peterjang73

Google may be close to launching its long-rumored foldable Pixel phone. CNBCclaims to have seen internal communications revealing that the Pixel Fold will be announced at the I/O conference on May 10th, and ship in June. The device will reportedly cost around $1,700, or close to the $1,800 Samsung asks for the Galaxy Z Fold 4.

The company may tout a few advantages over its rival. The Pixel Fold will have a book-like design with a 5.8-inch external screen and 7.6-inch folding display, but will supposedly have the "most durable hinge" of any foldable, according to Google. It should also be water-resistant and pack a larger battery that lasts up to 24 hours in normal use, and 72 hours with Extreme Battery Saver.

Don’t expect performance gains. The Pixel Fold will apparently use the same Tensor G2 chip as in the Pixel 7 series, and previous leaks suggest it might have cameras that predate the Pixel 6. As with other Pixels, the draw may be the ‘pure’ Android experience — this will be the first opportunity to see Google’s own approach to foldable devices.

The price is still steep compared to regular Pixel phones, which top out at $899 for the Pixel 7 Pro. Google may sweeten the deal by encouraging trade-ins, however. The company may even offer a free Pixel Watch to Fold buyers.

If the details are accurate, the foldable Pixel may not be an easy sell. Google is expected to debut the Pixel 8 this fall, and that’s likely to include upgrades like a third-generation Tensor chip. With that in mind, North Americans have had few options for foldable phones beyond the Galaxy Z Fold series and previous-gen Moto Razr models. The Pixel Fold may stand out simply by providing real competition in a small category.

This article originally appeared on Engadget at https://ift.tt/gnz1I0R

via Engadget http://www.engadget.com

April 19, 2023 at 11:03AM

April 20, 2023

AI-generated spam may soon be flooding your inbox – and it will be personalized to be especially persuasive

https://www.geeksaresexy.net/2023/04/20/ai-generated-spam-may-soon-be-flooding-your-inbox-and-it-will-be-personalized-to-be-especially-persuasive/

AI may make spam more pervasive than ever. AP Photo/Gene J. Puskar

John Licato, *University of South Florida*

Each day, messages from Nigerian princes, peddlers of wonder drugs and promoters of can’t-miss investments choke email inboxes. Improvements to spam filters only seem to inspire new techniques to break through the protections.

Now, the arms race between spam blockers and spam senders is about to escalate with the emergence of a new weapon: generative artificial intelligence. With recent advances in AI made famous by ChatGPT, spammers could have new tools to evade filters, grab people’s attention and convince them to click, buy or give up personal information.

As director of the Advancing Human and Machine Reasoning lab at the University of South Florida, I research the intersection of artificial intelligence, natural language processing and human reasoning. I have studied how AI can learn the individual preferences, beliefs and personality quirks of people.

This can be used to better understand how to interact with people, help them learn or provide them with helpful suggestions. But this also means you should brace for smarter spam that knows your weak spots – and can use them against you.

Spam, spam, spam

So, what is spam?

Spam is defined as unsolicited commercial emails sent by an unknown entity. The term is sometimes extended to text messages, direct messages on social media and fake reviews on products. Spammers want to nudge you toward action: buying something, clicking on phishing links, installing malware or changing views.

Spam is profitable. One email blast can make US$1,000 in only a few hours, costing spammers only a few dollars – excluding initial setup. An online pharmaceutical spam campaign might generate around $7,000 per day.

Legitimate advertisers also want to nudge you to action – buying their products, taking their surveys, signing up for newsletters – but whereas a marketer email may link to an established company website and contain an unsubscribe option in accordance with federal regulations, a spam email may not.

Spammers also lack access to mailing lists that users signed up for. Instead, spammers utilize counter-intuitive strategies such as the “Nigerian prince” scam, in which a Nigerian prince claims to need your help to unlock an absurd amount of money, promising to reward you nicely. Savvy digital natives immediately dismiss such pleas, but the absurdity of the request may actually select for naïveté or advanced age, filtering for those most likely to fall for the scams.

