Author: peterjang73

iPhone users who still use their phone as an actual—you know—phone will soon have the ability to filter out background noise to make those calls clearer, with the coming advent of iOS 16.4 and new implementations for Voice Isolation.

Apple quietly implemented a Voice Isolation feature in FaceTime calls last year, which helped filter out background noise, making voices sound clearer. To activate it, users had to select the feature from a menu in the app’s Control Center. While previously restricted to VOIP (voice over IP) calls, it’s now coming to cellular calls as well.

iOS 16.4 is scheduled for a full release sometime in the next week or so, to coincide with the upcoming Apple Music Classical app. The latest 16.4 RC (release candidate) version was released Tuesday to developers and some select users whose devices are listed as beta testers. Accessing Voice Isolation in calls works similar to how you do it on FaceTime. During a call, users can access the Control Center, tap on the Mic Mode, then choose Voice Isolation from the list of three options. The other option, called “Wide Spectrum,” actually makes listeners hear even more of the background.

Otherwise, iOS is bringing a few more minor improvements, bug fixes, and—perhaps most important for texters—21 new emojis with Unicode 15. Though these were shown off in last year’s emoji slate, this latest release onto Apple devices includes multiple new animals like a moose, a goose, a jellyfish and a donkey.

As for additional features, 16.4 will update phones to include notifications for web apps on the Home Screen and an accessibility setting that automatically dims video when it detects flashes or strobes of light. Photos will also now detect duplicate photos and videos in an iCloud Shared Photo Library.

There’s a few bug fixes as well, including crash detection optimizations on iPhone 14 and 14 Pro devices. The company also said it would fix an issue where Matter-compatible thermostats become unresponsive when paired to an Apple Home device.

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Google isn’t used to playing catch-up in either artificial intelligence or search, but today the company is hustling to show that it hasn’t lost its edge. It’s starting the rollout of a chatbot called Bard to do battle with the sensationally popular ChatGPT. 

Bard, like ChatGPT, will respond to questions about and discuss an almost inexhaustible range of subjects with what sometimes seems like humanlike understanding. Google showed WIRED several examples, including asking for activities for a child who is interested in bowling and requesting 20 books to read this year. 

Bard is also like ChatGPT in that it will sometimes make things up and act weird. Google disclosed an example of it misstating the name of a plant suggested for growing indoors. “Bard’s an early experiment, it’s not perfect, and it’s gonna get things wrong occasionally,” says Eli Collins, a vice president of research at Google working on Bard.

Google says it has made Bard available to a small number of testers. From today anyone in the US and the UK will be able to apply for access. 

The bot will be accessible via its own web page and separate from Google’s regular search interface. It will offer three answers to each query—a design choice meant to impress upon users that Bard is generating answers on the fly and may sometimes make mistakes.

Google will also offer a recommended query for a conventional web search beneath each Bard response. And it will be possible for users to give feedback on its answers to help Google refine the bot by clicking a thumbs-up or thumbs-down, with the option to type in more detailed feedback.

Google says early users of Bard have found it a useful aid for generating ideas or text. Collins also acknowledges that some have successfully got it to misbehave, although he did not specify how or exactly what restrictions Google has tried to place on the bot.

Bard and ChatGPT show enormous potential and flexibility but are also unpredictable and still at an early stage of development. That presents a conundrum for companies hoping to gain an edge in advancing and harnessing the technology. For a company like Google with large established products, the challenge is particularly difficult.

Both the chatbots use powerful AI models that predict the words that should follow a given sentence based on statistical patterns gleaned from enormous amounts of text training data. This turns out to be an incredibly effective way of mimicking human responses to questions, but it means that the algorithms will sometimes make up, or “hallucinate,” facts—a serious problem when a bot is supposed to be helping users find information or search the web. 

ChatGPT-style bots can also regurgitate biases or language found in the darker corners of their training data, for example around race, gender, and age. They also tend to reflect back the way a user addresses them, causing them to readily act as if they have emotions and to be vulnerable to being nudged into saying strange and inappropriate things.

China’s new state-sponsored dating app, Palm Guixi, is something right out of the dystopia fiction handbook and is receiving mixed responses. The app was reportedly created to streamline the dating process for residents in Jiangxi by matching single users based on background data uploaded by the app itself.

Palm Guixi works to create what it thinks will be an appropriate match for singles looking for love and unlike Tinder, Bumble, Hinge, or any of the other swipe-right dating apps, Palm Guixi uses their background data to pick suitors for the user. According to China Youth Daily, the platform also works to organize blind dates once a match is approved, The Guardian reported.

The Chinese government reportedly launched the app in an effort to boost the marriage rate which has steadily declined over the past decade. A report by China’s Ministry of Civil Affairs found that not only are fewer residents getting married but of those that are, roughly half were 30 years old and above. China reached its peak marriage rate in 2011, with 9.7 million registered marriages which, in 2021, plummeted to an all-time low of 7.6 million, Fortune reported back in 2022.

This significant drop has been welcomed by some younger people in China, who say the government’s hardened stance on divorce has deterred many of them from pursuing marriage, according recent reporting by the South China Morning Post. China introduced a new law requiring a 30-day “cooling off” period even if a couple mutually agrees to divorce. If, at the end of the 30 days, the couple still wants to divorce they are required to reapply for the split, but lawyers say the outcome of having the divorce approved can be unpredictable.

