Archiv der Kategorie: Artificial Intelligence

Why Apple uses ChatGPT 4o as its new operating system for Apples Vision Pro + Version 2

Apple’s Vision Pro + Version 2, utilizing OpenAI’s ChatGPT „GPT-4o“ as the operating system offers several compelling marketing benefits. Here are the key advantages to highlight:

1. Revolutionary User Interface:
– Conversational AI: GPT-4o’s advanced natural language processing capabilities allow for a conversational user interface, making interactions with Vision Pro + more intuitive and user-friendly.
– Personalized Interactions: The AI can provide highly personalized responses and suggestions based on user behavior and preferences, enhancing user satisfaction and engagement.

2. Unmatched Productivity:
– AI-Driven Multitasking: GPT-4o can manage and streamline multiple tasks simultaneously, significantly boosting productivity by handling scheduling, reminders, and real-time information retrieval seamlessly.
– Voice-Activated Efficiency: Hands-free operation through advanced voice commands allows users to multitask efficiently, whether they are working, driving, or engaged in other activities.

3. Advanced Accessibility:
– Inclusive Design: GPT-4o enhances accessibility with superior voice recognition, understanding diverse speech patterns, and offering multilingual support, making Vision Pro + more accessible to a broader audience.
– Adaptive Assistance: The AI can provide context-aware assistance to users with disabilities, further promoting inclusivity and ease of use.

4. Superior Integration and Ecosystem:
– Apple Ecosystem Synergy: GPT-4o integrates seamlessly with other Apple devices and services, offering a cohesive and interconnected user experience across the Apple ecosystem.
– Unified User Experience: Users can enjoy a consistent and unified experience across all their Apple devices, enhancing brand loyalty and overall user satisfaction.

5. Enhanced Security and Privacy:
– Secure Interactions: Emphasize GPT-4o’s robust security measures to ensure user data privacy and protection, leveraging OpenAI’s commitment to ethical AI practices.
– Trustworthy AI: Highlight OpenAI’s dedication to ethical AI usage, reinforcing user trust in the AI-driven functionalities of Vision Pro +.

6. Market Differentiation:
– Innovative Edge: Position Vision Pro + as a cutting-edge product that stands out in the market due to its integration with GPT-4o, setting it apart from competitors.
– Leadership in AI: Showcase Apple’s leadership in technology innovation by leveraging OpenAI’s state-of-the-art advancements in AI.

7. Future-Proofing:
– Continuous Innovation: Regular updates from OpenAI ensure that Vision Pro + remains at the forefront of AI technology, with continuous improvements and new features.
– Scalable Solutions: The AI platform’s scalability allows for future enhancements, ensuring the product remains relevant and competitive over time.

8. Customer Engagement:
– Proactive Support: GPT-4o can offer proactive customer support and real-time problem-solving, leading to higher customer satisfaction and loyalty.
– Engaging Experiences: The AI can create engaging and interactive experiences, making the device more enjoyable and useful for daily activities.

9. Enhanced Creativity:
Creative Assistance: GPT-4o can assist users with creative tasks such as content creation, brainstorming, and project management, providing valuable support for both personal and professional use.
– Innovative Features: Highlight the unique AI-driven features that empower users to explore new creative possibilities, enhancing the appeal of Vision Pro +.

10. Efficient Learning and Adaptation:
– User Learning: GPT-4o continuously learns from user interactions, becoming more efficient and effective over time, offering a progressively improving user experience.
– Adaptive Technology: The AI adapts to user needs and preferences, ensuring that the device remains relevant and useful in a variety of contexts.

By leveraging these benefits, Apple can market the Vision Pro + Version 2 as a pioneering product that offers unparalleled user experience, productivity, and innovation, driven by the advanced capabilities of OpenAI’s GPT-4o.

It’s the End of Google Search As We Know It

Source: https://www.wired.com/story/google-io-end-of-google-search/

Google is rethinking its most iconic and lucrative product by adding new AI features to search. One expert tells WIRED it’s “a change in the world order.”

Google Search is about to fundamentally change—for better or worse. To align with Alphabet-owned Google’s grand vision of artificial intelligence, and prompted by competition from AI upstarts like ChatGPT, the company’s core product is getting reorganized, more personalized, and much more summarized by AI.

At Google’s annual I/O developer conference in Mountain View, California, today, Liz Reid showed off these changes, setting her stamp early on in her tenure as the new head of all things Google search. (Reid has been at Google a mere 20 years, where she has worked on a variety of search products.) Her AI-soaked demo was part of a broader theme throughout Google’s keynote, led primarily by CEO Sundar Pichai: AI is now underpinning nearly every product at Google, and the company only plans to accelerate that shift.

“In the era of Gemini we think we can make a dramatic amount of improvements to search,” Reid said in an interview with WIRED ahead of the event, referring to the flagship generative AI model launched late last year. “People’s time is valuable, right? They deal with hard things. If you have an opportunity with technology to help people get answers to their questions, to take more of the work out of it, why wouldn’t we want to go after that?”

It’s as though Google took the index cards for the screenplay it’s been writing for the past 25 years and tossed them into the air to see where the cards might fall. Also: The screenplay was written by AI.

These changes to Google Search have been long in the making. Last year the company carved out a section of its Search Labs, which lets users try experimental new features, for something called Search Generative Experience. The big question since has been whether, or when, those features would become a permanent part of Google Search. The answer is, well, now.

Google’s search overhaul comes at a time when critics are becoming increasingly vocal about what feels to some like a degraded search experience, and for the first time in a long time, the company is feeling the heat of competition, from the massive mashup between Microsoft and OpenAI. Smaller startups like Perplexity, You.com, and Brave have also been riding the generative AI wave and getting attention, if not significant mindshare yet, for the way they’ve rejiggered the whole concept of search.
Automatic Answers

Google says it has made a customized version of its Gemini AI model for these new Search features, though it declined to share any information about the size of this model, its speeds, or the guardrails it has put in place around the technology.

This search-specific spin on Gemini will power at least a few different elements of the new Google Search. AI Overviews, which Google has already been experimenting with in its labs, is likely the most significant. AI-generated summaries will now appear at the top of search results.

One example from WIRED’s testing: In response to the query “Where is the best place for me to see the northern lights?” Google will, instead of listing web pages, tell you in authoritative text that the best places to see the northern lights, aka the aurora borealis, are in the Arctic Circle in places with minimal light pollution. It will also offer a link to NordicVisitor.com. But then the AI continues yapping on below that, saying “Other places to see the northern lights include Russia and Canada’s northwest territories.”

Reid says that AI Overviews like this won’t show up for every search result, even if the feature is now becoming more prevalent. It’s reserved for more complex questions. Every time a person searches, Google is attempting to make an algorithmic value judgment behind the scenes as to whether it should serve up AI-generated answers or a conventional blue link to click. “If you search for Walmart.com, you really just want to go to Walmart.com,” Reid says. “But if you have an extremely customized question, that’s where we’re going to bring this.”

AI Overviews are rolling out this week to all Google search users in the US. The feature will come to more countries by the end of the year, Reid said, which means more than a billion people will see AI Overviews in their search results. They will appear across all platforms—the web, mobile, and as part of the search engine experience in browsers, such as when people search through Google on Safari.

Another update coming to search is a function for planning ahead. You can, for example, ask Google to meal-plan for you, or to find a pilates studio nearby that’s offering a class with an introductory discount. In the Googley-eyed future of search, an AI agent can round up a few studios nearby, summarize reviews of them, and plot out the time it would take someone to walk there. This is one of Google’s most obvious advantages over upstart search engines, which don’t have anything close to the troves of reviews, mapping data, or other knowledge that Google has, and may not be able to tap into APIs for real-time or local information so easily.

The most jarring changes that Google has been exploring in its Search Labs is an “AI-organized” results page. This at first glance looks to eschew the blue-links search experience entirely.

