanford HAI released its Foundation Model Transparency Index, which tracked whether creators of the 10 most popular AI models disclose information about their work and how people use their systems. Among the models it tested, Meta's Llama 2 scored the highest, followed by BloomZ and then OpenAI's GPT-4. But none of them, it turned out, got particularly high marks."

"It is also self-improving. The 10-year-old grading software leverages deep learning algorithms to "compare notes" with human teachers' scores, suggestions, and comments. An engineer involved in the project compared its capabilities to those of AlphaGo, the record-breaking AI Go player developed by Google subsidiary DeepMind."

"Researchers at Columbia University sought to shed light on the problem by tasking 400 AI engineers with creating algorithms that made over 8.2 million predictions about 20,000 people. In a study accepted by the NeurIPS 2020 machine learning conference, the researchers conclude that biased predictions are mostly caused by imbalanced data but that the demographics of engineers also play a role."

"Those changes happened relatively slowly, but it seems to me that employment disruption is accelerating. A large reason for this is that what used to be a room-sized super computer now fits in my pocket. Over the last two decades, I have observed a fundamental change in how we can apply advanced algorithms to sensing and controlling systems-the kinds of technology that enable more sophisticated robotic manufacturing. I can remember discussing various algorithms and believing they were well beyond what we could ever implement. Now these same algorithms are considered elementary. They are just some of the changes that have fueled the revolution in manufacturing."

"Learning from rewards seems like the simplest thing. I make coffee, I sip coffee, I'm happy. My brain registers "brewing coffee" as an action that leads to a reward. That's the guiding insight behind deep reinforcement learning, a family of algorithms that famously smashed most of Atari's gaming catalog and triumphed over humans in strategy games like Go. Here, an AI "agent" explores the game, trying out different actions and registering ones that let it win."

"If you've been job hunting recently, chances are you've interacted with a resume robot, a nickname for an Applicant Tracking System, or ATS. In its most basic form, an ATS acts like an online assistant, helping hiring managers write job descriptions, scan resumes and schedule interviews. As artificial intelligence advances, employers are increasingly relying on a combination of predictive analytics, machine learning and complex algorithms to sort through candidates, evaluate their skills and estimate their performance. Today, it's not uncommon for applicants to be rejected by a robot before they're connected with an actual human in human resources. The job market is ripe for the explosion of AI recruitment tools. Hiring managers are coping with deflated HR budgets while confronting growing pools of applicants, a result of both the economic downturn and the post-pandemic expansion of remote work. As automated software makes pivotal decisions about our employment, usually without any oversight, it's posing fundamental questions about privacy, accountability and transparency."

"Concerns have been growing about AI's so-called "white guy problem" and now scientists have devised a way to test whether an algorithm is introducing gender or racial biases into decision-making."

""No-limit Texas Hold'em is a game of incomplete information where the AI must infer a human player's intentions and then act in ways that incorporate both the direct odds of winning and bluffing behaviour to try to fool the other player." The designers said their computer didn't "bluff" the human players. But by learning from its mistakes and practising its moves at night between games, the AI was working out how to defeat its human opponents."

"At the heart of the problem that troubles Ming is the training that computer engineers receive and their uncritical faith in AI. Too often, she says, their approach to a problem is to train a neural network on a mass of data and expect the result to work fine. She berates companies for failing to engage with the problem first - applying what is already known about good employees and successful students, for example - before applying the AI."

"Bias in Artificial Intelligence (AI) has been a popular topic over the last few years as AI-solutions have become more ingrained in our daily lives."

"Curtin University internet studies professor Tama Leaver posted about some of his tests with Emu's sticker generation to X, formerly known as Twitter. Leaver found, for example, that the AI will block a phrase like "child with gun" and display a warning message about how the prompt doesn't follow Meta's Community Guidelines. Emu will, however, generate stickers with the similar, more niche prompt "child with grenade." It not only creates stickers of kids holding the weapon but also produces stickers of children holding guns."

"Among other things, the declaration states that military AI needs to be developed according to international laws, that nations should be transparent about the principles underlying their technology, and that high standards are implemented for verifying the performance of AI systems. It also says that humans alone should make decisions around the use of nuclear weapons."

"Depending on how legal decisions shake out, AI systems could become a valuable tool to assist creativity, a nuisance ripping off hard-working human musicians, or both."

"As part of her undergraduate research, Alice Xue studied whether a machine could pass a Visual Turing Test by producing images that people cannot tell were made by a machine. Xue trained an algorithm using 2,192 traditional Chinese landscape paintings collected from art museums. The resulting AI-generated paintings were mistaken for being made by humans 55 per cent of the time."