Advances in AI, however, mean spammers might not have to rely on such hit-or-miss approaches. AI could allow them to target individuals and make their messages more persuasive based on easily accessible information, such as social media posts.

Inboxes are already bursting with spam. Epoxydude/fStop via Getty Images

Future of spam

Chances are you’ve heard about the advances in generative large language models like ChatGPT. The task these generative LLMs perform is deceptively simple: given a text sequence, predict which token – think of this as a part of a word – comes next. Then, predict which token comes after that. And so on, over and over.

Somehow, training on that task alone, when done with enough text on a large enough LLM, seems to be enough to imbue these models with the ability to perform surprisingly well on a lot of other tasks.

Multiple ways to use the technology have already emerged, showcasing the technology’s ability to quickly adapt to, and learn about, individuals. For example, LLMs can write full emails in your writing style, given only a few examples of how you write. And there’s the classic example – now over a decade old – of Target figuring out a customer was pregnant before she did.

Spammers and marketers alike would benefit from being able to predict more about individuals with less data. Given your LinkedIn page, a few posts and a profile image or two, LLM-armed spammers might make reasonably accurate guesses about your political leanings, marital status or life priorities.

Our research showed that LLMs could be used to predict which word an individual will say next with a degree of accuracy far surpassing other AI approaches, in a word-generation task called the semantic fluency task. We also showed that LLMs can take certain types of questions from tests of reasoning abilities and predict how people will respond to that question. This suggests that LLMs already have some knowledge of what typical human reasoning ability looks like.

If spammers make it past initial filters and get you to read an email, click a link or even engage in conversation, their ability to apply customized persuasion increases dramatically. Here again, LLMs can change the game. Early results suggest that LLMs can be used to argue persuasively on topics ranging from politics to public health policy.

Good for the gander

AI, however, doesn’t favor one side or the other. Spam filters also should benefit from advances in AI, allowing them to erect new barriers to unwanted emails.

Spammers often try to trick filters with special characters, misspelled words or hidden text, relying on the human propensity to forgive small text anomalies – for example, “c1îck h.ere n0w.” But as AI gets better at understanding spam messages, filters could get better at identifying and blocking unwanted spam – and maybe even letting through wanted spam, such as marketing email you’ve explicitly signed up for. Imagine a filter that predicts whether you’d want to read an email before you even read it.

Despite growing concerns about AI – as evidenced by Tesla, SpaceX and Twitter CEO Elon Musk, Apple founder Steve Wozniak and other tech leaders calling for a pause in AI development – a lot of good could come from advances in the technology. AI can help us understand how weaknesses in human reasoning might be exploited by bad actors and come up with ways to counter malevolent activities.

All new technologies can result in both wonder and danger. The difference lies in who creates and controls the tools, and how they are used.

John Licato, Assistant Professor of Computer Science and Director of AMHR Lab, *University of South Florida*

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

Click This Link for the Full Post > AI-generated spam may soon be flooding your inbox – and it will be personalized to be especially persuasive

image of screen showing email inbox with 316 spam messages — AI may make spam more pervasive than ever. AP Photo/Gene J. Puskar

John Licato, *University of South Florida*

Each day, messages from Nigerian princes, peddlers of wonder drugs and promoters of can’t-miss investments choke email inboxes. Improvements to spam filters only seem to inspire new techniques to break through the protections.

Now, the arms race between spam blockers and spam senders is about to escalate with the emergence of a new weapon: generative artificial intelligence. With recent advances in AI made famous by ChatGPT, spammers could have new tools to evade filters, grab people’s attention and convince them to click, buy or give up personal information.

As director of the Advancing Human and Machine Reasoning lab at the University of South Florida, I research the intersection of artificial intelligence, natural language processing and human reasoning. I have studied how AI can learn the individual preferences, beliefs and personality quirks of people.

This can be used to better understand how to interact with people, help them learn or provide them with helpful suggestions. But this also means you should brace for smarter spam that knows your weak spots – and can use them against you.