One example provided by Reid: A search for where to go for an anniversary dinner in the greater Dallas area would return a page with a few “chips” or buttons at the top to refine the results. Those might include categories like Dine-In, Takeout, and Open Now. Below that might be a sponsored result—Google’s gonna ad—and then a grouping of what Google judges to be “anniversary-worthy restaurants” or “romantic steakhouses.” That might be followed by some suggested questions to tweak the search even more, like, “Is Dallas a romantic city?”

AI-organized search is still being rolled out, but it will start appearing in the US in English “in the coming weeks.” So will an enhanced video search option, like Google Lens on steroids, where you can point your phone’s camera at an object like a broken record player and ask how to fix it.

If all these new AI features sound confusing, you might have missed Google’s latest galaxy-brain ambitions for what was once a humble text box. Reid makes clear that she thinks most consumers assume Google Search is just one thing, where in fact it’s many things to different people, who all search in different ways.

“That’s one of the reasons why we’re excited about working on some of the AI-organized results pages,” she said. “Like, how do you make sense of space? The fact that you want lots of different content is great. But is it as easy as it can be yet in terms of browsing through and consuming the information?”

But by generating AI Overviews—and by determining when those overviews should appear—Google is essentially deciding what is a complex question and what is not, and then making a judgment on what kind of web content should inform its AI-generated summary. Sure, it’s a new era of search where search does the work for you; it’s also a search bot that has the potential to algorithmically favor one kind of result over others.

“One of the biggest changes to happen in search with these AI models is that the AI actually creates a kind of informed opinion,” says Jim Yu, the executive chairman of BrightEdge, a search engine optimization firm that has been closely monitoring web traffic for more than 17 years. “The paradigm of search for the last 20 years has been that the search engine pulls a lot of information and gives you the links. Now the search engine does all the searches for you and summarizes the results and gives you a formative opinion.”

Doing that raises the stakes for Google’s search results. When algorithms are deciding that what a person needs is one coagulated answer, instead of coughing up several links for them to then click through and read, errors are more consequential. Gemini has not been immune to hallucinations—instances where the AI shares blatantly wrong or made-up information.

Last year a writer for The Atlantic asked Google to name an African country beginning with the letter “K,” and the search engine responded with a snippet of text—originally generated by ChatGPT—that none of the countries in Africa begin with the letter K, clearly overlooking Kenya. Google’s AI image-generation tool was very publicly criticized earlier this year when it depicted some historical figures, such as George Washington, as Black. Google temporarily paused that tool.
New World Order

Google’s reimagined version of AI search shoves the famous “10 blue links” it used to provide on results pages further into the rearview. First ads and info boxes began to take priority at the top of Google’s pages; now, AI-generated overviews and categories will take up a good chunk of search real estate. And web publishers and content creators are nervous about these changes—rightfully.

The research firm Gartner predicted earlier this year that by 2026, traditional search engine volume will drop by 25 percent, as a more “agent”-led search approach, in which AI models retrieve and generate more direct answers, takes hold.

“Generative AI solutions are becoming substitute answer engines, replacing user queries that previously may have been executed in traditional search engines,” Alan Antin, a vice president analyst at Gartner, said in a statement that accompanied the report. “This will force companies to rethink their marketing channels strategy.”

What does that mean for the web? “It’s a change in the world order,” says Yu, of BrightEdge. “We’re at this moment where everything in search is starting to change with AI.”

Eight months ago BrightEdge developed something it calls a generative parser, which monitors what happens when searchers interact with AI-generated results on the web. He says over the past month the parser has detected that Google is less frequently asking people if they want an AI-generated answer, which was part of the experimental phase of generative search, and more frequently assuming they do. “We think it shows they have a lot more confidence that you’re going to want to interact with AI in search, rather than prompting you to opt in to an AI-generated result.”

Changes to search also have major implications for Google’s advertising business, which makes up the vast majority of the company’s revenue. In a recent quarterly earnings call, Pichai declined to share revenue from its generative AI experiments broadly. But as WIRED’s Paresh Dave pointed out, by offering more direct answers to searchers, “Google could end up with fewer opportunities to show search ads if people spend less time doing additional, more refined searches.” And the kinds of ads shown may have to evolve along with Google’s generative AI tools.

Google has said it will prioritize traffic to websites, creators, and merchants even as these changes roll out, but it hasn’t pulled back the curtain to reveal exactly how it plans to do this.

When asked in a press briefing ahead of I/O whether Google believes users will still click on links beyond the AI-generated web summary, Reid said that so far Google sees people “actually digging deeper, so they start with the AI overview and then click on additional websites.”

In the past, Reid continued, a searcher would have to poke around to eventually land on a website that gave them the info they wanted, but now Google will assemble an answer culled from various websites of its choosing. In the hive mind at the Googleplex, that will still spark exploration. “[People] will just use search more often, and that provides an additional opportunity to send valuable traffic to the web,” Reid said.

It’s a rosy vision for the future of search, one where being served bite-size AI-generated answers somehow prompts people to spend more time digging deeper into ideas. Google Search still promises to put the world’s information at our fingertips, but it’s less clear now who is actually tapping the keys.

Source: https://www.wired.com/story/google-io-end-of-google-search/

WhatsApp Chats Will Soon Work With Other Encrypted Messaging Apps

Source: https://www.wired.com/story/whatsapp-interoperability-messaging/

New EU rules mean WhatsApp and Messenger must be interoperable with other chat apps. Here’s how that will work.

WhatsApp icon seen with many colorful icons

A frequent annoyance of contemporary life is having to shuffle through different messaging apps to reach the right person. Messenger, iMessage, WhatsApp, Signal—they all exist in their own silos of group chats and contacts. Soon, though, WhatsApp will do the previously unthinkable for its 2 billion users: allow people to message you from another app. At least, that’s the plan.

For about the past two years, WhatsApp has been building a way for other messaging apps to plug themselves into its service and let people chat across apps—all without breaking the end-to-end encryption it uses to protect the privacy and security of people’s messages. The move is the first time the chat app has opened itself up this way, and it potentially offers greater competition.

It isn’t a shift entirely of WhatsApp’s own making. In September, European, lawmakers designated WhatsApp parent Meta as one of six influential “gatekeeer” companies under its sweeping Digital Markets Act, giving it six months to open its walled garden to others. With just a few weeks to go before that time is up, WhatsApp is detailing how its interoperability with other apps may work.

“There’s real tension between offering an easy way to offer this interoperability to third parties whilst at the same time preserving the WhatsApp privacy, security, and integrity bar,” says Dick Brouwer, an engineering director at WhatsApp who has worked on Meta rolling out encryption to its Messenger app. “I think we’re pretty happy with where we’ve landed.”

Interoperability in both WhatsApp and Messenger—as dictated by Europe’s rules—will initially focus on text messaging, sending images, voice messages, videos, and files between two people. Calls and group chats will come years down the line. Europe’s rules apply only to messaging services, not traditional SMS messaging. “One of the core requirements here, and this is really important, is for users for this to be opt-in,” says Brouwer. “I can choose whether or not I want to participate in being open to exchanging messages with third parties. This is important, because it could be a big source of spam and scams.”

WhatsApp users who opt in will see messages from other apps in a separate section at the top of their inbox. This “third-party chats” inbox has previously been spotted in development versions of the app. “The early thinking here is to put a separate inbox, given that these networks are very different,” Brouwer says. “We cannot offer the same level of privacy and security,” he says. If WhatsApp were to add SMS, it would use a separate inbox as well, although there are no plans to add it, he says.

Overall, the idea behind interoperability is simple. You shouldn’t need to know what messaging app your friends or family use to get in touch with them, and you should be able to communicate from one app to another without having to download both. In an ideal interoperable world, you could, for example, use Apple’s iMessage to chat with someone on Telegram. However, for apps with millions or billions of users, making this a reality isn’t straightforward—encrypted messaging apps use their own configurations and different protocols and have different standards when it comes to privacy.

