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"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."

"Ege Gürdeniz: There are two components to Artificial Intelligence (AI) bias. The first is an AI application making biased decisions regarding certain groups of people. This could be ethnicity, religion, gender, and so on. To understand that we first need to understand how AI works and how it's trained to complete specific tasks."

"AI and the American Smile How AI misrepresents culture through a facial expression."

"The music industry is urging streaming platforms not to let artificial intelligence use copyrighted songs for training, in the latest of a run of arguments over intellectual property that threaten to derail the generative AI sector's explosive growth. In a letter to streamers including Spotify and Apple Music, the record label Universal Music Group expressed fears that AI labs would scrape millions of tracks to use as training data for their models and copycat versions of pop stars."

"Wooldridge says copyright is a "coming storm" for AI companies. LLMs are likely to have accessed copyrighted material, such as news articles. Indeed the GPT-4-assisted chatbot attached to Microsoft's Bing search engine cites news sites in its answers. "I didn't give explicit permission for my works to be used as training data, but they almost certainly were, and now they contribute to what these models know," he says. "Many artists are gravely concerned that their livelihoods are at risk from generative AI. Expect to see legal battles," he adds."

"Nelson said it's likely that AI will replace some jobs in Hollywood while also potentially creating more. He pointed to the entrance of computer-editing software, and how that replaced more manual movie-editing jobs and processes. "There are some jobs that might just go away entirely," he said. "There might be some pain, but through it all, I think there's just going to be more opportunities." Media and legal experts also said the use of AI in filmmaking raises several concerns - and the law is still unclear."

"And he emphatically doesn't see the process of building an AI image as dehumanised, or even one in which the human is sidelined. "I don't see it as a threat to creativity. For me, it really is setting me free. All the boundaries I had in the past - material boundaries, budgets - no longer matter. And for the first time in history, the older generation has an advantage, because AI is a knowledge accelerator. Two thirds of the prompts are only good if you have knowledge and skills, when you know how photography works, when you know art history. This is something that a 20-year-old can't do.""

"FOLLOW US Twitter / Facebook / RSS Machine learning image classifiers use context clues to help understand the contents of a room, for example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs. The downside of this powerful approach is that it means machine learning classifiers can be confounded by confusing, out-of-context elements in a scene, as is demonstrated in The Elephant in the Room, a paper from a trio of Toronto-based computer science academics."

"Expert Moshe Vardi told the American Association for the Advancement of Science (AAAS): "We are approaching a time when machines will be able to outperform humans at almost any task. "I believe that society needs to confront this question before it is upon us: if machines are capable of doing almost any work humans can do, what will humans do?""

"What do today's AI-generated lesson plans look like? AI-generated lesson plans are already better than many people realise. Here's an example generated through the GPT-3 deep learning language model:"

"Whereas most machine learning algorithms can't hone their skills beyond an initial training period, the researchers say the new approach, called a liquid neural network, has a kind of built-in "neuroplasticity." That is, as it goes about its work-say, in the future, maybe driving a car or directing a robot-it can learn from experience and adjust its connections on the fly."

"One great way to tell the difference is to ask AI recruiting companies what they use artificial intelligence, machine learning and/or deep learning for. Hopefully the hiring firm can what it's using the new technology for and not just that it is. If not it's time to dig a bit deeper."

"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."

"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?"

" Critics have pointed out that the old computing adage "garbage in, garbage out" also applies to ML. If the data from which a machine "learns" is biased, then the outputs will reflect those biases. And this could become generalised: we may have created a technology that - however good it is at recommending films you might like - may actually morph into a powerful amplifier of social, economic and cultural inequalities."

AI and machine learning, combined with the science of turning data into insightful information (aka data science), have become more important than ever in the "new normal" to guide innovation based on new market trends and consumer preferences This article was published in thejakartapost.com with the title "How artificial intelligence helps firms stay relevant amid pandemic - Opinion - The Jakarta Post". Click to read: https://www.thejakartapost.com/academia/2020/09/21/how-artificial-intelligence-helps-firms-stay-relevant-amid-pandemic.html. Download The Jakarta Post app for easier and faster news access: Android: http://bit.ly/tjp-android iOS: http://bit.ly/tjp-ios

"The opt-out movement is spreading, with tens of millions of artworks and images excluded in the last few weeks. But following the trail is tricky as images are used by clients in altered forms and opt-out clauses can be hard to find. Many photographers are also reporting that their "style" is being mimicked to produce cheaper work. "As these programs are devised to 'machine learn', at what point can they generate with ease the style of an established professional photographer and displace the need for their human creativity?" says Doran."

"Automatically produced texts use language models derived from statistical analysis of vast corpuses of human-generated text to produce machine-generated texts that can be very hard for a human to distinguish from text produced by another human. These models could help malicious actors in many ways, including generating convincing spam, reviews, and comments -- so it's really important to develop tools that can help us distinguish between human-generated and machine-generated texts."

"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."