Latest Generative AI news and analysis from startup Europe
However, there are also challenges and ethical considerations, such as the potential for AI to perpetuate biases present in training data or to generate misleading content. It’s essential to ensure that Generative AI in news is used responsibly and in a way that upholds journalistic integrity and ethical standards. Multilingual News AI-powered translation tools can swiftly translate news articles into multiple languages, making news more accessible to a global audience. This promotes cross-cultural understanding and information sharing. Tools such as ChatGPT, Copilot, and Dalle-E 2 can write code, take exams, write essays and create dramatic images.
In a 2-cell, within-subject design, participants saw both news items tagged as written by an AI and by a human reporter. Generative AI leverages large data sets and sophisticated models to mimic human creativity and produce new images, music, text and more. Indeed, AI-assisted software development has recently exploded, joining other technologies that have been used for years and are aimed at increasing developer productivity. In the past, using tools like low-code platforms to automate and speed up software development was met with suspicion.
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Generative AI has paved the way for applications ranging from image and audio generation to storytelling and game development by utilizing algorithms and training models on enormous amounts of data. As pre-registered, we performed the main analyses at the level of individual accuracy perceptions ratings (i.e., one data point per news item per participant) using a linear regression with robust standard errors clustered on participant. At the end of the survey, and in line with prior research , participants were asked (i) if they had searched for any of the headlines while responding to the survey, and (ii) if they had responded randomly at any point. A manipulation check in Experiment 1 assessed whether participants correctly recalled that the headlines they had viewed had been generated by a human or an AI reporter. Upon completion of the survey, participants could follow a link to know which of the news items were true and which were false. The alternative account is grounded in people’s resistance toward replacement of humans by automated systems [11, 21, 36].
Fact-Checking and Verification One of the issues facing the social media industry is the fake news generated. In recent years, we’ve seen how it impacts society in a negative way. Generative AI can assist journalists in fact-checking and verifying information.
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Their existence seems to some like a science fiction movie portraying a future that is doomed for employees in certain industries, especially creatives. Mark Haranas is an assistant news editor and longtime journalist now covering cloud, multicloud, software, SaaS and channel partners at CRN. He speaks with world-renown CEOs and IT experts as well as covering breaking news and live events while also managing several CRN reporters. AudioCraft works for music, sound, compression, and generation — all in the same place.
Creative industries have already started to feel the change of workflows due to generative AI. Copywriters, designers, coders, photo and video editors, and even strategists now have access to generative AI tools that can simplify their day-to-day tasks. However, it has the potential to disrupt some businesses and will spark backlash over accuracy, fairness, and plagiarism. Generative AI models simulate how we think by relying on algorithms that “learn” with each use. They start with millions of labeled pictures, text, or other media, and gradually identify patterns that allow them to understand and create content independently. Although OpenAI is the best-known generative AI company, it’s not the only one.
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
However, the coefficient on Heterogeneous Treatment Effect was negative and not precisely estimated. For the CATE estimates to pick up meaningful heterogeneity, the coefficient should instead have been positive and statistically different from 0. This provides further evidence that the variables examined did not precisely predict heterogeneity Yakov Livshits in treatment effects. It’s going to have the potential freedom, if you give it, to take actions. It’s truly a step change in the history of our species that we’re creating tools that have this kind of, you know, agency. Additionally, generative AI may unintentionally continue to reinforce biases that are present in the training data.
- As people want journalism to be impartial and neutral , this AI appreciation account predicts that people would perceive news from AI as more accurate than news from human reporters, with higher trust ascribed to AI than human reporters.
- As pre-registered, we performed the main analyses at the level of individual accuracy perceptions ratings (i.e., one data point per news item per participant) using a linear regression with robust standard errors clustered on participant.
- If not, it’s not really different from what everyone else was doing in terms of ethics.
- With even more controls, we think MusicGen can turn into a new type of instrument — just like synthesizers when they first appeared.
In a decade’s time, the impact of Generative AI on news media will likely manifest as a more diversified readership, enriched user experiences, enhanced credibility, and a deeper understanding of audience preferences. Personalized News AI algorithms use data about a reader’s preferences and behavior to tailor news content. This not only keeps readers engaged but also helps them discover stories they might have otherwise missed. Automated Content Generation Generative AI can generate news articles, summaries, and even reports with remarkable speed and accuracy. This is particularly valuable in breaking news situations, where getting information out quickly is crucial.
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Groundbreaking examples of artificial intelligence, protecting your enterprise from the risks of generative AI, and possible guardrails for generative AI. Structured enterprise data may just be generative AI’s next breakthrough area. We asked Nima Negahban, cofounder and CEO of Kinetica, to explain how the technology is being used in a variety of industries. Med-PaLM 2 advances language model capabilities in medicine Yakov Livshits by combining an improved base LLM (PaLM 2), domain-specific fine-tuning, and a new prompting strategy to enhance medical reasoning. Although if Adobe did charge “per use” for locally run tools like brushes and filters… I’d hope they’d at least they’d make the billing completely “usage based” with no minimum monthly cost. If not, it’s not really different from what everyone else was doing in terms of ethics.
So, teams need to have visibility and governance over the code the AI creates. Someone will have to be able to understand it because, eventually, they will have to change it, which brings me to the inescapable issue of technical debt. I expect many more people to start using these tools to speed up projects, from LLMs that generate code snippets, to highly complex low-code solutions that abstract the entire software development lifecycle. However, this does not mean AI will obliterate the software development job market. According to the US Bureau of Labor Statistics, the overall employment of software developers, quality assurance analysts, and testers is projected to grow 25 percent from 2021 to 2031 – much faster than the average for all occupations in the US.
Its surprisingly human-like capabilities fascinated the public but also brought concerns such as the dangers of misinformation, its potential for bias, and even the possibility of machines replacing people in jobs – including software developers. Generative AI models are also prone to challenges inherent to DL techniques. Additionally, the generative nature of the models can introduce artifacts into the generated data. They could produce strange looking images that are difficult to explain. Various approaches have been proposed to overcome these challenges. They may complete errors or give incorrect answers, given the data they are trained on.