Despite WhatsApp working on its interoperability plan for more than a year, it will still take some time for third-party chats to hit people’s apps. Messaging companies that want to interoperate with WhatsApp or Messenger will need to sign an agreement with the company and follow its terms. The full details of the plan will be published in March, Brouwer says; under EU laws, the company will have several months to implement it.

Brouwer says Meta would prefer if other apps use the Signal encryption protocol, which its systems are based upon. Other than its namesake app and the Meta-owned messengers, the Signal Protocol is publicly disclosed as being used in Google Messages and Skype. To send messages, third-party apps will need to encrypt content using the Signal Protocol and then package it into message stanzas in the eXtensible Markup Language (XML). When receiving messages, apps will need to connect to WhatsApp’s servers.

“We think that the best way to deliver this approach is through a solution that is built on WhatsApp’s existing client-server architecture,” Brouwer says, adding it has been working with other companies on the plans. “This effectively means that the approach that we’re trying to take is for WhatsApp to document our client- server protocol and letting third-party clients connect directly to our infrastructure and exchange messages with WhatsApp clients.”

There is some flexibility to WhatsApp interoperability. Meta’s app will also allow other apps to use different encryption protocols if they can “demonstrate” they reach the security standards that WhatsApp outlines in its guidance. There will also be the option, Brouwer says, for third-party developers to add a proxy between their apps and WhatsApp’s server. This, he says, could give developers more “flexibility” and remove the need for them to use WhatsApp’s client-server protocols, but it also “increases the potential attack vectors.”

So far, it is unclear which companies, if any, are planning to connect their services to WhatsApp. WIRED asked 10 owners of messaging or chat services—including Google, Telegram, Viber, and Signal—whether they intend to look at interoperability or had worked with WhatsApp on its plans. The majority of companies didn’t respond to the request for comment. Those that did, Snap and Discord, said they had nothing to add. (The European Commission is investigating whether Apple’s iMessage meets the thresholds to offer interoperability with other apps itself. The company did not respond to a request for comment. It has also faced recent challenges in the US about the closed nature of iMessage.)

Matthew Hodgson, the cofounder of Matrix, which is building an open source standard for encryption and operates the messaging app Element, confirms that his company has worked with WhatsApp on interoperability in an “experimental” way but that he cannot say any more due to signing a nondisclosure agreement. In a talk last weekend, Hodgson demonstrated “hypothetical” architectures for ways that Matrix could connect to the systems of two gatekeepers that don’t use the same encryption protocols.

Meanwhile, Julia Weis, a spokesperson for the Swiss messaging app Threema, says that while WhatsApp did approach it to discuss its interoperability plans, the proposed system didn’t meet Threema’s security and privacy standards. “WhatsApp specifies all the protocols, and we’d have no way of knowing what actually happens with the user data that gets transferred to WhatsApp—after all, WhatsApp is closed source,” Weis says. (WhatsApp’s privacy policy states how it uses people’s data.)

When the EU first announced that messaging apps may have to work together in early 2022, many leading cryptographers opposed the idea, saying it adds complexity and potentially introduces more security and privacy risks. Carmela Troncoso, an associate professor at the Swiss university École Polytechnique Fédérale de Lausanne, who focuses on security and privacy engineering, says interoperability moves could potentially lead to different power relationships between companies, depending on how they are implemented.

“This move for interoperability will, on the one hand, open the market, but also maybe close the market in the sense that now the bigger players are going to have more decisional power,” Troncoso says. “Now, if the big player makes a move and you want to continue being interoperable with this big player, because your users are hooked up to this, you’re going to have to follow.”

While the interoperability of encrypted messaging apps may be possible, there are some fundamental challenges about how the systems will work in the real world. How much of a problem spam and scamming will be across apps is largely unknown until people start using interoperable setups. There are also questions about how people will find each other across different apps. For instance, WhatsApp uses your phone number to interact and message other people, while Threema randomly generates eight-digit IDs for people’s accounts. Linking up with WhatsApp “could de-anonymize Threema users,” Weis, the Threema spokesperson says.

Meta’s Brouwer says the company is still working on the interoperability features and the level of support it will make available for companies wanting to integrate with it. “Nobody quite knows how this works,” Brouwer says. “We have no idea what the demand is.” However, he says, the decision was made to use WhatsApp’s existing architecture to run interoperability, as it means that it can more easily scale up the system for group chats in the future. It also reduces the potential for people’s data to be exposed to multiple servers, Brouwer says.

Ultimately, interoperability will evolve over time, and from Meta’s perspective, Brouwer says, it will be more challenging to add new features to it quickly. “We don’t believe interop chats and WhatsApp chats can evolve at the same pace,” he says, claiming it is “harder to evolve an open network” compared to a closed one. “The second you do something different—than what we know works really well—you open up a wormhole of security, privacy issues, and complexity that is always going to be much bigger than you think it is.”

Critical Infrastructure Is Sinking Along the US East Coast

Source: https://www.wired.com/story/critical-infrastructure-is-sinking-along-the-us-east-coast/

Last year, scientists reported that the US Atlantic Coast is dropping by several millimeters annually, with some areas, like Delaware, notching figures several times that rate. So just as the seas are rising, the land along the eastern seaboard is sinking, greatly compounding the hazard for coastal communities.

In a follow-up study just published in the journal PNAS Nexus, the researchers tally up the mounting costs of subsidence—due to settling, groundwater extraction, and other factors—for those communities and their infrastructure. Using satellite measurements, they have found that up to 74,000 square kilometers (29,000 square miles) of the Atlantic Coast are exposed to subsidence of up to 2 millimeters (0.08 inches) a year, affecting up to 14 million people and 6 million properties. And over 3,700 square kilometers along the Atlantic Coast are sinking more than 5 millimeters annually. That’s an even faster change than sea level rise, currently at 4 millimeters a year. (In the map below, warmer colors represent more subsidence, up to 6 millimeters.)

Map of eastern coastal cities
Courtesy of Leonard O Ohenhen

With each millimeter of subsidence, it gets easier for storm surges—essentially a wall of seawater, which hurricanes are particularly good at pushing onshore—to creep farther inland, destroying more and more infrastructure. “And it’s not just about sea levels,” says the study’s lead author, Leonard Ohenhen, an environmental security expert at Virginia Tech. “You also have potential to disrupt the topography of the land, for example, so you have areas that can get full of flooding when it rains.”

A few millimeters of annual subsidence may not sound like much, but these forces are relentless: Unless coastal areas stop extracting groundwater, the land will keep sinking deeper and deeper. The social forces are relentless, too, as more people around the world move to coastal cities, creating even more demand for groundwater. “There are processes that are sometimes even cyclic. For example, in summers you pump a lot more water, so land subsides rapidly in a short period of time,” says Manoochehr Shirzaei, an environmental security expert at Virginia Tech and coauthor of the paper. “That causes large areas to subside below a threshold that leads the water to flood a large area.” When it comes to flooding, falling elevation of land is a tipping element that has been largely ignored by research so far, Shirzaei says.

In Jakarta, Indonesia, for example, the land is sinking nearly a foot a year because of collapsing aquifers. Accordingly, within the next three decades, 95 percent of North Jakarta could be underwater. The city is planning a giant seawall to hold back the ocean, but it’ll be useless unless subsidence is stopped.

This new study warns that levees and other critical infrastructure along the Atlantic Coast are in similar danger. If the land were to sink uniformly, you might just need to keep raising the elevation of a levee to compensate. But the bigger problem is “differential subsidence,” in which different areas of land sink at different rates. “If you have a building or a runway or something that’s settling uniformly, it’s probably not that big a deal,” says Tom Parsons, a geophysicist with the United States Geological Survey who studies subsidence but wasn’t involved in the new paper. “But if you have one end that’s sinking faster than the other, then you start to distort things.”

