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"Most people who use AI-powered translation tools do so for commonplace, relatively unimportant tasks like understanding a single phrase or quote. Those basic services won't do for an enterprise offering technical documents in 15 languages - but Lengoo's custom machine translation models might just do the trick. And with a new $20 million B round, they may be able to build a considerable lead. The translation business is a big one, in the billions, and isn't going anywhere. It's simply too common a task to need to release a document, piece of software or live website in multiple languages - perhaps dozens."

"My job was to use technology to make the low-level content moderators' work more efficient. For example, we created a tool that allowed them to throw a video clip into our database and search for similar content. When I was at ByteDance, we received multiple requests from the bases to develop an algorithm that could automatically detect when a Douyin user spoke Uyghur, and then cut off the livestream session. The moderators had asked for this because they didn't understand the language. Streamers speaking ethnic languages and dialects that Mandarin-speakers don't understand would receive a warning to switch to Mandarin."

"That can include statistical or machine learning algorithms that rely on police records detailing the time, location, and nature of past crimes in a bid to predict if, when, where, and who may commit future infractions. In theory, this should help authorities use resources more wisely and spend more time policing certain neighborhoods that they think will yield higher crime rates."

"These experiments in computational creativity are enabled by the dramatic advances in deep learning over the past decade. Deep learning has several key advantages for creative pursuits. For starters, it's extremely flexible, and it's relatively easy to train deep-learning systems (which we call models) to take on a wide variety of tasks."

"As the lava lamps bubble and swirl, a video camera on the ceiling monitors their unpredictable changes and connects the footage to a computer, which converts the randomness into a virtually unhackable code.  Why use lava lamps for encryption instead of computer-generated code? Since computer codes are created by machines with relatively predictable patterns, it is entirely possible for hackers to guess their algorithms, posing a security risk. Lava lamps, on the other hand, add to the equation the sheer randomness of the physical world, making it nearly impossible for hackers to break through."

"Creating an algorithm that discriminates or shows bias isn't as hard as it might seem, however. As a first-year graduate student, my advisor asked me to create a machine-learning algorithm to analyze a survey sent to United States physics instructors about teaching computer programming in their courses."

"However, since machine learning algorithms are what they eat (in other words, they function based on the training data they ingest), they inevitably end up picking up on human biases that exist in language data itself."

"Why? Because it has an internal app store that puts its young player base to work making virtual stuff and selling it for scrip or peanuts while the company pockets the profits. It even promotes the far-right personalities and groups using it to recruit. It is a ruthless money machine that embodies the perverse incentives of social media, aimed directly at children and operated by amoral reptiles."

"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."

"Until recently, I was deeply sceptical of the idea of AI art. I saw it as hype and casuistry, and with some cause: widely publicised efforts such as Ai-Da the robot artist obviously exaggerate the independence of the machine and play on our fascination with sentient artificial beings. But now the dream is coming true, at least in art. And art is surely one of the most inimitable expressions of the human mind."

"If AI causes mass unemployment among the general populace, it will be the first time in history that any technology has ever done that. Industrial machinery, computer-controlled machine tools, software applications, and industrial robots all caused panics about human obsolescence, and nothing of the kind ever came to pass; pretty much everyone who wants a job still has a job. As Noah has written, a wave of recent evidence shows that adoption of industrial robots and automation technology in general is associated with an increase in employment at the company and industry level."

"There will be more where that came from. So it's time for a reality check. Quantum computers are interesting, but experience so far suggests they are exceedingly tricky to build and even harder to scale up. There are now about 50 working machines, most of them minuscule in terms of qubits. The biggest is one of IBM's, which has - wait for it - 433 qubits, which means scaling up to 20m qubits might, er, take a while. This will lead realists to conclude that RSA encryption is safe for the time being and critics to say that it's like nuclear fusion and artificial general intelligence - always 50 years in the future."

"Will this AI take jobs from artists? Where does copyright law land? Can machines ever truly produce something original? Should I feel guilty for making a picture of Tony Soprano having a cappuccino with Shrek and sharing it with my group chat?"

"The intriguing echo of Eliza in thinking about ChatGPT is that people regard it as magical even though they know how it works - as a "stochastic parrot" (in the words of Timnit Gebru, a well-known researcher) or as a machine for "hi-tech plagiarism" (Noam Chomsky). But actually we do not know the half of it yet - not the CO2 emissions incurred in training its underlying language model or the carbon footprint of all those delighted interactions people are having with it. Or, pace Chomsky, that the technology only exists because of its unauthorised appropriation of the creative work of millions of people that just happened to be lying around on the web? What's the business model behind these tools? And so on. Answer: we don't know."

"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."

"This is the broad goal of a new field known as embodied AI, and Li's not the only one embracing it. It overlaps with robotics, since robots can be the physical equivalent of embodied AI agents in the real world, and reinforcement learning - which has always trained an interactive agent to learn using long-term rewards as incentive. But Li and others think embodied AI could power a major shift from machines learning straightforward abilities, like recognizing images, to learning how to perform complex humanlike tasks with multiple steps, such as making an omelet."

"Green Light uses machine learning systems to comb through Maps data to calculate the amount of traffic congestion present at a given light, as well as the average wait times of vehicles stopped there. That information is then used to train AI models that can autonomously optimize the traffic timing at that intersection, reducing idle times as well as the amount of braking and accelerating vehicles have to do there. It's all part of Google's goal to help its partners collectively reduce their carbon emissions by a gigaton by 2030."

"A boarding prep school in West Sussex, Cottesmore School, has made history by appointing an AI robot, Abigail Bailey, as its "principal headteacher". Created in collaboration with an artificial intelligence developer, the robot is designed to support the school's headmaster, Tom Rogerson, by providing advice on various issues such as supporting staff members and helping pupils with ADHD. Abigail Bailey functions similarly to the AI service ChatGPT, where users ask questions and receive answers from the chatbot's algorithms. The AI principal has been developed with extensive knowledge in machine learning and educational management, allowing it to analyze vast amounts of data. According to Mr. Rogerson, having the AI robot to assist him is calming and reassuring. He considers the role of a school leader to be lonely and believes that having an entity to rely on is invaluable. He also intends to make the publicly-available online robot accessible to state school headteachers."

"Researchers involved in a recent study trained an artificial intelligence (AI) model to diagnose type 2 diabetes in patients after six to 10 seconds of listening to their voice. Canadian medical researchers trained the machine-learning AI to recognise 14 vocal differences in the voice of someone with type 2 diabetes compared to someone without diabetes. The auditory features that the AI focussed on included slight changes in pitch and intensity, which human ears cannot distinguish. This was then paired with basic health data gathered by the researchers, such as age, sex, height and weight. Researchers believe that the AI model will drastically lower the cost for people with diabetes to be diagnosed."