Spam, spam, spam

So, what is spam?

Spam is defined as unsolicited commercial emails sent by an unknown entity. The term is sometimes extended to text messages, direct messages on social media and fake reviews on products. Spammers want to nudge you toward action: buying something, clicking on phishing links, installing malware or changing views.

Spam is profitable. One email blast can make US$1,000 in only a few hours, costing spammers only a few dollars – excluding initial setup. An online pharmaceutical spam campaign might generate around $7,000 per day.

Legitimate advertisers also want to nudge you to action – buying their products, taking their surveys, signing up for newsletters – but whereas a marketer email may link to an established company website and contain an unsubscribe option in accordance with federal regulations, a spam email may not.

Spammers also lack access to mailing lists that users signed up for. Instead, spammers utilize counter-intuitive strategies such as the “Nigerian prince” scam, in which a Nigerian prince claims to need your help to unlock an absurd amount of money, promising to reward you nicely. Savvy digital natives immediately dismiss such pleas, but the absurdity of the request may actually select for naïveté or advanced age, filtering for those most likely to fall for the scams.

Advances in AI, however, mean spammers might not have to rely on such hit-or-miss approaches. AI could allow them to target individuals and make their messages more persuasive based on easily accessible information, such as social media posts.

Inboxes are already bursting with spam. Epoxydude/fStop via Getty Images

Future of spam

Chances are you’ve heard about the advances in generative large language models like ChatGPT. The task these generative LLMs perform is deceptively simple: given a text sequence, predict which token – think of this as a part of a word – comes next. Then, predict which token comes after that. And so on, over and over.

Somehow, training on that task alone, when done with enough text on a large enough LLM, seems to be enough to imbue these models with the ability to perform surprisingly well on a lot of other tasks.

Multiple ways to use the technology have already emerged, showcasing the technology’s ability to quickly adapt to, and learn about, individuals. For example, LLMs can write full emails in your writing style, given only a few examples of how you write. And there’s the classic example – now over a decade old – of Target figuring out a customer was pregnant before she did.

Spammers and marketers alike would benefit from being able to predict more about individuals with less data. Given your LinkedIn page, a few posts and a profile image or two, LLM-armed spammers might make reasonably accurate guesses about your political leanings, marital status or life priorities.

Our research showed that LLMs could be used to predict which word an individual will say next with a degree of accuracy far surpassing other AI approaches, in a word-generation task called the semantic fluency task. We also showed that LLMs can take certain types of questions from tests of reasoning abilities and predict how people will respond to that question. This suggests that LLMs already have some knowledge of what typical human reasoning ability looks like.

If spammers make it past initial filters and get you to read an email, click a link or even engage in conversation, their ability to apply customized persuasion increases dramatically. Here again, LLMs can change the game. Early results suggest that LLMs can be used to argue persuasively on topics ranging from politics to public health policy.

Good for the gander

AI, however, doesn’t favor one side or the other. Spam filters also should benefit from advances in AI, allowing them to erect new barriers to unwanted emails.

Spammers often try to trick filters with special characters, misspelled words or hidden text, relying on the human propensity to forgive small text anomalies – for example, “c1îck h.ere n0w.” But as AI gets better at understanding spam messages, filters could get better at identifying and blocking unwanted spam – and maybe even letting through wanted spam, such as marketing email you’ve explicitly signed up for. Imagine a filter that predicts whether you’d want to read an email before you even read it.

Despite growing concerns about AI – as evidenced by Tesla, SpaceX and Twitter CEO Elon Musk, Apple founder Steve Wozniak and other tech leaders calling for a pause in AI development – a lot of good could come from advances in the technology. AI can help us understand how weaknesses in human reasoning might be exploited by bad actors and come up with ways to counter malevolent activities.

All new technologies can result in both wonder and danger. The difference lies in who creates and controls the tools, and how they are used.