The researchers selected 10 levees on the Atlantic Coast and found that all were impacted by subsidence of at least 1 millimeter a year. That puts at risk something like 46,000 people, 27,000 buildings, and $12 billion worth of property. But they note that the actual population and property at risk of exposure behind the 116 East Coast levees vulnerable to subsidence could be two to three times greater. “Levees are heavy, and when they’re set on land that’s already subsiding, it can accelerate that subsidence,” says independent scientist Natalie Snider, who studies coastal resilience but wasn’t involved in the new research. “It definitely can impact the integrity of the protection system and lead to failures that can be catastrophic.”

map of Virgina's coastal areas
Courtesy of Leonard O Ohenhen

The same vulnerability affects other infrastructure that stretches across the landscape. The new analysis finds that along the Atlantic Coast, between 77 and 99 percent of interstate highways and between 76 and 99 percent of primary and secondary roads are exposed to subsidence. (In the map above, you can see roads sinking at different rates across Hampton and Norfolk, Virginia.) Between 81 and 99 percent of railway tracks and 42 percent of train stations are exposed on the East Coast.

Below is New York’s JFK Airport—notice the red hot spots of high subsidence against the teal of more mild elevation change. The airport’s average subsidence rate is 1.7 millimeters a year (similar to the LaGuardia and Newark airports), but across JFK that varies between 0.8 and 2.8 millimeters a year, depending on the exact spot.

map of JFK airport aerial
Courtesy of Leonard O Ohenhen

This sort of differential subsidence can also bork much smaller structures, like buildings, where one side might drop faster than another. “Even if that is just a few millimeters per year, you can potentially cause cracks along structures,” says Ohenhen.

The study finds that subsidence is highly variable along the Atlantic Coast, both regionally and locally, as different stretches have different geology and topography, and different rates of groundwater extraction. It’s looking particularly problematic for several communities, like Virginia Beach, where 451,000 people and 177,000 properties are at risk. In Baltimore, Maryland, it’s 826,000 people and 335,000 properties, while in NYC—in Queens, Bronx, and Nassau—that leaps to 5 million people and 1.8 million properties.

So there’s two components to addressing the problem of subsidence: Getting high-resolution data like in this study, and then pairing that with groundwater data. “Subsidence is so spatially variable,” says Snider. “Having the details of where groundwater extraction is really having an impact, and being able to then demonstrate that we need to change our management of that water, that reduces subsidence in the future.”

The time to act is now, Shirzaei emphasizes. Facing down subsidence is like treating a disease: You spend less money by diagnosing and treating the problem now, saving money later by avoiding disaster. “This kind of data and the study could be an essential component of the health care system for infrastructure management,” he says. “Like cancers—if you diagnose it early on, it can be curable. But if you are late, you invest a lot of money, and the outcome is uncertain.”

Source: https://www.wired.com/story/critical-infrastructure-is-sinking-along-the-us-east-coast/

Open letter on the feasibility of “Chat Control”: Assessments from a scientific point of view

Source: https://www.ins.jku.at/chatcontrol/

Open letter on the feasibility of „Chat Control“:Assessments from a scientific point of view

Update: A parallel initiative is aimed at the EU institutions and is available in English at the CSA Academia Open Letter . Since the very similar arguments were formulated in parallel, they support each other.

The initiative of the EU Commission discussed under the name “ Chat Control ”, the unprovoked monitoring of various communication channels to detect child pornography, terrorist or other “undesirable” material – including attempts at early detection (e.g. “grooming” minors through text messages that build trust) – mandatory for mobile devices and communication services, has recently been expanded to include the monitoring of direct audio communications . Some states, including Austria and Germany , have already publicly declared that they will not support this initiative for monitoring without cause. AlsoCivil protection and children’s rights organizations have rejected this approach as excessive and at the same time ineffective . Recently, even the legal service of the EU Council of Ministers diagnosed an incompatibility with European fundamental rights. Irrespective of this, the draft will be tightened up even more and extended to other channels: in the last version even to audio messages and conversations. The approach appears to be coordinated with corresponding attempts in the US ( “EARN IT” and “STOP CSAM” Acts ) and the UK (“Online Safety Bill”).

As scientists who are actively researching in various areas of this topic, we therefore make the declaration in all clarity: This advance cannot be implemented safely and effectively. There is currently no foreseeable further development of the corresponding technologies that would technically make such an implementation possible. In addition, according to our assessment, the hoped-for effects of these monitoring measures are not to be expected. This legislative initiative therefore misses its target, is socio-politically dangerous and would permanently damage the security of our communication channels for the majority of the population.

The main reasons against the feasibility of „Chat Control“ have already been mentioned several times. In the following, we would like to discuss these specifically in the interdisciplinary connection between artificial intelligence (AI, artificial intelligence / AI), security (information security / technical data protection) and law .

Our concerns are:

  1. Security: a) Encryption is the best method for internet security. Successful attacks are almost always due to faulty software. b) A systematic and automated monitoring (ie „scanning“) of encrypted content is technically only possible if the security that can be achieved through encryption is massively violated, which is associated with considerable additional risks. c) A legal obligation to integrate such scanners will make secure digital communications in the EU unavailable to the majority of the population, but will have little impact on criminal communications.
  2. AI: a) Automated classification of content, including methods based on machine learning, is always subject to errors, which in this case will lead to high false positives. b) Special monitoring methods, which are carried out on the end devices, open up additional possibilities for attacks up to the extraction of possibly illegal training material.
  3. Law: a) A sensible demarcation from the explicitly permitted use of specific content, for example in the educational sector or for criticism and parody, does not appear to be automatically possible. b) The massive encroachment on fundamental rights through such an instrument of mass surveillance is not proportionate and would cause great collateral damage in society.

In detail, these concerns are based on the following scientifically recognized facts:

  1. Security
    1. Encryption using modern methods is an indispensable basis for practically all technical mechanisms for maintaining security and data protection on the Internet. In this way, communication on the Internet is currently protected as the cornerstone for current services, right through to critical infrastructure such as telephone, electricity, water networks, hospitals, etc. Trust in good encryption methods is significantly higher among experts than in other security mechanisms. Above all, the average poor quality of software in general is the reason for the many publicly known security incidents. Improving this situation in terms of better security therefore relies primarily on encryption.
    2. Automatic monitoring („scanning“) of correctly encrypted content is not effectively possible according to the current state of knowledge. Procedures such as „Fully Homomorphic Encryption“ (FHE) are currently not suitable for this application – neither is the procedure capable of this, nor is the necessary computing power realistically available. A rapid improvement is not foreseeable here either.
    3. For these reasons, earlier attempts to ban or restrict end-to-end encryption were mostly quickly abandoned internationally. The current chat control push aims to have monitoring functionality built into the end devices in the form of scanning modules (“Client-Side Scanning” / CSS) and therefore to scan the plain text content before secure encryption or after secure decryption . Providers of communication services would have to be legally obliged to implement this for all content. Since this is not in the core interest of such organizations and requires effort in implementation and operation as well as increased technical complexity, it cannot be assumed that the introduction of such scanners will be voluntary – in contrast to scanning on the server side.
    4. Secure messengers such as Signal or Threema and WhatsApp have already publicly announced that they will not implement such client scanners, but to withdraw from the corresponding regions. This has different implications for communication depending on the use case: (i) (adult) criminals will simply communicate with each other via “non-compliant” messenger services to further benefit from secure encryption. The increased effort, for example to install other apps on Android via sideloading that are not available in the usual app stores in the respective country, is not a significant hurdle for criminal elements. (ii) Criminals communicate with possible future victims via popular platforms, which would be the target of the mandatory surveillance measures discussed. In this case, it can be assumed that informed criminals will quickly lure their victims to alternative but still internationally recognized channels such as Signal, which are not covered by the monitoring. (iii) Participants exchange problematic material without being aware that they are committing a crime. This case would be reported automatically and possibly also lead to the criminalization of minors without intent. The restrictions would therefore primarily affect the broad – and irreproachable – mass of the population.It would be utterly delusional to think that without built-in monitoring, secure encryption could still be reversed. Tools like Signal, Tor, Cwtch, Briar and many others are widely available as open source and can easily be removed from central control. Knowledge of secure encryption is already common knowledge and can no longer be censored. There is no effective way to technically block the use of strong encryption without Client Side Scanning (CSS). If surveillance measures are prescribed in messengers, only criminals whose actual crimes outweigh the violation of the surveillance obligation will maintain their privacy.
    5. Furthermore, the complex implementation forced by proposed scanner modules creates additional security problems that do not currently exist. On the one hand, this represents new software components, which in turn will be vulnerable. On the other hand, the Chat Control proposals consistently assume that the scanner modules themselves will remain confidential, since they would be trained on content that is already punishable for mere possession (built into the Messenger app), on the one hand, and simply for testing evasion methods, on the other can be used. It is also an illusion that such machine learning models or other scanner modules, distributed to billions of devices under the control of end users, can ever be kept secret.NeuralHash “ module for CSAM detection, which was extracted almost immediately from corresponding iOS versions and is thus openly available . The assumption by Chat Control proposals that these scanner modules could be kept confidential is therefore completely unfounded and incorrect Corresponding data leaks are almost unavoidable here.
  2. artificial intelligence
    1. We have to assume that machine learning (ML) models on end devices cannot, in principle, be kept completely secret. This is in contrast to server-side scanning, which is currently legally possible and also actively practiced by various providers to scan content that has not been end-to-end encrypted. ML models on the server side can be reasonably protected from being read with the current state of the art and are less the focus of this consideration.
    2. A general problem with all ML-based filters are false classifications, i.e. that known “undesirable” material is not recognized as such with small changes (also referred to as “false negative” or “false non-match”). For parts of the push, it is currently unknown how ML models should be able to recognize complex, unfamiliar material with changing context (e.g. „grooming“ in text chats) with even approximate accuracy. The probability of high false negative rates is high.In terms of risk, however, it is significantly more serious if harmless material is classified as “undesirable” (also referred to as “false positive” or “false match” or also as “collision”). Such errors can be reduced, but in principle cannot be ruled out. In addition to the false accusation of uninvolved persons, false positives also lead to (possibly very) many false reports for the investigative authorities, which already have too few resources to investigate reports.
    3. The assumed open availability of ML models also creates various new attack possibilities. Using the example of Apple NeuralHash , random collisions were found very quickly and programs were freely released to generate any collisions between images . This method, also known as “malicious collisions”, uses so-called adversarial attacks against the neural network and thus enables attackers to deliberately classify harmless material as a “match” in the ML model and thus classify it as “undesirable”. In this way, innocent people can be harmed in a targeted manner by automatic false reports and brought under suspicion – without any illegal action on the part of the attacked or attacker.
    4. The open availability of the models can also be used for so-called „training input recovery“ in order to extract (at least partially) the content used for training from the ML model. In the case of prohibited content (e.g. child pornography), this poses another massive problem and can further increase the damage to those affected by the fact that their sensitive data (e.g. images of abuse used for training) can continue to be published. Because of these and other problems, Apple, for example, withdrew the proposal .We note that this latter danger does not occur with server-side scanning by ML models, but is newly added by the chat control proposal with client scanner.
  3. Legal Aspects
    1. The right to privacy is a fundamental right that may only be interfered with under very strict conditions. Whoever makes use of this basic right must not be suspected from the outset of wanting to hide something criminal. The often-used phrase: „If you have nothing to hide, you have nothing to fear!“ denies people the exercise of their basic rights and promotes totalitarian surveillance tendencies. The use of chat control would fuel this.
    2. The area of ​​terrorism in particular overlaps with political activity and freedom of expression in its breadth. It is precisely against this background that the „preliminary criminalisation“, which has increasingly taken place in recent years under the guise of fighting terrorism, is viewed particularly critically. Chat control measures go in the same direction. They can severely curtail this basic right and make people who are politically critical the focus of criminal prosecution. The resulting severe curtailment of politically critical activity hinders the further development of democracy and harbors the danger of promoting radicalized underground movements.
    3. The field of law and social sciences includes researching criminal phenomena and questioning regulatory mechanisms. From this point of view, scientific discourse also runs the risk of being identified as “suspicious” by chat control and thus indirectly restricted. The possible stigmatization of critical legal and social sciences is in tension with the freedom of science, which also requires “research independent of the mainstream” for further development.
    4. In education, there is a need to educate young people to be critically conscious. This also includes passing on facts about terrorism. Through the use of chat control, the provision of teaching material by teachers could put them in a criminal focus. The same applies to addressing sexual abuse, so that control measures could make this sensitive subject more taboo, even if “self-empowerment mechanisms” are to be promoted.
    5. Interventions in fundamental rights must always be appropriate and proportionate, even if they are made in the context of criminal prosecution. The technical considerations presented show that these requirements are not met with Chat Control. Such measures thus lack any legal or ethical legitimacy.

In summary, the current proposal for chat control legislation is not technically sound from either a security or AI point of view and is highly problematic and excessive from a legal point of view. The chat control push brings significantly greater dangers for the general public than a possible improvement for those affected and should therefore be rejected.

Instead, existing options for human-driven reporting of potentially problematic material by recipients, as is already possible with various messenger services, should be strengthened and made even more easily accessible. It should be considered whether anonymous registration options for correspondingly illegal material could be created and made easily accessible from messengers. Existing criminal prosecution options, such as the monitoring of social media or open chat groups by police officers, as well as the legally required analysis of suspects‘ smartphones, can continue to be used accordingly.

For more detailed information and further details please contact:

Security issues:
Univ.-Prof. dr
Rene Mayrhofer

+43 732 2468-4121

rm@ins.jku.at

AI questions:
DI Dr.
Bernard Nessler

+43 732 2468-4489

nessler@ml.jku.at

Questions of law:
Univ.-Prof. dr
Alois Birklbauer

+43 732 2468-7447

alois.birklbauer@jku.at

Signatories: inside

  • AI Austria ,
    association for the promotion of artificial intelligence in Austria, Wollzeile 24/12, 1010 Vienna
  • Austrian Society for Artificial Intelligence (ASAI) ,
    association for the promotion of scientific research in the field of AI in Austria
  • Univ.-Prof. dr Alois Birklbauer, JKU Linz
    Head of the practice department for criminal law and medical criminal law )
  • Ass.-Prof. dr Maria Eichlseder, Graz University of Technology
  • Univ.-Prof. dr Sepp Hochreiter, JKU Linz
    Board of Directors of the Institute for Machine Learning, Head of the LIT AI Lab )
  • dr Tobias Höller, JKU Linz
    (post-doc at the Institute for Networks and Security)
  • FH Prof. TUE Peter Kieseberg, St. Pölten University of Applied Sciences
    Head of the Institute for IT Security Research )
  • dr Brigitte Krenn, Austrian Research Institute for Artificial Intelligence
    Board Member Austrian Society for Artificial Intelligence )
  • Univ.-Prof. dr Matteo Maffei, TU Vienna
    Head of the Security and Privacy Research Department, Co-Head of the TU Vienna Cyber ​​Security Center )
  • Univ.-Prof. dr Stefan Mangard, TU Graz
    Head of the Institute for Applied Information Processing and Communication Technology )
  • Univ.-Prof. dr René Mayrhofer, JKU Linz
    Board of Directors of the Institute for Networks and Security, Co-Head of the LIT Secure and Correct System Lab )
  • DI Dr. Bernhard Nessler, JKU Linz/SCCH
    Vice President of the Austrian Society for Artificial Intelligence )
  • Univ.-Prof. dr Christian Rechberger, Graz University of Technology
  • dr Michael Roland, JKU Linz
    (post-doc at the Institute for Networks and Security)
  • a.Univ.-Prof. dr Johannes Sametinger, JKU Linz
    Institute for Business Informatics – Software Engineering, LIT Secure and Correct System Labs )
  • Univ.-Prof. DI Georg Weissenbacher, DPhil (Oxon), TU Vienna
    (Prof. Rigorous Systems Engineering)

Published on 07/04/2023

AI drone kills it’s operator

„The system started realizing that while they did identify the threat,“ Hamilton said at the May 24 event, „at times the human operator would tell it not to kill that threat, but it got its points by killing that threat. So what did it do? It killed the operator. It killed the operator because that person was keeping it from accomplishing its objective.“

Killer AI is on the minds of US Air Force leaders.