Chinese residents took to Weibo to support the decreasing marriage rate, with one writing, “Marriage is like a gamble. The problem is that ordinary people can’t afford to lose, so I choose not to take part,” the outlet reported.

China’s push to engage young people in dating also comes as its population fell to its lowest level last year with only a record 6.77 births per 1,000 people in China last year. Some Jiangxi residents are now pushing back, saying the government introduced the app because it only wants to reverse the falling birth rate.

The Guardian reported commenters on Weibo are now speaking out against Palm Guixi, with one user saying the Chinese government expects its people to “breed like pigs.”

The prominent model of information access and retrieval before search engines became the norm – librarians and subject or search experts providing relevant information – was interactive, personalized, transparent and authoritative. Search engines are the primary way most people access information today, but entering a few keywords and getting a list of results ranked by some unknown function is not ideal.

A new generation of artificial intelligence-based information access systems, which includes Microsoft’s Bing/ChatGPT, Google/Bard and Meta/LLaMA, is upending the traditional search engine mode of search input and output. These systems are able to take full sentences and even paragraphs as input and generate personalized natural language responses.

At first glance, this might seem like the best of both worlds: personable and custom answers combined with the breadth and depth of knowledge on the internet. But as a researcher who studies the search and recommendation systems, I believe the picture is mixed at best.

AI systems like ChatGPT and Bard are built on large language models. A language model is a machine-learning technique that uses a large body of available texts, such as Wikipedia and PubMed articles, to learn patterns. In simple terms, these models figure out what word is likely to come next, given a set of words or a phrase. In doing so, they are able to generate sentences, paragraphs and even pages that correspond to a query from a user. On March 14, 2023, OpenAI announced the next generation of the technology, GPT-4, which works with both text and image input, and Microsoft announced that its conversational Bing is based on GPT-4.

‘60 Minutes’ looked at the good and the bad of ChatGPT.

Thanks to the training on large bodies of text, fine-tuning and other machine learning-based methods, this type of information retrieval technique works quite effectively. The large language model-based systems generate personalized responses to fulfill information queries. People have found the results so impressive that ChatGPT reached 100 million users in one third of the time it took TikTok to get to that milestone. People have used it to not only find answers but to generate diagnoses, create dieting plans and make investment recommendations.

ChatGPT’s Opacity and AI ‘hallucinations’

However, there are plenty of downsides. First, consider what is at the heart of a large language model – a mechanism through which it connects the words and presumably their meanings. This produces an output that often seems like an intelligent response, but large language model systems are known to produce almost parroted statements without a real understanding. So, while the generated output from such systems might seem smart, it is merely a reflection of underlying patterns of words the AI has found in an appropriate context.

This limitation makes large language model systems susceptible to making up or “hallucinating” answers. The systems are also not smart enough to understand the incorrect premise of a question and answer faulty questions anyway. For example, when asked which U.S. president’s face is on the $100 bill, ChatGPT answers Benjamin Franklin without realizing that Franklin was never president and that the premise that the $100 bill has a picture of a U.S. president is incorrect.

The problem is that even when these systems are wrong only 10% of the time, you don’t know which 10%. People also don’t have the ability to quickly validate the systems’ responses. That’s because these systems lack transparency – they don’t reveal what data they are trained on, what sources they have used to come up with answers or how those responses are generated.

For example, you could ask ChatGPT to write a technical report with citations. But often it makes up these citations – “hallucinating” the titles of scholarly papers as well as the authors. The systems also don’t validate the accuracy of their responses. This leaves the validation up to the user, and users may not have the motivation or skills to do so or even recognize the need to check an AI’s responses. ChatGPT doesn’t know when a question doesn’t make sense, because it doesn’t know any facts.

AI stealing content – and traffic

While lack of transparency can be harmful to the users, it is also unfair to the authors, artists and creators of the original content from whom the systems have learned, because the systems do not reveal their sources or provide sufficient attribution. In most cases, creators are not compensated or credited or given the opportunity to give their consent.

There is an economic angle to this as well. In a typical search engine environment, the results are shown with the links to the sources. This not only allows the user to verify the answers and provides the attributions to those sources, it also generates traffic for those sites. Many of these sources rely on this traffic for their revenue. Because the large language model systems produce direct answers but not the sources they drew from, I believe that those sites are likely to see their revenue streams diminish.

Large language models can take away learning and serendipity

Finally, this new way of accessing information also can disempower people and takes away their chance to learn. A typical search process allows users to explore the range of possibilities for their information needs, often triggering them to adjust what they’re looking for. It also affords them an opportunity to learn what is out there and how various pieces of information connect to accomplish their tasks. And it allows for accidental encounters or serendipity.

These are very important aspects of search, but when a system produces the results without showing its sources or guiding the user through a process, it robs them of these possibilities.

Large language models are a great leap forward for information access, providing people with a way to have natural language-based interactions, produce personalized responses and discover answers and patterns that are often difficult for an average user to come up with. But they have severe limitations due to the way they learn and construct responses. Their answers may be wrong, toxic or biased.

While other information access systems can suffer from these issues, too, large language model AI systems also lack transparency. Worse, their natural language responses can help fuel a false sense of trust and authoritativeness that can be dangerous for uninformed users.

Want to know more about AI, chatbots, and the future of machine learning? Check out our full coverage of artificial intelligence, or browse our guides to The Best Free AI Art Generators and Everything We Know About OpenAI’s ChatGPT.

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Chirag Shah, Professor of Information Science, University of Washington

This article is republished from The Conversation under a Creative Commons license. Read the original article.