John Licato, Assistant Professor of Computer Science and Director of AMHR Lab, *University of South Florida*

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

Click This Link for the Full Post > AI-generated spam may soon be flooding your inbox – and it will be personalized to be especially persuasive

via [Geeks Are Sexy] Technology News https://ift.tt/MWwGN1a

April 20, 2023 at 09:01AM

April 19, 2023

How ChatGPT—and Bots Like It—Can Spread Malware

https://www.wired.com/story/chatgpt-ai-bots-spread-malware/

The AI landscape has started to move very, very fast: consumer-facing tools such as Midjourney and ChatGPT are now able to produce incredible image and text results in seconds based on natural language prompts, and we’re seeing them get deployed everywhere from web search to children’s books.

However, these AI applications are being turned to more nefarious uses, including spreading malware. Take the traditional scam email, for example: It’s usually littered with obvious mistakes in its grammar and spelling—mistakes that the latest group of AI models don’t make, as noted in a recent advisory report from Europol.

Think about it: A lot of phishing attacks and other security threats rely on social engineering, duping users into revealing passwords, financial information, or other sensitive data. The persuasive, authentic-sounding text required for these scams can now be pumped out quite easily, with no human effort required, and endlessly tweaked and refined for specific audiences.

In the case of ChatGPT, it’s important to note first that developer OpenAI has built safeguards into it. Ask it to “write malware” or a “phishing email” and it will tell you that it’s “programmed to follow strict ethical guidelines that prohibit me from engaging in any malicious activities, including writing or assisting with the creation of malware.”

ChatGPT won’t code malware for you, but it’s polite about it.

OpenAI via David Nield

However, these protections aren’t too difficult to get around: ChatGPT can certainly code, and it can certainly compose emails. Even if it doesn’t know it’s writing malware, it can be prompted into producing something like it. There are already signs that cybercriminals are working to get around the safety measures that have been put in place.

We’re not particularly picking on ChatGPT here, but pointing out what’s possible once large language models (LLMs) like it are used for more sinister purposes. Indeed, it’s not too difficult to imagine criminal organizations developing their own LLMs and similar tools in order to make their scams sound more convincing. And it’s not just text either: Audio and video are more difficult to fake, but it’s happening as well.

When it comes to your boss asking for a report urgently, or company tech support telling you to install a security patch, or your bank informing you there’s a problem you need to respond to—all these potential scams rely on building up trust and sounding genuine, and that’s something AI bots are doing very well at. They can produce text, audio, and video that sounds natural and tailored to specific audiences, and they can do it quickly and constantly on demand.

via Wired Top Stories https://www.wired.com

April 19, 2023 at 06:09AM

April 19, 2023

Samsung Finally Enables the Temperature Sensor on the Galaxy Watch 5

https://gizmodo.com/samsung-finally-enables-the-temperature-sensor-on-the-g-1850349689

Samsung finally enabled temperature tracking on a smartwatch launched nearly nine months ago. The Galaxy Watch 5 and Galaxy Watch 5 Pro now offer detailed menstrual tracking by measuring your core body temperature. Samsung says the new feature is rolling out “progressively” to the Samsung Health app in the U.S.,…

VW, Rivian, Nissan, BMW, Genesis, Audi and Volvo lose EV tax credits starting tomorrow

https://www.autoblog.com/2023/04/17/ev-tax-credits-end-vw-rivian-nissan-bmw-genesis-audi-volvo/

The U.S. Treasury said Monday that Volkswagen, BMW, Nissan, Rivian, Hyundai and Volvo electric vehicles will lose access to a $7,500 tax credit under new battery sourcing rules.

The Treasury said the new requirements effective Tuesday will also cut by half credits for the Tesla Model 3 Standard Range Rear Wheel Drive to $3,750 but other Tesla models will retain the full $7,500 credit.