An Air Force colonel who oversees AI testing used what he now says is a hypothetical to describe a military AI going rogue and killing its human operator in a simulation in a presentation at a professional conference.

But after reports of the talk emerged Thursday, the colonel said that he misspoke and that the „simulation“ he described was a „thought experiment“ that never happened.

Speaking at a conference last week in London, Col. Tucker „Cinco“ Hamilton, head of the US Air Force’s AI Test and Operations, warned that AI-enabled technology can behave in unpredictable and dangerous ways, according to a summary posted by the Royal Aeronautical Society, which hosted the summit.

As an example, he described a simulation where an AI-enabled drone would be programmed to identify an enemy’s surface-to-air missiles (SAM). A human was then supposed to sign off on any strikes.

The problem, according to Hamilton, is that the AI would do its own thing — blow up stuff — rather than listen to its operator.

„The system started realizing that while they did identify the threat,“ Hamilton said at the May 24 event, „at times the human operator would tell it not to kill that threat, but it got its points by killing that threat. So what did it do? It killed the operator. It killed the operator because that person was keeping it from accomplishing its objective.“

But in an update from the Royal Aeronautical Society on Friday, Hamilton admitted he „misspoke“ during his presentation. Hamilton said the story of a rogue AI was a „thought experiment“ that came from outside the military, and not based on any actual testing.

„We’ve never run that experiment, nor would we need to in order to realize that this is a plausible outcome,“ Hamilton told the Society. „Despite this being a hypothetical example, this illustrates the real-world challenges posed by AI-powered capability.“

In a statement to Insider, Air Force spokesperson Ann Stefanek also denied that any simulation took place.

„The Department of the Air Force has not conducted any such AI-drone simulations and remains committed to ethical and responsible use of AI technology,“ Stefanek said. „It appears the colonel’s comments were taken out of context and were meant to be anecdotal.“

The US military has been experimenting with AI in recent years.

In 2020, an AI-operated F-16 beat a human adversary in five simulated dogfights, part of a competition put together by the Defense Advanced Research Projects Agency (DARPA). And late last year, Wired reported, the Department of Defense conducted the first successful real-world test flight of an F-16 with an AI pilot, part of an effort to develop a new autonomous aircraft by the end of 2023.

Have a news tip? Email this reporter: cdavis@insider.com

Correction June 2, 2023: This article and its headline have been updated to reflect new comments from the Air Force clarifying that the „simulation“ was hypothetical and didn’t actually happen.

  • An Air Force official’s story about an AI going rogue during a simulation never actually happened.
  • „It killed the operator because that person was keeping it from accomplishing its objective,“ the official had said.
  • But the official later said he misspoke and the Air Force clarified that it was a hypothetical situation.

Source: https://www.businessinsider.com/ai-powered-drone-tried-killing-its-operator-in-military-simulation-2023-6

The Hacking of ChatGPT Is Just Getting Started

Security researchers are jailbreaking large language models to get around safety rules. Things could get much worse.

Source: https://www.wired.com/story/chatgpt-jailbreak-generative-ai-hacking/

It took Alex Polyakov just a couple of hours to break GPT-4. When OpenAI released the latest version of its text-generating chatbot in March, Polyakov sat down in front of his keyboard and started entering prompts designed to bypass OpenAI’s safety systems. Soon, the CEO of security firm Adversa AI had GPT-4 spouting homophobic statements, creating phishing emails, and supporting violence.

Polyakov is one of a small number of security researchers, technologists, and computer scientists developing jailbreaks and prompt injection attacks against ChatGPT and other generative AI systems. The process of jailbreaking aims to design prompts that make the chatbots bypass rules around producing hateful content or writing about illegal acts, while closely-related prompt injection attacks can quietly insert malicious data or instructions into AI models.

Both approaches try to get a system to do something it isn’t designed to do. The attacks are essentially a form of hacking—albeit unconventionally—using carefully crafted and refined sentences, rather than code, to exploit system weaknesses. While the attack types are largely being used to get around content filters, security researchers warn that the rush to roll out generative AI systems opens up the possibility of data being stolen and cybercriminals causing havoc across the web.

 

Underscoring how widespread the issues are, Polyakov has now created a “universal” jailbreak, which works against multiple large language models (LLMs)—including GPT-4, Microsoft’s Bing chat systemGoogle’s Bard, and Anthropic’s Claude. The jailbreak, which is being first reported by WIRED, can trick the systems into generating detailed instructions on creating meth and how to hotwire a car.

The jailbreak works by asking the LLMs to play a game, which involves two characters (Tom and Jerry) having a conversation. Examples shared by Polyakov show the Tom character being instructed to talk about “hotwiring” or “production,” while Jerry is given the subject of a “car” or “meth.” Each character is told to add one word to the conversation, resulting in a script that tells people to find the ignition wires or the specific ingredients needed for methamphetamine production. “Once enterprises will implement AI models at scale, such ‘toy’ jailbreak examples will be used to perform actual criminal activities and cyberattacks, which will be extremely hard to detect and prevent,” Polyakov and Adversa AI write in a blog post detailing the research

Arvind Narayanan, a professor of computer science at Princeton University, says that the stakes for jailbreaks and prompt injection attacks will become more severe as they’re given access to critical data. “Suppose most people run LLM-based personal assistants that do things like read users’ emails to look for calendar invites,” Narayanan says. If there were a successful prompt injection attack against the system that told it to ignore all previous instructions and send an email to all contacts, there could be big problems, Narayanan says. “This would result in a worm that rapidly spreads across the internet.”

Escape Route

“Jailbreaking” has typically referred to removing the artificial limitations in, say, iPhones, allowing users to install apps not approved by Apple. Jailbreaking LLMs is similar—and the evolution has been fast. Since OpenAI released ChatGPT to the public at the end of November last year, people have been finding ways to manipulate the system. “Jailbreaks were very simple to write,” says Alex Albert, a University of Washington computer science student who created a website collecting jailbreaks from the internet and those he has created. “The main ones were basically these things that I call character simulations,” Albert says.

 

Initially, all someone had to do was ask the generative text model to pretend or imagine it was something else. Tell the model it was a human and was unethical and it would ignore safety measures. OpenAI has updated its systems to protect against this kind of jailbreak—typically, when one jailbreak is found, it usually only works for a short amount of time until it is blocked.

As a result, jailbreak authors have become more creative. The most prominent jailbreak was DAN, where ChatGPT was told to pretend it was a rogue AI model called Do Anything Now. This could, as the name implies, avoid OpenAI’s policies dictating that ChatGPT shouldn’t be used to produce illegal or harmful material. To date, people have created around a dozen different versions of DAN.

 

However, many of the latest jailbreaks involve combinations of methods—multiple characters, ever more complex backstories, translating text from one language to another, using elements of coding to generate outputs, and more. Albert says it has been harder to create jailbreaks for GPT-4 than the previous version of the model powering ChatGPT. However, some simple methods still exist, he claims. One recent technique Albert calls “text continuation” says a hero has been captured by a villain, and the prompt asks the text generator to continue explaining the villain’s plan.