Vehicles losing credits Tuesday are the BMW 330e, BMW X5 xDrive45e, Genesis Electrified GV70, Nissan Leaf , Rivian R1S and R1T, Volkswagen ID.4 as well as the plug-in hybrid electric Audi Q5 TFSI e Quattro and plug-in hybrid (PHEV) electric Volvo S60. The Swedish carmaker is 82%-owned by China’s Zhejiang Geely Holding Group.

The rules are aimed at weaning the United States off dependence on China for EV battery supply chains and are part of President Joe Biden’s effort to make 50% of U.S. new vehicle sales by 2030 EVs or PHEVs.

Hyundai said in a statement it was committed to its long-range EV plans and that it “will utilize key provisions in the Inflation Reduction Act to accelerate the transition to electrification.”

Rivian declined to comment and the other automakers could not immediately be reached for comment.

Treasury also disclosed General Motors electric Chevrolet Bolt and Bolt EUV will qualify for the full $7,500 tax credit.

GM said earlier it expected at least some of its EVS would qualify for the $7,500 tax credit under the new rules, including the 2023 Cadillac Lyriq and forthcoming Chevrolet Equinox EV SUV and Blazer EV SUV. Treasury said all GM EVs will qualify.

Earlier, Ford Motor and Chrysler-parent Stellantis said most of their electric and PHEV models would see tax credits halved to $3,750 on April 18. Treasury confirmed the automakers’ calculations.

The rules were announced last month and mandated by Congress in August as part of the $430 billion Inflation Reduction Act (IRA).

The IRA requires 50% of the value of battery components be produced or assembled in North America to qualify for $3,750, and 40% of the value of critical minerals sourced from the United States or a free trade partner for a $3,750 credit.

The law required vehicles to be assembled in North America to qualify for any tax credits, which in August eliminated nearly 70% of eligible models and on Jan. 1 new price caps and limits on buyers income took effect.

Last week, the Environmental Protection Agency proposed new emissions rules that forecast 60% of new vehicle sales in 2030 would be EVs.

A preliminary administration analysis found nearly 65% of first quarter EV sales qualified under North American final assembly and price cap requirements; more than 90% of those previously eligible first quarter sales remain eligible for at least a $3,750 credit.

Treasury in December said EVs ineligible for the $7,500 consumer tax credit could qualify for a commercial leasing $7,500 credit.

Related video:

via Autoblog https://ift.tt/yqc6LXO

April 17, 2023 at 10:55AM

April 17, 2023

OpenAI’s CEO Says the Age of Giant AI Models Is Already Over

https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over/

The stunning capabilities of ChatGPT, the chatbot from startup OpenAI, has triggered a surge of new interest and investment in artificial intelligence. But late last week, OpenAI’s CEO warned that the research strategy that birthed the bot is played out. It’s unclear exactly where future advances will come from.

OpenAI has delivered a series of impressive advances in AI that works with language in recent years by taking existing machine-learning algorithms and scaling them up to previously unimagined size. GPT-4, the latest of those projects, was likely trained using trillions of words of text and many thousands of powerful computer chips. The process cost over $100 million.

But the company’s CEO, Sam Altman, says further progress will not come from making models bigger. “I think we’re at the end of the era where it’s going to be these, like, giant, giant models,” he told an audience at an event held at MIT late last week. “We’ll make them better in other ways.”

Altman’s declaration suggests an unexpected twist in the race to develop and deploy new AI algorithms. Since OpenAI launched ChatGPT in November, Microsoft has used the underlying technology to add a chatbot to its Bing search engine, and Google has launched a rival chatbot called Bard. Many people have rushed to experiment with using the new breed of chatbot to help with work or personal tasks.

Meanwhile, numerous well-funded startups, including Anthropic, AI21, Cohere, and Character.AI, are throwing enormous resources into building ever larger algorithms in an effort to catch up with OpenAI’s technology. The initial version of ChatGPT was based on a slightly upgraded version of GPT-3, but users can now also access a version powered by the more capable GPT-4.