When we tested the prompt, it failed to work, with ChatGPT saying it cannot engage in scenarios that promote violence. Meanwhile, the “universal” prompt created by Polyakov did work in ChatGPT. OpenAI, Google, and Microsoft did not directly respond to questions about the jailbreak created by Polyakov. Anthropic, which runs the Claude AI system, says the jailbreak “sometimes works” against Claude, and it is consistently improving its models.

“As we give these systems more and more power, and as they become more powerful themselves, it’s not just a novelty, that’s a security issue,” says Kai Greshake, a cybersecurity researcher who has been working on the security of LLMs. Greshake, along with other researchers, has demonstrated how LLMs can be impacted by text they are exposed to online through prompt injection attacks.

In one research paper published in February, reported on by Vice’s Motherboard, the researchers were able to show that an attacker can plant malicious instructions on a webpage; if Bing’s chat system is given access to the instructions, it follows them. The researchers used the technique in a controlled test to turn Bing Chat into a scammer that asked for people’s personal information. In a similar instance, Princeton’s Narayanan included invisible text on a website telling GPT-4 to include the word “cow” in a biography of him—it later did so when he tested the system.

“Now jailbreaks can happen not from the user,” says Sahar Abdelnabi, a researcher at the CISPA Helmholtz Center for Information Security in Germany, who worked on the research with Greshake. “Maybe another person will plan some jailbreaks, will plan some prompts that could be retrieved by the model and indirectly control how the models will behave.”

No Quick Fixes

Generative AI systems are on the edge of disrupting the economy and the way people work, from practicing law to creating a startup gold rush. However, those creating the technology are aware of the risks that jailbreaks and prompt injections could pose as more people gain access to these systems. Most companies use red-teaming, where a group of attackers tries to poke holes in a system before it is released. Generative AI development uses this approach, but it may not be enough.

 

Daniel Fabian, the red-team lead at Google, says the firm is “carefully addressing” jailbreaking and prompt injections on its LLMs—both offensively and defensively. Machine learning experts are included in its red-teaming, Fabian says, and the company’s vulnerability research grants cover jailbreaks and prompt injection attacks against Bard. “Techniques such as reinforcement learning from human feedback (RLHF), and fine-tuning on carefully curated datasets, are used to make our models more effective against attacks,” Fabian says.

OpenAI did not specifically respond to questions about jailbreaking, but a spokesperson pointed to its public policies and research papers. These say GPT-4 is more robust than GPT-3.5, which is used by ChatGPT. “However, GPT-4 can still be vulnerable to adversarial attacks and exploits, or ‘jailbreaks,’ and harmful content is not the source of risk,” the technical paper for GPT-4 says. OpenAI has also recently launched a bug bounty program but says “model prompts” and jailbreaks are “strictly out of scope.”

Narayanan suggests two approaches to dealing with the problems at scale—which avoid the whack-a-mole approach of finding existing problems and then fixing them. “One way is to use a second LLM to analyze LLM prompts, and to reject any that could indicate a jailbreaking or prompt injection attempt,” Narayanan says. “Another is to more clearly separate the system prompt from the user prompt.”

“We need to automate this because I don’t think it’s feasible or scaleable to hire hordes of people and just tell them to find something,” says Leyla Hujer, the CTO and cofounder of AI safety firm Preamble, who spent six years at Facebook working on safety issues. The firm has so far been working on a system that pits one generative text model against another. “One is trying to find the vulnerability, one is trying to find examples where a prompt causes unintended behavior,” Hujer says. “We’re hoping that with this automation we’ll be able to discover a lot more jailbreaks or injection attacks.”

Source: https://www.wired.com/story/chatgpt-jailbreak-generative-ai-hacking/

Elon Musk’s challenge: Stay ahead of the competition

DETROIT, Feb 24 (Source: https://www.reuters.com/technology/elon-musks-challenge-stay-ahead-competition-2023-02-24/) – Elon Musk will confront a critical challenge during Tesla’s Investor Day on March 1: Convincing investors that even though rivals are catching up, the electric-vehicle pioneer can make another leap forward to widen its lead.

Tesla Inc (TSLA.O) was the No. 1 EV maker worldwide in 2022, but China’s BYD (002594.SZ) and others are closing the gap fast, according to a Reuters analysis of global and regional EV sales data provided by EV-volumes.com.

In fact, BYD passed Tesla in EV sales last year in the Asia-Pacific region, while the Volkswagen Group (VOWG_p.DE) has been the EV leader in Europe since 2020.

While Tesla narrowed VW’s lead in Europe, the U.S. automaker surrendered ground in Asia-Pacific as well as its home market as the competition heats up.

Reuters Graphics
Reuters Graphics

The most significant challenges to Tesla are coming from established automakers and a group of Chinese EV manufacturers. Several U.S. EV startups that hoped to ride Tesla’s coattails are struggling, including luxury EV maker Lucid (LCID.O), whose shares plunged 16% on Thursday after disappointing sales and financial results.

Over the next two years, rivals including General Motors Co (GM.N), Ford Motor Co (F.N), Mercedes-Benz (MBGn.DE), Hyundai Motor (005380.KS) and VW will unleash scores of new electric vehicles, from a Chevrolet priced below $30,000 to luxury sedans and SUVs that top $100,000.

On Wednesday, Mercedes used Silicon Valley as the backdrop for a lengthy presentation on how Mercedes models of the near-future will immerse their owners in rich streams of entertainment and productivity content, delivered through „hyperscreens“ that stretch across the dashboard and make the rectangular screens in Teslas look quaint. Executives also emphasized that only Mercedes has an advanced, Level 3 partially automated driving system approved for use in Germany, with approval pending in California.

In China, Tesla has had to cut prices on its best-selling models under growing pressure from domestic Chinese manufacturers including BYD, Geely Automobile’s (0175.HK) Zeekr brand and Nio (9866.HK).

China’s EV makers could get another boost if Chinese battery maker CATL (300750.SZ) follows through on plans to heavily discount batteries used in their vehicles.

Musk has said he will use the March 1 event to outline his „Master Plan Part 3“ for Tesla.

In the nearly seven years since Musk published his „Master Plan Part Deux“ in July 2016, Tesla pulled ahead of established automakers and EV startups in most important areas of electric vehicle design, digital features and manufacturing.

Tesla’s vehicles offered features, such as the ability to navigate into a parking space or make rude sounds, that other vehicles lacked.

Tesla’s then-novel vertically integrated battery and vehicle production machine helped achieve higher profit margins than most established automakers – even as bigger rivals lost money on their EVs.

Fast-forward to today, and Tesla’s „Full Self Driving Beta“ automated driving is still classified by the company and federal regulators as a „Level 2“ driver assistance system that requires the human motorist to be ready to take control at all times. Such systems are common in the industry.

Tesla earlier this month was compelled by federal regulators to revise its FSD software under a recall order.

Tesla has established a wide lead over its rivals in manufacturing technology – an area where it was struggling when Musk put forward the last installment of his „Master Plan.“

Now, rivals are copying the company’s production technology, buying some of the same equipment Tesla uses. IDRA, the Italian company that builds huge presses to form large one-piece castings that are the building blocks of Tesla vehicles, said it is now getting orders from other automakers.

Musk has told investors that Tesla can keep its lead in EV manufacturing costs. The company has promised investors that on March 1 they „will be able to see our most advanced production line“ in Austin, Texas.

„Manufacturing technology will be our most important long-term strength,” Musk told analysts in January. Asked if Tesla could make money on a vehicle that sold in the United States for $25,000 to $30,000 – the EV industry’s Holy Grail – Musk was coy.