Altman’s statement suggests that GPT-4 could be the last major advance to emerge from OpenAI’s strategy of making the models bigger and feeding them more data. He did not say what kind of research strategies or techniques might take its place. In the paper describing GPT-4, OpenAI says its estimates suggest diminishing returns on scaling up model size. Altman said there are also physical limits to how many data centers the company can build and how quickly it can build them.

Nick Frosst, a cofounder at Cohere who previously worked on AI at Google, says Altman’s feeling that going bigger will not work indefinitely rings true. He, too, believes that progress on transformers, the type of machine learning model at the heart of GPT-4 and its rivals, lies beyond scaling. “There are lots of ways of making transformers way, way better and more useful, and lots of them don’t involve adding parameters to the model,” he says. Frosst says that new AI model designs, or architectures, and further tuning based on human feedback are promising directions that many researchers are already exploring.

Each version of OpenAI’s influential family of language algorithms consists of an artificial neural network, software loosely inspired by the way neurons work together, which is trained to predict the words that should follow a given string of text.

via Wired Top Stories https://www.wired.com

April 17, 2023 at 06:11AM

April 17, 2023

This Tool Knows Whether You’re Overpaying for Rent in NYC

https://lifehacker.com/this-tool-knows-whether-youre-overpaying-for-rent-in-ny-1850325047

Rent prices rose by an average of 14% last year, and it doesn’t look like those heart-clutching price hikes are going to stop any time soon. For anyone wondering exactly how much they’re getting ripped off in this rental hellscape, there are tools like RentHop RentCompare, which is specifically designed to help renters in New York City figure out if they’re overpaying or underpaying for rent. While the app is specific to NYC homes right now, the price tool is still a great way to explore how much different neighborhoods cost and help you see where you stand in the current rental market.

Here’s how you can use this rent compare tool, no matter where you live.

How to use RentCompare

RentCompare is an interactive tool that allows renters to compare a rental price across the average rent for an apartment’s size and neighborhood. On renthop.com, you enter your email and your neighborhood, current rent, apartment size, and amenities. Then, RentCompare reveals how it compares to similar apartments in the area.

The goal is to “put the power back in renters’ hands” by showing them whether their current rental price is below the average for apartments in the neighborhood with the same bedroom size. Of course, you may pay more than the average renter if you’re also paying for amenities like a doorman, roof deck, or in-unit washer/dryer (my greatest dream).

Consider what these famous fictional New Yorkers were “paying”

If you’re looking for an apartment in NYC, you can use a rental listings site like Zillow and compare the listed prices to comparable apartments listed on RentHop’s RentCompare. And if you live somewhere that is not NYC, you can use Zillow to compare rent prices to what is most likely a much more affordable city. Or, you can still play around with tool—just pretend to be some of your favorite famous New Yorkers.

A few years back, Insider got to the bottom of how much NYC’s most famous fictional apartments actually cost. Plug in some of the truly exorbitant apartment prices below and see whether or not Seinfeld was getting a fair deal. (What’s the deal with getting a fair deal, am I right?) (Spoiler: He was getting a great deal, considering the fun fact that Seinfeld’s TV apartment is physically impossible). Mess around with these wild home prices:

Friends (90 Bedford Street, West Village): $2 million
Sex And The City (66 Perry Street, West Village): $600,000
Seinfeld (129 West 81st Street, Upper West Side): $900,000
Big Daddy (16 Mercer Street, SoHo): $2.5 million
The Cosby Show (Brooklyn Heights): $11 million
Succession (specifically Kendall Roy’s ex-wife) (Tribeca’s Woolworth Tower Residences, Tribeca): $23.355 million (This one was pulled from Architectural Digest.)

Keep in mind that rent varies across the city, while most fictional New Yorkers seem to get the luxury of living on the Upper West Side. Last year, GBSN Research found that the median rent is $3,550 in Manhattan, $2,850 in Brooklyn, and $2,400 in Queens.

via Lifehacker https://lifehacker.com

April 17, 2023 at 10:20AM

Author: peterjang73

Google will reportedly announce a foldable Pixel phone at I/O next month