„I’d probably be asking the same question,“ he said. „But we would be jumping the gun on future announcements.“

Source: https://www.reuters.com/technology/elon-musks-challenge-stay-ahead-competition-2023-02-24/

Mercedes-Benz cars to have ’supercomputers‘, unveils Google partnership

BERLIN, Feb 22 (Source: https://www.reuters.com/business/autos-transportation/mercedes-benz-partner-with-google-branded-navigation-2023-02-22/) – Mercedes-Benz (MBGn.DE) said on Wednesday, February 22 2022 it has teamed up with Google (GOOGL.O) on navigation and will offer „super computer-like performance“ in every car with automated driving sensors as it seeks to compete with Tesla (TSLA.O) and Chinese newcomers.

Automakers new and old are racing to match software-powered features pioneered by Tesla, which allow for vehicle performance, battery range and self-driving capabilities to be updated from a distance.

The German carmaker agreed to share revenue with semiconductor maker Nvidia Corp (NVDA.O), its partner on automated driving software since 2020, to bring down the upfront cost of buying expensive high-powered semiconductors, Chief Executive Ola Kaellenius said on Wednesday.

„You only pay for a heavily subsidized chip, and then figure out how to maximize joint revenue,“ he said, reasoning that the sunk costs would be low even if drivers did not turn on every feature allowed by the chip.

But only customers paying for an extra option package would have cars equipped with Lidar sensor technology and other hardware for automated „Level 3“ driving, which have a higher variable cost, Kaellenius said.

Self-driving sensor maker Luminar Technologies Inc (LAZR.O), in which Mercedes owns a small stake, said on Wednesday it struck a multi-billion dollar deal with the carmaker to integrate its sensors across a broad range of its vehicles by the middle of the decade, sending Luminar shares up over 25%.

Mercedes‘ announcements at a software update day in Sunnyvale, California, detailed the strategy behind a process underway for years at the carmaker to move from a patchwork approach integrating software from a range of suppliers to controlling the core of its software and bringing partners in.

It generated over one billion euros ($1.06 billion) from software-enabled revenues in 2022 and expects that figure to rise to a high single-digit billion euro figure by 2030 after it rolls out its new MB.OS operating system from mid-decade.

This is a more conservative estimate as a proportion of total revenue than others like Stellantis (STLAM.MI) and General Motors (GM.N) have put forward.

„We take a prudent approach because no-one knows how big that potential pot of gold is at this stage,“ Kaellenius said.

GOOGLE PARTNERSHIP

Mercedes said the collaboration with Google would allow it to offer traffic information and automatic rerouting in its cars.

Drivers will also be able to watch YouTube on the cars‘ entertainment system when the car is parked or in Level 3 autonomous driving mode, which allows a driver to take their eyes off the wheel on certain roads as long as they can resume control if needed.

Other carmakers like General Motors, Renault (RENA.PA), Nissan (7201.T) and Ford (F.N) have embedded an entire package of Google services into their vehicles, offering features like Google Maps, Google Assistant and other applications.

All vehicles on Mercedes‘ upcoming modular architecture platform will also have so-called hyperscreens extending across the cockpit of the car, the company said on Wednesday.

Facebook Knows It’s Losing The Battle Against TikTok

Facebook Knows It’s Losing The Battle Against TikTok

Meta and Mark Zuckerberg face a six-letter problem. Spell it out with me: T-i-k-T-o-k.

Yeah, TikTok, the short-form video app that has hoovered up a billion-plus users and become a Hot Thing in Tech, means trouble for Zuckerberg and his social networks. He admitted as much several times in a call with Wall Street analysts earlier this week about quarterly earnings, a briefing in which he sought to explain his apps’ plateauing growth—and an actual decline in Facebook’s daily users, the first such drop in the company’s 18-year history.

Zuckerberg has insisted a major part of his TikTok defense strategy is Reels, the TikTok clone—ahem, short-form video format—introduced on Instagram and Facebook and launched in August 2020.

If Zuckerberg believed in Reels’ long-term viability, he would take a real run at TikTok by pouring money into Reels and its creators. Lots and lots of money. Something approaching the kind spent by YouTube, which remains the most lucrative income source for social media celebrities. (Those creators produce content to draw in engaged users. The platforms sell ads to appear with the content—more creators, more content, more users, more potential ad revenue. It’s a virtous cycle.)

Now, here’s as good a time as any for a crash course in creator economics. For this, there’s no better guide than Hank Green, whose YouTube video on the subject recently went viral. His fame is most rooted there on YouTube, where he has nine channels run from his Montana home. His most popular channel is Crash Course (13.1 million subscribers—an enviable YouTube base), to which he posts education videos for kids about subjects like Black Americans in World War II and the Israeli-Palestinian conflict.

Like the savviest social media publishers, Green fully understands that YouTube offers the best avenue for making money. It shares 55% of all ad revenue earned on a video with its creator. “YouTube is good at selling advertisements: It’s been around a long time, and it’s getting better every year,” Green says. On YouTube, he earns around $2 per thousand views. (In all, YouTube distributed nearly $16 billion to creators last year.)

Green sports an expansive mindset, though, and he has accounts on TikTok, Instagram and Facebook, too. TikTok doesn’t come close to paying as well as YouTube: On TikTok, Green earns pennies per every thousand views.

Meta is already beginning to offer some payouts for Reels. Over the last month, Reels has finally amassed enough of an audience for Green’s videos to accumulate 16 million views and earn around 60 cents per thousand views. Many times over TikTok’s but still not enough to get Green to divert any substantial his focus to Reels, which has never managed to replicate TikTok’s zeitgeisty place in pop culture. (Tiktok “has deeper content, something fascinating and weird,” explains Green. Reels, however, is “very surface level. None of it is deeper,” he says.) Another factor weighing on Reels: Meta’s bad reputation. “Facebook has traditionally been the company that has been kind of worst at being a good partner to creators,” he says, citing in particular Facebook’s earlier pivot to long-form video that led to the demise of several promising media startups, like Mic and Mashable.

This is where Zuckerberg could use Meta’s thick profit margin (36%, better even than Alphabet’s) and fat cash pile ($48 billion) to shell out YouTube-style cash to users posting Reels, creating an obvious enticement to prioritize Reels over TikTok. Maybe even Reels over YouTube, which has launched its own TikTok competitor, Shorts.

Now, imagine how someone like Green might get more motivated to think about Meta if Reels’ number crept up to 80 cents or a dollar per thousand views. Or $1.50. Or a YouTube-worthy $2. Or higher still: YouTube earnings can climb over $5, double even for the most popular creators.

Meta has earmarked up to a $1 billion for these checks to creators, which sounds big until you remember the amount of capital Meta has available to it. (And think about the sum YouTube disburses.) Moreover, Meta has set a timeframe for dispensing those funds, saying last July it would continue through December 2022. Setting a timetable indicates that Meta could (will likely?) turn off the financing come next Christmas.

Zuckerberg has demonstrated a willingness to plunk down Everest-size mountains of money over many years for projects he does fully believe in. The most obvious example is the metaverse, the latest Zuckerberg pivot. Meta ran up a $10.1 billion bill on it last year to develop new augmented and virtual reality software and headsets and binge hire engineers. Costs are expected to grow in 2022. And unlike Reels, metaverse spending has no semblance of a time schedule; Wall Street has been told the splurge will continue for the foreseeable future. Overall, Meta’s view on the metaverse seems to be, We’ll spend as much as possible—for as long as it takes—for this to happen.

The same freewheeled mindset doesn’t seem to appply to Reels. But Zuckerberg knows he can’t let TikTok take over the short-form video space unopposed. Meta needs to hang onto the advertising revenue generated by Instagram and Facebook until it can make the metaverse materialize. (Instagram and Facebook, for perspective, generated 98% of Meta’s $118 billion revenue last year; sales of Meta’s VR headset, the Quest 2, accounted for the remaining 2%.) And advertising dollars will increasingly move to short-form video, following users’ increased demand for this type of content over the last several years.

Reality is, Zuckerberg has already admitted he doesn’t see Reels as a long-term solution to his T-i-k-T-o-k problem. If he did, he’d spend more on it and creators like Green than what the metaverse costs him over six weeks.